The Evolution of Yield Management in the Airline Industry: Origins to the Last Frontier (Management for Professionals) 3030704238, 9783030704230

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Management for Professionals

Ben Vinod

The Evolution of Yield Management in the Airline Industry Origins to the Last Frontier

Management for Professionals

The Springer series Management for Professionals comprises high-level business and management books for executives. The authors are experienced business professionals and renowned professors who combine scientific background, best practice, and entrepreneurial vision to provide powerful insights into how to achieve business excellence.

More information about this series at http://www.springer.com/series/10101

Ben Vinod

The Evolution of Yield Management in the Airline Industry Origins to the Last Frontier

Ben Vinod Charter and Go Grapevine, TX, USA

ISSN 2192-8096 ISSN 2192-810X (electronic) Management for Professionals ISBN 978-3-030-70423-0 ISBN 978-3-030-70424-7 (eBook) https://doi.org/10.1007/978-3-030-70424-7 # The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG. The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Innovation distinguishes between a leader and a follower Steve Jobs

To my wife, Ann, who never complained over the years when I traveled around the globe visiting customers.

Foreword

This is a book about “how to” create and operate a yield management system. It will likely be used by companies in a variety of businesses who seek to sell whatever service or product they offer at the highest price consistent with maximizing their total revenue. In years past, I knew a good deal about the techniques of yield management. In recent years, however, the practice has been very significantly refined and improved, and I can no longer claim either detailed knowledge or mastery of the techniques. The reader should understand, however, that it really works. I think it is fair to say that American Airlines, while I was its chief executive (1985–1998), applied yield management more creatively and aggressively than other airlines, and that our use of the technique contributed very materially to our competitive success. In the beginning, our effort was a pen-and-pencil effort to overcome the obvious fact that we were selling seats for less than people were willing to pay for them. While there were many examples of that problem, the one I found most outrageous was seeing signs on London’s sidewalks advertising trips to New York for very nominal amounts of money. In those days, travelers could buy the cheap seats and turn in tickets already purchased for a refund. That was not, of course, the only example. In the early 1980s, companies like American came under pressure from new entrant airlines that paid their employees less and used the savings to offer lower prices. In response, American came up with the idea of selling seats at matching prices but requiring travelers to stay away from home for a specified amount of time, thus preventing business travelers, who paid the highest prices and had demanding schedules, from using the lower fares. Business travelers were unwilling to either use the services of the new entrants, which were generally of lower quality, or tolerate the minimum stay requirements associated with our matching fares. We were also frustrated by our inability to appropriately match supply and demand. We often found, for example, that we were dispatching partially filled aircraft on long-haul international routes despite the fact that customers in cities on our network wishing to fly to those international destinations were unable to reach the hub city from which the international flight was departing. As we got into our study, it became obvious that the problem was far too complex to be solved without finding ways to assess the many ways in which a given seat on a vii

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particular aircraft might be combined with individual seats on other aircraft to create various products. As we began to understand the possibilities, we built mathematical models to identify options and the computational capabilities to massage and evaluate the combinations. As the years passed, we hired a lot of very smart people to build the models and program the computers. Tom Cook was the officer who led the effort, and Ben Vinod, the author of this book, was one of the many people who helped build our industry-leading capability. My colleagues and I at American were proud of what Tom and his team accomplished, and I am confident that those interested in this subject will find Ben’s book rewarding. President and Chief Executive Officer, American Airlines (1985–1998) Gloucester, MA, USA October 6, 2020

Robert L. Crandall

Foreword

I joined the American Airlines Operations Research (OR) group as a Director in 1982. Ben Vinod joined the team 4 years later as an OR analyst. The OR Group was relatively small at the time with several dozen people. At that time, the group was concentrating on developing innovative OR-based solutions for a few limited functional areas of the airline; most important among them was revenue management (RM). The RM team was developing American’s RM system called DINAMO. American Airlines won the 1991 Edelman Award from INFORMS for the best application of OR (revenue management) in the world. From 1982 to 1987, the AA OR group grew rapidly increasing its impact into many functional areas of the airline including marketing, capacity planning/scheduling, crew planning and scheduling, airport operations, maintenance and engineering, systems operations control, and others. In addition, the group executed several strategic studies such as whether to build an additional 1-billion-dollar terminal in DFW to enable the growth of American’s largest hub. These studies and the OR-based decision support systems that were leading-edge solutions in the airline industry were often sponsored by the CEO Robert L. Crandall who many thought was the industry’s best CEO. In 1987, American Airlines Decision Technologies, AADT, was formed as a wholly owned subsidiary of AMR Corp. (parent company of American Airlines) with the charter to provide OR-based solutions to organizations external to AA. Robert Crandall’s only restriction for AADT was to make a profit in the first year and not to engage with direct airline competitors. AA remained AADT’s primary client but grew very rapidly. AADT’s first external client was Amtrak, and the project was to build a RM system. AADT’s growth was explosive with many external clients worldwide and many of the larger engagements were to install or build a RM system. Airlines (not direct AA competitors), hotels, Club Med, Royal Caribbean Cruise Lines, Ryder Truck, the French National Railroad (SNCF), and even the U.S. Navy were major RM clients. Ben Vinod was critical to the growth of AADT and the evolution of the science of RM in the airline industry and other important types of business during his tenure at AA, AADT, and SABRE and is uniquely qualified to write this book. In the early 1990s, AADT and SABRE were merged and subsequently spun off from AMR. After I left SABRE in 1999, Ben Vinod continued to lead at SABRE providing both management and intellectual leadership applying RM and more ix

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generally OR to important problems to create competitive advantage. For people wanting a comprehensive and practical book on revenue management, I very much recommend Ben’s book. President, American Airlines Decision Technologies (1987–1999) Dallas, TX, USA October 23, 2020

Thomas M. Cook

Foreword

The History of Peanuts: The Rise and Rise of Yield Management When President Jimmy Carter, a peanut farmer from Georgia, signed into law the Airline Deregulation Act in 1978, the basic economic foundations or conditions for yield management were born. This event dismantled a comprehensive system of government controls. For example, before deregulation, airlines operated in tightly regulated environments in which Civil Aeronautics Board (CAB) approved routes and set fares that guaranteed airlines a 12% return on flights that were 55% full. After deregulation, there was an explosion of fares offered by competing airlines. Indeed, between 1976 and 1990, average fares for U.S. domestic passengers fell by 30% in real terms. As the conditions were right, what was needed was a system for airlines to manage this new business environment. Where shall we begin? One of the new carriers entering this deregulated market was PEOPLExpress, and it threatened the existence of American Airlines with deeply discounted tickets. These fares were relatively cheap and passengers were allowed to bring on one carry-on bag free of charge. PEOPLExpress charged moderate prices for food and beverages rather than being inclusive. A can of beer cost US$1 and peanuts 50¢. It was American Airlines under CEO Robert L Crandell that responded with the first effective and efficient yield management system. Led by an Operations Research team under Tom Cook, a system called DINAMO was developed with overbooking and multiple-class configurations. From a passenger perspective, the yield management system at American Airlines allowed for nonrefundable Ultimate Super Saver fares which were priced lower than PEOPLExpress’s fares. For the first time, yield management analysts at American Airlines were able to explicitly control the availability of the deeply discounted fares. PEOPLExpress responded with further discounts, which were unsustainable, thus forcing them out of business. Why is this account important? This is a question of history and why, how, when, and what has happened. Fundamentally Ben Vinod’s account of the rise of yield management (or revenue management) is the most comprehensive and accessible account that I have read. It is a book of significance that charts why yield xi

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management occurred, how yield management works in practice, the conditions of when it works, and what the details of revenue management are. Whether you are a student of yield management or the next CEO of American Airlines, you simply need to read this book and all of your questions will be answered. So, this book is a question of history for the future. The comprehensiveness and detail become the foundation for any of us that will be forecasting the future. The book provides a critical praxis of yield management, expectations and illustrations which will allow the reader to understand what comes next (although Ben Vinod illustrates the future very well in Chaps. 11 and 12). My final thoughts are, how much would you be willing to pay for a bag of peanuts? Maybe that will be the second edition. Professor and Tourism Futurist at Victoria University of Wellington Editor, Journal of Revenue & Pricing Management Wellington, New Zealand November 24, 2020

Ian Yeoman

Foreword

In my five-decade involvement in the global airline industry, I have witnessed numerous developments that led airlines to pivot their business modes. Some examples include the development of game-changing aircraft, computer reservation systems, hub-and-spoke systems, deregulation of the airline business, emergence of low-cost and low-fare airlines, yield management systems, loyalty programs, the Internet, alliances (and joint ventures within alliances), changes in the structure of travel agent fees, electronic ticketing, IATA’s New Distribution Capability (NDC) initiatives, and the unbundling of the airline product. Some of these developments followed each other directly, for example, yield management and loyalty programs, creating a one-two punch. Other developments piggybacked on each other, for example, changes in agent ticket fees and electronic ticketing. Dr. Ben Vinod’s book provides an excellent and extensive background on the ramifications of many of these developments on schedule, marketing, and sales planning in which yield management, now called revenue management, is the core component. The coverage of the material discussed is very well articulated and wide-ranging, encompassing the key elements of marketing planning—from scheduling, pricing, and yield management to sales and distribution. The author also discusses the impact of emerging technologies in travel, for example, artificial intelligence and blockchain. The heart of the book is obviously the discussion of revenue management. During the past 40 of my 50-year involvement in the global airline industry, I have seen a broad spectrum of material presented on the subject of revenue management. I have also worked in this area with airlines and related businesses, such as global distribution systems. I am, however, amazed at the breadth and depth of knowledge presented in this book by Dr. Vinod. This material will help both academic researchers and practitioners in the business. On the research side, for example, one only needs to read his exceptional description of the airline spill models, for example, his description of the cumulative probability functions, and the estimation of spill. On the pragmatic side, one can gain valuable insights from his perspectives on offer management, dynamic pricing, alternative approaches to demand forecasting, and inventory control. Dr. Vinod’s thinking is at the cutting edge, exemplified by his discussion of individual seat inventory control and individual seat pricing. And, going forward, readers will see tremendous value in the role of operations research, big data, and xiii

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emerging technologies. And, having worked for many years with American Airlines and Sabre, Dr. Vinod is also able to shed light on the changes needed in organizational structures to leverage emerging technologies and big data. Finally, Dr. Vinod’s book contains an incredibly comprehensive list of references, over 400, for those who want to pursue the subject in more detail. This book should be of tremendous interest to practitioners, researchers, and graduate students. Executive-in-Residence, Fisher College of Business, Ohio State University Columbus, OH, USA November 26, 2020

Nawal K. Taneja

Preface

After a 2-year stint at Rutgers University as a faculty member, I moved to Dallas/Fort Worth to start a new career as an Operations Research (OR) analyst at American Airlines (AA) in June 1985. Tom Cook, the Director of the OR Group, had managers Steve Clampett, Barry Smith, Paul Stephens, and Mary Lynch reporting to him. The rapid growth of the OR Group led to the creation of American Airlines Decision Technologies (AADT) to sell advanced OR models to airlines around the world. As President of AADT, Tom reported to Robert L. Crandall, the President and CEO of American Airlines. Tom Cook was the chief visionary responsible for the creation of advanced applications in Airline Planning, Airline Operations, and Crew Planning, and provided me the opportunity to work with many airline and other travel industry executives on advanced revenue management implementations. Under Barry Smith, I worked on Systems Operations Control (SOC) recovery from irregular operations, crew planning, and yield management, the area that I most enjoyed. Over the years, I had the opportunity to work on all aspects of revenue management in the airline industry: leg/segment, origin and destination, low-cost carrier, reservations, inventory control, airline connectivity, availability processing, distributed availability, competitive revenue management, offer management, branded fares, air ancillaries, and individual seat pricing. Sabre, a division of AA and, later, an independent travel distribution and technology company, gave me the opportunity to work with many airlines and define the new generation of solutions in this space. Related areas that held my interest were in air shopping and loyalty programs. In addition to airlines, I also had the opportunity to work with some of the leading hotel chains, rental car companies, passenger rail companies, cruise lines, and ferry lines. My interest in OR began when I was a research assistant under Professor Helmut T. Zwahlen, a professor’s professor, a master of the craft, at Ohio University in Athens, Ohio. I am also deeply indebted to Professor James W. Barany at Purdue University who mentored me. This was further augmented under Professor James J. Solberg who was my PhD thesis advisor, along with Professor Arnold Sweet, Professor Herb Schwetman, and Professor Joe Talavage. During my tenure at American Airlines and Sabre, I had the opportunity to work with the best and the brightest. There were several individuals who were passionate about the revenue management space with whom I collaborated. Robert L. Crandall, xv

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CEO of American Airlines, inspired us to excel. Tom Cook was passionate about customers who trusted us to deliver these advanced next-generation solutions on an accelerated timeline. Steve Clampett had a razor-sharp focus on execution to meet customer commitments. I learned the basics of revenue management from Barry Smith at American Airlines. At Sabre I learned the intricacies of airline pricing from Yanjun Zhang, and Ross Darrow was the ideal sounding board for new innovations in travel. I had the pleasure to work with many smart individuals at American Airlines and Sabre across Airline Planning, Air Shopping, Airline Operations, Reservations, Distribution, and Hospitality. They include (in alphabetical order): Wendy Albright, Venkat Anganagari, Sean Arena, Michael Askew, Karen Baghdasaryan, Paul Bachand, Vidhya Balakrishnan, Ramji Balasubramanian, Randy Balzer, Wenkai Bao, Gene Bartholf, Shane Batt, Brad Beakley, Rajeev Bellubbi, Michael Benzinger, Tom Bertram, Anwesha Bhattacharjee, John Blankenbaker, Brad Boston, Jack Burkholder, Michael Byrd, Chad Callaghan, Wassim Chaar, Ninan Chacko, Scott Chandler, Rama Cherukuri, Shelene Chang, Umit Cholak, Grzegorz Cholewiak, Hubert Chrobak, Alessandro Ciancimino, Michael Clarke, Peter Comiskey, John Dabkowski, Phil Dale, Ross Darrow, Ignacio deCardenas, Sally deFina, Vinit Doshi, Lisa Douglas, Gavin Duffy, John Elieson, Denis Fernandes, Bill Fite, Michal Gandor, Greg Gilchrist, Gupta Gogula, David Gray, Richard Green, Dirk Guenther, Max Gurdian, Phil Haan, Brian Harrison, Alan Heard, Renee Henderson, Mark Haneke, Hamid Herzai, Dave Hobt, Christian Huff, Youngbum Hur, Christophe Imbert, Sunny Ja, Vikram Jayaram, Sai Jayaraman, Joe Jennings, Bill Jessiman, Hai Jiang, Jeff Johnson, Doak Jones, Jay Jones, Terry Jones, Vibhu Kalyan, Jeffrey Katz, Cuneyd Kaya, Jackie Kee, Mike Kennedy, Deborah Kerr, Mike King, Konrad Koch, Vamsidhar Kodam, Bogusz Komarzynski, Piotr Kosiorowski, Steve Kretsch, Dasha Kuksenko, Sundar Kuppuswamy, Lorenzo Laohoo, Tony Lee, John Leimkuhler, Ladislav Lettovsky, Dong Liang, Paula Lippe, Sheng Liu, Mike Malik, Sekhar Mallipeddi, Chito Manansala, Josephine Kintanar Manjot, Gianni Marostica, Bryan McVicker, Sterling Miller, Shankar Mishra, Amod Mital, Kyle Moore, Peter Moore, Keith Murgatroyd, Craig Murphy, Tom Murray, Mona Naguib, Ashwin Naik, Sundar Narasimhan, C.P. Narayan, Bob Newman, Pavlo Ovcharenko, Steve Packwood, Srinivas Palamarthy, Saunvit Pandya, Angela Payne, Aleksandra Rajska, Gopal Ranganathan, Beju Rao, Richard Ratliff, Norbert Remenyi, Darren Rickey, Carlo Ripiccini, Adam Roach, Marshall Romberg, Santosh Sah, Mitra Sanyal, Vish Saoji, George Schenck, Rob Schermerhorn, Dave Schwarte, Tina Shaw, Sergey Shebalov, Dave Shirk, Armando Silva, Judy Simm, Lohit Singh, Mateusz Slomka, Elliot Smith, Russell Smith, Steve Speer, Hari Subramanian, Milorad Sucur, Kuba Syska, Tomasz Szymanski, Judi Theis, Hunkar Toyoglu, Hande Tuzel, Antonella Vecchio, Suresh Vellanki, Vish Viswanathan, Chris Waitman, Gayle Waitman, Alan Walker, Tracey Weber, Chris Wilding, Robert Winkler, Michal Winkler, David Wood, Melvin Woodley, Lisa Woods, Peng Xie, Kartik Yellepeddi, Rick Zeni, Yanjun Zhang, and Yifei Zhang.

Preface

xvii

Ian Yeoman, Professor and Tourism Futurist at Victoria University of Wellington; Guillermo Gallego, Crown Worldwide Professor of Engineering at the Hong Kong University of Science and Technology; Nawal Taneja, Executive-in-Residence, Fisher College of Business, Ohio State University; and Ellis Johnson, Professor Emeritus and the Coca-Cola Chaired Professor at Georgia Tech, have also influenced me over the years with their unique perspectives. I have also had the opportunity to interact with leading academics including Chris Anderson, Peter Belobaba, John-Paul Clarke, Laurie Garrow, Sherri Kimes, Diego Klabjan, Una McMahon-Beattie, Kalyan Talluri, Garret van Ryzin, and Larry Weatherford. Former Sabre CEOs, Sam Gilliland and Tom Klein, and Travelocity CEO, Michelle Peluso, challenged us to excel in applying core airline revenue management concepts to Sabre Travel Network (agency channel, the Sabre GDS) and Travelocity across several key initiatives. Improving the customer experience with relevant offers by optimizing screen displays for brick-and-mortar travel agencies and online travel agencies (OTA) was a key priority. Individuals who have offered their perspectives, past and present, when we worked together and influenced the content in this book are Bob Crandall, Tom Cook, Barry Smith, Steve Clampett, Sam Gilliland, Tom Klein, Barry Vandevier, Michelle Peluso, Ian Yeoman, Nawal Taneja, and members of Sabre Research. There is an abundance of content in the literature on the mathematical models for revenue management from academics and practitioners and many are referenced in this book. This is a practice book on pricing and revenue management for professionals, in an easy-to-read style, which provides an end-to-end view of the airline revenue management space. In my various roles at American Airlines and Sabre, I was fortunate to have had the opportunity to work with several smart people on many facets of the travel value chain. There are several aspects of revenue management that are not covered in books and journal articles, and it is a gap I wanted to address in this book. Special thanks to Ross Darrow, Ann Vinod, Yanjun Zhang, and Richard Ratliff who offered their suggestions and constructive feedback as I wrote this book. I would also like to thank creative artist and user experience designer Joe Jennings for creating many of the figures in this book. Grapevine, TX April 20, 2021

B. Vinod

Contents

1

2

Origins . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 Origins of the Airline Reservations System . . . . . . . . . . . . . . . 1.3 Airline Deregulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.4 Yield Management: The Early Period . . . . . . . . . . . . . . . . . . . 1.5 Origins of the Frequent Flyer Programs . . . . . . . . . . . . . . . . . 1.6 Origins of the GDS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.6.1 Industry Standards and Governance . . . . . . . . . . . . . 1.6.2 Communications Partners . . . . . . . . . . . . . . . . . . . . 1.6.3 Settlement Partners for Airlines and Agencies . . . . . . 1.6.4 Industry Partners for Airline Fares . . . . . . . . . . . . . . 1.6.5 Industry Partners for Airline Schedules . . . . . . . . . . 1.6.6 GDS and Collaborative Entities . . . . . . . . . . . . . . . . 1.6.7 Government Oversight . . . . . . . . . . . . . . . . . . . . . . 1.6.8 Airline Divestiture and Deregulation of the GDS . . . 1.6.9 Air Shopping and the GDS . . . . . . . . . . . . . . . . . . . 1.6.10 Legacy Technology . . . . . . . . . . . . . . . . . . . . . . . . 1.7 The Growth of the Internet and Online Channels . . . . . . . . . . . 1.8 The Travel Value Chain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.9 Revenue Management Storefronts . . . . . . . . . . . . . . . . . . . . . 1.10 Travel Agents: How They Make Money . . . . . . . . . . . . . . . . . 1.11 Changes in the Distribution Landscape with IATA’s New Distribution Capability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.12 The Airline Marketing Planning Process . . . . . . . . . . . . . . . . . 1.12.1 The Time Frames . . . . . . . . . . . . . . . . . . . . . . . . . . 1.12.2 Industry Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . 1.12.3 Scheduling, Pricing, Revenue Management and Distribution Synergies . . . . . . . . . . . . . . . . . . . . . . . 1.13 Pricing and Yield Management for Competitive Advantage . . . 1.14 Yield Management: The Onward Journey . . . . . . . . . . . . . . . .

1 1 2 6 7 12 14 18 19 19 20 20 21 21 22 22 23 24 26 27 28

Airline Pricing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Fare Products . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

41 41 43

29 34 34 35 36 38 40

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2.3 2.4

Fare Dimensions and Fare Types . . . . . . . . . . . . . . . . . . . . . . Booking Class, Fare Category and Fare Basis Code . . . . . . . . . 2.4.1 Fare Classes and Booking Classes . . . . . . . . . . . . . . Classification of Fare Products . . . . . . . . . . . . . . . . . . . . . . . . 2.5.1 Public Fares . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5.2 Private Fares . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5.3 Web Fares . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fare Rule Categories . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Circumventing Fare Rules . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.7.1 Overlapping Flights . . . . . . . . . . . . . . . . . . . . . . . . 2.7.2 Hidden Cities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Journeys . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Itinerary Pricing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . IATA Traffic Conference Areas . . . . . . . . . . . . . . . . . . . . . . . Constructed Fares . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Savvy Travelers, Stopovers, Open Jaws and Frequent Flyer Redemptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Market Segmentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . How Many Price Points in a Market? . . . . . . . . . . . . . . . . . . . The Fare Management Planning Process . . . . . . . . . . . . . . . . . Pricing Strategy and its Impact on Tactical Pricing . . . . . . . . . Reactive Pricing Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . Proactive Pricing Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fare Rationalization in the Price Planning Process . . . . . . . . . . Multilateral and Bilateral Prorate Agreements . . . . . . . . . . . . . 2.20.1 The SPA Lifecycle . . . . . . . . . . . . . . . . . . . . . . . . . Airline Ancillaries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.21.1 Branded Fares Record (S-8) . . . . . . . . . . . . . . . . . . Total Itinerary Pricing with Ancillaries . . . . . . . . . . . . . . . . . .

44 44 45 46 46 46 48 48 50 50 51 51 52 53 54

The Airline Spill Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Recapture and Upsell . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 Spill Model Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4 Spill Model Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5 The Boeing Spill Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5.1 Logit Approximation to the Normal Distribution . . . . 3.6 The Gamma Spill Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.6.1 The Passenger Closing Rate . . . . . . . . . . . . . . . . . . 3.7 Calibration of Input Parameters . . . . . . . . . . . . . . . . . . . . . . . 3.7.1 Coefficient of Variation of Demand . . . . . . . . . . . . . 3.7.2 Estimation of Load Factor on Closed Flights . . . . . . 3.8 Estimation of Spill . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.8.1 Estimating Spill for a Group of Flights . . . . . . . . . . . 3.9 First Class Spill Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

69 69 70 70 71 72 75 76 79 80 80 82 83 84 84

2.5

2.6 2.7

2.8 2.9 2.10 2.11 2.12 2.13 2.14 2.15 2.16 2.17 2.18 2.19 2.20 2.21 2.22 3

54 55 56 59 60 61 62 62 64 65 66 67 67

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3.9.1 3.9.2 4

Negative Exponential Distribution . . . . . . . . . . . . . . Two-Stage Cox Distribution . . . . . . . . . . . . . . . . . .

85 86

Revenue Management of the Base Fare . . . . . . . . . . . . . . . . . . . . . . 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Definition of Flight Leg, Flight Segment, Service and Market . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3 Revenue Management Alternatives . . . . . . . . . . . . . . . . . . . . . 4.4 Leg/Segment Revenue Management . . . . . . . . . . . . . . . . . . . . 4.4.1 Host CRS Data Collection . . . . . . . . . . . . . . . . . . . . 4.4.2 Demand Forecasting . . . . . . . . . . . . . . . . . . . . . . . . 4.4.3 Competitive Air Shopping Data . . . . . . . . . . . . . . . . 4.4.4 Overbooking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4.5 Discount Allocation Controls . . . . . . . . . . . . . . . . . 4.4.6 Reservations Inventory Controls by Leg/Segment . . . 4.4.7 Performance Measurement . . . . . . . . . . . . . . . . . . . 4.4.8 Critical Situation Identification . . . . . . . . . . . . . . . . 4.5 Origin and Destination (O&D) Revenue Management . . . . . . . 4.5.1 First, Second and Third Order Network Effects . . . . . 4.5.2 Virtual Nesting . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.5.3 Continuous Nesting (Bid Price Controls) . . . . . . . . . 4.5.4 Network Optimization Models . . . . . . . . . . . . . . . . . 4.5.5 Calculation of Seat Availability . . . . . . . . . . . . . . . . 4.5.6 Fare Qualification Rules in Passenger Valuation . . . . 4.5.7 Alternatives for Creation of Market Values . . . . . . . . 4.5.8 Post Process Nested Inventory Controls . . . . . . . . . . 4.6 Inventory and Legacy Systems . . . . . . . . . . . . . . . . . . . . . . . . 4.7 Industry Impact of O&D Revenue Management . . . . . . . . . . . 4.8 Branded Fare Products . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.8.1 Branded Fare Family Example . . . . . . . . . . . . . . . . . 4.8.2 Fare Family Attributes . . . . . . . . . . . . . . . . . . . . . . 4.8.3 Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.9 Connectivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.9.1 AVS/AVN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.9.2 Basic Booking Record (BBR) . . . . . . . . . . . . . . . . . 4.9.3 Direct Access Interactive (DAI) . . . . . . . . . . . . . . . . 4.9.4 Seamless Sell and Seamless Availability . . . . . . . . . Market Restricted Flights . . . . . . . . . . . . . . . . . . . . 4.9.5 4.9.6 Married Segments . . . . . . . . . . . . . . . . . . . . . . . . . . 4.9.7 Journey Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.9.8 Married to Journey . . . . . . . . . . . . . . . . . . . . . . . . . 4.9.9 Interactive Seat Maps . . . . . . . . . . . . . . . . . . . . . . . 4.9.10 Interactive Pre-reserved Seats . . . . . . . . . . . . . . . . . 4.9.11 Point of Sale . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

91 91 91 92 93 93 93 113 127 136 142 151 156 157 159 160 167 170 175 178 180 180 181 181 182 183 183 183 184 185 185 185 186 187 189 189 190 190 190 190

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4.10 4.11 4.12

4.13

4.14

4.15 4.16

4.17

4.18 4.19 5

4.9.12 Point of Commencement . . . . . . . . . . . . . . . . . . . . . Regaining Control of Off-tariff Fares with O&D Controls . . . . Availability versus Inventory . . . . . . . . . . . . . . . . . . . . . . . . . Maintaining Integrity of O&D in Inventory . . . . . . . . . . . . . . . 4.12.1 Codeshare Availability . . . . . . . . . . . . . . . . . . . . . . 4.12.2 Out of Sequence Bookings . . . . . . . . . . . . . . . . . . . 4.12.3 Integrity of O&D Controls and Mixed Classes . . . . . Significance of Seat Availability for Online and Offline Distribution Channels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.13.1 Approaches to Determining Availability . . . . . . . . . . 4.13.2 Impact of Cached Availability on the Revenue Management Value Proposition . . . . . . . . . . . . . . . . 4.13.3 Proxy Based Availability as an Alternative to Cached Availability . . . . . . . . . . . . . . . . . . . . . . . . 4.13.4 Distributed Availability . . . . . . . . . . . . . . . . . . . . . . Alliances and Partnerships . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.14.1 Origins of Codeshare . . . . . . . . . . . . . . . . . . . . . . . 4.14.2 Origins of Global Alliances . . . . . . . . . . . . . . . . . . . 4.14.3 The Modern Alliances . . . . . . . . . . . . . . . . . . . . . . . 4.14.4 Codeshare Flights . . . . . . . . . . . . . . . . . . . . . . . . . . Alliance Revenue Management . . . . . . . . . . . . . . . . . . . . . . . What Revenue Management Capability Does My Airline Need? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.16.1 Phased Adoption . . . . . . . . . . . . . . . . . . . . . . . . . . Revenue Management for Groups . . . . . . . . . . . . . . . . . . . . . 4.17.1 Types of Groups . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.17.2 Group Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . 4.17.3 Allotment Planning . . . . . . . . . . . . . . . . . . . . . . . . . 4.17.4 Group Demand Forecasting . . . . . . . . . . . . . . . . . . . 4.17.5 Group Attrition Estimation . . . . . . . . . . . . . . . . . . . 4.17.6 Group Performance Measurement . . . . . . . . . . . . . . Role of Revenue Integrity . . . . . . . . . . . . . . . . . . . . . . . . . . . Impact of Revenue Management in Travel and Other Industries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Low-Cost Carriers and Impacts on Revenue Management . . . . . . . 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 Value Pricing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3 Low-Cost Carrier Dynamics . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4 Inventory Control with Restriction Free Fares . . . . . . . . . . . . . 5.5 Coexistence of Inventory Controls for Network Carriers . . . . . 5.6 Impact of LCC Pricing on Revenue Management . . . . . . . . . . 5.6.1 Multi-class and Multi-class Multi-flight models . . . . 5.6.2 Impact on Revenue Management Models: Demand Forecasting . . . . . . . . . . . . . . . . . . . . . . . .

191 192 192 192 193 194 194 199 201 202 204 205 206 206 206 207 209 210 213 216 217 218 219 220 221 222 223 224 225 229 229 230 231 233 234 235 236 237

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5.6.3 6

7

8

Impact on Revenue Management Models: Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 239

Offer Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1 Origins of Merchandising . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2 Offer Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3 An Omni-Channel Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . 6.4 The Stages of Travel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.4.1 Customer Segmentation . . . . . . . . . . . . . . . . . . . . . 6.4.2 Personas for Offer Creation . . . . . . . . . . . . . . . . . . . 6.4.3 Personalizing the Best Fare Based on Trade-off Analytics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.4.4 Types of Recommendation Engines . . . . . . . . . . . . . 6.4.5 Recommendation Engine for Bundles . . . . . . . . . . . 6.4.6 Offer Engine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.4.7 Displaying Offers on the Consumer Direct Channel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.4.8 Test and Learn Experimentation . . . . . . . . . . . . . . . 6.5 Dynamic Pricing of Offers and the Role of the GDS . . . . . . . . 6.6 Corporate Travel and Offer Management . . . . . . . . . . . . . . . . 6.7 Attribute-Based Room Pricing for Hotels . . . . . . . . . . . . . . . . 6.8 Extensions to Non-Air with Stopovers . . . . . . . . . . . . . . . . . . 6.9 Offer Management and Value Scoring for GDS Displays . . . . . 6.10 Limitations of Supplier and GDS Influenced Offers . . . . . . . . . 6.11 The Universal Profile . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.12 The Universal Data Exchange . . . . . . . . . . . . . . . . . . . . . . . . 6.13 Altering the Customer Value Chain . . . . . . . . . . . . . . . . . . . .

241 241 243 245 245 248 251

Competitive Revenue Management . . . . . . . . . . . . . . . . . . . . . . . . . 7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2 Leveraging Competitive Shopping Data . . . . . . . . . . . . . . . . . 7.3 Dynamic Availability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.3.1 Pros and Cons of Dynamic Availability . . . . . . . . . . 7.4 Dynamic Pricing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.4.1 Pros and Cons of Dynamic Pricing . . . . . . . . . . . . . 7.4.2 Bridging the Chasm Between the Market Value and Ticketed Fare . . . . . . . . . . . . . . . . . . . . .

271 271 272 273 274 274 276

Agency Revenue Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.2 Aspects of Agency Revenue Management . . . . . . . . . . . . . . . . 8.2.1 Front End Commissions . . . . . . . . . . . . . . . . . . . . . 8.2.2 Back End (Override) Commissions . . . . . . . . . . . . . 8.2.3 Net Fare Markup . . . . . . . . . . . . . . . . . . . . . . . . . . 8.2.4 Bulk Fares and Packages . . . . . . . . . . . . . . . . . . . . . 8.2.5 Optimizing Screen Real Estate . . . . . . . . . . . . . . . . .

279 279 280 280 281 281 282 283

254 255 256 257 257 259 261 262 263 264 264 266 266 268 269

277

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8.2.6 8.2.7 8.3

Hotel Product Normalization . . . . . . . . . . . . . . . . . . 284 Collaboration with Corporations to Optimize Travel Spend . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 284 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 284

9

The Last Frontier: Individual Seat Pricing . . . . . . . . . . . . . . . . . . . 9.1 Individual Seat Inventory Control . . . . . . . . . . . . . . . . . . . . . . 9.1.1 Seat Map Cache for GDS Shopping . . . . . . . . . . . . . 9.1.2 Seat Map Cache for the Direct Channel . . . . . . . . . . 9.1.3 Seat-Led Shopping: Agency and Direct Channels . . . 9.1.4 Pricing of Seats . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.1.5 Impact of NDC on Revenue Management . . . . . . . . 9.2 Milestones in Airline Revenue Management . . . . . . . . . . . . . .

285 285 286 287 288 289 290 292

10

Influence of Revenue Management on the Airline Business Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.1 Impact of Revenue Management on the Airline Business . . . . . 10.1.1 Reservations and Inventory Control . . . . . . . . . . . . . 10.1.2 Network Planning and Flight Scheduling . . . . . . . . . 10.1.3 Close-in Re-fleeting . . . . . . . . . . . . . . . . . . . . . . . . 10.1.4 Fare Management . . . . . . . . . . . . . . . . . . . . . . . . . . 10.1.5 Air Shopping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.1.6 Loyalty and Coalition Programs . . . . . . . . . . . . . . . 10.1.7 Screen Display Optimization . . . . . . . . . . . . . . . . . . 10.1.8 Offer Management . . . . . . . . . . . . . . . . . . . . . . . . . 10.1.9 Pricing of Air Ancillaries . . . . . . . . . . . . . . . . . . . . 10.1.10 Inflight Catering . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.1.11 Interactive Marketing . . . . . . . . . . . . . . . . . . . . . . . 10.1.12 Airline Operations . . . . . . . . . . . . . . . . . . . . . . . . . 10.2 Coping with the COVID-19 Pandemic . . . . . . . . . . . . . . . . . . 10.2.1 Flight Scheduling . . . . . . . . . . . . . . . . . . . . . . . . . . 10.2.2 Airline Pricing and Cash Flow . . . . . . . . . . . . . . . . . 10.2.3 Robust Revenue Management . . . . . . . . . . . . . . . . .

293 293 293 294 295 296 298 300 302 303 304 305 305 306 307 308 308 309

Artificial Intelligence and Emerging Technologies in Travel . . . . . . 11.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2 Travel Complexity and AI . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2.1 Growth in Air Shopping Volumes . . . . . . . . . . . . . . 11.2.2 Growth in Air Traffic Volumes . . . . . . . . . . . . . . . . 11.2.3 Content Fragmentation . . . . . . . . . . . . . . . . . . . . . . 11.2.4 IATA New Distribution Capability . . . . . . . . . . . . . 11.2.5 Dynamic Pricing . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2.6 Payment Systems . . . . . . . . . . . . . . . . . . . . . . . . . . 11.3 Approach for Adoption of AI in Travel . . . . . . . . . . . . . . . . . . 11.3.1 Robotic Process Automation . . . . . . . . . . . . . . . . . . 11.3.2 Cognitive Insight . . . . . . . . . . . . . . . . . . . . . . . . . .

313 313 314 314 315 315 315 315 315 316 316 316

11

Contents

11.4 11.5

11.6 11.7 11.8 11.9

11.10 11.11

11.12 11.13

11.14 12

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11.3.3 Cognitive Engagement . . . . . . . . . . . . . . . . . . . . . . Operations Research at the Crossroads . . . . . . . . . . . . . . . . . . Role of AI in Travel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.5.1 Passenger Name Recognition . . . . . . . . . . . . . . . . . 11.5.2 Customer Segmentation . . . . . . . . . . . . . . . . . . . . . 11.5.3 Test and Learn Experimentation . . . . . . . . . . . . . . . 11.5.4 Fare Prediction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.5.5 User Interfaces and Experiential Learning . . . . . . . . Challenge of Interpretability . . . . . . . . . . . . . . . . . . . . . . . . . . COVID-19 and AI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Quantum Computing and AI . . . . . . . . . . . . . . . . . . . . . . . . . Building an Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.9.1 Identifying Opportunities for AI . . . . . . . . . . . . . . . 11.9.2 How to Scale . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Future of AI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Role of Big Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.11.1 Demand Forecasting Based on Consumer Preferences . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.11.2 Hotel Shopping and Dynamic Ranking . . . . . . . . . . 11.11.3 Optimizing Air Screen Display . . . . . . . . . . . . . . . . 11.11.4 Dynamic Intervention . . . . . . . . . . . . . . . . . . . . . . . 11.11.5 Hotels Dynamic Pricing . . . . . . . . . . . . . . . . . . . . . 11.11.6 Hotel Competitive Sets . . . . . . . . . . . . . . . . . . . . . . 11.11.7 The Chatter Index . . . . . . . . . . . . . . . . . . . . . . . . . . Shopping Query Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Blockchain in Travel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.13.1 Loyalty Programs . . . . . . . . . . . . . . . . . . . . . . . . . . 11.13.2 Interline Ticketing . . . . . . . . . . . . . . . . . . . . . . . . . 11.13.3 Airline/Agency Contracts . . . . . . . . . . . . . . . . . . . . 11.13.4 Revenue Management . . . . . . . . . . . . . . . . . . . . . . . 11.13.5 Known Traveler Digital Identity . . . . . . . . . . . . . . . The Role of Machine Learning with Blockchain . . . . . . . . . . . 11.14.1 Maturity of Blockchain in Travel . . . . . . . . . . . . . . .

Future State . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.1 Future of Travel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.2 Core Airline Revenue Management . . . . . . . . . . . . . . . . . . . . Future of the GDS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.3 12.4 E-Commerce Giants and Travel . . . . . . . . . . . . . . . . . . . . . . . 12.5 Seamless Customer Experience for Travel . . . . . . . . . . . . . . . . 12.6 Beyond Travel for a Seamless E-commerce Experience . . . . . . 12.7 Administration of Key Horizontal Enablers by a Neutral Entity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

316 317 318 318 319 319 320 321 321 322 322 323 323 324 324 325 328 328 328 329 329 329 330 330 331 332 333 334 335 335 336 336 339 339 340 341 342 343 345 345

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Appendix A: Traffic Freedoms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . First Freedom . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Second Freedom . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Third Freedom . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fourth Freedom . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fifth Freedom . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sixth Freedom . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Seventh Freedom . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Eighth Freedom . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ninth Freedom . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

347 347 347 348 348 349 349 350 351 351

Appendix B: Airline Industry Acronyms . . . . . . . . . . . . . . . . . . . . . . . . 353 Appendix C: Glossary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 363 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 375 Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 395

1

Origins

1.1

Introduction

In the early days of commercial aviation, private contractors had to bid for the routes to carry mail after the passage of the Contract Air Mail Act on February 2, 1925, more commonly known as the Kelly Act after Representative Clyde Kelly of Pennsylvania. For his work to revolutionize the delivery of mail service in the United States, he earned the title “father of the air mail.” The Kelly Act was an important piece of legislation to free the airmail from total control by the U.S. Post Office. The Kelly Act allowed the Postmaster General to negotiate independent contracts with private companies to carry mail. The airmail planes had one seat available for sale to a commercial passenger. Passengers would contact the airmail company in the departure city and make a reservation if a seat were available on the requested departure date. When airlines started carrying more than a single passenger on a flight, automation was required to determine availability, accept the reservation, and update the inventory counts. During the 1930s most airlines operated a request and reply system (Copeland, 1995). Seat inventory was still controlled at the flight’s departure city, which resulted in a roundabout process of the airline agent communicating to the agent in charge of inventory from a departure city for changes to the reservation on behalf of the customer. After the sale was confirmed, the agent would record the details from the passenger on a PNR card before it was transmitted to inventory by teletype or phone. In 1939, the request and reply system was replaced by a sell and report system at American Airlines. Agents in the Boston office realized that agents could sell seats freely until the flight was almost sold out, thereby reducing the number of phone calls, and speeding up the process of processing customer requests, boosting agent productivity. Once the bookings on the flight had reached a threshold, a ‘stop sale’ message was transmitted to the agents, and the process reverted to the request and reply environment. Although an improvement, this still suffered from inefficiencies and a lack of automation. # The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 B. Vinod, The Evolution of Yield Management in the Airline Industry, Management for Professionals, https://doi.org/10.1007/978-3-030-70424-7_1

1

2

1

Origins

The Civil Aeronautics Authority was formed by the United States government in 1938, which was renamed as the Civil Aeronautics Board (CAB) in 1940. CAB was given regulatory power over civil aviation including airline tariffs, airmail rates, mergers, and competition. CAB virtually controlled the performance of the U.S. airlines until the late 1970s by regulating prices for each route and governing which carrier could operate on which route.

1.2

Origins of the Airline Reservations System

The inventory control domain of the airline reservations system, also known as the host computer reservations system (host CRS) serves as the execution component of a revenue management system to accept and reject bookings. It is the place that inventory controls recommended by revenue management are executed to accept and reject booking requests to maximize an airline’s network revenues. The core components of a host CRS include flight schedules, inventory, passenger name record (PNR), ticketing, and departure control. Even with the growth of commercial aviation after World War II, the reservation process was antiquated. The sell and report system operated by airlines was inefficient. To avoid oversales, airlines had to maintain seats in reserve so that the last few seats could be processed by the slower request and reply system. The age of interactive real time computing technology had not yet arrived. In the 1940s, the President of American Airlines, C.R. Smith, was looking for a breakthrough in reservations processing with the aid of electromechanical devices. However, at that time no manufacturer of business equipment could fulfill American’s requirements for reservations processing: find flight availability, record the booking or cancellation, retain processed transactions on the agent’s device until it was manually cleared and automatically notify other stations on the status of the flight. Besides, the data processing had to be economical over the current manual environment. The first electromagnetic system designed to determine seat availability was the Boston Reservisor in February 1946 to replace the card files. Because of the time it took to reconcile the passenger’s name record against seat inventory, it required a buffer of a few seats to avoid denied boardings. In 1952, the Magnetronic Reservisor was installed at LaGuardia airport in New York which could handle 1000 flights for 10 days. With this system, many operators could look up information simultaneously and notify ticket agents over the telephone whether a seat was available. It was still considered inefficient because an operator and agent had to communicate at each end of the phone line. In addition, the issue of reconciling the passenger name record with seat inventory after each booking continued to persist. The chance encounter between American Airlines CEO C.R. Smith and a young IBM International Business Machines Corporation (IBM) salesman R. Blair Smith on a Los Angeles to New York flight in 1953 revolutionized airline reservations processing (Copeland, 1995). Blair Smith was on his way to an IBM training session in New York.

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Blair Smith1 later recalled the conversation with C.R. Smith: “I told [C.R. Smith] I was going back to study a computer that had the possibility of doing more than just keeping availability. It could even keep a record of the passenger’s name, the passenger’s itinerary, and, if you like, his phone number. Mr. C.R. Smith was intrigued by this. He took out a card and wrote a special phone number on the back. He said, ‘Now, Blair, . . . when you get through with your school, our reservation center is at LaGuardia Airport. You go out there and look it over. Then you write me a letter and tell me what we ought to do.’” At the training session Blair Smith informed IBM’s CEO Thomas J. Watson Jr. about his conversation with C.R. Smith. Watson urged Smith to do what C.R. Smith had requested: tour the reservation center, write a letter with his recommendation, and send a copy to Watson. Blair Smith recommended a joint project between IBM and American to create a computerized reservations system. The research project was called SABER—Semi Automatic Business Environment Research, to study the technical feasibility for automating the reservations process by integrating a passenger’s name to a seat reservation. When this project was completed in 1958, American signed a new contract with IBM to create the functional specifications for the world’s first passenger name record (PNR) system to match passengers to seats. In 1951, IBM on behalf of the United States Air Force set up project SAGE (Semi-Automatic Ground Environment) to develop a real time computer system for air traffic control. In 1959, the federal government declassified SAGE. The air defense system, partially developed by IBM, had the promise of interactive real time computing. SAGE was a major invention and made several technical advances (Astrahan & Jacobs, 1983; Copeland & McKenney, 1988)—magnetic core memory, active standby dual processors, modems for digital communications over voice-band channels, time sharing the central processor, input-output control with memory cycle stealing and branch and index instructions. The IBM SAGE computer, the AN/FSQ7 also had a communication front-end that received data in real time from tracking devices over communications lines. The knowledge gained from SAGE was invaluable to develop the world’s first computerized airline reservations system. IBM viewed the Sabre partnership as a high-risk undertaking and hired the services of the consulting firm Arthur D. Little to evaluate the feasibility of the endeavor. At that time IBM only sold or leased hardware and relied on customers to develop the software. To avoid failure, IBM decided to make an investment to support program development. From American, Roger Burkhardt and Wilfred (Fred) Plugge were the driving force for this initiative. To augment the programming staff, American hired mathematician Mal Perry who played a central role in the development of the reservations system. Phased deployment was planned to begin in 1961. The joint IBM/American team moved into the American Airlines headquarters at 99 Park Avenue in Manhattan, NY. Bill Elmore programmed the first PNR

1

IBM Archive: Sabre: The First Online Reservations System, https://www.ibm.com/ibm/history/ ibm100/us/en/icons/sabre/s

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demonstration on an IBM 650, which at 25,000 instructions was an early indication of how powerful the Sabre system could be. There was a software bug in the program that caused the 650’s drum memory to zero itself out unexpectedly, leading to more skepticism (Head, 2002). Losing sight that application programming in assembly language was the primary challenge, IBM made mistakes by proposing incompatible hardware in their proposals to Pan Am, American and Delta. American’s system used the binary IBM 7090 computers. Delta’s DELTAMATIC used the IBM 7070 and Pan Am’s PANAMAC used the IBM 7080 computers, which were decimal machines (Copeland & McKenney, 1988). These computers had different throughput capabilities and processors (Siwiec, 1977). In retrospect, to lower development costs by spreading the cost across the three airlines, they should have standardized on the more expensive 7090 computers. By 1960 Pan American and Delta had signed contracts with IBM and the umbrella project for all three carriers was called SABER. This prompted American to seek a new moniker for its system, and it was called SABRE (Semi-Automated Business Research Environment). In 1962, the Sabre central site for the reservations system was in Braircliff Manor. Robert V. Head, who joined the Sabre team in October 1959, had an interesting story to tell. During a visit to Bill Elmore’s office, which overlooked the exercise yard of Sing Sing Prison, Elmore explained to Head the difference between the prisoners and Sabre programmers—the prisoners knew when they were going to get out! After his tenure on the Sabre project, Robert V. Head went on to become a prolific writer and contributed articles to banking, automation, and computer journals. He also wrote several books on information systems and management. In 1964, after several cost overruns and delays, SABRE was launched. It was the first fully operational computerized reservations system. DELTAMATIC and PANAMAC became operational in 1965 followed by Eastern’s System One. Eastern’s System One was an important milestone since it was based on IBM’s Programmed Airline Reservations System (PARS), which became the new baseline for reservations processing in the early 1970s. TWA and United were allied with hardware vendors Burroughs and Univac, respectively. Neither Burroughs nor Univac had any experience with teleprocessing systems (unlike IBM’s work on SAGE) and these two initiatives ran into a range of technical problems. Forced to seek an alternate solution, both carriers decided independently to purchase Eastern’s software with installation support from IBM (Copeland & McKenney, 1988). The SABRE system had two binary IBM 7090 mainframe computers in a new data center located in Briarcliff Manor, New York. With the new computing environment, American could economically check availability, update inventory, and record a passenger name record (PNR) automatically. American’s reservation error rate dropped to under 1%, and a reservation could be processed in seconds. The new system could process 7500 reservations per hour. The average time to process a reservation in the manual card system was 90 min, which was now done in a few seconds. It was the world’s first commercial online transaction processing (OLTP) system. This fulfilled American’s requirement to economically check availability, update inventory, and record a PNR automatically. When the first fully operational

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computerized reservations system was launched in 1964, Sabre was the largest private real time commercial data processing system in the world, second only to the U.S. government. Agents accessed the mainframe reservations system with Raytheon cathode ray tube terminals (CRT) that were completely dependent on the host. The industry called these “dumb terminals” because they had extremely limited capabilities on their own. A few years later, IBM released a generalized version of its reservations system called PARS for Eastern Airlines on the powerful System/360 computers. By 1971, PARS became the industry standard with the application software for a passenger name reservation and seat inventory. United introduced the Apollo system in 1971 based on PARS. The operating system for the reservations systems was called the Airline Control Program (ACP). ACP was a low-level programming language in Assembly and code was written in 128-byte, 381-byte, 1 K or 4 K blocks. If the code could not be contained in a block, the blocks were chained for continuity. Remarkably, conceived in the 1960s, ACP has survived conceptually unchanged into the twenty-first century. ACP eventually became Transaction Processing Facility (TPF). Sabre went through a series of upgrades to the PARS-based system from 1971 to 1973 which culminated with the replacement of the IBM Selectric terminals with cathode ray tubes in 1973. After Sabre upgraded to PARS based system in 1971, nine out of the top ten U.S. major airlines were on PARS (IPARS is the international version). These transaction-oriented systems were developed in TPF, a low-level assembly language that could process a high volume of messages per second. They supported all reservations related functions—schedules, inventory, passenger name record, shopping, itinerary pricing, ticketing, and departure control. ACP and later TPF high performance operating systems revolutionized transaction processing on an unprecedented scale, improving efficiency of airline passenger operations and ultimately airline profitability. The generic name for IBM-based reservations systems is Passenger Service System (PSS). Today, Amadeus’ Altea, Sabre’s SabreSonic Customer Sales and Service and Navitaire New Skies reservations systems host many airlines. Navitaire, a wholly owned subsidiary of Accenture, was acquired by Amadeus in 2015. Other vendors that provide hosting services for reservations are Shares, SITA Horizon Customer Sales and Service, Hitit Computer Services and Radixx International. Radixx was acquired by Sabre in 2019. UniSys Aircore® in a partnership with TravelSky provides hosted reservations services for carriers based in China. The Civil Aviation Administration of China (CAAC) mandates that all Chinese carriers should be hosted for reservations in China. While the industry has been consolidating and using the services of CRS vendors that provide hosted services, a third of the world’s airlines continue to use proprietary systems for reservations processing.

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1.3

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Origins

Airline Deregulation

The Airline Deregulation Act that was signed into law in 1978, despite the unanimous opposition from airline executives, under the administration of President Jimmy Carter. Deregulation resulted in increased service, a wider choice of air services to choose from and increased competition. Airline deregulation dismantled a comprehensive system of government controls (Kahn, 1988a, 1988b). Prior to deregulation, airlines operated in a tightly regulated environment; regulated by governments and self-regulated through organizations such as the Civil Aeronautics Board (CAB) and the International Air Transport Association (IATA). A powerful voice in favor of deregulation of the airline industry, economist Alfred E. Kahn, was the chairman of the CAB. Kahn was the chief architect and promoter of deregulating the airlines in the United States despite opposition from labor unions and industry executives. He is acknowledged worldwide as the “Father of Airline Deregulation”. The act called for the complete deregulation of all fares by January 1, 1983, a year after the CAB’s route authority was to be terminated. The deregulation of the airline industry led to the birth of pricing and revenue management as it is called today. For the first time in commercial aviation history, airlines had the liberty of changing their routes and fare structure in response to consumer demand and competitive pressures. With airlines around the world going through various phases of deregulation today, the luxury of investing in new technology and solutions in the pursuit of incremental revenues has become a necessity to survive in a fiercely competitive environment. Every one of the major airlines has been practicing some form of pricing and yield management since deregulation, although with varying degrees of sophistication. The pricing and yield management process is the most important determinant of airline profitability. The practice is mature on several fronts such as business process adoption of best practices, decision support capabilities for sophisticated forecasting and inventory control recommendations and cost-effective product distribution. Deregulation saw a major transformation of a regulated industry (Crandall, 1995; Dempsey & Gesell, 1997) overnight. During the regulated era, competition was artificially limited, and profitability was all but guaranteed. The CAB approved routes and set fares that guaranteed airlines a 12% return on flights that were 55% full. Deregulation changed the dynamics of airline profitability, enhanced competition and productivity and ushered an era of survival of the fittest. The U.S. Domestic Airline Deregulation Act mandated the end of route restrictions by December 31, 1981 and the end of rate regulation by January 1, 1983. The CAB accelerated this effort and ended route regulation in 1979 and rate regulation in 1980. With deregulation, CAB was sunset on January 1, 1985. Airline deregulation also had its negative impacts on the population at large. Several smaller cities lost commercial service which in turn resulted in the movement of people toward the larger cities at the expense of the smaller cities. At that time this had an adverse impact on the general health of the U.S. economy. With deregulation, the government mandated fare levels through the CAB’s Standard Industry Fare Level (SIFL) came to an end (Kretsch, 1995) for the domestic U.S. market. This led to an explosion of fares offered by competing airlines from the

1.4 Yield Management: The Early Period

7

traditional full fare to discounted and deeply discounted fares that brought air travel within reach of a whole segment of the population that had never flown before. Between 1976 and 1990 average yields per passenger mile declined 30% after adjusting for inflation (Kahn, 1988a). Besides changing the domestic U.S. market, deregulation also influenced the airline industry worldwide. The dismantling of government control with deregulation of the U.S. airline industry in 1978 resulted in an exponential increase in fare filings and new markets that were served by airlines. Faced with intense competition after deregulation, several airlines like Braniff Airlines (1982), PEOPLExpress (1986), Eastern Airlines (1991) and Midway Airlines (1991) ceased to operate. There was also a wave of airline consolidations after deregulation.

1.4

Yield Management: The Early Period

Yield management is discussed in the context of scheduled air carriers that operate under a Federal Aviation Regulations (FAR) 121 certificate granted by the Federal Aviation Administration (FAA). Charter operations (FAR 135 certificate and variations) is not addressed in this book. Prior to deregulation, airlines operated in a tightly regulated environment; regulated by governments and self-regulated through organizations such as the Civil Aeronautics Board (CAB) and the International Air Transport Association (IATA). An overview of key economic, regulatory and market factors that led to the discipline of yield management is discussed in Cross (1998). In the 1940s CAB had regulatory powers over tariffs, air mail rates and competition. Following United’s lead in 1940, airlines introduced a second class of service (coach class) at a lower fare in the 1940s. Charter passenger airlines (airlines that offered non-scheduled passenger services), offered lower fares than scheduled airlines in 1948. Faced with this competition, schedules airlines like Capital Airlines sought permission from CAB to offer discounted fares. The era of discount travel had begun and led to a range of restricted fares in coach class. Common restrictions were off-peak travel, no stopovers, and no refunds. Discounts expanded into the 1960s with qualified fares such as youth fares, family fares with discounts for children, clergy fares (required an identification card) and Discover America excursion fares. Discount fares continued to flourish in the 1970s with a wider range of discounted fares. The “Peanut Fares” were introduced by Donald Burr when he was President of Texas International Airlines in a few markets with low load factors with CAB approval. A month later, with CAB approval, American introduced the SuperSAAver ™ fares valid only on roundtrips with a 45% discount over coach fares, a 30-day advance purchase restriction and limited between 7-days to 45-days for the return. The justification provided by American to CAB was the competitive threat from transcontinental charter flights from New York to Los Angeles and New York to San Francisco. The CAB introduced a “free zone” in 1978 in which

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fares that ranged between 70% discount over coach fare formula and a 10% increase over the coach fare formula were deemed lawful. Southern Airways conduced the first “planned overbooking/controlled oversales” experiment in 1949 (Cross, 1998). In the mid to late 1950s and 1960s, airlines practiced overbooking, the process of accepting more bookings than available capacity to compensate for the effects of cancellations and no-shows. It was reported by Marvin Rothstein (1985), Director of O.R. at American Airlines, that prior to 1961, U.S. airlines did not publicly acknowledge overbooking, even though they practiced it discreetly. In 1961, the CAB acknowledged that one in every 10 passengers on the 12 leading airlines at that time no showed for flights which caused economic distress to the airlines. Even though the CAB did not officially approve the practice of overbooking, it mandated a no-show penalty of 50% of the value of the ticket to passengers and also put in place a reciprocal policy wherein airlines paid a penalty equal to 50% of the value of the ticket for passengers who were left at the gates. These penalties were abandoned in 1963 and a subsequent study was conducted by the CAB in 1965–1966. Their study determined that there were 7.69 denied boardings for every 10,000 passengers boarded (CAB economic Regulation Docket 16563, 1967). The CAB concluded a year later in 1967 that airlines could accommodate more passengers by practicing controlled overbooking. Hence, overbooking became an accepted practice, but the CAB also raised the penalty to 100% of the value of the ticket. The involuntary denied boardings reported as the number of denied boardings for every 10,000 passengers boarded was monitored by the CAB and today is still reported by the U.S. Department of Transportation (DOT). Prior to airline deregulation, the first discount allocation model was proposed by Kenneth Littlewood (1972) from the British Overseas Airways Corporation (BOAC). Littlewood proposed that airlines maximize revenues instead of passenger occupancy on a flight for the perishable seat inventory. Known as Littlewood’s rule for two booking classes, this can be extended to multiple classes. In 1980, William Swan, a member of the O.R. department at American, made extensions to Littlewood’s model, with the logit approximation to the normal distribution. This was first deployed in 1982 (Smith, Leimkuhler, & Darrow, 1992). Yield management came into existence on a significant scale a few years after the deregulation of the U.S. airline industry. The term “yield management” is attributed to Robert L. Crandall when he was Senior Vice President at American Airlines (Cross, 1995). When Robert Crandall was named President of American Airlines in 1980, airline deregulation was already underway. American, under Crandall, was concerned about relentless price cutting by competitors in an unregulated market that would wipe out profits. When this came to pass, innovation began in the airline industry. Carriers operating in a deregulated environment quickly realized the importance of passenger mix to improve yields and the importance of increasing the seats occupied by revenue passengers because the cost of carrying a marginal passenger on a flight was negligible, and it was almost pure profit.

1.4 Yield Management: The Early Period

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Crandall succeeded Albert Casey as American’s Chairman, President and CEO in 1985 (Serling, 1985). The first yield management system was created in 1985–1986 by American Airlines under the leadership of Robert Crandall to counter the competitive threat from PEOPLExpress (Crandall, 1995; Cummings, 2007). For American, yield management served as a strategic and tactical weapon to counter the competitive threat. An Operations Research team under Tom Cook, Director of the Operations Research group (and later President of American Airlines Decision Technologies) developed the system which was deployed in 1986 (Cook, 1999; Smith et al., 1992). This system, called DINAMO (Dynamic INventory Allocation Maintenance Optimizer), with overbooking and multi-class discount allocation controls was launched in 1985 (Cummings, 2007). Work on yield management at American started in 1979 and went through five iterations (Smith, 2007); starting with a rules-based system to the launch of DINAMO in 1986. The five iterations were the Multiclass Optimization Modeling System (MOMS) in 1979, Discount Allocation Decision System (DADS) in 1980, City Allocation Reporting System (CARS) in 1981, the Super City Analysis and Reporting System (SCARS) in 1982 leading to DINAMO in 1986. These first-generation applications were deployed on IBM mainframe computers with the MVS operating system. The SCARS reports were produced from a MARK IV program and given to the analysts for review. By the time DINAMO arrived, the yield management analysts accessed data and reports with an IBM mainframe 3270 terminal emulator. As President of Texas International Airlines under Chairman Frank Lorenzo, Donald Burr revitalized the airline industry with the introduction of the “Peanut Fares” in 1977. This was before airline deregulation, and Texas International Airlines sought permission from the CAB to offer these fares at half the price on selected low-density routes, making it the fastest growing airline in the country, earning $8.2 million on revenues of $145 million (Easterbrook, 1987). Burr left Texas International Airlines in January 1980 (Gordon, 1989) to start his own low-cost airline, PEOPLExpress which was launched in April 1981. The airline grew at a rapid pace with its primary hub in Newark, New Jersey. By 1985, Donald Burr’s PEOPLExpress Airlines was listed by Fortune as one of the fastest growing companies. By undercutting fares offered by competing airlines, PEOPLExpress market share increased rapidly to become the fifth largest airline in the U.S. Left with no choice but to match the low fares, Crandall relied on American’s new yield management system, DINAMO, to capacity control the availability of these deeply discounted fares. Changes were also made in the reservations system, Sabre PSS, to accept these new discounted inventory controls from DINAMO, and seat availability was based on the net nesting method (Vinod, 2006) with segment limits. On January 17, 1985 American introduced the nonrefundable Ultimate Super Saver fares which were priced lower than the PEOPLExpress fares. For the very first-time yield management analysts were able to explicitly control the availability of the deeply discounted fares. In the absence of yield management controls, PEOPLExpress sold all seats at the deeply discounted prices, which were not sustainable over the long run to support the airline’s cost structure. The implementation of yield management

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by American Airlines had a significant impact on PEOPLExpress. By September 1986, PEOPLExpress was in deep financial trouble and eventually ceased operations on February 1, 1987 when they merged with Continental Airlines. PEOPLExpress failed for two reasons: the absence of yield management controls and the rapid uncontrolled expansion of the fleet with the acquisitions of Denver-based Frontier Airlines, Britt Airways and Provincetown-Boston Airlines which placed an enormous debt burden on the airline. American Airlines used their new strategic weapon to control seat inventory and maximize network revenues. American matched PEOPLExpress fares but offered only a few seats at these deeply discounted prices. Donald Burr, Chairman and CEO of PEOPLExpress Airlines summed it up in 1986 (Cross, 1997). We were a vibrant, profitable company from 1981 to 1985, and then we tipped right over into losing $50 million a month. We were still the same company. Still at Newark. There were no changes in this company. What changed was American’s ability to do widespread Yield Management in every one of our markets. We had been profitable from the day we started until American came at us with Ultimate Super Savers. That was the end of our run because they were able to underprice us at will and surreptitiously. There was nothing left to defend us. All we had left was our cost structure, which at the time was a billion dollars a year less than American. You figure that at a billion dollars cheaper, you ought to be safe. We kept naively hoping that our billion- dollar cushion would give us enough room even if they underpriced us here and there. But all they needed to take away from us was that marginal traffic above breakeven. You don’t have to take away half the guy’s market. All you have to do is take away a few seats on every flight and the guy’s dead. Donald Burr Chairman and CEO PEOPLExpress Airlines

Burr realized the significance of yield management late in the game and was quoted as saying (Bryan, 1989) “What you don’t know about Revenue Management could kill you!” There is another aspect of what American did. When American Airlines introduced the deeply discounted Ultimate SuperSAAver™ fare in 1985, prices were discounted 70% and ranged between $39 and $129 but were capacity controlled, bringing air travel within reach of an entirely new population that had never flown before. Yield management maximizes revenue from perishable inventory based on the fundamental premise that all customers are not created equal. Seat inventory is perishable, since once the flight departs, the unsold seat inventory is lost forever. Yield management is the process of selling the right seat to the right customer at the right price at the right time. Yield management is called “revenue management” today since it is revenue and not yield (revenue per revenue passenger mile) that is maximized. The transition happened officially in 1993 when the IATA Conference on Yield Management, which was organized in 1988, was rebranded as the IATA Conference on Revenue Management.

1.4 Yield Management: The Early Period

11

Development of the first yield management system started off as a rules-based system (Smith, 2007) before being enhanced to a decision support system based on marginal revenues. The economic overbooking model (Smith, 1982) in DINAMO evaluated tradeoffs between denied boarding costs and the cost of a spoiled seat. The discount allocation model was an expected marginal seat revenue model, an extension of Littlewood’s Rule (Littlewood, 1972) to multiple booking classes, which generated the joint protection levels for higher valued booking classes relative to the lower value booking class. This method was later called EMSRB (Belobaba, 1992) as a computationally viable heuristic to optimal booking limits (Belobaba, 1992; Curry, 1990) that requires the evaluation of multi-dimensional numerical quadrature (convolution integrals) and more compute power. Seat inventory controls for this first-generation yield management system were based on leg/segment controls—leg class nested inventory controls with segment close indicators and segment limit sales by booking class. Beyond the decision support capabilities, the success of yield management at American was made possible with advances in inventory control on American’s Sabre Passenger Sales and Service (PSS) system (the host CRS) (Vinod, 1990) where American’s schedules and fares were stored. American Airlines established its hub at Dallas/Fort Worth International Airport (DFW) on June 11, 1981. Central to the launch of Growth Plan I and Growth Plan II in the 1980s by American’s CEO Robert Crandall was the introduction of the McDonnell Douglas MD-80 (Super 80) aircraft into the fleet in 1983. The large number of new MD-80s entering service spurred the transition from a point-to-point network to the creation of a hub and spoke network at American’s two primary hubs, Dallas/Fort Worth and Chicago O’Hare to provide air services for a wide geographic area. With the growth plan the fleet more than doubled in size between 1984 and 1990. Crandall also created the “B-scale”, a lower pay scale for new pilots. New hubs were added in Nashville, Tennessee, Raleigh-Durham, North Carolina, San Jose, California, Miami, Florida and San Juan, Puerto Rico. Expansion of the fleet and workforce led to lower operating costs, increased revenues, and profits. At one point, American had 363 MD-80s in its fleet. Crandall also found other creative ways to cut costs (Mayerowitz, 2011). In 1987 he famously removed one olive from each salad service on American’s flights. He reasoned that passengers would not notice, and the airline would save at least $40,000 per year. With cost in mind, he also preserved the airline’s distinctive look. The planes were polished but not painted. “No paint means less weight,” Crandall once explained. The unpainted look, keeps “the sun glinting off our ‘silver birds.’” In a post deregulated world, a hub and spoke network provided service to more markets through the hubs and increased aircraft utilization. Hub economics were based on a mix of 30% local traffic and 70% connecting traffic. With the transition from a point-to-point network to a hub and spoke network, leg-based inventory controls generated by DINAMO had limitations and steps were undertaken to transition from a leg/segment yield management system that optimized controls by flight to an Origin and Destination (O&D) based system that maximized network

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revenues by capturing the value of the reservation request by O&D. The first O&D yield management system was deployed in 1987 with virtual nesting (Smith, 1986; Smith et al., 1992) controls, which won the prestigious Edelman award in 1992. With virtual nesting, O&D classes were mapped to virtual buckets to control inventory by O&D. Virtual nesting produced an additional 1%–2% in incremental revenues over leg-based controls. Inventory in the Sabre PSS was upgraded to support virtual nesting controls. Optimal mapping of itineraries to virtual buckets based on value produced an additional 0.4% in revenues (Vinod, 1989). American calculated that the systematic use of yield management enabled the company to generate $1.4 billion in incremental revenue between 1989 and 1991, while AMR’s (the parent company) profits were $892 million over the same period. Among the European carriers, SAS deployed O&D controls in 1993 (Petersen, 1996). Keeping abreast with the evolution of yield management, the real time transaction systems, the Global Distribution Systems (GDS) and airline central reservations systems (host CRS), brought to market some of the key enablers for O&D control such as seamless (interactive) availability and sell for true last seat availability by point of sale (POS), married segment control and journey control from the 1980s to early 1990s. In the 1990s, yield management was renamed revenue management since revenue and not yield is maximized. Based on the premise that customers pay different fares for the same origin and destination, airline customers are conditioned to paying different amounts for the same product depending on when they book and the details of the itinerary. Every time a person boards a flight, it is highly likely that the person in the next seat paid a different price. When Tom Cook was asked to compare his management style with that of Bob Crandall after they had both departed from American Airlines, he remarked (Horner, 2002): “Bob is a very unique and incredibly talented chief executive. I admire him tremendously. As for my own management style, you would have to ask the people who work with and for me. I consider myself a pretty much hands-on guy and I share that with Bob. I look for the right people and give them the authority to get things done. I’m results-oriented.”

1.5

Origins of the Frequent Flyer Programs

Texas International Airlines created the first mileage-based frequent flyer program in 1979, but it was dismantled within a year and later merged with Continental Airlines in 1982. A year later, in 1980, Western Airlines created the Travel Bank program. This program introduced by Western Airlines (‘the only way to fly’), which awarded a US$50 travel certificate for every five trips, was not mileage based. American Airlines launched the AAdvantage frequent flyer program on May 1, 1981. Robert Crandall is credited for launching the frequent flyer program as an instrument to promote the brand to repeat customers, track frequent flyer consumption patterns and create targeted offers for the most loyal customers. The idea evolved from William

1.5 Origins of the Frequent Flyer Programs

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Table 1.1 Frequent Flyer Programs in 1981 Airline American Airlines United Airlines Trans World Airlines Delta Air Lines Western Airlines Braniff Airlines Continental and Eastern Airlines Republic Airways Northwest (Orient) Airlines Air Canada

Frequent Flyer Program AAdvantage Mileage Plus Frequent Flight Bonus/Aviators Frequent Flyer Program (later called SkyMiles) Air Pass II Travel Bonus Bonanza Frequent Traveler/OnePass Frequent Flyer Free Flight Plan/WorldPerks Altitude

Bernbach (Levenson, 1987), the legendary founder, and CEO of Doyle Dane Bernbach, American’s advertising agency, based on his observation of commercial banks who were wooing their best customers with free products such as toasters and electric blankets. He advocated a special “loyalty fare” for frequent flyers to American Airlines. Though the “loyalty fare” was never rolled out, it eventually led to the mileage based AAdvantage program. The first wave of mileage based frequent flyer programs, all launched in 1981 after American’s AAdvantage program, is summarized in Table 1.1. It is a well-accepted fact that the loyalty programs are a resounding success for the airlines. These loyalty programs create new demand, build brand equity although the end product is more or less a commodity, build a lifetime relationship with loyal customers who are married to the frequent flyer program and above all serve as a secondary source of revenue through joint marketing programs with credit card companies, telephone companies, and numerous promotions with packaged goods companies. At present the largest airlines in the world boast memberships of more than 50 million and partnerships with hundreds of companies. Since inception, frequent flyer programs have been embraced by airlines worldwide in a highly competitive marketplace to lock-in customers with frequent flyer miles and add-on incentives. Loyalty programs have expanded into hotels, rental car, casinos, financial services, grocery chains, apparel retailers and restaurants. Loyalty programs offered by travel suppliers such as airlines and hotels have become so pervasive that it is difficult to imagine a world without them. Over the years, they have enabled travel suppliers to extend their reach by selling miles and points to credit card companies and retailers who in turn use it as an incentive to sign up new customers, stimulate demand and gain repeat sales. Although loyalty programs should be leveraged for revenue growth, they are frequently wrongly perceived as a necessary evil, the cost for doing business. A challenge faced by a loyalty program is to estimate the share of wallet of a customer. A customer may be loyal based on the amount of business transacted. However, the customer may spend twice as much on a competitor. Unfortunately,

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this data are not accessible. A combination of surveys and predictive analytics based on a loyal customer’s purchase behavior patterns can be used to make inferences on this important statistic. Large brick-and-mortar retailers like Walmart and Target face a somewhat similar problem. To gain insight into the share of wallet of a customer, credit card companies monetize their data by selling aggregate spend data by postal code back to the retailers. This in turn influences marketing spend by retailers on free standing inserts (FSI), print advertising in local newspapers and circulars. Harrah’s uses predictive analytics to determine share of wallet. A lot has been said on Harrah’s successful total rewards program (Loveman, 2003). The loyalty card tracks a customer’s playing time, money won and lost, and credits accumulated to build history of each gambler’s behavior at Harrah’s properties. Central to the success of this program are customer segmentation and the use of advanced predictive analytic tools to determine which customers should be targeted with offers and incentives to return and play. Although data are readily available on past customer visits from historical data, predictive models reveal which customers are likely visiting other casinos in the market and be used to determine which customers should be targeted with offers and incentives to return and play. Loyalty programs also have a significant impact on revenue management. Every frequent flyer redemption displaces revenue passengers, which represents a cost to the airline. Frequent flyer redemption policy is influenced by its impact on revenue management. Frequent flyer redemption bookings are capacity controlled.

1.6

Origins of the GDS

From the mid-1960s to the mid-1970s, both airlines and travel agencies promoted the concept of a neutral industry-wide reservations system to standardize reservations workflows and enhance agent productivity (Vinod, 2009). The Reuben H. Donnelly Corp. (publisher of the Official Airline Guide) initiated development of the Donnelly Official Airline Reservations System (DOARS) on a Univac platform in 1967. It subsequently failed because of lack of financing from the 21 airline participants. The Automated Travel Agency Reservations System (ATARS), a system that was exclusively for airlines and travel agents based on PARS, sought approval from the Civil Aeronautics Board (CAB). The U.S. Justice Department interpreted the exclusivity feature as a per se violation of anti-trust laws, which in turn triggered a CAB investigation. The ATARS agreement was modified to meet the requirements but was abandoned before CAB could reach a verdict. The contract to develop a common integrated travel agency system between the American Society of Travel Agents (ASTA) and Control Data Corporation (CDC) in 1973 also failed when the airline constituents could not accept a computer vendor controlling access to travel agents. To gain control of the distribution channel, American proposed the creation of a joint task force consisting of airlines, ASTA and hardware vendors to develop the Joint Industry Computerized Reservations System (JICRS) in 1974. American’s

1.6 Origins of the GDS

15

Robert Crandall and Max Hopper played key roles in defining the functional scope, benefits, and costs of JICRS. In July 1975, the technical evaluation team concluded that the development of JICRS was technologically feasible and would produce significant cost savings to the airline participants. However, United did not find the terms of the financing of JICRS acceptable. It was tied to passenger volumes and United, by virtue of being the largest domestic U.S. carrier, would become the largest investor. JICRS failed when United announced that it would actively promote its PARS-based Apollo system to travel agents within 9 months. Carriers such as American and TWA were forced to adopt similar plans and the race was on to develop and actively promote these systems to the travel agency community. ASTA made a final attempt to create a joint system, known as the MultiAccess Agent Reservations System (MAARS). It failed when CAB refused antitrust immunity over concerns with exposing the other airline CRSs being promoted to the travel agencies. When attempts to create a joint system for travel agents failed in the 1970s, airlines started promoting their reservations systems to travel agents to sell their products. Agency features were added to the reservations system and the host CRSs with the enhanced capabilities used by travel agents identified during the JICRS study came to be known as the Global Distribution Systems (GDS). These systems developed agency point-of-sale support worldwide, whereas the airline system where seat inventory was stored, is known today as host CRSs or simply CRSs. Hosting multiple airlines in a single system followed quickly. The display of flights on the Sabre system did not follow the OAG (Official Airline Guide) format. The OAG format displayed nonstops followed by direct flights, online connections and interline connections. The Sabre display was much more sophisticated and based on total travel time (also called elapsed time), displacement from requested time and carrier preference. Differences in the display order between OAG and Sabre led to the perception that the display was biased toward American. Interestingly each of these three schedule parameters feature very prominently in consumer choice model calibrations today to understand consumer behavior and how they select an itinerary from a choice set. In 1976 there were 130 travel agencies that subscribed to Sabre. Max Hopper, the chief visionary of the Sabre system owned by American Airlines was the first to market with a product for travel agents. American aggressively marketed the reservations system as an extension of the airline sales force, to sign up new subscribers. It was perceived by competitors that the product display of flight schedule and availability favored American over other carriers. United’s Apollo was introduced to the travel agency community a few months later. Apollo did not include the travel agency features, identified in the JICRS study, which Sabre supported and United quickly realized that travel agents preferred the Sabre system. Trans World Airlines (TWA) introduced their system (TWA PARS) shortly after Apollo. They also introduced PARS II with limited features, which found a niche with small travel agencies., followed by the Trans World Airlines (TWA) PARS system. The common perception was that the airline owned

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Origins

proprietary reservations systems gave these airlines an unfair competitive edge over airlines that were not promoting a host CRS of their own. MAARS Plus, a system not owned by airlines or travel agencies was launched in 1977 by ITT. They made the lofty claim that it was the only unbiased system, which was not true. This system had unique features; it offered direct connections to airline reservations systems of all participating carriers. MAARS Plus also stored the reservations on the airline reservations systems. This system failed for two reasons. First, the absence of a common language made it difficult for travel agents to understand the various codes from the individual airline systems and, second, the revenue model was flawed because investors could not recover subscription fees from the travel agents. From a product development and capability perspective, Sabre was a year ahead of the other CRSs. Most of the CRSs had similar marketing strategies—in return for the hardware and training that was provided at no cost, the agencies agreed to pay a subscriber fee based on the volume of bookings made by the agency. Agencies offset these expenses with the standard commissions paid by the airlines, about 10%. In addition, they also received override commissions to encourage bookings on specific airlines, based on thresholds achieved for key performance indicators (KPIs). The volume of business at travel agencies expanded rapidly with the agency channel providing most of the bookings for the airlines. After airline deregulation, facing stiff competition, airlines with vested interests in agency reservations system started investing in back office systems. American bought Agency Data Systems (ADS), after United had acquired a license to use ADS. All systems today have back office systems which are required for agents to view agent sales reports (ASR) and commissions. When smaller airlines and travel agencies complained, CAB identified four anticompetitive behaviors that were prevalent in 1983. The Code of Federal Regulations (CFR) was introduced in November 1984 which prohibited anti-competitive behavior from airlines that owned the dominant CRSs. They were: 1. Carrier preferencing in displays was eliminated and all displays were required to display results which best met the query parameters submitted by a travel agent. Hence, on United’s Apollo, for a specific market, an American flight may be ranked first and appear on the first line of the display. 2. Capabilities available in the CRS for a specific airline should be universally available to all participating airlines. 3. Discrimination between CRS providers was also banned which ensured that all airlines that owned and marketed a CRS to travel agencies participated in all the CRSs. Hence an airline that participated in one CRS had to participate in all of them. It was also mandated that travel agency booking data collected by a CRS were required to be made available to the competing CRSs for a fee. This is now referred to as MIDT (Marketing Information Data Tapes). 4. Booking fees charged by the CRSs to participating carriers were required to be non-discriminatory. Before this ruling, price discrimination prevailed, and some carriers paid no segment booking fees whereas others did. Booking fee billing

1.6 Origins of the GDS

17

data previously provided on microfiche or paper is provided as BIDT (Billing Information Data Tapes) by the CRS vendors. The CRS rules went into effect on November 11, 1984, though it did not dispel the debate over anti-competitive behavior. During this period, a notable feature launched by Sabre was Sabre Traveler Automation Records (STARS) that eliminated the manual customer contact list maintained by travel agents. The CRSs came to be known as the GDSs as the systems developed agency point-of-sale support worldwide, whereas the airline system where seat inventory was stored is known today as host CRSs, or simply CRSs. Hosting multiple airlines in a single system followed quickly. When airlines realized the value of GDS ownership, several new GDSs appeared in the market in the 1980s. Eastern Airlines launched System One Direct Access (SODA) in 1981. It became operational in 1982 and was based on PARS. Delta Air Lines launched DATAS II in 1982 based on PARS technology, terminating the joint marketing agreement with United’s Apollo system. Abacus was founded in 1987 by Cathay Pacific Airways, Singapore Airlines and Thai Airways International PLC. Thai Airways later dropped out of the partnership and other airlines joined the partnership. PARS and Abacus signed an agreement that created the foundation for Worldspan. Amadeus Global Travel Distribution was created in 1987 as a joint venture between Air France, Lufthansa, Iberia, and Scandinavian Airlines System (SAS). System One, developed by Eastern Airlines was the baseline for the Amadeus reservations system (passenger name record) code running on TPF and the pricing engine was from Air France, running on Unisys. The system became operational in 1991 by integrating four national reservations systems, Esterel in France, Savia in Spain, Smart in Sweden and START in Germany (Kärcher, 1996). These national systems were controlled and (partly) owned by the founding airlines of Amadeus. A wave of consolidations resulted in just four major domestic GDSs. These are Sabre, Apollo, Worldspan and System One/Amadeus. Sabre was partially spun off from AMR Corp, parent of American Airlines, in June 1996. Apollo was operated by Galileo International, which was owned by United Airlines. PARS and DATAS II merged to create Worldspan in February 1990 and was initially owned by Delta Air Lines, Northwest Airlines and Trans World Airlines. Galileo International was created in 1993 by 11 North American and European airlines based on the Apollo system: Aer Lingus, Air Canada, Alitalia, Austrian Airlines, British Airways, KLM Royal Dutch Airlines, Olympic Airlines, Swissair, TAP Air Portugal, United Airlines and US Airways. Travelport was created in 2001 by Cendant following the acquisition of Galileo GDS and Cheap Tickets. Travelport acquired Orbitz in 2004. Travelport for sold to the Blackstone Group in 2006. Travelport acquired Worldspan in 2007. The GDS is viewed as a marketplace that links buyers to the suppliers. It is also known as an aggregator or intermediary. Besides airlines, GDSs also have access to content from hotels, rental cars, rail operators and cruise lines. It is used by

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Origins

traditional brick-and-mortar travel agents, consolidators, wholesalers, and online travel agencies. Today, the major global GDSs are Amadeus, Sabre and Travelport.

1.6.1

Industry Standards and Governance

To participate in a GDS, airlines and GDSs have a participating carrier agreement (PCA). Full content is a provision in the PCA for an airline to provide the same content through the GDS that they provide to their consumer direct website, call centers or any other channel. There are two primary entities that define how the airline CRS and GDS work with each other, IATA and A4A. IATA, the International Air Transport Association, represents, leads, and serves the airline industry comprised of over 250 airlines worldwide. A4A, Airlines for America, previously known as the Air Transport Association of America, Inc. (ATA), is the premier trade organization of the principal U.S. airlines. A4A members transport over 90% of all U.S. airline passenger and cargo traffic. IATA and A4A publish a wide variety of manuals for all aspects of the airline industry. IATA published AIRIMP (Air Interline Messaging Procedures), the reservations interline message procedures passenger manual. A4A publishes the Standard Interline Passenger Procedures, known as SIPP. The standards define several messaging protocols. The teletypewriter message, referred to as teletype, is a simple asynchronous message. In the 1920s, airlines recognized the need to communicate reservation activity between themselves and started sending messages to printers using teletype. Messaging standards were established after World War II as the airline industry flourished. These teletype standards were used as the foundation of system-tosystem communications. IATA standardized teletype messages in the airline industry. There are many message types, including name messages for sell, update and cancel reservations, availability status messages advising when flights are open/ closed for sale, airline schedule updates, ticketing, and special formats to support codeshare agreements. Interactive messaging using EDIFACT (Electronic Data Interchange for Administration, Commerce and Transport) was defined by IATA Passenger and Airport Data Interchange Standards (PADIS). This is also referred to as Type A messaging. The Open Travel Alliance (OTA) is a non-profit standards body that has developed messaging standards for interoperability to disparate systems across all verticals (air, hotel, car, etc.) in the travel industry. XML (extended markup language) message standards are being defined by the Open Travel Alliance (OTA) and Open AXIS using EDIFACT as a reference. IATA is also involved in developing the new XML and JSON (Java Script Object Notation) messaging standard for the New Distribution Capability (NDC). These new message standards make it easier for new types of standard and rich content from airlines and service providers to be transmitted to a GDS.

1.6 Origins of the GDS

19

All GDSs participate in the IATA forums as non-voting members, alongside airlines and other service providers.

1.6.2

Communications Partners

SITA (Société Internationale de Télécommunications Aéronautiques) was founded in 1949 by 11 airlines to combine their individual communication networks to realize cost efficiencies. The 11 original airlines were Air France, KLM Royal Dutch Airlines, Sabena, Swissair, TWA, British European Airways Corporation (BEAC), British Overseas Airlines Corporation (BOAC), British South American Airways (BSAA), Swedish A.G. Aerotransport, Danish Air Lines and Norwegian Air Lines. SITA manages complex communication solutions for its air transport, government and GDS customers. It has one of the world’s largest, fastest, and most reliable messaging services backbone and supports the world’s largest teletype messaging community and routinely exchanges over 25 million messages a day. SITA’s international data network operates in over 200 countries and territories. The GDSs and online service providers use the SITA network to supplement their own. ARINC (Aeronautical Radio, Inc.) was chartered by the U.S. Federal Radio Commission, which later became the Federal Communications Commission (FCC), to serve as the airline industry’s single licensee and coordinator of radio communications outside of the government. The airline industry uses teletype messages over ARINC or SITA networks to communicate between reservations systems. ARINC also introduced ACARS (Aircraft Communications Addressing and Reporting System) in 1978 to support aircraft to ground communications via aircraft band radio or satellite.

1.6.3

Settlement Partners for Airlines and Agencies

Interline travel uses the services of settlement partners to distribute payment to the appropriate parties. The IATA Clearing House (ICH) enables the world’s airlines and airline-associated companies to settle their interline billing. There are over 475 members and associates and settles over 50 billion in interline and service transactions annually. The scope of clearance is further expanded through its interclearance agreement with the U.S.-based Airline Clearing House (ACH) which settles over $12 billion in receivables on behalf of 91 airlines. There are two settlement partners for travel agencies—BSP and ARC. Both are owned and governed by the airlines. Travel agents issue one sales report and remit one amount to a central point and airlines receive one settlement covering all agents. The Billing and Settlement Plan (BSP) facilitates the selling, reporting, and remitting procedures for IATA accredited passenger sales agents. All IATA accredited agents in the BSP country of operation are automatically eligible for participating in a BSP. General Sales Agents (GSA) and Airport Handling Agents (AHA) may also participate in a BSP based on nomination from the airline they represent. The Airline

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Origins

Reporting Corporation (ARC) serves the travel industry with financial services, ticket distribution and settlement for ARC accredited travel agency locations and corporate travel departments in the United States, Puerto Rico, and the U.S, Virgin Islands. The financial settlement process supports two major workflows and their variants. First is the scenario where the travel agent is the merchant of record and second, where the airline is the merchant of record. When the travel agent is the merchant of record, the travel agent collects payment from the traveler, and then submit the payment to BSP/ARC clearing houses for settlement to the airline. When the airline is the merchant of record, the traveler accesses a payment gateway on the agent’s system to pay for the ticket directly to the airline.

1.6.4

Industry Partners for Airline Fares

The Airline Tariff Publishing Company (ATPCO) and SITA are the main publishers of airfares and rules. The Air Traffic Conference of America which was part of the Air Transport Association of America (ATA) was founded in 1945 to publish passenger tariffs. In 1965 they divested from ATA as an independent company and continued to publish passenger tariffs to travel agents. ATPCO was established in 1975 by 11 founding airlines (Air Canada, Air France, Air Nippon Airways, American Airlines, British Airways, Delta Air Lines, Hawaiian Airlines, KLM Royal Dutch Airlines, LATAM Airlines, Lufthansa Airlines and United Airlines) and was branded as ATPCo and later changed to ATPCO. These fare aggregators collect fare information from over 400 airlines and distributes it to GDSs and CRSs both domestically (U.S.) and internationally on varying frequencies. All GDSs also have a capability to receive fares directly, used primarily for private fares, thereby bypassing ATPCO and SITA.

1.6.5

Industry Partners for Airline Schedules

The Official Airline Guide (OAG) has been publishing airline schedules since 1929. Innovata emerged as a competitor to OAG in 1998. OAG and Innovata are two of several vendors that distribute airline schedules, Standard Schedule Information Manual (SSIM), to the GDS. Airlines submit their schedules to schedule aggregators for worldwide distribution. The fare aggregators also distribute connect point and minimum connect time (MCT) information. IATA standard MCT are set by airport authorities and airlines can file exceptions to the standards.

1.6 Origins of the GDS

1.6.6

21

GDS and Collaborative Entities

The primary role of the GDS to support these entities is content sourcing. The GDSs have relationships with travel management companies (TMC), corporations, online travel agencies (OTA) and leisure travel agencies. The Computerized Airline Sales and Marketing Association (CASMA) is an industry conference that brings GDSs, airlines, and vendors in travel distribution together to discuss current trends and state of the industry. TMCs support corporate travel programs and compete on service, price, technology, duty of care and reporting. Examples are American Express Global Business Travel (AmEx GBT), Carlson Wagon-Lit (CWT), BCD Travel and Corporate Travel Management (CTM). Corporations can contract directly with a GDSs corporate booking tool or indirectly with a TMC for travel technology and services to support their corporate travel program. Corporations can also source content directly from suppliers, though this is the exception than the norm. Corporations can be Fortune 500 companies as well as smaller companies. OTAs provide travelers with access to leisure content and online self-service purchase options. OTAs rely on GDSs for shopping and booking APIs. The older APIs are SOAP (Simple Object Access Protocol) based while the newer APIs are REST (Representational State Transfer) APIs. REST APIs provides simpler method of accessing web services, are JSON compliant, and can benefit mobile applications. Examples of OTAs are Booking Holdings, Expedia, Svenska Resegruppen AB, CheapOair, Hopper and Despegar. The leisure travel agencies, both online and brick-and-mortar, provide end-to-end travel service to customers based on their goals, finding the best options, and booking travel. Examples are dnata, American Express and Flight Centre Travel Group. Beyond these entities there are other technology providers that may use products and services offered by GDSs. Examples are Google Flights, farecompare, Bing, Yapta, TripBam, etc.

1.6.7

Government Oversight

The travel and transportation industries are controlled by many government bodies. There are transportation boards such as the U.S. Department of Transportation (DOT) which mandates on-time reporting in the U.S. The European Commission (EU) publishes a GDS code of conduct that governs many aspects of the GDS business such as air availability displays, contract terms and conditions. Various governmental bodies dictate data privacy standards. Immigration and security organizations dictate policy. A GDS must comply with all governmental requirements in countries where they do business.

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1.6.8

1

Origins

Airline Divestiture and Deregulation of the GDS

Starting in 1994, paper tickets were gradually replaced with electronic tickets, known as e-tickets. The digital version of the paper tickets was stored on the reservations systems making the ticketing process cheaper. In the late 1990s, the airlines started divesting their interests in the GDSs. Galileo acquired Apollo in 1992 and went public in 1997 as an independent company. Sabre separated from American in 2000 and became independent. Amadeus took back ownership from the founding airlines in the early 2000s and is listed on the Madrid stock exchange. In July 2004, the U.S. Department of Transportation (DOT) deregulated the U.S. GDS industry and sunset all the CRS regulations in the United States. This included: 1. Display bias, which prohibited GDSs from preferential displays to favor any airline. This rule did not apply to hotels or rental car companies. 2. Parity clauses that required participating airlines to maintain the same level of service in all GDSs. This was significant since airlines no longer had to subscribe to all the GDSs, but they could be selective, but this has not happened because every airline wants access to the premium corporate segment. 3. Full content agreements negotiated between a GDS and an airline. The GDSs previously would mandate full content agreements as a condition for participation to ensure access to all fares inclusive of web fares. Before the Internet era, the volume of bookings from the agency channel had increased steadily over the years and the resultant mix was 80% of bookings came from the GDSs. The remaining bookings came from airline call centers, ATOs and CTOs. After GDS deregulation, very few airlines have stopped participating in a GDS. GDS share of bookings worldwide has slipped to the 40%–50% range. The major global GDSs are Amadeus, Sabre and Travelport (Apollo, Galileo and Worldspan). From an air market share perspective, Amadeus is the largest followed by Sabre and Travelport. TravelSky is a state-owned GDS based in China and operates in a regulated environment. Other regional GDSs are Kiu System (Latin America), Sirena-Travel (Russia), TOPAS (South Korea) and INFINI (Japan).

1.6.9

Air Shopping and the GDS

Air shopping, the agent entry that returns a range of itineraries to select from, is the single largest application and infrastructure supported by a GDS. In the 1980s all GDSs supported rudimentary air shopping capabilities. For example, the Apollo/Galileo flight search product was called Best Buy Quote and Worldspan’s was called Power Pricing. Until the late 1990s Sabre maintained a Fare Pricing Complex (FPC) running on TPF with mainframes to support shopping and pricing. Sabre launched Bargain FinderSM in 1984 which returned nine itineraries for

1.6 Origins of the GDS

23

a shopping request. It was the industry’s first automated low fare search capability. These shopping algorithms required an itinerary to be booked by a travel agent before new itineraries were priced and displayed. In 1993, an enhanced version, called Bargain Finder PlusSM returned 19 itineraries. It was popular with travel agents, though the shopping results lacked adequate diversity. When the OTAs arrived in 1996, demand for air shopping and related requirements increased dramatically. OTAs required a shopping service that had to return a larger number of itineraries (200–1000+) for each shopping request without having to book an itinerary first. Hence, results had to be based on the standard shopping parameters of origin, destination, number in party, departure date and return date. Fare-led algorithms were augmented with schedule-led algorithms to ensure diversity of itineraries. To support the growing demands for air shopping from Travelocity and other OTAs who depended on Sabre’s web services for shopping services, an open systems version of shopping and pricing was initiated by Sabre’s then Chief Technology Officer Craig Murphy in 2001 and launched in 2004, called the Air Travel Shopping Engine (ATSE). ATSE went through significant enhancements with the Estimated Seat Value (ESV) algorithm for Travelocity in 2008 (Benzinger et al., 2008) followed by the high-performance shopping engine (Steeb & Sohn, 2006), where data are organized by VTCR-Fare Class (Vendor, Tariff, Carrier, Rules, Fare Class). ITA Software (de Marcken, 2003) provides an alternative to GDS shopping, and was deployed when Orbitz was launched and is used today by several airline websites and Google Flights.

1.6.10 Legacy Technology Host CRS and GDS technology is legacy and was based on IBM’s Airline Control Program (ACP) which evolved to TPF (Transaction Processing Facility), also a low-level assembly programming language. Reservations systems on TPF 4.1 migrated to IBM’s latest Z/TPF version over the past decade. Based on the recognition that total cost of ownership, flexibility, and time to market are critical, all the GDSs have a plan to migrate reservations processing from a TPF environment to a service-based architecture based open systems technology platform. A key argument is that an open systems architecture that scales vertically with the transaction volume can be processed on commodity hardware, which lowers the cost of transaction processing than current costs. It is a phased migration strategy for all components of transaction processing—reservations, inventory, departure control shopping, pricing, schedules, and ticketing. Over the years, it has become increasingly difficult to recruit programmers who are well versed in TPF, a legacy programming language, which provides an added incentive to migrate to an open systems environment that use modern programming languages. Amadeus was the first to successfully complete the migration to open systems in 2018. They however had an advantage that only their core PNR processing was in TPF, on the System One baseline, while inventory and departure control were developed directly on open systems.

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1.7

1

Origins

The Growth of the Internet and Online Channels

With the rapid growth of the Internet in the mid-1990s, several online travel agencies, both direct and indirect, appeared in the market. In 1985, American launched eAAsy Sabre, a green screen interface for a home user to access schedules, availability, and fares with any modem-equipped computer. It was an important first step in the online era, but it was also mostly symbolic. Market penetration was low and fewer than 150,000 customers had booked and purchased tickets using eAAsy Sabre (Gutis, 1989). In 1990, the three largest online service providers that subscribers could access for a wide range of services such as news, weather, banks, and travel were Compuserve with 550,000 members followed by Prodigy and Nynex (Lavin, 1990). Using an online service provider, customers accessed one or more of the three largest airfare services: eAAsy Sabre, the Official Airline Guide (OAG) electronic edition travel services and Travelshopper from TWA and Northwest Airlines. American and Sabre were the first to realize the potential of the Internet and launched Travelocity, the world’s first online travel reservations system on March 12, 1996 even before the arrival of airline websites. Sabre’s former CIO, Terry Jones, was responsible for developing the Travelocity booking engine. From its early origins, the OTAs transformed themselves into web supermarkets to serve as a one-stop shop for air, hotels, rental car, and cruise lines. The OTA is an indirect channel. They use a GDS as the back end to manage bookings and a fulfilment agency for ticketing, customer service and accounting. Expedia was launched by Microsoft in late 1996 and began offering online services on the Microsoft Network. Microsoft later sold its interest in Expedia to media conglomerate USA Networks, Inc. in 2002. Priceline was founded in 1997 by Jay S. Walker. Priceline introduced the novel reverse auction “name your price” model in 1998 for which they were granted a patent (Walker, Schneier, & Jorasch, 1998). This model reversed the buyer-seller relationship since the buyer submits a guaranteed price, they are willing to pay to travel to a destination. Priceline aggregates requests from buyers and submits them to sellers for a response. Airlines participate in the reverse auction model to get rid of surplus inventory. However, the model has its flaws; it does not offer the identity of the seller or the travel schedule, that may be less than appealing, until have the purchase has been made. The popularity of this model is limited to leisure travelers who are seeking deep discounts. The OTAs provided market transparency to travelers on an unprecedented scale. Travelers could view schedules and fares on the Internet without having to talk to a brick-and-mortar travel agent. Giving access to consumers with information and choice at their fingertips was a significant improvement over traditional distribution. This market transparency has led to innovations in revenue management. A few years after Expedia, Travelocity and Priceline had consolidated their market positions in the online space, Orbitz was launched in 2001 with investment from American, Continental, Delta, Northwest, and United. Unlike the other OTAs, Orbitz supported a lower-cost model with direct supplier links and a percentage of

1.7 The Growth of the Internet and Online Channels

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their online bookings bypassed the Worldspan GDS and were booked directly in the airline CRSs. Travelocity’s former Chief Executive Sam Gilliland (Hansell, 2002) remarked on the direct link to suppliers “If you have a direct connection, you are just pushing costs from one place to another. In the end, it will have very little economic benefit to the airlines.” Orbitz was acquired by Cendant Corporation in 2004. In 2006, Travelport, the travel distribution business of Cendant was acquired by The Blackstone Group and Orbitz was later acquired by Expedia in 2015. Expedia also acquired Travelocity from Sabre in 2013. Today, the largest OTAs are Expedia (Expedia Holdings) and booking.com (Booking Holdings). The Priceline Group, which owned booking.com was renamed Booking Holdings in 2018. Niche OTAs Hotwire.com, Hotels.com, and CheapTickets.com are owned by Expedia Holdings. TripAdvisor.com, the online travel company known for its user generated content (e.g., hotel reviews, restaurant reviews, etc.) was founded in 2000. It was owned by Expedia from 2005 to 2011 and it now an independent company. The latest of the new entrants in the online space are metasearch engines. Kayak and Skyscanner emerged in 2004 and Google Flights in 2011. Metasearch sites have become mainstream. A metasearch site looks at several sites, supplier websites as well as OTAs, and produces a display based on itineraries returned from these individual sites and deep links to the various sites to make a booking. They receive the referral fee even though the booking is not guaranteed since the customer can abandon the shopping cart after the deep link to the supplier site. With metasearch, if an airline does not participate in a GDS, a metasearch engine can determine the lowest fare, if it exists, from the supplier site directly. However, metasearch engine sites also have their preferences and, in some cases, only search sites that will pay them a commission for a customer referral to the site. Today many retailers advertise on Google and manufactured goods are available through Amazon. Platforms such as Google, Amazon and Kayak are expensive for users of these systems. These intermediaries also threaten the existence of businesses since if they do not agree with their terms and fees, they would no longer be allowed to participate in the marketplace. Strategies to counter this threat is a high priority to control the cost of online advertising (Edelman, 2014). Google Flights is reshaping the metasearch business. ITA Software, which Google acquired on July 1, 2010 is the backbone for Google Flights, but there are key differences. ITA Software’s QPX shopping engine powers airline websites and does not resort to cached results. Google Flights on the other hand is a combination of live and cached results. While Google Flights provides deep links to supplier websites, it is only recently that they have provided deep links to OTA sites outside the U.S. Google’s recent decision (Sullivan, 2020) to offer referrals at no cost to both airlines and OTAs is likely to have a significant impact on other metasearch players who are deeply dependent on the referral revenue. Google plans to offset the revenue loss by introducing new ad formats that offers suppliers and travel partners other options for promoting their products. Google’s justification for the move is to display flights ranked by relevance to the user by factors such as price and convenience.

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1.8

1

Origins

The Travel Value Chain

The travel value chain is shown in Fig. 1.1. Revenue management is an advanced decision support application that generates optimal inventory controls which are implemented in the airline’s host reservations system. Inventory control in the host CRS serves as the execution component of recommendations from the revenue management system. A customer can access and book travel through direct channels or indirect channels. The direct channels are those that access schedules, fares, and availability from an individual supplier’s reservations system (host CRS). All bookings that originate from brick-and-mortar travel agencies and OTAs are indirect bookings since the GDS is an intermediary. Today’s GDS is more than just air supplier content, but links travel supplier like airlines, hotels, rental cars, cruise lines and rail with travel agencies. It is a businessto-business (B2B) model. The GDS is considered a very efficient and cost-effective channel to distribute and sell supplier inventory. However, today it is a legacy platform with limitations on how suppliers want their content displayed and sold. For travel agencies, it provides a platform for comparison shopping across suppliers so that they can select the supplier of choice to make a booking based on a customer’s preferences. A GDS has connectivity to supplier reservations systems to access availability. In the future in a New Distribution Capability (NDC) world, suppliers will also price itineraries and ancillaries for travel agents. GDSs earn their main source of revenue from booking fees which are negotiated with the travel supplier. For example, for airlines, it is based on segment booking fees and for hotels

Fig. 1.1 The travel value chain

1.9 Revenue Management Storefronts

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it is priced by transaction regardless of the length of stay. On the average, there are 2.5 bookings per ticket. Travel agents that subscribe to a GDS earn incentives and in some situations the incentives could exceed 50% of the segment booking fees.

1.9

Revenue Management Storefronts

Until the late 1980s and early 1990s the sale of airline products was strictly offline and limited to travel agencies, airline call center agents, airline ticket offices (ATO) and city ticket office (CTO). The advent of the Internet in the 1990s spawned new online channels through which products could be distributed and sold. These new channels offered end consumers unprecedented transparency to airline schedules and available selling fares. Storefronts through which an airline’s products are distributed represent the output of the revenue management process. The challenge is that the same content—schedules, fares, and availability, should be displayed across all channels of distribution. This requires the right level of connectivity to ensure availability is identical across all channels. In today’s product distribution landscape, Fig. 1.2 illustrates the four broad categories of storefronts that display the end-product of the revenue management process for sale to customers. The airline direct channels have an online and offline component which represent an airline’s consumer direct website and call center, respectively. Airport ticket offices (ATOs) and city ticket offices (CTOs) are also offline direct channels. The direct channels transact directly with an airline’s host CRS. The indirect channels also have an online and offline component, online travel agencies (OTAs) and brick-and-mortar travel agencies, respectively. The indirect channels typically transact with a GDS for schedules, fares, and availability. Although there has been a shift from offline to online over the past decade, the indirect channels contribute over 50%t of the bookings worldwide. Based on the fundamental premise that an airline’s revenue management process is only as good as the accuracy of its product displays through the various channels, the link between revenue management and product distribution is of critical importance.

Offline

Online

Fig. 1.2 Channels for product distribution

Direct

Indirect

Airline Website

Online Travel Agency

Airline Call Center Airline Ticket Office City Ticket Office

Brick and Mortar Travel Agency

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Travel Agents: How They Make Money

Travel agents subscribe to a GDS to access schedules and fares to book travel for their customers for which they pay a subscription fee. They have four primary sources of revenues. GDS incentives, front end commissions, override or backend commissions and service fees. Until the mid-1990s, travel agency business was a lucrative business. The GDSs provide an incentive to travel agents when they book travel. A percentage of the segment booking fees negotiated between an airline and a GDS is paid to the travel agency on a per transaction basis. Agency incentives paid by GDSs are fiercely competitive since most large travel agencies have a dual GDS strategy where they transact bookings through multiple GDSs, usually two and in some cases three. Agency incentives have a negative impact on GDS revenues, and these agency incentives frequently exceed 50% of the segment booking fees. In addition, financial instruments may include signing bonuses or growth bonuses. Front end commissions changed dramatically on February 9, 1995 when Delta Air Lines rescinded the 10% travel agent commissions on U.S. domestic fares by imposing a cap on commissions of $50 on round-trip fares above $500. One-way fare commissions were capped at $25 for one-way fares above $250. Other airlines followed Delta’s lead and the American Society of Travel Agents (ASTA) filed a lawsuit against the airlines that imposed the cap claiming that they illegally conspired to cap the commissions. While the lawsuit was settled in 1996 (McDowell, 1996), the cap on commissions prevailed, and it set the trend for declining travel agency commissions. Today in U.S. domestic markets, front end commissions are limited to specific markets on which an airline wants to sell more tickets. Travel agencies earn front end commissions on international markets and find them more lucrative. Contrary to common belief, commissions are still a large part of a travel agent’s revenue stream. While U.S. domestic front-end commissions have declined, it is still lucrative for travel agents to sell cruise lines and high-end tour packages. Travel agents also earn performance-based incentives, commonly called override commissions or back-end commissions. When an agent achieves the negotiated performance targets established by the airline such as a specific number of bookings per quarter in a market or market entity, they receive an agreed-upon lump sum payment from the airline. Agencies also negotiate net fares with airlines and favorable rates with hotels that they can markup and sell for a profit. Agencies prefer to sell net fares to their customers since they make higher margins per transaction. The final source of revenue is service fees levied to customers for their services. For international trips, travel agents likely can find cheaper fares than those found on OTAs like Booking.com and Expedia. Many customers, especially the millennials, do not realize the value provide by travel agents. Besides, agents have connections to airlines and have access to better seats and upgrades, better rooms and can book ground transportation and local attractions. Figure 1.3 illustrates the logical flow of payments in the travel distribution market.

Changes in the Distribution Landscape with IATA’s New Distribution. . .

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Front-end Commissions and Backend (Override) Commissions Subscription Fees

Airlines

Booking Fees

Global Distribution Systems

Incentives

Travel Agents Service Fees / OTAs Ticket Price

Traveler

Form of Payment Settlement to Airline

BSP / ARC Clearing House

Agent makes periodic payment

Credit Card, Debit Card, Cash, Check, Other

Fig. 1.3 Flow of payments in the travel distribution market

1.11

Changes in the Distribution Landscape with IATA’s New Distribution Capability

From an airline CFO’s perspective, GDS costs are one of the last controllable expenses for an airline. It is not just GDS booking fees, but also credit cards and agency commissions. For over a decade (2005–present), the long-term strategy of network carriers has been to advance distribution from fare and schedule-led selling to merchandising to transform themselves from suppliers of a commodity, an airline seat, to product marketers of airline bundles of base fare and air ancillary products. Despite the value provided by the GDS in providing price transparency and delivering higher valued corporate customers through managed travel programs, this is seen as a shortcoming. GDSs have always been under the threat of disintermediation. “Disintermediation” is technology and/or business process that will cut the GDS out of booking channels and fees. Full content agreements negotiated between airlines and GDSs do not apply for the sale of ancillary products sold by airlines. Further, travel agents and GDSs are not compensated for the sale of ancillary products such as bags, pre-reserved seats, lounge access and meals at time of booking. Ancillary products are not part of the full content agreements, which implies that airlines do not have to distribute ancillary products to ATPCO and the GDS. Low-cost carriers (LCC) are budget airlines that have seen double-digit growth over the past decade and most of them do not participate in the GDS to avoid the cost of indirect distribution. An evolving trend has been the introduction of surcharges by airlines for indirect agency bookings. Lufthansa introduced surcharges for travel agency bookings in 2015. This was followed by the International Airline Group (IAG) members British Airways and Iberia in 2017 and Air France KLM in 2018 (Vellapalath, 2018). Singapore Airlines plans to impose a surcharge for agency bookings in 2021 (Parsons, 2020). While GDSs continue to play a prominent role in the travel landscape, their role is changing, if not diminishing. To support the future of distribution and adoption of the New Distribution Capability (NDC), they are focused on broadening the content

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offerings with multi-source, source agnostic content. It is not clear how their role is going to change. They may evolve into a tool for direct corporate bookings and direct leisure travel. They are challenged with the changes in the travel industry such as growth of direct bookings, incentives offered by airlines to customers to book directly and avoid GDS booking fees, surcharges imposed by some airlines for tickets booked through a GDS and the NDC messaging standard for data transmission initiated by IATA for airlines to communicate with intermediaries such as the GDS. With NDC, airlines can take control of distribution and transmit personalized rich content, priced itineraries, and air ancillary bundles to a travel agency customer. NDC is also a threat to the future of fare aggregators like ATPCO and SITA and schedule aggregators like OAG, Innovata and Cirium since airlines in the future may decide to not file their fares and schedules through fare aggregators and schedule aggregators, respectively. This means that every travel agency booking will have to go to the airline for schedules, itinerary, base fare, and ancillary prices. NDC today is in the early stages of adoption, and it will eventually render the full content agreements obsolete since pricing power is shifting from the GDS to the airline for indirect bookings. One Order is also an integral part of the NDC program to introduce a single customer order record to capture all data elements that require to be fulfilled. This streamlines the sale of ancillary products with the elimination of multiple reservations records and the elimination of the e-ticket/Electronic Miscellaneous Document (EMD) with a single reference order. Figure 1.4 illustrates the current transaction flow. Travel agents use GDSs to shop, price, generate offers (itineraries with ancillaries), book, ticket and collect payment. Before NDC, the number of travel agency and OTAs who sourced content directly from an airline were the exception rather than the rule. Figure 1.5 illustrates the transaction workflow with IATA NDC. The key difference is that pricing power shifts from the GDS to the airline to generate offers and take control of distribution of the airline product through the indirect channel. Itineraries booked by a travel agent in the airline host CRS can be stored in the GDS as passive segments. The segments are passive since any change to the itinerary must be done by the airline. It also allows airlines to share rich content (e.g., images, text, video, etc.) and personalize the offer with dynamic pricing and ancillary bundles for product differentiation from competitors. NDC also gives flexibility to airlines to connect directly with OTAs, TMCs and travel search engines without the need for a GDS. Several airlines and aggregators are going through the IATA NDC certification process. There are 5-levels, and this information is maintained in a NDC registry. 1. 2. 3. 4. 5.

Level 1: Post Booking Ancillaries Level 2: Offer Management Level 3: Offer and Order Management Level 4: Full Offer and Order Management NDC@Scale

Changes in the Distribution Landscape with IATA’s New Distribution. . .

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Fig. 1.4 Current product distribution transaction workflow

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Fig. 1.5 Product distribution transaction workflow with NDC

32 Origins

1.11

Changes in the Distribution Landscape with IATA’s New Distribution. . .

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With NDC the future of schedule aggregators and fare aggregators is far from certain. An airline may decide to not publish schedules and fares to aggregators. While the NDC messaging standard provides clear benefits for airlines, the benefits to OTAs, wholesalers, and TMCs is less clear. However, it is a mandate: when an airline is NDC-ready, they may choose to use the NDC messaging standard as the only conduit for OTAs and TMCs to communicate with them. The GDSs have multimillion-dollar investments to handle the new communication protocol since they realize that without the investment, they may no longer have content. NDC is also a radical transformation of capabilities required in a GDS in the future. For example, air shopping constitutes the single largest recurring annual investment in advanced algorithms and infrastructure for a GDS. For example, at Sabre, these volumes averaged over 675 million shops a day in 2019 (Vinod, 2020a). When all airlines are using the NDC messaging standard, pricing power will shift from the GDS to the airline and this investment is no longer required since it is now the responsibility of the airline to provide priced itineraries. The same is true for itinerary pricing which requires investment to maintain its accuracy. By the end of 2020, there were approximately 40 aggregators that had achieved NDC certification level 3 or greater as noted in the NDC registry. Several nimble aggregators have entered the market with modern technology and APIs that provide airline connectivity to travel agencies out of the box that are NDC compliant and at a price point that is acceptable to airlines. What they lack is expertise in agency workflow management and security. To eliminate the legacy costly infrastructure and capabilities that will not be required in the future, GDSs should consider acquiring or partnering with a new entrant that uses modern technology and augment their weak spots of agency workflow management and security. This enables a GDS to rapidly migrate away from aging technology. For GDSs operating in a legacy TPF environment, it can provide a faster and more cost-effective path forward than an expensive TPF offload. It can also rapidly accelerate reducing the cost of distribution, which is an airline priority. An often-asked question is: What does the customer lose with NDC? Customers lose schedule and fare transparency as we know it today. In the current environment, OTA and GDS customers have access to multi-supplier content in an instant when they conduct a low fare search. With NDC, booking an airline ticket will be like buying a new car. To buy a car, a customer goes from dealership to dealership comparing prices, features and packages offered without instant visibility to all car dealerships and their offers. With air bundles offered by airlines using the NDC messaging standard, the content across airlines is not homogeneous. Hence, a customer must make a judgement call on which airline has a better offer since the ancillary content is never the same across airlines. The GDSs will have to normalize the content returned from multiple airlines from a request, thereby making it easier for travel agencies to compare offers and make a booking. The science of normalizing non-homogenous content for display is far from perfect and is discussed in Chap. 6.

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Origins

The Airline Marketing Planning Process

Yield management is a core component of airline planning. Its role in marketing planning and relationship to other functional areas is outlined in Fig. 1.6. The airline marketing planning function (Smith, Barlow, & Vinod, 1998) has a fundamental objective to match demand and supply at distinct levels of granularity. Fleet planning acquires different types of aircraft from aircraft manufacturers characterized by attributes such as range, operating cost, capacity, and cost to profitably serve the future route network of the airline. Pricing establishes the market prices based on market competitive fare filings and yield management fine tunes the demand and supply matching process by determining the optimal mix of passengers that should be accepted. This is followed by sales and distribution, where an airline distributes its product through the various channels at its disposal. They include online direct and indirect channels as well as offline direct and indirect channels. An important consideration for airlines is to lower the cost of sale or the cost of distribution over time. Scheduling, pricing, yield management and sales and product distribution make up the four primary components of the marketing planning process. Fleet planning and loyalty programs also play key roles. For an airline to be profitable, every aspect of the marketing planning process is of critical importance. The level of coordination among these major functional areas influences the effectiveness of the marketing planning process. Pricing and yield management play key roles in the marketing planning process for an airline. Planning decisions in pricing and yield management influence fleet planning, flight scheduling and product distribution. Figure 1.4 illustrates the marketing planning process.

1.12.1 The Time Frames The airline marketing process begins with long range planning which takes place 1–5 years into the future. Core elements of long-term planning are fleet planning, network restructuring and re-design, new market opportunities assessment, and alliances/joint ventures/partnerships. The budget planning process typically starts with a multi-year outlook that spans 3 years. Budgets begin to be finalized about

Strategic Planning Fleet Planning

Future Schedules

Tactical Planning Current Schedules

Airline Pricing

Yield Management

Product Distribution

Loyalty Program

Feedback to all entities

Flight Scheduling

Fig. 1.6 Airline marketing planning process

1.12

The Airline Marketing Planning Process

35

3 months before the start of the operating budget cycle. The financial plans are created based on known measures and forecast measures. Examples of known measures that undergo adjustments during the planning process are sector revenue, yield (revenue/revenue passenger mile), revenue per available seat mile and load factor. Forecast measures are new market penetration, planned closures of existing markets, GDP growth and macro-economic trends. Intermediate term planning starts a year before departure up to approximately 3 months before departure. This phase includes the introduction of new routes, network planning to retime flights, evaluate codeshare opportunities and introduction of new frequency of flights in markets. Pricing and revenue management strategy are also addressed based on global outlook, market forecasts and competitors in the landscape. The short-term planning begins 3 months before flight departure and ends on the departure day of the flight. This phase addresses close-in re-fleeting to better match supply to demand, competitive fare filings based on market conditions and revenue management inventory controls.

1.12.2 Industry Datasets To support the schedule development process, aggregation of data from multiple sources is required to establish a comprehensive database of traffic data. Some of these datasets are also useful for yield management applications, particularly competitive revenue management (see Chap. 7). The list of industry data sources described below it not exhaustive but are the most frequently used. MIDT Marketing Information Data Tapes is a dataset based on bookings made by travel agents that subscribe to a GDS. The data are collected before a ticket is issued. In its raw form, almost all the data stored in the passenger name record (PNR) is available. The main suppliers today of MIDT data are Amadeus, Sabre and Travelport. It has been actively marketed and sold since 1987. T100 Provided by DOT, this is leg level data that includes point of commencement. Hence, from this data we know the departure airport, arrival airport, carrier, month, and passengers. For example, we can tell how many passengers flew FRA-JFK on Lufthansa in January 2019. It is a monthly extract based on passenger arrivals and hence and includes direct sales. DB1B Data Bank 1B has the origin and destination survey data for a 10% sample of airline tickets from reporting carriers. Data includes origin, destination and other itinerary details of passengers transported. This is O&D level data but is based on a 10% sample. Since the entire itinerary is provided, we know the country of origin. It is

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more accurate for U.S. domestic, less so for international because only first international port touched before/beyond the U.S. is recorded. For example, for the itinerary PVG-NRT-LAX-LAS-LAX-NRT-PVG, only NRT-LAX-LAS-LAX-NRT is recorded. DB1A Data Bank 1A was replaced in 2003 by Data Bank 1B. These datasets are similar except for a few differences. For instance, DB1B identifies both the operating carrier and the ticketing carrier while DB1A assumes they were the same. IATA DDS This dataset is superior to MIDT since it includes visibility into direct and indirect (agency) ticket sales across all geographic regions. This dataset was created in partnership with ARC, Cirium and airlines who contribute their sales data. OAG Official Airline Guide (OAG) is a schedule aggregator across all airlines. This data are also available from companies like Innovata and Cirium. ATPCO/SITA The Airline Tariff Publishing Company (ATPCO) and SITA are fare aggregators. U.S. Bureau of Economic Analysis (BEA) Travel and tourism statistics measures how much tourists spend on lodging, airfare, and travel related expenses.

1.12.3 Scheduling, Pricing, Revenue Management and Distribution Synergies To create schedules, the total demand for air travel is first collected, validated, and estimated by market based on data from public and commercial sources from the data sources discussed above. This data are required for long-term planning activities and applicable to airlines, airports, and aircraft manufacturers. Market sizes and fares produced by this process then consider various factors such as time of day, elapsed time, aircraft type, codeshare, and competitor schedules to estimate market share for an airline. Operational cost data are next used to determine schedule profitability for an airline. Flight scheduling is more commonly referred to as capacity planning, which includes the creation of future flight schedules and managing current flight schedules. Scheduling identifies the routes and associated markets that will produce profitable demand given the capacity and operating costs of a specific aircraft type. Identifying the needs of the customer base influences the route structure of the airline. Considerable planning and analysis go into determining the operating airline schedule to ensure that the scheduled services offered by the airline are profitable.

1.12

The Airline Marketing Planning Process

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The schedule identifies the services offered by an airline. In addition, the schedule defines a level of in-flight service that differentiates the airline from the competition. Offering a competitive product in the economy, executive/business class and premium/first class is vital in retaining and enhancing market share. It is a combination of the services offered to meet the needs of the customer, price, and above all exceeding customer expectations that make an airline profitable. In addition, adding value to the product with frequent flyer programs, aircraft reconfiguration to provide passengers more room, improving the quality of in-flight services, upgrading and improving passenger amenities, enhancing the quality of economy class services, etc., are essential components to retain and enhance market share. A key ability to effectively use yield management techniques to maximize revenues is to segment passengers based on their needs, business constraints, passenger characteristics and willingness to pay. The right price is the highest price the customer will pay for a product. A pricing strategy to maximize revenue potential requires customer segmentation based on a deep understanding of the customer base. Price elasticity is a measure of the change in sales resulting from a change in price. Different market segments have different price elasticities. Leisure passengers are elastic wherein a small change in fare can result in a significant demand shift. Business passengers are generally inelastic and small fare changes generally cause a small demand shift. Airlines can benefit by increasing revenues and profitability through yield management by adjusting reservation availability based on the value of the customer to accommodate the most profitable number and mix of passengers. The level of detail at which inventory can be controlled on the airline’s central reservations system (the “host CRS”) dictates the level of sophistication of the yield management system. While the scheduling and pricing decisions are made at the market or service level, yield management decisions have been leg or segment class based due to the inherent shortcomings of the reservations systems. To realize the maximum revenue potential, inventory must be controlled by origin and destination. Thus, origin and destination yield management is the process of selectively accepting and rejecting customers by fare, service class and departure date to maximize total network revenues. The incremental revenues generated depend on several factors such as the sophistication of the inventory control mechanism used, the network load factor, mix of short haul versus long haul passengers and proportion of individual demand versus group demand. When loyalty programs were launched, the term Customer Relationship Management (CRM) was not well known. CRM enables airlines to target customers with special campaigns and offers. It serves as a vehicle for creating new demand and converting the new demand into loyal customers. CRM initiatives of an airline complement their well-established loyalty programs. CRM applications rely on the loyalty customer database as a starting point to build on, to develop intimate knowledge and insight into customers, allowing recognition of customers at different contact points and providing information on individual customer value. Based on advanced analytics with varying degrees of sophistication, it also provides a

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framework to target customers with cross-sell and upsell opportunities based on the customer profile and to provide better service to the individual customer. Cost effective electronic sales and distribution of the services offered by an airline is a fundamental requirement to improve overall advance bookings, improve load factor and be profitable. The effective use of various distribution channels is of critical importance. Full-service carriers typically participate in both the direct and indirect channels, while most of the low-cost carriers (LCC) only participate in the direct channel. For the GDSs to survive long-term, the financial economics of LCC participation in the GDS must be addressed. While GDSs contribute higher valued corporate customers, who are on a managed travel program subject to corporate travel policy compliance, efforts continue to dis-intermediate the GDS to eliminate the costs associated with indirect distribution. Regardless of the future of the indirect channel, the airline’s financial budget planning process determines how to spend the available budget to distribute the product. Performance monitoring and feedback to the functional areas of marketing planning are fundamental building blocks for an airline to enforce the process of continuous improvement. Business process integration across these functional areas is key to effective decision making in an airline. This entails the optimal usage of yield management data (both inputs and derived outputs) for effective decision making across the major functional areas in airline planning and operations. Besides the value of integrated planning in commercial aviation, the French National Railroad (SNCF) has also realized significant benefits with integrated scheduling, pricing and revenue management decision making. Sabre and SNCF won the prestigious Edelman award for this work (Ben-Khedher, Kintanar, Queille, & Stripling, 1998).

1.13

Pricing and Yield Management for Competitive Advantage

Pricing and yield management analysts play a critical role in the success of a yield management program at an airline. Knowing the market dynamics, competitor behavior patterns and competitor strategy is critical for success. An essential trait of a pricing and yield management analyst is to get into the mind of a competitor. When a pricing manager for example plans to affect a price change, an important question that needs to be asked is, “how will my competitors react to a price change?” Sophisticated pricing models and decision support applications cannot answer that question, but a pricing manager who understands how each competitor will react to a pricing action can answer that question with a reasonable degree of confidence. The practice of pricing and yield management is a fundamental enabler for competitive advantage. While these decision support systems can be sophisticated, technology is only part of the solution. People and business process play an equally important or greater role in the success of a pricing and yield management program. Analysts who manage flights and markets in this field need to have an intimate knowledge of the key performance indicators that are monitored by the system to

1.13

Pricing and Yield Management for Competitive Advantage

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take preemptive and corrective actions. Practitioners of revenue management are modern day warriors who have a single-minded objective of improving margins in the face of adversity and competition. Revenue management is a competitive weapon in the heat of battle to overcome the competition, gain improvements in market share and simultaneously improve margins and hence profitability. But, to win a war, the attack plan must be clearly thought out and articulated into a winning strategy or a sequence of strategies and tactics. Every practitioner of pricing and yield management will be well served to read the book by Sun Tzu entitled “The Art of War,” (Sawyer, 1994) for ideas to formulate the competitor attack plan. Sun Tzu’s writings are over 2500 years old and they are applicable today as it was back then. The consensus among historians is that Sun Tzu lived during the Wu Dynasty in late sixth century BC. It was reputed that Napoleon Bonaparte’s tactics and strategy during the Napoleonic wars were based on Sun Tzu’s teachings. The Grand Duke said. . . One who is confused in purpose cannot respond to his enemy.

In this quote, Sun Tzu is emphasizing the importance of clearly stated missions. The quote from the Grand Duke can be interpreted to mean: In order to act, the strategic direction of the enterprise must be clearly articulated and understood by the rank and file of the organization at all levels. In other words, if the mission of the enterprise is not well understood or if it is not well communicated to all levels in the organization, it is not possible to do battle with a competitor and win. The pricing and yield management leadership team in an airline is responsible to define strategy and set the direction that is well articulated and communicated to all levels of the organization. Leadership is one of the main themes in Sun Tzu’s writings, the qualities of leaders and how they should act. Leadership in pricing and yield management is about people and process to move an enterprise to a new level of competitive excellence. In his book, Sun Tzu states the five factors “from which victory can be known.” Each of these factors is directly applicable for a practitioner of pricing and yield management to do battle and conquer the competition. Sun Tzu’s five factors are: “One who knows when he can fight, and when he cannot fight, will be victorious.” “One who recognizes how to employ large and small forces will be victorious,” “One whose upper and lower ranks have the same purpose will be victorious,” “One who is fully prepared and waits for the unprepared will be victorious.” “One whose general is capable and not interfered with by the ruler will be victorious.”

This is what Sun Tzu had to say about the enemy (competitors). Thus, it is said that one who knows the enemy and knows himself will not be endangered in a hundred engagements. One who does not know the enemy but knows himself will sometimes be victorious, sometimes meet with defeat.

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One who knows neither the enemy nor himself will invariably be defeated in every engagement.

In the highly competitive world of price setting and allocation management for survival in a highly competitive industry and marketplace, Sun Tsu’s writing is essential reading from senior management to all levels within the pricing and yield management organization as a whole to advance to the next level of competitive readiness with the available tools.

1.14

Yield Management: The Onward Journey

When Bob Crandall introduced the term “Yield Management” into the airline vocabulary, no one realized at that time the far-reaching future impacts of this new concept. Fundamentally it ushered in a new era of competitiveness in the airline industry, travel verticals like hotels, cars, cruise lines, rail and into a range of industries beyond travel like high-tech manufacturing, consumer electronics, and retail. It spawned active research and innovation from industry and academic institutions and rapid improvements in business process adoption that continues unabated to this day for competitive advantage.

2

Airline Pricing

2.1

Overview

Airline pricing is not revenue management, though efforts are underway to unify the capabilities of airline pricing and airline revenue management with dynamic pricing. Before airline deregulation, airlines had operated in a regulated environment. The environment was regulated by government bodies and self-regulated by organizations such as the International Air Transport Association (IATA). Deregulation of the airline industry in 1978 led to an explosion in fares offered in the marketplace, and airlines were not prepared to handle the volume of fare changes. This was because, in the absence of competition, and with the view that air travel was only for the privileged few, most travelers were relatively price inelastic and competition was limited. Besides, the industry had grown accustomed to government-mandated fare levels either through the Civil Aeronautics Board (CAB) mandated Standard Industry Fare Level (SIFL) for U.S. domestic fares or through government-sanctioned collusion in IATA tariff coordination meetings. These fare levels ensured airline profitability with limited competition. In this environment, airlines had no incentive to create and promote fares for specific customer segments. Deregulation transformed the airline industry by offering fares that were within the reach of a whole new population that had never flown before. Overnight, guidelines established by SIFL disappeared and international carriers, while still participating in IATA fares, focused their attention on carrier-specific fares within the bounds of the regulatory environment for international routes. The primary fare management objective of airlines that has perpetuated since deregulation is to match a competitor’s fare (Kretsch, 1995). This is based on the premise that in a capital and asset intensive industry, the marginal cost of adding an additional passenger is very low and hence the focus is to protect and retain market share and pay down fixed costs. Consequently, an airline’s fare actions are mostly reactive. This behavior is consistent with the desire of most airlines to maintain market share. Research dating back to the nineteenth century confirms that price matching in a competitive marketplace can be an optimal strategy (Curry, 1995). # The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 B. Vinod, The Evolution of Yield Management in the Airline Industry, Management for Professionals, https://doi.org/10.1007/978-3-030-70424-7_2

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Airline Pricing

Fig. 2.1 Airline fare management process

Fare changes could be classified into two categories, regional or systemwide fare changes and market specific changes. The former consists mainly of sales and general increases or decreases on all fares. Fare changes initiated by an airline are always matched except when an existing sale provides a better incentive to the public than the new one. Market-specific fare changes are typically triggered by a single carrier based on factors such as demand stimulation, carrier’s perceived dominance in a market, or schedule-related service changes in the market. When a fare action is triggered by an airline, other carriers typically respond with an identical response to protect market share regardless of the revenue impacts. Reactive responses dominate proactive fare changes to demonstrate price leadership. Sometimes, the reaction ripples through other markets or differs from the original change, inducing a series of cascading changes. This behavior of reactive fare response is consistent with the desire of most airline executives to retain and protect market share. Figure 2.1 illustrates the fare management process. Typically, an airline files fares containing various purchase or travel restrictions with fare distributors such as Airline Tariff Publishing Company (ATPCO) or SITA. Previously known as ATPCo, it is now branded as ATPCO. 87% of the world’s fare filings are done through ATPCO and the fares database has more than 223 million fares. It is owned by many of the major airlines and provides data collection and distribution clearing house services for more than 400 carriers worldwide. Daily, the fare aggregator and distributor consolidate fares received from other carriers and broadcasts the fares to all participating airlines and major Global Distribution Systems (GDS) such as Amadeus, Sabre, Travelport, and regional GDSs such as TOPAS (South Korea), INFINI (Japan) and Travelsky (China). Airlines submit their fare changes to ATPCO and SITA which in turn aggregates the content from airlines and transmits the fares to the participation airline

2.2 Fare Products

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reservations systems and GDSs who process this information and store it in a format to support fare quotes and pricing of itineraries based on fare rules for the travel agency community, airline ticket office (ATO) and city ticket office (CTO) agents.

2.2

Fare Products

Besides the expensive standard full fare unrestricted product, restrictions are a way to publish alternate prices for the same unit of inventory. In the travel and transportation industries, there are three fundamental types of discounts. They are restricted discounts, qualified discounts and unqualified discounts (Vinod, 2010). Restricted discounts fundamentally change the nature of the fare product that is being offered. Examples include advanced purchase restrictions such as 3-day, 7-day, 14-day and 21-day advance purchase restrictions, Saturday night stay, minimum/maximum stays, day-of-week, seasonality, etc. It is an effective way to segment customers by enforcing the restrictions when a booking is made for an airline seat. These fares can be inventory controlled since they are different fare products. While these customers may be on the same flight, they have essentially purchased different fare products. One of the behavior modifications airlines have focused on is to have customers purchase restricted fares well before departure to improve their inventory planning. The second discount is categorized as qualified discounts. These are difficult to control independent of the public fares since there is no product differentiation, but the passenger paid a lower price because the passenger “qualified.” For example, affinity groups such as AAA and AARP, may have a negotiated lower fare. The same is true with corporate discounts, where the discount is a percentage and floats with the prevailing public fares and associated fare rules. Qualified fares can result in revenue dilution but generate sales volume. The third discount is an unqualified discount. The hotel and car rental industries have the most of this type of rates. The airline industry is dealing with restriction free pricing (RFP) when the Low-Cost Carriers (LCC) introduced these unrestricted tariffs. Here, a range of prices is set up with no restrictions. In the absence of fences, essentially, the lowest available fare is the fare of the moment (although most hotels start at the top and negotiate downward). These prices present the most confusion and resentment in the market. In addition, if they are not effectively managed, they can result in revenue dilution of the overall market. While it is possible to control these discounts, traditional revenue management approaches tend to fail. This is because revenue management has never focused on the problem of what the customer is willing to pay. Rather, it focuses on what the supplier is willing to accept, resulting in revenue dilution. Upsell probabilities have tried to address this concern by inflating the high revenue demand by the probability a passenger eligible for the lower revenue product would sell up to the higher valued fare product. However, given that upsell probability estimates are notoriously imprecise, historically it has been difficult to apply and in truly low demand situations when demand is less than capacity, everything will typically be open (for example, without restrictions, if a $100 fare is available, there is no demand for the $120 fare).

44

2.3

2

Airline Pricing

Fare Dimensions and Fare Types

There are six dimensions to a fare, as described in Table 2.1: These six dimensions are physically separate. In other words, the fare basis code does not define a rule, but it refers to a rule that is defined elsewhere. The same is also true with footnotes, general rules, routings, and RBD validation.

2.4

Booking Class, Fare Category and Fare Basis Code

Figure 2.2 illustrates the relationship between fare basis codes, fare categories (also known as fare groups) and booking classes. A fare basis code is up to 15 characters long. It can include letters, numbers and up to two slashes, and is a shorthand method for describing pricing rules. It is a compilation of the fare class or ticketing code and one or two ticketing designators. The fare basis code is not published, it is constructed by the pricing process and is shown on the ticket. Booking classes, are also known as reservation booking designators or RBDs, are used by revenue management to forecast demand, set inventory controls on the reservations system, and distribute airline seat availability to the GDSs. Fare categories and fare basis codes are in the domain of airline pricing. A many-to-one relationship exists between fare basis codes and fare categories. While ideally each fare category should map to a unique booking class, this is not the case, and a many-to-one relationship exists between fare categories and booking classes. For example, both Premium Economy and unrestricted frequent flyer redemptions may be mapped to the same booking class Y as shown in Fig. 2.2. Booking classes play an important role in both online fares and interline agreements. Incorrect mapping of a fare to a booking class or using the wrong designator on an interline agreement can result in loss of revenue. A booking class is a required element for auto pricing of passenger itineraries by GDSs. Booking classes are distributed globally through fare aggregators ATPCO and SITA. The fare categories define the market segments. The fare basis codes represent the fares with its associated rules, restrictions, routings, and footnotes represent the selling products that are ultimately distributed by an airline to the target channels. Table 2.1 Dimensions to a fare Dimensions Fare record Rules (the fare categories) Footnotes General rules Routing RBD validation

Description Consists of the origin and destination, the fare amount, fare basis code, footnote and the routing number Restrictions applicable to a fare Additional rule restrictions applicable to a fare. Commonly used to include travel restrictions or ticketing restrictions Additional rule restrictions that may be applicable to a fare The origin and destination and allowable cities in between Validate availability of booking class

2.4 Booking Class, Fare Category and Fare Basis Code Booking Class

Fare Category

45 Fare Basis Code

Premium Economy

Y B M H V Z Q

Reward Miles Unrestricted Reward Miles Restricted Economy Unrestricted Economy Restricted Visiting Friends/ Relatives (VFR) Military / Government

KECO1403

MILYX7D7 MILYX7D7

Travel Industry Discount Basic Economy Restricted

Fig. 2.2 Relationship between booking classes, fare categories and fare basis codes

The fare basis is the code that appears on the ticket. It can include letters, numbers and up to two slashes (/). It is a compilation of the fare class or ticketing code and one or two ticketing designators. The fare category is an internal classification by an airline to identify and group fares into predefined market segments. Fare basis codes, and booking classes are external classifications that are published to travel agencies, tour operators, and consumers.

2.4.1

Fare Classes and Booking Classes

A fare class specifies the rules of an airline’s fare, specified in one to eight characters. Every fare has a fare class code which is used for pricing. Revenue management analysts incorrectly refer to booking classes (RBDs) and fare classes interchangeably, which should be avoided.

46

2.5

2

Airline Pricing

Classification of Fare Products

In airline pricing there are three distinct fare products—public fares, private fares, and web fares.

2.5.1

Public Fares

These are fares filed by an airline and are then distributed by the fare distribution vendors to all GDSs for worldwide access. Also referred to as published fares, but the term is confusing since all fares, public and private, are published for access to some entity or the other. All customer segments have access to a public fare and these fares are automatically subject to travel agency commissions where applicable. When a travel agent sells a public fare to a passenger, the gross fare is written on the ticket and the agent submits the net amount to the airline after the commission. Public fares are sometimes referred to as gross fares or IATA fares.

2.5.2

Private Fares

Private fares are fares created under private tariffs. Private fares are subject to limited distribution and serve as a discreet sales outlet. There are several variations to create private fares. The fare filed and controlled by an airline with limited distribution and usage is a private fare and uses security in ATPCO Category 15 (CAT 15) or Category 35 (CAT 35) that identifies who can sell and the Category 1 (CAT 1) rule that specifies who can buy this fare. Corporate fares are created with a Category 25 (CAT 25) fare-by-rule (FBR). They are negotiated between an airline and a corporation and unlike other industries, do not go through the request for quote (RFQ) process. CAT 25 allows the creation of new fares using rules data to specify the market fares and the amounts. The fares can either be calculated from existing fares and rules in the market or specified to create a new fare using the rule provisions in Category 25. The generated fares will not have a fare class application or be published fares in ATPCO systems. CAT 25 requires that the Fare by Rule Index, also commonly known as the Record 8, be coded for a given rule. The FBR index acts as a pointer for pricing. A CAT 25 enables an airline to create new fares by filing discounts on a public fare (e.g., an ADT adult fare, a Passenger Type Code (PTC)) but the same rules apply to the discounted fare. There is also the capability to use, override or combine, the public fare rules when the FBR fare is created. It is typically accessed at a point of sale, and a discount applies to the amount of a public fare with its associated rules and restrictions. It can also permit changes with a fee. For corporations, the CAT25 discount is not applicable across all RBDs but is usually limited to the higher fare RBDs in the hierarchy. Hence, situations can arise when the lowest public fare that is available is cheaper than the lowest corporate discounted fare since the RBD is

2.5 Classification of Fare Products

47

higher in the fare hierarchy. In some cases, if the security available in CAT 35 is filed, it will replace the CAT 15 security, but CAT 15 may still be used to enforce ticketing date and sales restrictions. The CAT 35 negotiated fare with markup represents a subset of the private fares with limited distribution. Negotiated fares are designed to handle the requirements of negotiated type fares, such as net remit programs, IT fares, corporate fares, and other types of private fares that can include multiple, related fare amounts, special ticketing, fare markups, and enhanced security over existing sales restrictions (Category 15). Negotiated fares are a contract between an airline and a travel agency or other entity such as a consolidator that allows the agency or consolidator to sell a published fare for less or to offer a special fare authorized by the airline. These fares are always private data. CAT 35 negotiated fares are generally sold through wholesalers, tour operators and travel agents. When a travel agent sells an unpublished fare, it is based on an agreement with the airline on the net fare amount for the tariff. The agent then marks up the fare and sells to a retail agent or a passenger. A negotiated fare may consist of multiple fare levels, fare amount, security, and ticketing data unique to the seller (e.g., the travel agent) and uses CAT 25 for fare generation and CAT 35 where the markup (or markdown) is defined subject to the specified range. When a travel agent sells a private fare to a customer, the net fare negotiated with the airline is marked up and the agent submits the net fare amount to the airline through the BSP1 for settlement. Private fares of this type are very prevalent in the Asia/Pacific region, Latin America and the Middle East and they introduce some unique challenges to the airline such as channel conflict, revenue dilution, attractiveness of the final price to the customer and absence of control of the selling fare, which is in the domain of the travel agents. Negotiated fares are also frequently referred to as off-tariff fares, sanction fares, unpublished fares, market fares, confidential fares, net fares, secured fares, wholesaler fares, consolidator fares, bucket fares and gray market fares. Knowing and managing the net/net fare is important (net of commissions and overrides) since the gross fares do not in any way represent what the airline receives for these unpublished tariffs. In some cases, the net/net fare could be as low as 40% of the gross fare. Another common private fare is a negotiated fare with CAT 35 rule to specify the security (“who can sell” and ticketing requirements), markup and commission for selling the fare by a travel agency. Negotiated fares of this type can be created with a CAT 25 fare by rule (FBR) with or without a discount to which a markup and/or commissions can be specified. For example, consider a $1000 public fare with an FBR 10%t discount and a 5% markup. In this scenario, the airline receives $900 (90% of $1000), the agency receives $45 (5% markup on $900) and the customer pays $900 + $45 ¼ $945. With negotiated fares, airlines can promote the sale of specific high-valued fares.

1

Billing and Settlement Plan (BSP) for IATA accredited passenger sales agents.

48

2

Airline Pricing

Airlines can also promote fare sales by offering a commission to travel agencies with negotiated fares. In the agency/airline direct model where tickets are sold by an airline through an agency, which exists in places like Brazil, the fare is handled with a CAT 35 over CAT 25. Fares are generated with CAT 25 based on the available public fares and viewed with CAT 35 security, the net fare and the FBR fare amounts are the same, and the airline can specify a sales commission (e.g., 1%) for the agency even though the ticket is sold by the airline. Travel agents can simplify the management of negotiated fare contracts with airlines by filing the fares directly with the GDS where they have a subscription. All GDSs support a negotiated fares database and provide tools to easily create, manage and distribute negotiated contracts.

2.5.3

Web Fares

Web fares can be classified as private fares since distribution of these fares is typically limited to the airline’s website. Web fares came into existence in the early 2000s, an outgrowth of the growing importance of the consumer direct channel to augment brand recognition and reduce distribution costs. The GDS has always been at odds with airline web fares. From their perspective, they offer a balanced solution for the efficient marketplace with the well-publicized full content renewal agreements that they negotiate with airlines, most notably the U.S. majors, global pure play, and international flag carriers. The full content renewals promoted by GDSs are based on six tenets for participating carriers: full content (schedules and fares), long-term agreements, service fee protection, parity treatment, flexibility for travel agents to choose the tools they choose and flexibility to pay incentives based on value. Hence, airlines that sign the full content renewals cannot publish lower fares on their websites that are not available through the GDS channel. However, there are still many carriers that do not fit this category and hence, publish web only fares that can only be purchased and ticketed through the airline’s website. The full content agreements do not apply to the sale of ancillary products sold by airlines. This means that airlines do not have to distribute ancillary products to ATPCO and the GDS. Further with the introduction of IATA’s New Distribution Capability (NDC), which is in the early stages of adoption, will eventually render the full content agreements obsolete since pricing power shifts from the GDS to the airline.

2.6

Fare Rule Categories

The complexity in pricing an itinerary is related to the different types of rules that may apply to a fare. Table 2.2 summarizes some of the most prevalent fare categories that are in use today.

2.6 Fare Rule Categories

49

Table 2.2 Summary of fare rule categories (ATPCO) Rule Category 1

2 3 4 5 6 7 8

9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 25

26 27 28 29

Description Eligibility, identification requirement and age range of a passenger type. E.g., ADT: Adult, CHD: Child, UNN: Unaccompanied Child, INF: Infant without a seat, INS: Infant with a seat, BEV: Bereavement Passenger, STU: Student, etc. There are over 150 passenger type codes (PTC) Day/time application that defines the days and or times when travel is permitted Seasonal and promotional date restrictions Flight application indicates a fare is only valid on specific flight numbers Advance reservations, advance purchase/ticketing requirements Minimum stay restriction, specifying when return travel may commence Maximum stay restriction, specifying the last day when travel may commence or may be completed Stopovers defines the number, locations, and charges of allowable stopovers within a fare component. A U.S. domestic flight is considered a stopover when the scheduled time on the ground is longer than 4 h. An international stopover exceeds 24 h. This category can override the default Transfers, conditions, and restrictions for transfers to occur Permitted Combinations, process of using multiple fares to arrive at an itinerary price for the passenger Blackout dates, used to define single dates or date ranges when travel is not permitted Surcharges, conditions when surcharges are applicable and the corresponding charge Accompanied travel when travel with one or more passengers is required to qualify for the fare Travel restrictions when travel dates are specifically stated in a rule Sales restrictions—where the fares can be sold and/or ticketed Penalties, if applicable for a fare and what charges will be assessed Higher Intermediate Point (HIP)/Mileage exceptions Ticket endorsements requirements as specified in a rule Children discounts Tour conductor discounts Agent discounts All other discounts Miscellaneous provisions, states if fares should or should not be used in construction, proration and differential Fare by Rule application to dynamically generates new fares by using a carrier’s existing fares and rules as a base. Allows airlines to file in ATPCO increasingly complex corporate contract terms, thereby improving faring accuracy and reducing agency debit memos (ADM) Groups, to define the requirements to qualify for a group fare. Not used in pricing but will display the rules text Tours, tour requirements for a fare Visit another country, requirements to qualify for a visit another country fare Deposits, defines deposit requirements to qualify for a fare, if any (continued)

50

2

Airline Pricing

Table 2.2 (continued) Rule Category 31 33 35

50

Description Voluntary changes, process the reissue transactions programmatically Voluntary reroute and refund Negotiated/Net fares. Enables travel agents to add their own markup to airline filed net fares, so that the agents’ selling fare is displayed in the GDS. Includes security and agency commissions Rule title/application assumption contains the rule title, geographic application, type of journeys and other conditions. This category exists for every rule and is used for rules text purposes only

Routings are used in conjunction with both domestic and international fares. Routing data are also made available to host CRSs and GDSs with a subscription.

2.7

Circumventing Fare Rules

Travelers have a bargain hunting mentality and frequently attempt to break the fare rules. Two examples are discussed below.

2.7.1

Overlapping Flights

Passengers book overlapping flights to circumvent minimum stay restriction and reduce the total cost of airline tickets purchased. This is also called back-to-back ticketing, and airlines do not allow it. Consider the following bookings by the same customer: Itinerary 1: Roundtrip. DFW-SEA departing May 1, returning July 15. Itinerary 2: Roundtrip, SEA-DFW departing May 4, returning July 11. The passenger can now travel on the first coupon of the first itinerary to SEA and return on the first coupon of the second itinerary on May 4. Subsequently, the passenger travels on the second coupon of the second itinerary to SEA on July 11 and returns on the second coupon of the first itinerary to DFW. Revenue integrity software is used to scan active PNRs to identify customers who are breaking the rules. Booking overlapping flights run the risk of termination of the frequent flyer accounts. In 2020, in the U.S. domestic market, the Saturday night stay restriction has been largely eliminated, eliminating the benefit of back-to-back ticketing. Fare rules have a history of reappearing, and when Saturday night stay restrictions return, savvy travelers will continue to game the airlines. Another common trick used in international travel involves non-overlapping trips, and it is perfectly legal. Consider a traveler that makes six to nine trips a year from Dallas/Fort Worth (DFW) to Krakow, Poland (KRK) with an average

2.8 Journeys

51

length of stay of 3 weeks. Business class tickets when the point of commencement is DFW is typically about 30% more expensive on many carriers than point of commencement KRK. A traveler can purchase a roundtrip ticket from DFW to KRK where the return date is before the intended departure date from KRK. After flying to KRK, the passenger can discard the return ticket and for the remaining trips in a year purchase KRK-DFW roundtrip tickets which are significantly cheaper. Note that the KRK-DFW roundtrip tickets have KRK as the point of commencement and hence the tickets will be priced in Polish złoty.

2.7.2

Hidden Cities

Leisure travelers realized the value of hidden city ticketed itineraries with one-way fares. Travelers explore the availability of hidden city tickets to reduce their cost of travel. Consider a passenger who wants to go from LaGuardia, New York (LGA) to Omaha, Nebraska (OMA), and the lowest available one-way fare is $500. There is an alternative flight for $300 from LGA to Phoenix, Arizona (PHX) with a layover (a long connection) in OMA. A passenger can purchase the LGA-OMA-PHX trip for $300 and not continue on the second leg of the journey at OMA for a savings of $200. The website skiplagged (www.skiplagged.com) pioneered the identification of cheap flights by exploiting hidden cities. When a passenger no-shows for the second leg of the journey, the entire reservation is cancelled. Also checked bags is not an option if the passenger does not complete the journey to the ticketed destination. Unlike overlapping flights, hidden city ticketing is considered legal though some carriers claim it is a violation of contract of carriage.

2.8

Journeys

There are three types of journeys that are shown on OTA and airline websites. They are one-way, roundtrip and multi-stop. A multi-stop trip can be a circle trip, open jaw (with surface sector), or around the world. One-Way The traveler goes from an origination airport to a destination airport. Examples are AUS-DFW, JFK-LON. Roundtrip The traveler goes from an origination airport to a destination airport and returns to the same origination airport where the trip began. Examples are BNA-JFK-CDG-JFK-BNA, AUS-DFW-AUS. Circle Trip The traveler goes from an origination airport, goes to multiple locations, and returns to the same origination airport where the trip began. A circle trip has at least two stopovers. For examples JFK-FCO-DEL-JFK is a circle trip with stopovers in FCO and DEL.

52

2

Airline Pricing

Round the World The traveler goes from an origination airport in one direction (eastbound or westbound) around the world and crosses the international date line and returns to the same origination city where the trip began. The fares have restrictions such as the minimum and maximum number of flights, direction of travel, open jaws are not permitted and a minimum number of stopovers. They are also limited to carriers within an alliance. Open Jaw There are three variations. Destination open jaw, where the traveler goes from an origination airport to the first destination airport, transfers to a different city using an alternate mode of transportation and takes a return flight back to the origination city where the trip began. Examples are JFK-LON (open jaw) CDG-JFK and JFK-AMS (open jaw) BRU-JFK. Origin open jaw where the traveler departs from an origination airport to a destination airport and returns to a different airport/city. Examples are DFW-LONSAT, DFW-FCO-AUS. Double open jaw where two totally separate fares exist. It is a single return ticket where the O&D for the first flight and the second flight are different. An example is DFW-FRA for the first flight and VIE-AUS for the second flight.

2.9

Itinerary Pricing

Itinerary pricing requires an understanding of fare components and priceable units (PU) which are described below. Priceable units are also called pricing units. A customer’s journey constrains the PU possibilities. PUs are responsible for much of the complexity in pricing an itinerary. The fare component is the most basic unit of fare construction and represents a specific fare between two city pairs. A fare component is a section between two points along the travel path. One or more fare components make up a PU. One or more PU combinations produce a pricing solution for a trip. A PU is a domain where multiple fare rules may apply and consists of a group of fare components. Fares may require that other fares in the same PU are from the same airline. Tickets are built from one or more PUs. PUs can take several forms. The PU types are one-way, roundtrip, circle trips and open jaw. Pricing units are patterns and combination of PU types can form a pricing solution for a trip. Open jaw PUs are like circle trips with a missing fare component where the gap exists and normally the gap must be shorter than the distance flown in any of the fare components. In summary, flights can be broken into fare components and PUs in many ways. Consider the itinerary from LAX to LGA outbound connecting over ORD and LAA to LAX inbound connecting over DFW. There are several combinations of fare components and possible PU patterns. A few are shown in Fig. 2.3.

2.10

IATA Traffic Conference Areas ORD

53 ORD

1 Round Trip Fare Components (2) LGA LAXLGA LAX LGALAX Pricing Units (1)

LAX

DFW

DFW ORD

ORD Open Jaw +1 One-Way Fare Components (3) LGA LAXORD LAX ORDLGA LGALAX Pricing Units (2)

LAX

DFW ORD

LAX

DFW

1 Circle Trip Fare Components (3) LGA LAXLGA LGADFW DFWLAX Pricing Units (1)

1 Circle Trip Fare Components (4) LAXORD LGA ORDLGA LAX LGADFW DFWLAX Pricing Units (1)

DFW ORD

DFW

2 Open Jaws Fare Components (4) LGA ORGLGA LGADFW DFWLAX LAXORD Pricing Units (2)

4 One-Ways Fare Components (4) LAXORD LGA ORDLGA LGADFW DFWLAX Pricing Units (4)

Pricing Unit (PU), fare component arrow design indicates number of PUs

Fig. 2.3 Examples of possible fare components and pricing units

In summary, this is the process of dividing an itinerary into fare components and assembling fare components into one of more priceable units. From the possible pricing solutions, itinerary pricing finds the cheapest solution that is applicable and available, with all taxes and fees taken into consideration. The government taxes mandated by the DOT may include the following: passenger facility charges (PFC), Federal Excise Tax (FET), Segment Fee and September 11 Security Fee, U.S. or International Departure and Arrival charges.

2.10

IATA Traffic Conference Areas

Since deregulation airline pricing has grown in complexity and a detailed explanation of all the intricacies is beyond the scope of this book. From the public and private fares, a specific itinerary can be priced based on the fundamentals of fare construction. The IATA (IATA, 2006) Fare Construction Handbook serves as the primary source for applying the fundamentals of fare construction. Fare construction for an itinerary may include specific routings, stop over charges, security fees, combinability of one-way pricing units, mixed classes of service, roundtrips, circle trips, open jaw, special mileage provisions, journeys with surface sectors, and ticket reissue or exchange. Fare construction is based on the areas of the world the customer is travelling to, from or connecting through. IATA divides the world into three traffic conference

54

2

Airline Pricing

Table 2.3 IATA traffic conference areas IATA area identifier IATA Area 1 (TC1) IATA Area 2 (TC2) IATA Area 3 (TC3)

Geographical locations North America, U.S. Territories, Central America, Caribbean, South America Europe, Russia (RU), Middle East, Africa, Indian Ocean Islands. Asia, South East Asia, Indian Sub-continent, Japan/Korea, North/Central Pacific, South West Pacific, Oceania.

(TC) areas which are further divided into sub areas. The composition of the three primary IATA Traffic Conference Areas is shown in Table 2.3. For example, a conference area of TC23 could incorporate fares between Europe (Area 2) and India (Area 3), while TC12 could incorporate fares between the United States and Germany. For example, TC3 could incorporate fares within the Pacific, between India and Fiji and between Hong Kong and Singapore.

2.11

Constructed Fares

Constructed fares fall into a unique category since the fare amount is only determined after going through a fare construction. For example, add-on fares are widely used by international carriers which enable them to create a fare between two locations without having to explicitly publish each market for which an airline wishes to participate. The new fare record is constructed by combining a published gateway fare with an add-on fare. For example, Thai Airways may publish a gateway fare from their hub in Bangkok to London, Heathrow. They may also publish an add-on fare from a domestic city in Thailand, say Phuket to Bangkok. Hence, the fare from Phuket to London can be constructed based on the published gateway fare and the add-on fare.

2.12

Savvy Travelers, Stopovers, Open Jaws and Frequent Flyer Redemptions

Many airlines use the neutral unit of construction (NUC) to file frequent flyer redemption mileage amounts to be processed as CAT 25 fare by rule. The NUC superseded the older fare construction unit (FCU) in July 1989. The NUC is a private currency that is pegged to the U.S. dollar and used to calculate a fare when the itinerary involves a combination of local currencies. For revenue travel, payment is based on the currency at the point of commencement of the itinerary. Rules for redemption of frequent flyer miles for award travel vary widely between airlines. Savvy travelers stretch the value of accumulated miles in their frequent flyer account with stopovers and open jaws on airlines that permit them. Leisure travelers find stopovers very appealing since they can extend their stay at the stopover city as

2.13

Market Segmentation

55

specified by the airline. Some are as short as 3 days and some are much longer. Some airline programs allow travelers to combine stopovers with one or more open jaw segments, allowing travelers to add multiple cities to the itinerary for a single award fee. In 2009, American introduced one-way redemption miles on all itineraries, thereby giving greater flexibility to customers. For example, a U.S. domestic unrestricted redemption was 50,000 miles for a roundtrip and restricted redemption was 25,000 miles. With one-way redemptions, for the roundtrip a traveler can redeem 25,000 miles, 37,500 miles or 50,000 miles, thereby giving customers more opportunities for redemption. With one-way redemptions, open jaws are permitted systemwide, but American does not permit stopovers. International carriers, Air Canada, Cathay Pacific, and Japan Air Lines allow stopovers with surface sectors (though there are maximum limits for each) and redemptions may only be applicable for carriers in the alliance.

2.13

Market Segmentation

In a deregulated airline environment, the significance of an efficient and rational fare management process cannot be understated. It is the critical first step toward effective revenue management with the creation of customer segments. Even though every seat on an aircraft is viewed as a commodity, the perceived value that each customer attaches to a seat is vastly different. In the absence of a sound fare management discipline, the potential benefits of a sophisticated revenue management process can never be realized. Customers flying on business tend to be more schedule sensitive and less sensitive to the prevailing fare. The broad-based market segments and their respective attributes are shown in Fig. 2.4. Leisure passengers, by far the larger segment consisting of short getaways, longer vacations and people visiting friends and relatives, tend to be more flexible and willing to consider alternative flights to get a lower fare. A pricing strategy needs to

Schedule Sensitive

Business Customer

Price Sensitive

Fig. 2.4 Broad-based market segments

MORE

LESS

Leisure Customer

LESS

MORE

56

2

Airline Pricing

Table 2.4 Customer segments Customer segment Business Travelers—First Class Business Travelers—Full Y Industry Approved Reduced Fares Corporate

Frequent Flyer Redemptions (Unrestricted) Frequent Flyer Redemptions (restricted) Tour groups Visiting Friends and Relatives Labor Groups Group Fares

Private Fares Emergency Child Fares

Description Last minute availability, no penalties, full refundability based on time related flexibility, seat pitch and comfort Last minute availability, no penalties, full refundability based on time related flexibility Limited availability, advance reservation requirements, maximum stay limits, with and without cancellation penalties Corporate negotiated fares with the airline based on anticipated volume of bookings, discounted fares with some or no restrictions Unrestricted redemptions apply if seats are available on the requested flight. Redemption costs are typically double mileage points Restricted or capacity-controlled redemptions based on the availability of the specific frequent flyer class Discounted tickets, air component with a land package, advance purchase and related restrictions apply Deeply discounted tickets purchased well in advance, longer length of stay, less flexibility, maximum stay restrictions and cancellation penalties apply Timely availability, one-way fares Negotiated fares based on the size of the group. Usually, the negotiated fare is mapped to a published fare with a pre-defined discount. Cancellation and ticketing time limit flexibility Unpublished fares negotiated between an airline and a travel agency Bereavement fares, available only with validation Discounted fare when accompanied by an adult

consider the types of travelers targeted by the proposed fare. Table 2.4 summarizes the broad customer segments that should be taken into consideration when determining a tariff structure for a market.

2.14

How Many Price Points in a Market?

A fundamental question that arises is: how many price points are required in a market? It is always in an airline’s best interest to participate in every segment of the market if revenue management can forecast demand for the segment and set inventory controls in the host CRS. Simply put, the more price points to capture all customer segments the better, as long as the associated inventory can be controlled. Deregulation resulted in an exponential increase in the number of fares offered in a market. For example, when Southwest Airlines, the low-cost leader in the industry, offered low affordable fares and American Airlines introduced the SuperSAAver™ fare, these actions attracted a whole new segment of the population that had never

2.14

How Many Price Points in a Market? $125 100

Fare

Fig. 2.5 Theoretical pricedemand curve with a single price point

57

Unrealized Revenue

75 50

Realized Revenue (with single price) $75 x 40 = $3,000

25

Unrealized Revenue

0 0

20

40 60 Demand

80

100

Realized Revenue

$125 Unrealized Revenue

100

Fare

Fig. 2.6 Theoretical pricedemand curve with multiple price points

75 50 25 0 0

20

40 60 Demand

80

100

Realized Revenue

flown before. The key is the ability to file fares to target all customer segments if there is a capability to control the availability of the fares (Crandall, 1998) in the reservations system. Figure 2.5 illustrates the theoretical price demand curve that shows the realized and unrealized revenues with a single price point. Figure 2.6 illustrates the theoretical price demand curve that shows the realized and unrealized revenues with multiple price points. As observed there remains unrealized revenues and hence the need for an infinite number of price points that can be inventory controlled to maximize revenues. Figure 2.7 illustrates the price demand curve for various price elasticities in a competitive marketplace. Note that the demand is highly elastic at the lower price points and less elastic at the higher price points. As the elasticity increases, the curve becomes more sensitive (steeper) along the demand axis implying that for the same change in price, the change in demand becomes larger, as the definition implies.

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2

Fig. 2.7 Price demand curve for different price elasticities

Airline Pricing

$300 Inelastic Region

Fare

$250 Lower Price Elasticity

$200

Elastic Region

$150 $100

Higher Price Elasticity

0 0

25

50 75 Demand

100

125

To understand passenger behavior characteristics requires an understanding of the underlying price elasticities for the different fare types. To gauge the impact of price on demand, a functional relationship between demand and price is required. Econometricians define a quantity called price elasticity (ε). The price elasticity of demand is the ratio of the percentage change in demand for a unit percentage (1%) decrease in price. The price elasticity (ε) is always a negative number (ε < 0) since price increases generally produce a decline in demand and price decreases generally results in an increase in demand. A product is called elastic if |ε| < 1, inelastic if |ε| > 1 and unit elastic if |ε| ¼ 1. When price increases; revenue decreases in the elastic range, revenue does not change for unit elasticity and revenue increases in the inelastic range. There are various models that can be used to establish the relationship between demand and price. A common approach for calibration is to use polynomial distributed lag models (Almon, 1965). Two models that are frequently used are the log-log linear and semi-log linear transformations. In the former, the log of the demand is a linear function of log of price, while in the latter, the log of demand is a linear function of the price itself. It can be shown mathematically that the former leads to a constant elasticity model, which means that ε is constant at each price point. If price elasticity is directly modeled by revenue management to determine optimal inventory controls, the constant price elasticity model is typically not valid over the life of the flight. Price elasticity changes with time to departure. Demand is highly elastic long before departure and less elastic closer to departure. This can be addressed by calibrating price elasticities for specific predeparture time periods. Table 2.5 summarizes the relationship between price elasticity and total revenue. A second measure of sensitivity to demand is the cross-price elasticity of demand which measures the responsiveness of demand for a product to the change in price of another product. If the cross-price elasticity is negative, the two fare products are complementary and if it is positive, the two fare products are substitutes. Typically, an air product bundled with a hotel product is complementary and the cross elasticity

2.15

The Fare Management Planning Process

59

Table 2.5 Relationship between price elasticity and total revenues Price elasticity E ε¼0

Price elasticity |ε| |ε| ¼ 0

–1 < ε < 0 ε ¼ –1 –1 B > M > H > V > Z > Q. Ideally, the tariff structure for a market should have no overlap in fare ranges between booking classes. Fare range overlaps make the revenue management process inefficient and incapable of generating incremental revenues due to minimal separation in average fare values between booking classes and fare inversions, when

2.19

Fare Rationalization in the Price Planning Process

63

After Fare Realignment

Range of Fares ($) Mapped to a Booking Class

Range of Fares ($) Mapped to a Booking Class

Before Fare Realignment

Y

B

M

H

V

Z

Q

Booking Class Hierarchy

Y

B

M

H

V

Z

Q

Booking Class Hierarchy

Fig. 2.9 Mapping of fares in a market before and after realignment

a booking class higher in the hierarchy has a lower fare than the booking class below it. The hierarchy of reservation booking designators (RBDs) assumes that the value of each RBD is in descending order. Purity of the RBDs is a critical requirement for the success of a revenue management program for the following reasons: 1. Overlapping fare ranges reduce the separation of the average fare associated with each booking class. When the separation is reduced, it automatically reduces the number of seats protected for the higher classes leading to revenue dilution 2. Fare inversions can result in zero protection for the perceived higher valued class from the lower valued class. For example, from Fig. 2.9, it is obvious that the average B class fare is greater than the average Y class fare, which is undesirable. This problem is referred to as fare inversion because the revenue ratio RB/RY, which is always expected to be less than 1, exceeds 1, and impacts the expected marginal seat revenue calculation resulting in no protection for Y from B. When inventory control recommendations made by a revenue management system are perceived to open the lower classes, the first step in the diagnosis is the tariff structure and its relationship to the booking classes. Though frequently overlooked, addressing the booking class mis-alignment problem is mandatory for revenue management to produce positive results. Ideally, the tariff structure for a market should be such that there is no overlap in fare ranges between booking classes. When the mapping of fares to booking classes is inconsistent with an airline’s revenue maximization goals, booking class realignment must be undertaken for all the affected markets. The process begins by examining the relationship between fares and booking classes on a market-bymarket basis, making the requisite adjustments, and refilling the fares. This process also provided the opportunity to eliminate fares that are deemed unnecessary. The

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Airline Pricing

figure below illustrates the tariff structure and its relationship to booking classes after a booking class realignment exercise for a specific market. Hence, the process of booking class realignment will result in a clear distinction of average fare values by booking class, which is a requirement for revenue management. Figure 2.9 illustrates the fare ranges associated with a booking class before and after the fare realignment process. The booking classes are in a hierarchy based on value and hence Y > B > M > H > V > Z > Q.

2.20

Multilateral and Bilateral Prorate Agreements

Interline traffic is an integral component of airline traffic and ranges between 8% and 12% worldwide. The problem with interlining is that airlines may pay more than their fair share to the validating carrier or receive less than their fair share for transporting passengers on the validating carrier’s ticket stock. Contrary to common belief, two or more airlines do not have to belong to an alliance to have a special prorate agreement (SPA) for interlining passengers. However, SPAs are also one of the fundamental building blocks between partner airlines in an alliance. IATA developed the multilateral prorate agreement (MPA) through the Prorate Agency which defines a methodology for determine the revenue share for each airline of an interlining itinerary. Consider an interline itinerary where one airline flies the passenger 700 miles, and the second airline flies the passenger 300 miles. The revenue share based on mileage is 70/30. This is called straight rate prorate (SRP). However, a regional cost factor is applied to these mileages to compensate airlines operating in a high-cost environment. The calculations also compensate higher cost short distance sectors. For passenger proration, the mileage is constructed by taking the ticketed point mileage (TPM) which is the shortest distance between any two points on an operating route, used in airline fare calculations regardless of the airports used. The worldwide cost weighted formula, adjusted by an area cost weighting based on the geographic sector where the flight operates, is applied to determine the resultant weighted mileage, known as the prorate factor. IATA’s Prorate Agency calculates the prorate factor for each city pair for which there is a scheduled passenger service and publishes these factors in the IATA Electronic Prorate Manual—Passenger (ePMP). Prorate factors are also called cost weighted mileage factors (CWMF). An exception to the standard prorates, called provisios, compensates airlines whose operating costs are higher on short haul segments. Airlines pay an annual subscription fee for the Prorate Manuals published by IATA, which details all the provisios. The MPA also includes protective measures that ensure that an unreasonably low revenue share is not allocated to any airline involved in the itinerary. When all the airlines involved in an interline itinerary are signatories to the MPAs, these rules apply for each airline. The ePMP contains airline provisios, prorate factors and base amounts.

2.20

Multilateral and Bilateral Prorate Agreements

65

The exception is when a bilateral prorate agreement, called the special prorate agreement (SPA), exists between the carriers in which case the SPA overrides the MPA. The SPA is an agreement between two or more airlines on the apportionment of through-fares on journeys with two or more legs operated by different carriers. SPAs are unilateral or bilateral which means that they apply to tickets issued by one or two carriers. In rare cases, the SPA may apply to tickets issued by more than two carriers. A SPA with the right terms for revenue allocation allows an airline to offer additional markets at competitive fares. There are many methods to derive special prorates. The most used are fixed rate which is a fixed dollar amount that a carrier pays another when they carry another airline’s passenger. Other types of special prorates are mileage based prorate, square root of the miles, a fixed percentage of the full local fare, etc. When a carrier establishes a SPA, it should provide a commercial advantage over the MPA. Generally, a carrier will want to negotiate a lower prorate for tickets they issue and a higher prorate for tickets they accept from other carriers. In summary, proration is the process of sharing revenue between two or more carriers that participate in an interline itinerary. When a carrier sells an interline ticket, payment is collected for the entire trip. After the interlining carrier transports the passenger, the airline will bill the carrier who issued the ticket, the validating carrier, to receive payment.

2.20.1 The SPA Lifecycle Figure 2.10 illustrates the SPA lifecycle. The creation of SPAs between partner airlines begins with the identification of opportunities. If the airline is part of an alliance, then the alliance teams would create the proposal. Once a SPA proposal is in place, it needs to be reviewed by airline pricing to validate the market opportunity and determine the soundness of the parameters that make up the SPA. Next, the Identify New Partnership Opportunities

Create / Update SPA Proposal

Airline Pricing Review/Analysis of SPA Proposal

Revenue Accounting Review/Analysis of SPA Proposal

Management Approval of SPA Proposal

Proposed Revisions Proposed Revisions

Monitoring and Evaluation

Fig. 2.10 The special prorate agreement request and approval process

SPA Team Executes Proposal

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revenue accounting team reviews the proposal. These steps are iterative until a consensus is reached. Once the SPA has been finalized and approved by senior management of both participating airlines, the SPA team executes the agreement. This is followed by periodic monitoring and evaluation of the agreement. SPA agreements also exist for the transportation of air cargo. For example, Emirates and Qantas Airways can establish a SPA. Cargo under an Emirates air waybill (AWB) can be transported on a Qantas Airways flight for a defined rate ($/kilogram). The benefit of SPAs for cargo is the same as passenger traffic. It enables an airline to carry cargo and passengers to destinations which they do not serve. By offering more destinations by interlining, a carrier can increase market presence. Prorate agreements generate incremental revenues to services offered by a carrier. Prorate agreements promote the onboarding of passengers who were issued tickets by another airline, thereby supplementing their own customers and augmenting flight load factors. This is valuable in low load factor markets. MPAs based on prorate factors and SPAs play an important role in determining availability of the host airline segments of an interline itinerary with O&D revenue management. This is discussed in Chap. 4. Consulting engagements with airlines have shown that the top five SPA agreements can contribute 40% of interline revenue and the top 25 SPA agreements can contribute 80% of SPA revenue (Peluso, 2014).

2.21

Airline Ancillaries

To differentiate an airline’s brand, create brand loyalty and generate incremental revenues, airlines are offering a no-frills base fare and adding back services that customers are truly willing to pay for at the time of booking. Some airlines see ancillary revenues as a new revenue stream that can potentially grow in the years ahead (Gottfredson, 2007; Straus, 2008). Examples of ancillary revenues are fuel and insurance surcharges, referred to as YQ and YR which are settled during the airline ticket invoicing process. Ancillary services introduce a new dimension to the fare management process. On a broader scale, ancillary revenues can be classified as ticket transaction fees, air extras and travel extras. Ticket transaction fees pass on the merchant fee imposed by a credit card to the customer. Another example is channel fees for travel agents. These additional fees typically do not appear on the airline ticket but on a new passenger receipt which totals the airfare and additional fees. Air extras are ancillary services that are consumed on board the aircraft. Examples are Internet service, pre-paid seat selection, meals, in-flight on demand entertainment, etc. This requires the issuance of a separate electronic miscellaneous document (EMD) and messaging infrastructure to support it. Travel extras are travel related services that are consumed either before or after the flight. Examples are ground transportation, home to airport baggage forwarding services, lounge access fees, destination activities, etc.

2.22

Total Itinerary Pricing with Ancillaries

67

With the renewed focus on ancillary products as a revenue stream that can augment the bottom line, airlines require the capability to sell, distribute and settle ancillary services across all channels of distribution. This implies that a capability is required to set the prices for ancillary services, distribute products with differentiated content and conduct financial settlement across all channels. This has significant impacts on the capabilities of current airline reservations systems, GDSs and revenue accounting. ATPCO in conjunction with IATA is providing an optional service fee solution that will radically change the industry. Table 2.6 illustrates the new types of fees being introduced. Pricing of ancillary services will eventually be market based instead of a systemwide flat fee that is mostly the case today. For example, a pre-reserved seat is perceived as more valuable by customers on long haul segments than short haul segments. In the absence of any real data, the calibration of a customer’s willingness to pay for ancillary services to establish market prices must rely on in-flight and on online booking survey data.

2.21.1 Branded Fares Record (S-8) ATPCO introduced the branded fares record, called the S-8 record, to standardize brand definitions and ancillary products offered by airlines. Specifically, this record provides the brand name definition, a mapping of RBDs and/or fare basis codes to the airline branded fare product. ATPCO Table 166 provides a cross reference of Optional Services to the branded fare product. ATPCO Table 189 serves as the fare identification table which cross references fares associated with a brand.

2.22

Total Itinerary Pricing with Ancillaries

With the growth in ancillary sales, GDSs have the capability to compute the total price which includes the base fare and ancillary services based on customer preferences and frequent flyer tier by airline. Hence a traveler who has elite status on carrier A will not have to pay for the first and second bag which will be reflected in the total price, while the total price on carrier B where the traveler does not have status will have to pay for the first and second bag which will be reflected in the total price. Other ancillaries that customers select such as lounge access, advance seat selection, in-flight Wi-Fi access and meals will also be reflected in the total price based on frequent flyer status. For corporate customers, negotiated discounts or waivers of ancillary services will also be considered. Similar capability for total pricing also exists on individual airline websites.

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Airline Pricing

Table 2.6 Fee types Fee type YQ/YR (S1, S2 Records)

OB (S4 Record)

OC (S5, S7 records)

OA

Description Carrier imposed fees. A standardized, automated collection, distribution, and pricing method that provides marketing carriers (carriers that appear on the flight coupon) the ability to control and collect fees at the sector (coupon), at the portion of travel (multiple sectors), or on the journey. ATPCO’s application handles fuel, insurance, and carrier-imposed miscellaneous fees • Fuel surcharges filed as YQF or YRF • Insurance surcharges filed as YQI or YRI • Not a tax, but a validating carrier specific fee • Included in the total amount of the ticket, therefore no changes to ticketing or reporting are required • Not applicable for interlines • Not commissionable • Filed by over 200 carriers worldwide • S1, S2 Records IATA defined code used for ticketing fees (optional, validating carrier only, not interline able) • IATA authorized the use of the OB tax code • Distribution channel fees • Carrier fees can be specified by call center, city/airport ticket office, department/station id • Form of payment fees—one fee for all cards or a card specific fee based on BIN number • Always part of the ticket transaction • Calculated for the validating carrier like YQ/YR fees • Available since 1Q 2007, but adoption has been limited pending resolution of several open issues IATA defined code used for fare related optional service or rule-buster service fees (optional, validating carrier only, not applicable for interlining). • Passenger choices (seats, meals, movies, etc.) • May be linked to flights, not tickets • The fees are operating carrier based not validating carrier as with YQ/YR and OB • Issued as a separate EMD and can be issued simultaneously or after the ticket has been issued • Involves inventory controls and messaging. Will involve inventory control and new messaging standards for pre-reserved seats, meals, headsets, etc. • Not applicable for interlines Baggage fees • Excess baggage charges • Overweight baggage fees IATA defined code used for booking fees (optional, validating carrier only, not applicable for interlines

3

The Airline Spill Model

3.1

Introduction

Spill is defined as the displacement of passengers from a flight when their first choice for travel is not available. The spill model estimates the number of passengers who are turned away. The airline spill model is a fundamental cornerstone toward the development of yield management in the airline industry. Passenger spill occurs due to three key reasons: 1. There is insufficient capacity available on a flight. 2. Discount allocation controls that protect more seats for higher valued passengers that were not sold by departure. 3. Incorrect overbooking levels that result in empty seats on closed out flights. Traffic represents the actual revenue passengers onboard a flight. Historical data on flown traffic can be collected from the departure control system after flight departure. The unconstrained demand for a flight is always greater than or equal to the onboard traffic. It represents the true demand for a flight and includes customers who were turned away or “spilled” because the flight was closed prior to departure. Data on customers spilled is never readily available and hence must be estimated. There are two techniques used in the airline industry to estimate spill. First is the “spill model” that estimates the number of spilled passengers by cabin. This model is used for a range of ad hoc analysis by financial and marketing analysts. The second is the detailed model based on inventory open and close information by booking class at specific points in time before departure. Booking profile and consumer choice model-based approaches can be used to estimate the number of passengers who were not accommodated by booking class. The detailed model by booking class is required for forecasting unconstrained demand by revenue management systems. There is a significant body of research on demand untruncation in the literature for both independent and dependent booking classes.

# The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 B. Vinod, The Evolution of Yield Management in the Airline Industry, Management for Professionals, https://doi.org/10.1007/978-3-030-70424-7_3

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70

3.2

3 The Airline Spill Model

Recapture and Upsell

The terms recapture and upsell are used frequently in several chapters in this book. Hence, definitions are in order. The capture rate is the likelihood that a customer books travel on any carrier when their first choice was not available. Recapture is a special case of capture onto the same airline. The recapture rate defines the fraction of passengers that were not accommodated on their first choice of travel who will accept a booking on an alternate flight with the host airline. For example, consider the situation where there are two nonstop flights in the same market departing at 7:00 am and 8:00 am. The passengers first choice of travel is the 8:00 am departure which is unavailable. A percentage of passengers will accept a reservation on the 7:00 am departure. If the recapture rate is 25%, it implies that 75% of the spilled demand is lost to competing airlines in the same market. Upsell is a special case of recapture onto the same flight with a higher fare. The upsell rate defines the percentage of passengers who would sell up to a higher booking class on the host airline because their first choice was unavailable. For example, if a 14-day advance purchase fare at $300 dollars were unavailable, a certain percentage of passengers will sell up to a 7-day advance purchase fare. If the upsell rate is 25%, it means that 25% of passengers who wanted to book the 14-day advance purchase fare would buy the more expensive 7-day advance purchase fare.

3.3

Spill Model Metrics

When demand exceeds capacity, the airline spill model determines the expected spill for a nonstop flight based on the observed historical onboard traffic. The inputs required to run the spill model typically consists of capacity, the uncertainty in demand expressed as the coefficient of variation of demand, recapture rate, the load factor on closed flights (LFCF) and if spill is being estimated for a single flight or a group of flights. While the spill model is easy to use, it determines a powerful statistic; the nominal demand for a flight given the onboard traffic or load factor. Conversely, given the nominal demand (or nominal load factor), the spill model can estimate the traffic on a given flight. The key metrics are summarized below: Nominal Demand The nominal demand is the unconstrained demand for a flight. Expected Spill Expected spill is the primary statistic derived from the spill model and is simply the difference between the nominal demand and the observed demand expressed in load factor units or seats.

3.4 Spill Model Applications

71

Spill Rate The spill rate is the number of spilled passengers on a flight divided by the nominal demand. Flight Closing Rate The flight closing rate is the probability that passenger demand (unconstrained net demand) will exceed the capacity of the aircraft. Passenger Closing Rate The passenger closing rate is the probability that an incremental passenger will be denied a seat. The passenger closing rate is always greater than the flight closing rate since more passengers demand seats on days when a flight is full than empty.

3.4

Spill Model Applications

A spill model is useful for a variety of applications. Some of the well-known applications of the spill model are: Alternate Aircraft Capacities Evaluate alternate configuration of aircraft on existing routes. For example, if the demand on a flight is remarkably high, an analyst may evaluate switching from a B737 (12F/110Y seat capacity) to a B767 (12F/210Y seat capacity). The justification for changing aircraft types on the flight is based on estimating the incremental passengers that can be accommodated on the larger aircraft. Conversely, if the capacity is decreased, the spill model can estimate the expected loss in traffic. Alternate Aircraft Configuration In international markets where demand for business class is high, adding seats in business class reduces premium spill and generates incremental revenues. However, reducing capacity in the economy cabin increases spilled passengers and reduces revenues. The spill model can be used to do the trade-off analysis of incremental premium revenues versus loss in economy revenues. Impact of Corporate Discounts Corporations negotiate discounts with airlines for corporate travel. When the discount is negotiated, it stipulates the booking classes for which the corporate discount applies. The discounts are off the prevailing adult selling fares in the market and the same fare rules apply to the corporate fares. The spill model can be used to estimate the displacement of adult fare passengers because of the corporate passengers on the flight and hence determine the cost of the corporate programs. Schedule Profitability Schedule profitability models are used to determine the profitability of a schedule during the schedule creation process. This application uses the spill model to

72

3 The Airline Spill Model

untruncate traffic and estimate demand on flights, which is then used to evaluate the profitability of the schedule. Evaluation of the Cost of an Airline’s Frequent Flyer Program From historical flight information, the number of passengers who flew by redeeming miles is known. With the spill model, an estimate of the expected incremental revenue traffic that may have flown if the frequent flyer redemptions were denied can be estimated. The incremental traffic can be estimated for all flights over a 1-year period based on the expected displacement of revenue traffic. The cost of the frequent flyer program, expressed in cost per seat mile, can then be estimated from the revenue passenger displacement cost and costs associated with administering the frequent flyer program. Entry into New Markets The spill model can justify entry into new markets. For example, if a flight is being introduced into a new market, the spill model can determine the type of aircraft based on seat capacity that should be used on the new service.

3.5

The Boeing Spill Model

The original spill model was developed by Boeing (DeSylva, 1976), based on the assumption that demand is normally distributed. Figure 3.1 illustrates a normal distribution with mean μ and variance σ 2 . The expected value of the curve to the right of the capacity constraint is the spilled demand. In the airline industry, the spill tables are commonly referred to as the Boeing Spill Tables. While the Boeing Spill Tables were useful, in the working paper, Boeing did not provide the closed form expressions to calculate spill. Hence, an analyst had to lookup the spill tables for various values of the coefficient of variation (known as the “kfactor” in the working paper) such as 0.25, 0.30, 0.35, 0.40 and 0.45. The Boeing Spill Tables are based on two fundamental assumptions. First, demand follows a normal distribution and, second, the load factor on closed flights (LFCF) was assumed to be 1.0. The LFCF is the correction for overbooking and is never equal to 1.0. The calibrated correction for overbooking typically ranges between 0.98 and 0.945. Not considering the LFCF on closed flights in the spill model computation underestimates spilled passengers.

Fig. 3.1 Spill estimation with the normal distribution

μ Nominal Demand t Traffic c Capacity

–∞

Capacity Spilled Passengers

t

μ

c

+∞

3.5 The Boeing Spill Model

73

Assuming a load factor on closed flights of 1.0 implies that there is no correction for less-than-optimal overbooking levels. The spill model discussed below is more flexible than the Boeing Spill Tables since a load factor on closed flights of less than 1 can be an input parameter to calculate the spill (Vinod, 1987a). A closed form expression for the spill model may be based on the following fourth order polynomial approximation to the normal curve (Abramowitz & Stegun, 1965). Let c1 = 0.196854 c2 = 0.115194 c3 = 0.000344 c4 = 0.019527 The cumulative probability distribution function for the normal is given by: )–4 ( F (z) = Pr(X ≤ z) = 1:0 – 0:50 1 + c1 z – c2 z2 + c3 z3 + c4 z4 With the standard normal z = x–μ σ . Now, the spill performance measures are: Flight Closing Rate The flight-closing rate is given by: ∫∞ Pr(D > c) =

f (x)dx c

∫∞ Pr(D > c) =

∅(k)dk = 1:0 – φ(k) c

where k =

c–μ σ

and ∅(k) is the probability density function of the standard normal distribution and φ(k) is the cumulative density function. Expected Spill Let the random variable D denote the passenger demand for a flight. The conditional expectation of D given that demand exceeds capacity can be expressed as: ∫∞ f (x|x > c)dx

E (D|D > c) = x c

Hence, the expected number of passengers spilled

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3 The Airline Spill Model

Table 3.1 Boeing (normal) spill table Spill model parameters CV = 0.30

CV = 0.40

Observed load factor (%) 55 60 65 70 75 80 85 90 95 55 60 65 70 75 80 85 90 95

Nominal load factor (%) 55.02 60.08 65.30 70.83 76.92 84.03 92.96 105.71 129.60 55.17 60.52 66.28 72.72 80.30 89.81 102.94 124.38 176.60

Spilled passengersa 0.02 0.08 0.30 0.83 1.92 4.03 7.96 15.71 34.60 0.17 0.52 1.28 2.72 5.30 9.81 17.94 34.38 81.60

Spill rate (%) 0.03 0.14 0.46 1.17 2.50 4.79 8.56 14.86 26.70 0.31 0.86 1.93 3.75 6.60 10.92 17.43 27.63 46.21

Flight closing rate (%) 0.32 1.34 3.83 8.49 15.86 26.32 40.04 57.14 77.68 2.11 5.15 10.17 17.42 26.98 38.83 52.85 68.78 86.09

“Spilled Passengers” is based on a capacity of 100 seats Assumed Load Factor on Closed Flights (LFCF) = 1.0 Aircraft Capacity: 100 seats a

E(Spill) = [E (D|D > c) – c]Pr(D > c) This simplifies to ( E(Spill) =

) σ ∅(k ) μ+ – c (1 – φ(k )) (1 – φ(k ))

where k = c–μ σ E [Traffic] = μ – E [Spill] E[Traffic] = μφ(k) – σ ∅(k ) + c(1 – φ(k )) Table 3.1 illustrates the values of the nominal load factor for a range of values of the observed load factor for various values of the coefficient of variation of demand. The first derivation of the normal spill model should be attributed to Shlifer and Vardi (1975), but it was done in the context of overbooking.

3.5 The Boeing Spill Model

75

Table 3.2 The Boeing (normal distribution) spill table Spill model parameters CV = 0.30

CV = 0.40

Observed load factor (%) 55 60 65 70 75 80 85 90 55 60 65 70 75 80 85 90

Nominal load factor (%) 55.03 60.17 65.52 71.32 77.96 86.10 97.31 116.67 55.28 60.80 66.88 73.89 82.51 94.05 112.02 150.30

Spilled passengersa 0.03 0.17 0.52 1.32 2.96 6.10 12.31 26.67 0.28 0.80 1.88 3.89 7.51 14.05 27.02 60.30

Spill rate (%) 0.06 0.27 0.78 1.85 3.79 7.09 12.65 22.86 0.51 1.32 2.81 5.26 9.10 14.94 24.12 40.12

Flight closing rate (%) 0.65 2.36 6.05 12.44 22.03 35.08 51.79 72.26 3.28 7.394 13.82 22.72 34.14 47.93 63.97 81.68

“Spilled Passengers” is based on a capacity of 100 seats Assumed Load Factor on Closed Flights (LFCF) = 96% Aircraft Capacity: 100 seats a

∫∞ E[Spill] =

(x – c)f (x)dx = σ [φ(k) – k (1 – ∅(k))] c

where k = c–μ σ The Boeing Spill Tables are widely used in the industry. Table 3.2 illustrates the measures after considering the correction for overbooking. The load factor on closed flights is assumed to be 0.96.

3.5.1

Logit Approximation to the Normal Distribution

An alternative logit approximation to the normal distribution was proposed (Swan, 1983). φ( z ) = 1 –

1 (1 + e1:702k )

The expected spill has a simpler representation.

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3 The Airline Spill Model

Table 3.3 Comparison with the logit approximation Observed load factor (%) 55 60 65 70 75 80 85 90 95

Nominal load factor (%) 55.17 60.52 66.28 72.72 80.30 89.81 102.94 124.38 176.60

Spilled Passengersa (Normal distribution) 0.17 0.52 1.28 2.72 5.30 9.81 17.94 34.38 81.60

Spilled passengersa (Logit approximation) 0.40 0.86 1.69 3.15 5.69 10.15 18.30 34.94 82.78

a “Spilled Passengers” is based on a capacity of 100 seats Assumed Load Factor on Closed Flights (LFCF) = 1.0 CV = 0.40 Aircraft Capacity: 100 seats

E[Spill] =

( ) σ ln 1 + e–1:702k 1:702

where k = c–μ σ This logit approximation to the normal distribution, unlike the fourth order polynomial approximation, is less accurate when estimating probabilities at the tails of the normal curve. This approximation overestimates the spill compared to the fourth order polynomial approximation. However, this formula is reasonably accurate and frequently used to determine discount allocation controls. Table 3.3 illustrates the difference in spilled passengers with the logit approximation to the normal distribution.

3.6

The Gamma Spill Model

The fundamental problem with the Boeing spill model is that the derivation is based on the normal distribution (Vinod, 1987a). There are two primary issues with this assumption. First, the normal distribution is valid for negative random variables and demand cannot be negative. Second, the normal distribution is bell shaped (symmetric) but demand is rarely symmetrical and assumes a shape that is skewed to the right. Empirical evidence confirmed with the Kolmogorov-Smirnov one sample test (Siegel, 1956) and the χ 2 goodness-of-fit test indicate that the distribution of demand is not normally distributed. The gamma distribution does not have the limitations of the normal distribution to model demand variability. The gamma distribution function has the probability density function

3.6 The Gamma Spill Model Fig. 3.2 Spill estimation with the gamma distribution

77

μ Nominal Demand t Traffic c Capacity

Capacity Spilled Passengers

0

f (x) =

t μ

c

+∞

βα xα–1 e–βx ;x ≥ 0 Γ(α)

where α is the shape parameter, β is the scale parameter and Γ is the gamma function. The gamma distribution, a two-parameter distribution characterized by its shape, α, and scale, β, is versatile and can be used to model demand very effectively. This is because; based on the demand profile, the α and β parameters can adjust to model the distribution of demand accurately. In addition, the gamma has the important property, unlike the normal distribution, that the random variable X is only defined for non-negative values x > 0. By matching the first two moments of the sample mean ^ μ and sample standard σ , we have variance ^ ^ μ = αβ σ 2 = αβ2 ^ from which α and β can be calculated. α=

μ2 ^ ^ σ2

β=

^ σ2 ^ μ

Figure 3.2 illustrates a gamma distribution of demand with mean demand μ. The derivation of the spill model when the demand is gamma distributed is summarized below (Vinod, 1987a, 1992). Flight Closing Rate The flight-closing rate is given by: ∫∞ Pr(D > c) =

( ) c f (x)dx = 1:0 – F G (x ≤ c) = 1:0 – F G α, β

c

where FG is the cumulative density function of the gamma distribution.

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3 The Airline Spill Model

Expected Spill Let the random variable D denote the passenger demand for a flight. The conditional expectation of D given that demand exceeds capacity is given by: ∫∞ f (x|x > c)dx

E (D|D > c) = x c

Hence, the expected number of passengers spilled = [E(D| D > c) – c] Pr (D > c) ∫∞

∫∞ xf (x)dx=

E(D|D > c) = c

[ = E (D|D > c) = |

c

∫c

∫∞ xf (x)dx – 0

f (x)dx ]

xf (x)dx|=

∫∞

0

f (x)dx c

Since ∫x

( ) x xf (x)dx = μF G α + 1, β

0

This simplifies to ( ( )) μ – μF G α + 1, βc ( )) E (D|D > c) = ( 1 – F G α, βc The expected number of spilled passengers is given by E(Spill) = [E (D | D > c) – c]Pr(D > c) = ( )) [ ( ] ( ( )) μ 1 – F G α + 1, βc c | ( ( )) – c| 1 – F G α, β 1 – F G α, βc ( ) ( ( )) c c E(Spill) = μ – μF G α + 1, – c 1 – F G α, β β ( ( )) ( ( )) c c – c 1 – F G α, E (Spill) = μ 1 – F G α + 1, β β

3.6 The Gamma Spill Model

79

Expected spill can also be derived from the definition of expected traffic, which is the demand truncated at flight capacity. E (Traffic) = E (D|D < c)Pr(D < c) + c Pr(D ≥ c) The conditional expectation ∫c

∫∞ xf (x|x < c)dx =

E (D|D < c) = 0

xf (x)dx=Pr(D < c) 0

Hence, ( E (Traffic) = μF G

) ( ( )) c c α + 1, + c 1 – F G α, β β

Since, E(Spill) = E(D) – E (T ) We get the same result ( ) ( ( )) c c – c 1 – F G α, E (Spill) = μ – μF G α + 1, β β ( ( )) ( ( )) c c – c 1 – F G α, E (Spill) = μ 1 – F G α + 1, β β

3.6.1

The Passenger Closing Rate

The passenger closing rate is the probability that an incremental passenger will be denied a seat. To estimate the passenger-closing rate, we are interested in random incidence (Larson & Odoni, 1981). Given the demand density f(x) we define the density function gW ( d ) =

xf (x) E (D)

where f(x) is the probability density function of demand process, E(D) is the expected nominal demand and gW(d) is the probability density function for the total duration of the passenger inter-arrival gap entered by random incidence. The passenger closing rate =

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3 The Airline Spill Model

( gW ( D > c ) =

1 E (D)

) ∫∞ xf (x|x > c)dx c

The passenger closing rate = ( gW (D > c) = ∫∞

1 E (D)

xf (x|x > c)dx c

∫∞ xf (x)dx =

c

) ∫∞

∫c xf (x)dx –

0

0

( μ – μF G

xf (x)dx

c α + 1, β

)

Since E(D) = μ,the passenger closing rate = ( ) c 1:0 – F G α + 1, β The passenger closing rate will always be greater than the flight closing rate. Tables 3.4 and 3.5 illustrate the gamma spill table for a load factor on closed flights of 100% and 96% respectively. Figure 3.3 contrasts the spill rate for the gamma and normal spill models. Figure 3.4 illustrates the impact of load factor on closed flights on the spill rate.

3.7

Calibration of Input Parameters

There are two parameters that need to be calibrated for a spill model. They are the coefficient of variation of demand (CV) and the load factor on closed flights.

3.7.1

Coefficient of Variation of Demand

Depending on the application of the spill model, the coefficient of variation of demand should be estimated at different levels of granularity. Common levels are flight for a month, flight leg for a month, flight leg for a year, system for a month and system for a year. Different techniques may be used to estimate the coefficient of variation of demand. For example, to estimate the CV for a flight for a month, a simple technique is to fit a regression model of the form

3.7 Calibration of Input Parameters

81

Table 3.4 The gamma spill table Spill model parameters CV = 0.30

CV = 0.40

Observed load factor (%) 55 60 65 70 75 80 85 90 95 55 60 65 70 75 80 85 90 95

Nominal load factor (%) 55.09 60.27 65.63 71.34 77.58 84.70 93.31 104.81 123.65 55.56 61.16 67.19 73.84 81.41 90.46 101.94 118.13 146.71

Spilled passengersa 0.09 0.27 0.63 1.34 2.58 4.70 8.31 14.81 28.65 0.56 1.16 2.19 3.84 6.41 10.46 16.94 28.13 51.71

Spill rate (%) 0.17 0.44 0.97 1.87 3.33 5.55 8.91 14.12 23.17 1.01 1.90 3.26 5.20 7.88 11.56 16.61 23.81 35.24

Flight closing rate (%) 1.07 2.62 5.41 9.85 16.31 25.19 36.94 52.17 71.94 3.96 7.12 11.63 17.65 25.34 34.92 46.57 60.63 77.73

“Spilled Passengers” is based on a capacity of 100 seats Load Factor on Closed Flights (LFCF) = 100% a

Y = β 0 + β1 X + E where Y = standard deviation for a flight for a month X = average load factor for a flight for a month It is important that the sample only include flights with a load factor that is less than 60%, to ensure that a displaced passenger bias is not introduced into the calibration. The estimate of the slope, β1, is the coefficient of variation for a flight for a month. Table 3.6 illustrates representative values of the coefficient of variation for various applications of the spill model. An alternative is to estimate the true demand for a flight by unconstraining traffic data by booking class based on open and close information, determining the total demand for a flight, and estimating the coefficient of variation.

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Table 3.5 The gamma spill table Spill model parameters CV = 0.30

CV = 0.40

Observed load factor (%) 55 60 65 70 75 80 85 90 95 55 60 65 70 75 80 85 90 95

Nominal load factor (%) 55.15 60.40 65.92 71.90 78.62 86.62 96.97 112.94 159.44 55.77 61.55 67.86 74.99 83.38 93.84 108.16 131.78 211.00

Spilled passengersa 0.15 0.40 0.93 1.90 3.62 6.73 11.85 22.96 64.44 0.77 1.55 2.86 4.99 8.38 13.84 23.16 41.78 116.00

Spill rate (%) 0.27 0.67 1.40 2.64 4.61 7.64 12.35 20.31 40.42 1.37 2.52 4.22 6.66 10.05 14.74 21.41 31.71 54.97

Flight closing rate (%) 1.66 3.84 7.58 13.34 21.55 32.69 47.32 66.48 92.72 5.26 9.20 14.68 21.92 31.10 42.44 56.25 73.08 94.52

a

Spill is based on a capacity of 100 seats Assumed Load Factor on Closed Flights (LFCF) = 96%

Gamma vs Normal Spill Rate Load Factor on Closed Flights (LFCF) = 100%, CV=0.40 Spill Rate (%)

50.00% 40.00% 30.00% 20.00% 10.00% 0.00% 55%

60%

65%

70%

75%

80%

85%

90%

95%

Observed Load Factor Gamma Spill Rate

Normal Spill Rate

Fig. 3.3 Comparison of Gamma and Normal Spill Models

3.7.2

Estimation of Load Factor on Closed Flights

When the load factor on closed flights is less than 1.0, the number of passengers spilled is higher. In practice, the load factor on closed flights is never equal to 1.0 because of uncertainty in the overbooking process caused by uncertainty in the

3.8 Estimation of Spill

83

Gamma Spill Rate

Gamma Spill Model Comparison of LFCF = 100% vs. 96%, CV = 0.40 60.00% 50.00% 40.00% 30.00% 20.00% 10.00% 0.00% 55%

60%

65%

70%

75%

80%

85%

90%

95%

Observed Load Factor LFCF=100%

LFCF=96%

Fig. 3.4 Spill Rate vs. Observed Load Factor at different values of LFCF Table 3.6 Representative values of CV for spill model applications

Application Flight for a month Flight leg for a month Flight leg for a year System for a month System for a year

Coefficient of variation 0.30 0.35 0.38 0.42 0.46

inputs used to determine the optimal overbooking levels: boarding rates, cancellation rates, no-records and mis-connects. There are different approaches to estimating the load factor on closed flights. The estimate can be developed at different levels of granularity. When a systemwide correction factor is required by month, one approach is as follows: 1. Calculate the average load factor on closed flights by reading day (predeparture points in time) and booking class for a given historical month across all flights. 2. Estimate the percentage of spilled passengers by reading day and booking class for the same historical time period. 3. Determine a composite value of the load factor on closed flights by computing a weighted average of the load factor on closed flights from step 1 above, weighted by the percentage of spilled passengers from step 2 above.

3.8

Estimation of Spill

Spill is estimated through an iterative procedure, using the derived formulas. The iterative process is initiated by assuming a value for the nominal load factor that is higher than the observed load factor. An estimate of the predicted load factor is calculated as follows for a single flight.

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3 The Airline Spill Model

Predicted Load Factor = (1:0 – Spill Rate) * Nominal Load Factor If the predicted load factor is less than the observed load factor, the nominal load factor is incremented, and the process repeated. When the predicted load factor is equal to the observed load factor, the iterations are terminated since an estimate for the nominal load factor has been obtained.

3.8.1

Estimating Spill for a Group of Flights

Spilled demand from a flight may travel on another flight on the same airline, fly on an alternate airline or not travel by air. Passengers that are spilled on one flight, may be reattracted to another flight which is the next best alternative. A naïve estimate of the reattraction rate of spilled passengers is proportional to an airline’s market share. A better estimate is using consumer choice models to determine the probability of selection of the desired itinerary from the schedule and then estimating the revised probability of selection of alternate itineraries when the desired itinerary is not available. The reattraction rate can vary from 0 to 100% of spilled demand. Reattracted demand that is accommodated is recaptured demand. For a group of flights, an estimate of recapture withing the group is required to estimate spill. The recapture rate is the fraction of spilled traffic that is accommodated on other departures within the group. This ensures that the nominal demand for the group of flights is not overestimated. Predicted Load Factor = (1:0 – Spill Rate * (1:0 – Recapture Rate)) * Nominal Load Factor Spill can be estimated under varying assumptions of the underlying demand distribution: normal, gamma, logistic, log normal and gamma (Li & Oum, 2000).

3.9

First Class Spill Model

The normal coach spill model cannot be adapted for estimating spill in first class cabin since demand is not normally distributed. Table 3.7 illustrates sample airline data for the first class cabin for flight departures over a period. As seen from the table and the associated graph, the number of passengers paying full fare F or and/or frequent flyer reward travel follows an exponential distribution. With the coefficient of variation approximately equal to 1, the demand for first class cabin can be modeled as a negative exponential distribution. However, there are situations when the coefficient of variation is less than 1 and greater than 1. In low variance situations when the CV < 1, an erlang distribution (continuous) or compound Poisson (discrete) can be used. Alternately the gamma distribution can be

3.9 First Class Spill Model Table 3.7 Demand for first class cabin

85

Number of passengers 0 1 2 3 4 5 6 7 8 9 10 11 12

Occurrences 4561 2282 1660 973 658 413 299 172 105 55 36 16 7

Frequency (%) 40.6 20.3 14.8 8.7 5.9 3.7 2.7 1.5 0.9 0.5 0.3 0.1 0.1

used when CV < 1 and empirical studies showed that the two stage Coxian distribution was the best fit when CV > 1.

3.9.1

Negative Exponential Distribution

The exponential distribution is a special case of the gamma distribution with α = 1, β = μ. Let f(x) denote the probability density function for the negative exponential distribution with mean μ. Hence, f (x) = γe–γx ; x > 0 where γ = 1/μ and γ is the rate of the negative exponential distribution. For the negative exponential distribution, the derivation of the spill measures is straightforward. They are: ∫∞ Flight Closing Rate = Pr(D > c) =

f (x)dx = 1:0 – F E (x ≤ c) = e–γc = e–c=μ

c

And FE is the cumulative density function of the negative exponential distribution. The expected traffic is the demand truncated at flight capacity. E (Traffic) = E (D|D < c)Pr(D < c) + c:Pr(D ≥ c) ( c) E (Traffic) = μ 1 – e–μ Expected Spill = Nominal Demand – Expected Traffic

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3 The Airline Spill Model

Table 3.8 First class cabin spill table Observed load factor (%) 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95

Nominal load factor (%) 15.02 20.14 25.51 31.28 37.64 44.81 53.05 62.76 74.40 88.80 107.10 131.32 165.06 215.36 299.10 466.22 966.50

Spilled passengersa 0.02 0.14 0.51 1.28 2.64 4.81 8.05 12.76 19.40 28.88 42.10 61.32 90.06 135.36 214.10 376.22 871.50

Flight closing rate (%) 0.13 0.70 1.98 4.09 7.02 10.74 15.18 20.32 26.08 32.43 39.31 46.70 54.56 62.86 71.58 80.70 90.17

“Spilled Passengers” is based on a capacity of 100 seats Assumed Load Factor on Closed Flights (LFCF) = 100% a

Expected Spill = μe–c=μ The spill rate is defined as the fraction of demand that is turned away. Spill Rate =

Expected Spill = e–c=μ μ

Table 3.8 illustrates the spill in first class cabin when demand follows a negative exponential distribution with a load factor on closed flights of 100%. Table 3.9 illustrates the spill in first class cabin when demand follows a negative exponential distribution with a load factor on closed flights of 85%. Figure 3.5 illustrates the impact of load factor on closed flights on the spill rate.

3.9.2

Two-Stage Cox Distribution

Phase type distributions are formed by a convolution of exponentially distributed phases, in series or in parallel. They evolved from Erlang (1917), where he used a series of identical exponential distributions to model telephone traffic. Phase type distributions are widely used in queueing theory (Neuts, 1981, 1989) and can be used to approximate any positive, continuous distribution with exponentially distributed phases.

3.9 First Class Spill Model

87

Table 3.9 First class cabin spill table Observed load factor (%) 15 20 25 30 35 40 45 50 55 60 65 70 75 80

Nominal load factor (%) 15.05 20.31 25.99 32.33 39.65 48.32 58.92 72.34 90.00 114.50 151.00 211.60 332.40 693.50

Spilled passengersa 0.05 0.31 0.99 2.33 4.65 8.32 13.92 22.34 35.00 54.50 86.00 141.60 257.40 613.50

Flight closing rate (%) 0.35 1.52 3.80 7.21 11.72 17.22 23.63 30.88 38.89 47.60 56.95 66.92 77.44 88.46

a “Spilled Passengers” is based on a capacity of 100 seats Assumed Load Factor on Closed Flights (LFCF) = 85%

Negative Exponential Spill Model Comparison of LFCF = 100% vs. 85% 100.00%

Spill Rate

80.00% 60.00% 40.00% 20.00% 0.00% 15

20

25

30

35

40

45

50

55

60

65

70

75

80

Observed Load Factor LFCF = 100%

LFCF = 85%

Fig. 3.5 Spill Rate vs. Observed Load Factor at different values of LFCF

For modeling first class demand, the negative exponential distribution is limited to a coefficient of variation (CV) of one. In high variance situations when the CV > 1, a two-stage Coxian distribution (Vinod, 1987b) can be used to match the first two moments. The two-phase Coxian distribution (Cox, 1972) is a mix of two exponential phases with rates γ 1and γ 2 with a probability of transition, a, to the second exponential phase. Figure 3.6 illustrates a two stage Coxian distribution. Y is a two-stage Coxian random variable when

88

3 The Airline Spill Model

Fig. 3.6 Two-stage cox distribution

a

g1

g2

1–a Stage 1: g 1e–g 1x Stage 2: g 2e–g 2x

Fig. 3.7 Alternate Representation of the two-stage Cox Distribution

γ1

α

1–α

γ2

γ1

Y = X 1 + UX 2 X1 and X2 are exponentially distributed random variables with rates γ 1 and γ 2and U is a two-valued indicator variable with P(U = 1) = a P(U = 0) = 1 – a The value of a (a < 1) can be calibrated from the demand data. An alternate representation of the two-stage Coxian distribution is shown in Fig. 3.7. The probability density function of the two-stage Coxian distribution can be derived by taking the Laplace transform of the individual components and inverting back into the time domain. The probability density function of the two-stage Coxian distribution is given by f (x) = Ae–γ1 x + Be–γ2 x where A = (1 – a)γ 1 –

aγ 1 γ 2 γ1 – γ2

3.9 First Class Spill Model

89

B=

aγ 1 γ 2 γ1 – γ2

The parameters of the two-stage Coxian distribution can be derived by the method of matching moments. From the data if the sample mean is ^ μ and standard deviation of the sample ^ σ The mean and variance of the sample are 1 –1 ^ μ = γ– 1 + aγ 2

^ σ2 =

1 a ( 2 – a) + γ 21 γ 22

from which the parameters γ 1, γ 2 and a can be derived. γ1 =

γ2 ^ μγ 2 – a

CV 2 = 1 –

γ2 =

–a ±

2a 2a + 2 2 ^ μγ 2 ^ μ γ2

√-----------------a2 + 2ak ; k = CV 2 – 1 ^ μk

2 The value of a is bounded by a < 1+CV 2 From empirical data the value of a can be calibrated. From past studies, a value of a = 0.1 has been used from calibration. For the two stage Coxian distribution, the spill measures are:

∫∞ f (x)dx =

Flight Closing Rate = Pr(D > c) = c

∫∞ Expected Spill =

Pr(X > x) = c

Spill Rate =

Ae–γ1 c Be–γ2 c + γ1 γ2

Ae–γ1 c Be–γ2 c + γ 21 γ 22

Expected Spill μ

Figure 3.8 illustrates the spill rate against the observed load factor for a CV of 1.40 and load factor on closed flights of 100%. A comparative analysis when the nominal demand is assumed to follow a normal, a logistic, a lognormal and a gamma distribution, was studied by Li and Oum (2000).

90

3 The Airline Spill Model

Spill Rate (%)

Cox Distribution Spill Model LFCF=100%, CV=1.40 100.00% 90.00% 80.00% 70.00% 60.00% 50.00% 40.00% 30.00% 20.00% 10.00% 0.00%

15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95

Observed Load Factor (%) Fig. 3.8 Spill rate vs. observed load factor

4

Revenue Management of the Base Fare

4.1

Introduction

The airline industry is labor intensive, fuel intensive and above all capital intensive, requiring significant investments in aircraft and facilities. Historically, airlines have spent 15% of annual revenues on capital equipment, which is more than double the average for manufacturing companies (Arpey, 1995). The airline industry also has a disproportionate distribution of fixed costs versus variable costs. Fixed costs range from 80 to 90% of total costs. Hence the incremental cost of carrying a passenger on a scheduled flight is minimal. The fare paid by the passenger is, however, extremely important to ensure airline profitability. Airlines realize that the dynamics of customer segmentation, targeted pricing and revenue management have become a necessity for survival rather than a luxury. Because direct variable costs are a small fraction of the total operating expenses, airlines have never really focused on capturing the true variable cost by flight. Moreover, the distribution of variable costs in an airline’s network has traditionally been on an allocation basis by station. Of the total variable cost for an airline, only 20% of the total variable cost is truly variable and can be attributed to meals, fuel, and travel agency commissions. Given the asset intensive nature of the industry, it matters less if revenue is maximized or profit is maximized to determine the inventory controls. This is quite unlike manufacturing, where variable costs are substantial and are an essential input into determining prices. An overview on various aspects of revenue management have been outlined in the following papers (Chiang, Chen, & Xu, 2007; McGill & van Ryzin, 1999; Van Ryzin & Talluri, 2005).

4.2

Definition of Flight Leg, Flight Segment, Service and Market

Consider the sample network shown in Fig. 4.1. # The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 B. Vinod, The Evolution of Yield Management in the Airline Industry, Management for Professionals, https://doi.org/10.1007/978-3-030-70424-7_4

91

92

4

Revenue Management of the Base Fare

Fig. 4.1 Sample network

A flight leg represents a nonstop city pair. Hence, the flight legs in this network are LAX-ORD, ORD-LHR and ORD-BOS. The origin and destination pairs on a flight number are the segments. Hence the segments on flight number 1 are LAX-ORD, ORD-LHR and LAX-LHR. An origin and destination (O&D) service can be a nonstop or a connecting market with specific departure, arrival and connect points. In this example LAX-BOS is an O&D service with a connect point over ORD. A market is a collection of all O&D services that have the same origin and destination of the market.

4.3

Revenue Management Alternatives

There are two types of revenue management systems; they differ based on how airline seat inventory is controlled. Most airlines worldwide control their inventory by leg or segment (“leg/segment”). The larger network carriers control their inventory by origin and destination (O&D). While most airlines worldwide adopt leg/ segment-based inventory controls, about 25–30 of the world’s largest network carriers have adopted O&D based inventory controls. We will first start with an introduction to leg/segment controls and transition to O&D controls. Traditional revenue management assumes that demand for a booking class is independent of the other booking classes. This is only true when fares have strong restrictions, thereby strongly segmenting the different customer types. When there are restriction-free fares in the market (something that came into existence with LCCs in the 1990s), demand dependencies across fares are often very prevalent. In this chapter, independent demands for booking classes are assumed. Advances in dependent demand forecasting and optimization are discussed in the next chapter.

4.4 Leg/Segment Revenue Management

4.4

93

Leg/Segment Revenue Management

A leg/segment revenue management system consists of the core components shown in Fig. 4.2. A successful revenue management business process must ensure that there is a continuous feedback process to improve predictability and accuracy of the models over time.

4.4.1

Host CRS Data Collection

Data collection is the first step. For carriers that are using leg/segment inventory controls on the host CRS, bookings data are collected by flight leg/date or flight segment/date on a nightly basis. In addition, open or close status is required for booking classes to unconstrain traffic and estimate the unconstrained demand. The data from the host CRS can be sourced directly from the inventory detail records (called IND on Sabre PSS, common term is IDR) which have information on bookings by cabin and class and availability of each booking class. Post departure data are collected from the Departure Control System (DCS). For leg/segment carriers, the estimate of revenue by flight leg or flight segment by booking class is a separate process, usually derived from revenue accounting data. The revenue by booking class for a leg or segment is an estimate arrived at by proration of the O&D ticketed fares.

4.4.2

Demand Forecasting

Demand forecasting is an integral part of the revenue management process. Of all the components of a revenue management system, improving forecast accuracy probably has the biggest impact. The uncertainty associated with demand materializing for a flight as forecast has a direct bearing on the accuracy of the inventory controls to maximize revenues. The lower the demand uncertainty, the more aggressive the inventory controls. The higher the demand uncertainty, the more conservative the inventory controls must be, causing revenue dilution. An improvement in demand forecasting accuracy means lower demand uncertainty. This results in improved matching of the unconstrained demand to available Host CRS / DCS Continuous Feedback Reservations Booking Data Post Departure Data

Demand Forecasting

Historical Data

Overbooking

Discount Allocations

Critical Situation Identification

Update Host CRS Inventory Controls

Fig. 4.2 Core components of a leg/segment revenue management system

Performance Reporting

94

4

Revenue Management of the Base Fare

Revenue Improvement versus Forecast Accuracy Revenue Improvement (%)

2.52%

2.30% 2.05% 1.75% 1.40% 0.95% 0.50%

0% 0%

10%

20%

30%

40%

50%

60%

70%

80%

Demand Forecast Accuracy Improvement (%)

Fig. 4.3 Relationship between demand forecast accuracy and revenue improvement

capacity which produces more aggressive and accurate inventory controls which generates incremental revenues. Figure 4.3 illustrates the expected revenue improvements when forecast accuracy can be improved. The graph provides average statistics from simulation studies across a cross section of carriers—two U.S. majors, two European flag carriers, a Central American carrier, and a flag carrier in the Asia Pacific region. Poelt (1998) estimated that a 20% reduction of forecast error could translate into 1% incremental revenues. Studies by Lee (1990) and Fiig, Weatherford, and Whitman (2019) confirm the importance of lower forecast errors to generate revenues. So, what is the implication of a bad forecast? Over-forecasting will result in too many seats being protected for the higher booking classes than there is demand leading to empty seats at departure. Under-forecasting will result in fewer seats being allocated and booked for the higher more valuable booking classes, leading to the acceptance of a larger number of discounted and deeply discounted passengers. This is called the spiral down effect (Cooper, Homem-del-Mello, & Kleywegt, 2006). While under-forecasting leads to loss in revenue, which is the fare difference between a higher valued passenger and a deeply discounted passenger, overforecasting has a larger revenue impact due to empty seats. Beyond the demand forecast models used, there are several factors that contribute to forecast accuracy. Besides having an adequate amount of data, accuracy of data used for forecasting, and identification of outliers in the data are a key requirement. There are also unusual events such as the worldwide COVID-19 pandemic from 2020 that has had a catastrophic impact on the airline industry, demand patterns and where future demand is not representative of the past. This warrants a new approach to revenue management based on continuous demand management (Vinod, 2021b) and a tactical workflow to achieve the key performance indicators. This is discussed in Chap. 10.

4.4 Leg/Segment Revenue Management

95

4.4.2.1 Key Differences from Traditional Forecasting Demand forecasting for revenue management is different from traditional forecasting. Unlike traditional forecasting which works with one-time dimension, revenue management works with two dimensions for time that need to be considered to generate a forecast. The two dimensions are time of booking and the departure or consumption date. Figure 4.4 shows the pace of bookings for the same flight across departure dates for the same departure day of week. Common explanatory variables (independent variables) that have been used to generate a demand forecast for a flight are bookings to date, day of week for departures and purchases, time of year (month, quarter) for departures and purchases, correlated demand across other flights with the same characteristics such as departure day of week and time and adjustments for special events and holidays. Demand forecasts for future flight departures can be based on historical bookings for past departures and current bookings for future departure dates. Traditional forecasting methods can be applied keeping in mind that revenue management deals with two time variables. The forecast models fall into three major categories: time series methods, regression (or causal) methods (which includes customer choice models), and combined forecasting models. The first step in demand forecasting is to take the available traffic (booking) data by booking class and estimate the unconstrained demand based on inventory open/ close information by booking class. Calibration of the selected forecast models require unconstrained demand, which is the true demand, including customers spilled, because a booking class was closed for sale. The revenue management discount allocation optimization models can work with either the forecast of total demand at departure and capacity or remaining demand and remaining capacity. Two measures closely related to demand forecasting are upsell and recapture. They are applicable for both independent demand and dependent demand models.

Departure Dates Demand

June 29 June 22 June 15 June 8 June 1 0

6 13 20 27 34 41 48 55 62 Days to Departure

Fig. 4.4 Bookings by flight for future departure dates

76

90

104

96

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4.4.2.2 Leg Class and Segment Class Demand Forecasting Demand forecasts are generated by leg class or segment classes when airlines adopt a leg/segment inventory control model to maximize revenues. The steps in the demand forecast process are shown in Fig. 4.5. 4.4.2.3 Booking Profiles The pace of bookings for a flight before departure is captured in a booking profile. Booking profiles are an important component of demand forecasting. A booking profile illustrates the pace of bookings at distinct points in time (reading days (RD)) leading up to the departure date. Predeparture booking data are stored by reading day. Reading days are also called data collection points (DCP). Figure 4.6 illustrates a booking curve for a typical flight for an unrestricted full fare booking class. Note that bookings typically peak close to departure. In contrast, Fig. 4.7 illustrates the booking behavior for a deeply discounted leisure fare with restrictions. Most bookings are made well in advance of the departure date to satisfy the applicable advance purchase restrictions. This fare sees no booking activity closer to departure since advance purchase rules, for example, prohibit bookings closer to departure.

Unbiased Booking Profiles

Outlier Detection and Data Cleansing

Historical Data Collection

Continuous Feedback

Demand Untruncation

Forecast Models Parameter Estimation

Forecast Accuracy Measurement

Generate Demand Forecasts

Fig. 4.5 Demand forecasting components

Reservations Holding

30 25 20 15 10 5 0 0

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13

20

27

34 41

48 55

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Fig. 4.6 Unrestricted fare booking profile

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Reservations Holding

30 25 20 15 10 5 0 0

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48 55

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Fig. 4.7 Restricted deep discount fare booking profile

For the purposes of forecasting demand and setting inventory controls, booking profiles are normally constructed at the segment booking class level of detail since open and close information by segment booking class are available in most reservations systems. The alternative is to construct booking profiles by leg class. The construction of unbiased segment class or leg class booking profiles requires open and close indicators (0 ¼ open, 1 ¼ closed) by reading day. The profiles are constructed based on historical bookings during open-for-sale time intervals. If a snapshot of the data are collected daily, fractional closures between reading days can be calculated. The individual leg class or segment class profiles are constructed from post departure data. Each leg class or segment class is checked for availability at each reading day by observing the historical seats available on the inventory detail records. Reservations flowing in between two consecutive open reading days are considered unconstrained (untruncated) demand. The average bookings per interval divided by the total number of bookings made in a leg class or segment class determines the average percent booked per reading day interval.

4.4.2.4 Standard Profiles To improve the robustness of the individual booking profiles, they can be clustered to create standard booking profiles. A standard profile therefore is a representation of several individual profiles. For example, even a small airline with only a hundred flights a day can have hundreds of profiles, being created by leg or segment, booking class, and day of week. Assuming that there are no through flights, 100 flights/day, 10 booking classes per flight and 7 days of week, this translates into 100 × 10 × 7 ¼ 7000 leg or segment-class-day of week profiles. Individual profiles may tend to be volatile. Volatility is introduced for the following reasons: inadequate historical observations to create the profile, and closed predeparture time intervals that were disregarded during the unbiased profile creation process. The creation of standard booking profiles can be accomplished with well-known clustering models

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(Anderberg, 1973) such as k-means, where k represents the number of clusters or standard profiles. The individual profiles that are input into the k-means clustering algorithm must be normalized. K-means is a centroid based algorithm, wherein the variance from the centroid is determined for each cluster and the clusters are remapped one step at a time based on the highest violation observed in the current mapping of profiles to clusters. It is an iterative process and as the iterations continue, the total squared error within a cluster is reduced. K-means is a greedy approach and does not guarantee optimality because the swaps are made one step at a time, and n–swaps are not considered simultaneously. For a fixed number of iterations, the optimal clusters are influenced by the starting solution. The stopping criteria for the iterations are based on a pre-defined threshold for the total squared error. Given the combinatorial nature of the problem, the k-means heuristic is quite fast compared to other clustering algorithms.

4.4.2.5 Hierarchical Profiles The creation of standard booking profiles by clustering frequently has its limitations from its acceptability as a representative profile for a specific flight by a yield management analyst. This is because, clustering is a grouping and frequently there may be actual bookings for a flight that deviate significantly from the standard profile that is mapped to a specific flight. An alternative approach is to store the actual unbiased booking profiles at different levels of the hierarchy—flight leg, flight segment, market, market entity, and system for example. This approach usually has wider acceptance from yield management analysts. 4.4.2.6 Reservations Holding Cancellation Rate Profile The reservations holding cancellation rate profile is widely used because of its inherent stability. Figure 4.8 illustrates a cancellation rate profile for reservations holding at various points in time before flight departure. The cancellation rate is expressed as a percentage of the bookings on hand.

Reservations Holding Cancellation Rate (%)

60% 50% 40% 30% 20% 10% 0 0

6

13 20

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34 41

48 55

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Fig. 4.8 Reservations holding cancellation rate profile

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The reservations holding cancellation rate is constructed by developing a ratio of the total reservations holding in a reading day interval (successive snapshots before departure) that cancelled by departure. Cancellation RatePer Interval¼ Number of PassengersHolding ReservationsonaReadingDaywhoCancelbyDeparture Total ReservationsHoldingonReadingDay

4.4.2.7 Net Demand Profiles Some revenue management systems use the net demand profile since it exhibits the key property of monotonicity. The net demand in each predeparture time interval is simply the new bookings made in the interval minus the number of new bookings that will cancel by departure date. By accumulating net demand by predeparture time interval, the profile can be constructed. A sample profile is shown in Fig. 4.9. Note that the curve is monotonic increasing all the way until the departure date. If normalized, the net demand curve can be expressed as a percentage from 0 to 100%. The net demand profile is not frequently used since most leg/segment revenue management systems do not process PNR data, which is required to create the net demand booking profile. Processing PNR data are a significant investment that most leg/segment revenue management carriers do not invest in. 4.4.2.8 Booked Cancellation Rate Profiles The booked cancellation rate profile also requires PNR data. It is constructed by developing a ratio of the total new bookings in a reading day interval (successive snapshots before departure) that cancelled by departure.

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100 80 60 40 20 0 0

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Fig. 4.9 Net demand booking profile

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60% 50% 40% 30% 20% 10% 0 0

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Bookings (%)

Fig. 4.10 Booked cancellation rate profile

40% 0

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34

41

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Days to Departure

Fig. 4.11 Estimating unconstrained demand with the booking profile

Booked Cancellation Rateper Interval¼ Number of PassengersBooked onReadingDayIntervalwhoCancelbyDeparture Total Reservations Booked ontheReadingDayInterval The booked cancellation rate is not recommended because of its inherent volatility. A sample booked cancellation rate profile is illustrated in Fig. 4.10.

4.4.2.9 Untruncating Traffic (Censored) Data Estimation of the unconstrained demand by leg/segment and booking class from the booking data are the first step toward demand forecasting. There are various techniques at varying degrees of sophistication that can be applied to unconstrain demand. Booking (traffic) data are censored data since the inventory by booking class may have been closed leading to spilled demand. Untruncating traffic to estimate the

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unconstrained demand is performed at a leg class or segment class level. Open and close indicators by reading day (the data collection points) at the segment class level are required to determine the unconstrained segment class demand forecast. Flown traffic can be untruncated to estimate the unconstrained historical demand to forecast future demand. Spill is estimated for each future flight departure by using the standard profile in conjunction with open and close status by reading day for a flight. Consider the booking profile shown in Fig. 4.11. If the current reading day is 20 (20 days before flight departure) and it represents 40% of bookings to date from the standard profile, then the unconstrained total demand and remaining demand for the flight are given by: Reading day ¼ 20 Current bookings ¼ 50 %of total curve at reading day 20 ðdays to departureÞ ¼ 40% current bookings 50 ¼ ¼ 125 Total demand estimate ¼ %of profile 40% Remaining demand estimate ¼ total demand estimate – current bookings ¼ 125 – 50 ¼ 75 This is a simple illustration of the application of booking profiles to untruncate traffic to estimate the unconstrained demand.

4.4.2.10 Demand Untruncation Example To explain how demand is untruncated, consider the profile shown in Table 4.1. The unbiased booking profile percentages are estimated from historical data. For each time interval (reading day), open and close indicators are also shown. Time interval 2 is closed and the remaining intervals are open. We further assume that there were no bookings during the closed interval. The first step is to determine the number of bookings that occurred during open time intervals. Let us assume that the total bookings were 50 during open time intervals. The open profile percentage is 85%. The unconstrained total number of bookings is given by 50/85% ¼ 58.8. The estimate of bookings in time interval 2 is 8.8 (58.8 – 50). Next, from the standard reservations holding cancellation rate profile, we can estimate the number of spilled customers. Assuming the cancellation Table 4.1 Booking profile % by reading day

Time interval 1 2 3 4 5 6

Open/close Open Closed Open Open Open Open

Percent of bookings 10 15 20 30 11 14

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rate is 18% from the profile, then the number of bookings that would not cancel if they were accepted is given by 8.8 × (100% – 18%) ¼ 7.2 passengers. To determine the net demand for the closed interval, the boarding rate forecast is applied. For example, if the boarding rate forecast is 90%, then the number of passengers that would show up is 7.2 × 90% ¼ 6.5. Hence, the untruncated demand is equal to 6.5 if the closed interval were open. If the closed interval were partially open, the untruncated demand is simply the maximum of actual net bookings in the interval and 6.5.

4.4.2.11 Expected Maximization Method The expected maximization (EM) algorithm is usually applied to incomplete data. The development of the EM method is attributed to Professor Ray Fair (Greene, 2003) who extracted a sample of 601 responses from about 20,000 received from a 101-question survey on sex in Psychology Today in 1969. Fair analyzed responses on extramarital responses from men and women married for the first time using a tobit model (Tobin, 1958), a class of regression models in which the observed range of the dependent variable is censored, that led to the development of the EM algorithm (Dempster, Laird, & Rubin, 1977; Fair, 1978). The EM algorithm was popularized by Talluri and van Ryzin (2004) with its applicability for estimating unconstrained demand. The algorithm iteratively calculates the maximum likelihood estimates of parameters with constrained booking data. The EM method is widely used today to estimate unconstrained demand. 4.4.2.12 Demand Forecast Models The literature on the merits of using various demand forecast models is very vast (Levenbach & Cleary, 1984) and the details are not addressed in this book. The demand forecast for a flight is based on several factors such as historical data, current bookings on hand, open/close status of booking classes, seasonality and independent variables that influence the demand for a flight. Understanding seasonal demand patterns is critical to improve forecast accuracy. Seasonality can be broadly classified as peak season, shoulder season and off-season. In addition, forecast adjustments will be required for special events and holidays. There are different types of holidays—fixed week of month holidays (e.g., Thanksgiving in the U.S., which falls on the 4th Thursday in November), moving holidays (e.g., Easter), and fixed date holidays (July 4, Christmas) where the day of week changes. There are a wide range of forecast models that are used to model and predict demand for future flights. A comprehensive treatment of the various demand forecasting techniques is beyond the scope of this book. Weatherford (2016) provides a concise history of forecasting models in revenue management. Popular demand forecast models used by revenue management practitioners are time series, regression (causal) models and combined forecasts, which are briefly discussed in this chapter. Traditional approaches to demand forecasting are based on the multiplicative or additive Holt-Winters model with constant, trend and seasonality by day of week and week of year. Predeparture bookings can also be used to forecast demand at departure based on open/close information and the shape of the booking profile.

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More recently machine learning techniques have shown promise (Acuna-Agost, Thomas, & Lhéritier, 2021). Common techniques in use are random forests, support vector machines, and neural networks. A few well-known and popular demand forecasting techniques are described below; booking profile, time series, regression and combined.

4.4.2.13 Time Series Forecasting A long-term forecast is a time series model that is calibrated based on historical unconstrained demand data. To forecast demand for flights several weeks before departure, time series models can be used to predict demand. Let the observed n variables in the time series be y1, y2, . . ., yn and let bxt be the average forecast at time t. 4.4.2.13.1 Simple Moving Average The simplest of these time series models is the simple moving average where each observation carries the same weight. The p-term moving average is bxt ¼

p–1 1X y ; ðt ¼ p, p þ 1, . . . , nÞ p i¼0 t–i

4.4.2.13.2 Exponential Smoothing The exponential smoothing model assumes that the most recent observation (yt – 1) has a greater weight than past observations that have exponentially decreasing weights. In the equation below, bxt–1 represents the most recent forecast from the previous period, and t represents the time period. bxt ¼ bxt–1 þ αðyt–1 – bxt–1 Þ This equation is equivalent to bxt ¼ αyt–1 þ ð1 – αÞxbt–1 So, the forecast for the upcoming period, t, is based on both the previous forecast and the latest observation. This approach provides similar forecast results as the simple moving average method, but the data requirements are greatly reduced; thus, exponential smoothing is much more commonly used in practice. Double exponential and triple exponential smoothing models are simple extensions of the basic exponential smoothing model. 4.4.2.13.3 Constant, Trend and Seasonality Models When the data has seasonal trends, low-cost exponential smoothing methods to forecast trend and seasonal patterns are required (Holt, 1957; Winters, 1960). These models have three parameters, α, β, and γ, where α smooths randomness, β smooths trend and γ smooths seasonality.

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A commonly used time series model is the multiplicative seasonal time series model (Winters, 1960) with constant, trend and seasonality coefficients. The time series is represented by the model xt ¼ ða þ bt Þst þ εt where a b st εt

is the permanent component is the linear trend component is the multiplicative seasonal factor is the random error component

The length of a season is T time periods or time buckets. For example, if seasons are defined using weekly patterns, then T ¼ 52 weeks, if seasons are defined using month as the time bucket, then T ¼ 12 months and so on. The seasonal factors, st, are normalized such that the factors sum to the length of the period T. XT s ¼T t¼1 t The model defined above has both a linear trend and seasonal component. If the trend component is not required, the term b can be set to zero in the above equation. At the end of each time period, the Winters model updates for the permanent component, trend and seasonality. b at ¼ α

xt

bstðt¼t–T Þ

h i þ ð1 – αÞ b at–1 þ b bt–1

where 0 < α < 1 is the smoothing constant. Note that dividing xt by bsðt¼t–TÞ deseasonalizes the data so that only the trend component and the prior value of the permanent component are used for updating b at . The second equation updates the estimate of the trend component. b bt–1 at – b at–1 ] þ ð1 – βÞb bt ¼ β½b where 0 < β < 1 is the trend smoothing constant. As seen from the equation above, the estimate of the trend component is simply the smoothed difference between two successive estimates of the permanent component. The third equation updates the estimate of the seasonal factor for period t. bst ¼ γ

xt þ ð1 – γ Þbstðt¼t–T Þ b at

where 0 < γ < 1 is the seasonal smoothing constant. As observed from the equation, at Þ is smoothed with the estimate of the the current observed seasonal variation (xt =b seasonal factor for period t computed T periods ago, which was the last time the seasonal pattern was observed. For example, if T ¼ 52 weeks, if we are in week

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26 (t ¼ 26), the last time this pattern was observed is t – T ¼ 26 – 52 ¼ –26; which is the same week in the prior year. The seasonality coefficients st, should be re-normalized periodically. Forecasts for future time period t + t0 is given by: h i at þ t 0 b bt bst¼ðtþt0 –T Þ xtþt0 ¼ b

Initialization Twenty-four months of historical data are ideally required to determine the initial bt¼0 and bst for t ¼ 1, 2, . . . T. If data are available for the last values for b at¼0 , b m seasons, then let x j ; j ¼ 1, 2, . . . m denote the mean for each of the seasons. The initial value of the trend is given by: x – x1 b bt¼0 m ðm – 1ÞT The initial value of the permanent component at the start of the first time period can be estimated as follows: b at¼0 ¼ x1 –

Tb b 2 0

The initial values of the seasonal factors are computed for each historical time period t ¼ 1, 2, . . ., mT. bst ¼

xi –

xt ⌈Tþ1 2

⌉ ; t ¼ 1, 2, . . . , mT –j b b0

where xi is the average for a season corresponding to the t index and j is the position of period t within the season. This equation should produce m estimates of the seasonal factor for each time period t. These should be averaged to find a single estimate of the seasonal factor for each representative time period. st ¼

1 Xm–1 bs ; t ¼ 1, 2, . . . , T k¼0 tþkT m

The seasonal factors should be normalized to ensure that they add up to the number of time periods T, over which the seasons were defined. T bst ¼ st PT

t¼1 st

; t ¼ 1, 2, . . . , T

Alternative time series models that are popular are the simple moving average, exponential smoothing, double exponential smoothing and triple exponential smoothing models. When there is little or no booking activity on a flight, the time series model provides a robust long-term forecast based on historical data.

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4.4.2.13.4 Kalman Filter Exponential smoothing techniques are widely used for forecasting and parameter estimation in yield management because of their inherent simplicity, low memory requirements, ease of implementation and computational efficiency. The Kalman Filter (Kalman, 1960; Kalman & Bucy, 1961) is a variation of the exponential smoothing model. It is a powerful technique because it explicitly models noisy data (observation noise) versus changing parameters (process noise). The difference lies in the fact that the smoothing constant, called the Kalman gain, changes over time and adapts quickly in a rapidly changing environment. With a Kalman Filter the estimated parameter is described by a state equation and a measurement equation (observations) as shown below xtþ1 ¼ xt þ wt ytþ1 ¼ xtþ1 þ vtþ1 where xt is the unknown parameter at time t, wt is the process noise and is assumed to be independent from one time period to the next with a covariance of q. yt + 1 is the observation at time t + 1 and consists of the value of the parameter xt + 1 plus some observation noise vt + 1 and is assumed to be independent from one time period to the next with a covariance of r. ⌈ ⌉ ⌈ ⌉ E w2t ¼ q and E v2tþ1 ¼ r The state estimate and the error variance are propagated between measurements (Gelb, 1974) as follows. bxt ¼ bxt–1 þ kt ðyt – bxt–1 Þ; t ¼ 1, 2, ::N The equation above describes the change in the estimate. Note that the smoothing “constant” is no longer a constant, but varies with time, which makes it adapt faster to process changes. The Kalman gain, kt, is given by () kt–1 þ qr () kt ¼ kt–1 þ qr þ 1 The initial value of the gain, k0, is 1. In contrast to exponential smoothing, a Kalman Filter does not require separate preprocessing for initialization and the estimate of the gain is optimal after each measurement. As observed from the equation, the Kalman gain, kt, is time varying and computed online. The initialization process of the filter consists of providing bx0 and k0, which is a measure of the uncertainty. The calibration parameters consist of selecting the process and measurement noise variances q and r. If required, the problem can be scaled so that r is equal to 1 and thus q becomes the only calibration parameter, which may be computed by minimizing the mean absolute error (MAE) of the forecast of reservations holding at departure.

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4.4.2.13.5 ARMA and ARIMA Models ARMA and ARIMA models are based on the general theory of linear filters (Whittle, 1963) and much of the original work was done by the well-known mathematicians Kolmogorov and Weiner in the 1930s for automatic control problems. Autoregressive (AR) models were first introduced by Yule (1927). Another filter, called the moving average (MA) model was developed by Slutsky (1937) and autoregressive moving average (ARMA) theory which combined these models were developed by Wold (1954). ARMA requires that the time series be stationary, which signifies that the mean and variance do not change over time. Box and Jenkins (1979) developed a structured class of time series models that addressed both stationary and non-stationary time series with the Autoregressive Integrated Moving Average (ARIMA). A non-stationary time series can be made stationary with the method of differencing (the “I” in ARIMA stands for integration, because after differencing, integration is required to put the forecasts back on the original scale). ARMA and ARIMA models are univariate models which limits the explanatory capability of these models. They have been used in revenue management for demand forecasting but are not used widely because they require a minimum of 2 years of data and preferably 5 or more years of data. Further the booking curves have a mean and variance which are highly time varying which contribute to the limitations of the ARMA and ARIMA models.

4.4.2.14 Regression Forecasting Regression models quantify relationships between the dependent variable and a set of independent variables. Traditional regression models for aggregate forecasting of airline demand are described in Taneja (1978). The objective is to predict the dependent variable based on knowledge of the independent variables. The multiple linear regression model is of the form bxtþ1 ¼ β0 þ β1 X 1 þ . . . þ βk X k where X1, X2, . . ., Xk are the independent variables and β0, β1. . .βk are the coefficients. There are several estimation techniques to determine the coefficients of the regression model; the method of least squares is the most popular and available in statistical packages. Regression models tend not to do well with airline data since the estimates of the β coefficients can be biased because of collinearity. This is because the independent variables are bookings on the same flight for different periods (or related) flights, and they tend to be very correlated. This can be overcome with a dynamic regression model bxtþ1 ¼ β0 þ β1 X 1 þ . . . þ βk X k þ be where be is the error from one period to the next. Another issue with regression-based forecasts is that they usually work best when the data are within the range of the calibration exercise. Out of sample forecasts,

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when the data observed are not in the range of the calibration can produce unreliable forecasts.

4.4.2.15 Booking Profile Forecasts A short-term forecast can be estimated from standard booking profiles, created from uncensored data, and the actual booking activity on a flight. The booking profilebased forecast assumes that the shape of the uncensored booking profile is independent of the actual magnitude of the demand. The volume of booking activity for a leg class or segment class does not change the shape of the booking profile. The booking profile-based approach captures current bookings to estimate the remaining demand. The use of short-term booking data to forecast demand has been discussed by many airline practitioners (Harris & Marucci, 1983 at Alitalia; L’Heureux, 1986 at Canadian Airlines International; Adams & Vodicka, 1987 at Qantas; Barber and Ratliff at Ansett Australian Airlines, 1994; and Smith, Leimkuhler, & Darrow, 1992 at American Airlines). There are two types of models, the multiplicative and additive demand forecast models. The multiplicative model forecasts remaining demand or pick-up by dividing the bookings on hand by the percentage of booking expected at the specific predeparture point in time. If bookings to date is 100 two weeks before departure and 50% of bookings were expected at that point in time from the uncensored profile, the remaining demand is 100/50% ¼ 200 – 100 ¼ 100. Forecast bp ¼

Reservations Holding %open booking periods

The additive model starts with the expected demand at departure and determines remaining demand to come until departure based on the profile percentage at the predeparture point in time. The difference between these two approaches is that the additive model is conservative. If demand to date is higher than expected, the additive model will predict lower remaining demand forecast while the multiplicative model will predict higher remaining demand forecast.

4.4.2.16 Combined Forecasting A common method employed to forecast demand for a flight is based on a combination of long-term and short-term forecasts. Combining forecast models improves the accuracy of the individual forecasts (Makridakis, Wheelwright, & McGhee, 1983). It is also a way to offset forecast bias which is influenced by selection of data and the forecasting method. By using different forecast methods that use different data, the forecast bias tends to dampen out in the aggregate. A question that arises are the weights that should be used to combine forecasts. Contrary to common belief, empirical evidence suggests that a simple average of the individual forecasts often provides more accurate demand forecasts than advanced approaches (Makridakis & Winkler, 1983; Winkler & Makridakis, 1983).

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The time series forecast, Forecastts, can then be augmented with the booking profile forecast Forecastbp to create the composite forecast. Combining the long-term forecast with the short-term forecast produces the composite forecast for a flight by leg class or segment class. Let us assume the mean squared error (MSE) from the time series is MSEts and the mean squared error from the booking profile forecast is MSEbp. To compute the weighted average of the time series and booking profile forecasts, the weight for a specific reading day is given by Weight ðwÞ ¼

1 MSE ts 1 MSE ts

1 þ MSE bp

The composite weighted average forecast, Forecastcf, is given by Forecast cf ¼ wForecast ts þ ð1 – wÞForecast bp With this approach, a greater weight is given to the time series forecast far from departure. Closer to the departure date, the booking profile forecast, which uses information of the actual reservations build-up for a flight is given a higher weight. Similarly, independent forecasts can be combined. For example, a Kalman Filter forecast, Forecastkf, can be combined with the existing forecasting process. The weights to be used by reading day to combine the two forecasts, Forecastkf and Forecastcf are as follows: Weight ðwÞ ¼

1 MSE kf 1 MSE kf

1 þ MSE cf

In the equation above, the mean squared error (MSE) is frequently replaced by the mean absolute deviation (MAD) since it is computationally efficient even though MSE has the advantage that outliers are penalized. The resultant combined forecast is Forecast ¼ wForecast kf þ ð1 – wÞForecast cf

4.4.2.17 Alternative Approaches to Demand Unconstraining and Forecasting In this chapter we only discussed single booking class demand untruncation and demand forecasting when demand is independent between booking classes. Demand untruncation is equivalent to unconstraining traffic to estimate unconstrained demand. There are several approaches to demand untruncation for a single-booking class in the literature. Poelt (2000) and Zeni (2001a) discuss a booking profile-based approach based on predeparture open/close availability status. One of the earlier methods for demand unconstraining, it is still widely used.

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Zeni (2001b) also proposed a mean imputation approach where historical data from closed predeparture periods was discarded and borrowed from previous departures for the same predeparture period. A method like the Expected Maximization (EM) method was proposed where constrained observations are replaced by a quantile (Weatherford & Poelt, 2002), typically 50% of the same median, instead of the mean. The accuracy of the model is related to selection of the value of the quantile. A widely used statistical estimation technique is the EM two step iterative method (Talluri & van Ryzin, 2004; Zeni, 2001b) until convergence is reached. The two steps are replacement of the censored observations with the sample mean and computation of the new mean and variance for the updated sample. To project demand turned away during closed periods, a historical time series of incremental demands between predeparture time periods was proposed with a double exponential smoothing model to forecast demand (Ferguson, Crystal, Higbie, & Kapoor, 2007). Besides the traditional additive and multiplicative time series methods to forecast unconstrained demand, some frequently used techniques are Croston’s (Croston, 1972) and modified Croston’s (Boylan, 2007) for forecasting sparse intermittent demand, multivariate models such as principal component analysis and canonical variate analysis, adaptive transfer function models, neural networks, detailed passenger choice models, wavelets and multivariate adaptive regression splines. Neural networks for forecasting have shown mixed results. A study by Weatherford, Gentry, and Wilamowski (2003) found that neural networks performed marginally better than exponential smoothing, linear regression and moving average models.

4.4.2.18 O&D Demand Forecasting: The First Generation With the advent of origin and destination (O&D) revenue management (Vinod, 1995, 1996a, 1996b) in the mid-1990s, airlines continued to resort to forecasting demand by origin, destination and class by forecasting demand by leg class or segment class followed with the estimation of origin and destination class demand forecast based on historic traffic flows from revenue accounting (ticket) data. This approach is referred to as the forecast enrichment approach; since a segment class forecast is transformed into an O&D class forecast based on the historical traffic distribution from ticket data. The technique also relies on a bi-directional reconciliation to ensure that the transformation from leg class to O&D class has consistent counts upline and downline over the connecting hub. This technique was proposed during the early years of origin and destination revenue management because airline reservations systems did not have the capability to deliver passenger name record (PNR) data, new PNRs and modifications to existing PNRs, daily for nightly batch processing. While this technique has remarkable consistency, robustness, and low processing overhead, it suffers from two inherent problems. First, revenue accounting data are not available immediately after a flight departs and it can take anywhere from 4 to 12 weeks for the data to be accessible. This poses a problem since aged historical data may not be

4.4 Leg/Segment Revenue Management Fig. 4.12 The forecast enrichment approach

111

Bookings by Leg Class or Segment Class

Season Assignment

Data Cleansing and Outlier Detection

Bookings Profiles

Spill Estimation, Untruncated Demand

Forecast Model Calibration

Forecasts by Leg Class or Segment Class

Historic Parsing % from Ticket Data

Service Class (O&D) Demand Forecasting

representative of future traffic flows. Second, the technique cannot adapt to market conditions quickly, a problem also faced by time series models that rely on the calibration of historical data to forecast demand. Figure 4.12 illustrates the methodology for generating service class forecasts from leg/segment class forecasts and parsing percentages from historical flown ticket data.

4.4.2.19 O&D Demand Forecasting: The Second Generation Improvements in PNR extraction processes from mainframe reservations systems in the 1990s led to the second-generation demand forecasting models frequently referred to as direct O&D forecasting. Forecasting directly at the service class level poses some unique challenges. It starts with the creation of service classes from flown PNR data, based on the rules for GDS display. Forecasting small numbers is a challenging task. The problem is further exacerbated if the O&D forecast must be generated by point of sale. A baseline forecast can be created from historical booking trends and current bookings by calibrating a time series model to measure trend and seasonal factors. In addition, booking pace for services can be considered to create this baseline forecast. A large volume of historical data must be processed to create a baseline forecast. In addition, important considerations for the baseline forecast include estimating spill based on

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actual service classes that were closed, forecast adjustments for holidays or special events and maintaining an active list of “must forecast” services. Specialized techniques need to be used to forecast low volume intermittent demand (Croston, 1972) directly at a service class level. PNR based direct O&D forecasting techniques, if calibrated correctly, can produce improvements in forecasts compared to the forecast enrichment approach. Forecasting demand directly at the O&D class level requires access to new PNRs and net changes to existing PNRs daily. This data are then processed to determine the number of O&D class bookings that have occurred followed by demand untruncation and demand forecasting for service classes on future departure dates. Various techniques have been used to forecast demand directly from PNR data. These include the well-known time series models, causal models, multivariate models, adaptive models like the Kalman Filter, and adaptive transfer function models (e.g., Auto Regressive Integrated Moving Average (ARIMA)). An improvement over the forecast enrichment approach is that forecasting demand directly at the origin, destination and class level follows the booking process. In other words, forecasts are generated at the same level at which they are booked. United Airlines (Brad & Singh, 1997) was the first to introduce direct O&D forecasting for revenue management with the Orion project. The direct O&D forecasts were used in conjunction with the virtual nesting controls in Apollo. To address demand volatility at a service class level, an alternative is to forecast demand directly at the market class level with a traditional time series model and distribute the demand to the individual service classes associated with the market with a consumer choice model (Ben-Akiva & Lerman, 1985) calibrated with PNR data. Figure 4.13 illustrates this approach. Causal factors that may be considered are type of service (nonstop, direct or connection), type of aircraft (widebody, turbo, jets), requested time, departure/arrival times, elapsed (travel) times, displacement time (difference between requested time and departure time) between services, route frequency, fares, and applicable restrictions. Introducing price into the equation has the advantage of being able to react quickly to major fare specials. The CCM calibration is limited to the host airline data.

4.4.2.20 Limitations of Single Booking Class Models The single booking class models are structurally incapable of addressing two specific demand interactions in the real world. First, is the modeling of upsell and downsell between booking classes. Second is the ability to address cross flight recapture. These innovations occurred in the context of dependent demand which is addressed in Chap. 5 with choice modeling. Before discussing the third generation of demand forecasting models, it is first important to discuss air shopping data.

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Fig. 4.13 Market to service class forecast

O&D Summary Data

Season Assignment

Data Cleansing and Outlier Detection Untruncation

Market Class Untruncated Demand Profile

Consumer Choice Model (MNL)

Market Class Untruncated Demand

Forecasts by Market Class

CCM Weights Service Class (O&D) Demand

4.4.3

Competitive Air Shopping Data

Competitive shopping data are a relatively new source of data that has a range of applications in travel. When a customer or travel agent submits a query for itineraries from an origin to a destination, the air shopping algorithms respond with a list of itineraries for display. This data has immense value and supports a range of applications to understand customer preferences and manage the screen real estate. Air shopping volumes at GDSs have grown in leaps and bounds over the past decade, and the shopping request and response data are typically stored in a Big Data environment. Some of the applications where air shopping data are used are summarized below: Demand Forecasting Shopping data are an ideal data source for calibrating customer choice models (CCM) used in demand forecasting. Revenue management systems forecasts demand by market, and creation of the service level forecasts requires a choice model to determine the weights for each service/class to generate the forecast.

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Dynamic Availability Dynamic availability is a competitive revenue management capability to override current inventory controls based on current market conditions. Dynamic availability requires shopping data either in batch or real time mode to calibrate the choice models to support both expected revenue maximization and maximize net contribution (difference between the market value and the total bid price) models. Dynamic Pricing Dynamic pricing is a competitive revenue management capability to determine the optimal price point for every customer request based on demand, remaining capacity and competitor selling fares. Dynamic pricing requires shopping data in batch or real time to calibrate the choice models to determine the dynamic price point that would maximize expected revenues or net contribution. Schedule Profitability To determine schedule profitability, MIDT data are typically used to calibrate the choice models. MIDT has been actively marketed since 1987. MIDT has two shortcomings. First, we do not know what itineraries were presented to a travel agent and what was booked and, second, itinerary price is not available. Calibrating the model with shopping data will greatly improve the robustness of the choice models used to determine schedules that are profitable. Air Shopping Algorithms Modern air shopping algorithms support customer segmentation based on context for travel and personalization. Shopping data are required to determine the dominant schedule and fare attributes that should be considered to return itineraries consistent with the preferences of the customer segment. To rank the itineraries returned from a shopping request requires a choice model to determine the utility value of each itinerary from shopping data. Air Shopping Display Algorithms To display itineraries with the right diversity and price to maximize conversion rates requires access to shopping data. Shopping diversity requires a choice model calibrated from shopping data to determine the right mix of schedule attributes such as nonstop, single connect, double connect, interline, and inbound diversity for every outbound to be displayed. It is also required for optimizing the real estate on a screen. Reduce Cost of Shopping Shopping data are required to conduct experiments to optimize shopping system parameters to reduce costs and improve accuracy (book-ability). Examples of parameters include cache time-to-live, shops to use cache results, number of itineraries to return, etc.

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NDC and Screen Displays for Travel Agents In an NDC world, it is required to determine the ranking of non-homogenous products (branded fares, base fare plus ancillaries) returned from multiple airlines. Shopping data are required to develop normalized solutions for comparison shopping of non-homogenous content to make it easier for a travel agent to select and book an itinerary. Further, once an itinerary/bundle is booked, scoring and sorting of closely matched itineraries/bundles for comparison purposes provides guidance to travel agents (e.g., 90% match, 85% match, etc.) Contract Optimization for Override Commissions To optimize back-end commission (overrides) opportunities for travel agencies, demand forecasts for future time periods are required as input. In addition, the order of the display can be biased to maximize the override commissions opportunity. Robotic Shop Detection Pattern detection models can evaluate shopping data to separate robotic and organic shops and flag each shopping record in the shopping data. It enables better insights on customer behavior and identifies caching opportunities. Flagging robotic shops makes the GDS data products more valuable for clients who typically exclude robotic shops for predictive modeling purposes. Net Fare Markup Model Large Travel Management Companies (TMC) negotiate net fares with airlines. Their preference is to markup and sell the net fare to customers. Estimating the market opportunity for net fare markups requires shopping data for analysis. Fare Prediction Model Shopping data can be calibrated with a Q-learning model, a special case of reinforcement learning, to predict when airfares will go up or down. A fare prediction model can recommend BUY or WAIT recommendations to advise customers. OTAs typically only use the BUY recommendation since airfares are going up. When airfares are going down, OTAs do not display the WAIT recommendation, since customer affinity to an OTA is very weak. Travel agents on the other hand are dealing with managed travel and a captive audience, and hence the WAIT recommendation is what they need to advise their customers to realize cost savings. Demand Signals Demand signals from flight search, which are forward looking, can support decision making for route planning, operations, and marketing.

4.4.3.1 O&D Demand Forecasting: The Third Generation Consumer choice modeling with market size and market share estimations from competitive shopping and/or MIDT data represents the third generation in O&D

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demand forecasting. Discrete choice models can be used to predict a customer’s decision to select one itinerary from a finite set of mutually exclusive and collectively exhaustive alternatives. The attractiveness of an itinerary over others in the choice set is determined by the customer’s perceived value of the relevant schedule and fare attributes. This approach follows the actual demand process by modeling the choices that were observed by a customer and what itinerary was selected and subsequently booked. A customer’s itinerary selection process is influenced by various factors such as competitive airline schedules, carrier preference in a market, price, departure time, displacement from requested time, elapsed time, number of connections, type of aircraft (turbo veruss regional jet versus jet), and other factors that can be explicitly modeled in arriving at a demand forecast. Introducing price into the equation has the distinct advantage of being able to react quickly to major fare specials. Results using consumer choice models with competitive shopping data indicate that price and schedule are the biggest determinants of demand for an origin and destination. Traditional techniques ignore competitive schedules and price information in forecasting demand, leading to higher forecast errors. The availability of shopping data from online and offline (brick-and-mortar travel agencies) channels makes this new generation of demand forecasting possible. For the first time, we have access to information on what the customer shopped for (display of competitive schedules and price) and what was booked from the available choices. Calibration of a forecast model of this type requires large volumes of data including competitive schedules, competitive fares and rules, revenue accounting, historical GDS bookings (MIDT), bilateral traffic data and actual passenger booking sessions reflecting the options available and the choices made. The actual data required depends on the scope of the forecast model. Two methods are available for predicting the percentage of total demand for each itinerary in each market, which are the quality-of-service index (QSI) model and consumer choice models (CCM). QSI is a simpler linear form of CCM used in flight scheduling to measure itinerary share. QSI relies on rating each of the available choices within an origin and destination pair by assigning weights to each preference. The weights are developed based on the relative importance of the preferences from statistical analysis. QSI, despite its limitations, is a widely used technique for estimating market share in the airline industry. Discrete choice models may also be used to estimate upsell based on the fare products offered for sale at a given point in time (Talluri & van Ryzin, 2003). Upsell is an integral input into the network optimization model. If upsell probabilities are not considered, it will result in inventory controls that are less aggressive, resulting in revenue dilution.

4.4.3.2 Stated Preferences Versus Revealed Preferences The theory of consumer behavior, where a consumer selects an itinerary after evaluating a set of alternatives is an active area of research across a wide spectrum of industries and travel is no exception. The satisfaction derived from making a

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purchase is contained in the consumer’s utility function (Henderson & Quandt, 1980). Many products we buy today are characterized by its unique characteristics identified as a bundle. Examples are: 1. A Toshiba Laptop, that comes with a 16GB Memory, 1 terabyte hard drive, Windows 10.0 Operating System, Microsoft Office (Word, Excel, Power Point, Visio) 2. A Branded Fare Family product offered by an airline—unique attributes offered at no extra cost such as Air Canada’s Latitude product which includes no change fees, pre-reserved seats, access to the lounge, free baggage allowance for two bags and a newspaper. 3. AT&T U-verse bundle options to save—Double Play (TV + High Speed Internet), Triple Play (Phone + TV + High Speed Internet), Double Play + HBO, etc. The key to product design is to understand the consumer preferences that influence the decision to make a purchase. What attributes in a bundle resonate with customers? What is the customer willing to pay for the bundle? There are two approaches to understanding consumer preferences. They are: 1. Stated Preferences, this is usually accomplished with a survey 2. Revealed Preferences, from actual purchases data collected. The data are used to infer consumer preferences based on choices provided. With the survey-based approach, conjoint analysis is an established technique that can be used to analyze the data. This is a popular among marketers, and it attempts to simulate a realistic shopping scenario. It has been around since the 1980s and started with index cards. The Courtyard brand by Marriott was launched based on conjoint analysis with index cards (Wind, Green, Shifflet, & Scarbrough, 1989). Conjoint analysis is used by automakers to determine the base product and option packages to go with a new car. Dell, Toshiba, Acer, HP, Lenovo, and other laptop manufacturers have used conjoint analysis to develop preconfigured product bundles. The single biggest drawback with this approach is that we simply do not know if the customer who took the survey were truthful. With the revealed preferences approach, it is assumed that consumers possess a utility function. The theory of revealed preferences allows prediction of the consumer’s behavior without specifying an explicit utility function. Inferences are made from actual purchase data of consumer preferences and consumer willingness to pay. The consumer choice modeling approach assumes that consumers choose an itinerary from among a set of alternatives during the flight search process. Each alternative is evaluated based on the set of attributes associated with the alternative. The utility function is estimated for each alternative and the consumer selects the alternative with the highest utility. Marketing plays a key role in segmenting consumers into groups with similar purchasing characteristics to whom these products will be offered. The single biggest drawback is availability of data to

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support the consumer choice modeling analysis. Subject to the availability of data, the revealed preferences approach is superior to the stated preferences approach.

4.4.3.3 Consumer Preference Modeling Approach to Demand Forecasting The consumer preference modeling approach to demand forecasting has gained significant momentum due to the availability of online and offline shopping data. Consumer choice modeling explicitly compares customer preferences to forecast demand. Unlike traditional time series models, the choice modeling approach follows the actual demand process by modeling the itinerary selection process. This approach models how a decision maker selects an itinerary from a set of distinct alternative itineraries, called the choice set. The customer’s behavior to select an itinerary is influenced by the attractiveness of the available alternatives, characterized by the utility value of each itinerary in the choice set. The consumer choice modeling approach (Ben-Akiva & Lerman, 1985) assumes that passengers select their itineraries with the intent of maximizing their utility. Customer utility is a function of market size, market share, competitive schedule changes, type of service (nonstop, direct or connection), carrier preference in a market, type of aircraft (widebody, turbo, jets), requested time, departure/arrival times, elapsed times, displacement time between services, route frequency, fares and applicable restrictions. Introducing price to the equation has the advantage of being able to react quickly to major fare specials. The utility of an itinerary, Ui, can be expressed as the sum of a deterministic component, Vi which represents the value of each itinerary in the choice set and a random component, εi. Hence, U i ¼ V i þ εi The value of each itinerary i can be represented as a linear function of a customer’s choice variables as follows: V i ¼ β1 X 1i þ β2 X 2i þ . . . þ βn X ni Vi ¼

n X

β j X ji ; i ¼ 1, 2, . . .

j¼1

where Xji represents the j-th explanatory variable for itinerary i and βj represents the corresponding parameter estimate that describes the relative importance of the different attributes on a consumer’s available choices. There are many types of choice models, and the multinomial logit (MNL) is the simplest and the most popular for modeling customer choice behavior in a homogeneous market. With the MNL model, the likelihood of purchase can be instantly recalculated if the availability of the products changes. A shortcoming of the MNL model is the property of independence from irrelevant attributes (IIA). This property essentially states that demand from closed items is re-attracted to the remaining open alternatives in proportion to their attractiveness. In other words, the ratio of purchase

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Table 4.2 Commonly used choice variables for calibration Choice variables Time of day Displacement time Price Number of connections Elapsed time Equipment type Quality of service

Description Time period (bucket) when the flight departs The difference between the requested time and the departure time The price for the itinerary (roundtrip fare/2) Determines nonstop versus number of connections (1, 2, . . .) Total travel time from start of trip to the destination Type of service: jet, regional jet, turbo propeller On time performance, baggage handling and overbooking metrics

probabilities for two available alternatives is constant regardless of the choice set containing them. Proportional reattraction may not be reasonable in situations when flight departure times are not adjacent and are separated by several hours. While there are other choice models, such as nested choice models, that overcome this problem, the MNL is still widely used. The choice model determines the probability of each itinerary being selected based on schedule and fare attributes such as departure time, number of stops, elapsed time for the origin and destination, origin point presence (defined as the carrier’s capacity share at the origination airport), fare, etc. Table 4.2 illustrates some of the commonly used choice variables for demand forecasting. The multinomial logit model can be used to estimate market share. The method computes the probability of booking a specific itinerary from the available options displayed on the screen, which is equivalent to estimating the market share of each option. If Ei is restricted to be independent and identically distributed Gumbel with a location parameter ϑ and a scale parameter μ > 0, the estimated market share, Si of each itinerary, expressed as a fraction of the total demand in a market is given by: e μV i Si ¼ P n eμV j j¼1

This is equivalent to an independent shopper selecting itinerary i. The scale parameter μ is not identifiable from β and can be conveniently set to 1. Care should be exercised if the scale parameter is varied because it can significantly change the share estimates of the MNL model. By varying the scale parameter to zero, the resulting model will estimate share equally across all the alternatives; at the other extreme, setting the scale parameter to an arbitrarily large value results in a maximum utility model. The market share or probability of selecting an itinerary from a shopping session adds up to 1. With an estimate of the market size, demand for each itinerary can be estimated. A popular model to calculate market share is the MNL model that is calibrated using Maximum Likelihood Estimation (Garrow, 2016). American Airlines has reported significant improvement in forecast accuracy using Random Forest

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Fig. 4.14 Sample network to illustrate market share estimation

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B D

A C

(Agrawal & Dasgupta, 2019) by training the model to match expected market share for each itinerary in the market to actual market share obtained from historical bookings for all airlines. Consider the sample network shown in Fig. 4.14. The next few examples illustrate how demand can be redistributed based on the attractiveness of an itinerary (Shebalov, 2013). Figure 4.15 is used as the baseline and illustrates with a simple example the estimation of market share and demand from the market size of a hypothetical market A–D. In this example, three itineraries are returned in a shopping response for which the market share and demand are estimated. If the attributes associated with one of the itineraries in the market (Fig. 4.15) changes, the demand can be redistributed using a simple utility adjustment as shown in Fig. 4.16, where the connection time for the third itinerary was reduced from 3 h to 90 min. No incremental data collection or analysis is required to estimate the share of the itinerary that has changed. If a schedule change eliminated one of the itineraries as a valid connection, the recalculation of market share for the remaining options does not require a recalibration of the consumer choice model. In Fig. 4.17, the third itinerary shown in Fig. 4.16 is no longer an option and market share is recalculated with the utilities of the remaining valid itineraries. In the scenario shown in Fig. 4.18, the capacity has been reduced on the first itinerary to 30 seats. Since capacity is constrained, excess demand (spill) is redistributed to the remaining itineraries in proportion to their attractiveness. There is a wealth of research in the literature on the use of consumer choice models for demand forecasting and estimation of unconstrained demand from booking data. The iterative expected maximization (EM) method can be used to unconstrain demand (Abdallah & Vulcano, 2016; Vulcano, van Ryzin, & Ratliff, 2012) for non-homogenous product sets. A recent algorithm was proposed to split booking transaction data observed under partial availability and extend the EM method for non-homogenous product sets (Remenyi & Luo, 2021).

4.4.3.4 Multinomial Probit and Nested Logit Models The multinomial logit models assume that the distribution of the random utility component is independent and identical over all the available alternatives. This leads to a well-known property called the Independent from Irrelevant Alternatives (IIA) property which implies that for a customer, the ratio of the choice probability of alternative i to that of alternative j is not relevant to any of the remaining alternatives.

Fig. 4.15 Choice model market share estimation

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Fig. 4.16 Redistribution of market demand based on revised utility

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Fig. 4.17 Redistribution of market demand after a schedule change

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Fig. 4.18 Redistribution of market demand when capacity is constrained

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4.4 Leg/Segment Revenue Management Table 4.3 Time bands used for calibration

Departure time window (di) 1 2 3 4 5

125

Time band 6:00 am–10:00 am 10:00 am–2:00 pm 2:00 pm–6:00 pm 6:00 pm–10:00 pm 10:00 pm–6:00 am

The IIA property assumes that the random components are independent and identically distributed. When the IID assumption is violated, the multinomial logit model will produce higher forecast errors. To overcome the IIA assumption, two classes of problems are available—the multinomial probit and the nested logit model. The multinomial probit model requires the estimation of the covariance matrix which can be a daunting task when the choice set is large, which is quite common when solving the airline itinerary choice problem. The nested logit model, though more time consuming to calibrate, is far more tractable, addresses the IIA problem, and has a closed form solution for the choice probabilities. Invalid use of the IIA assumption can lead to unexpected model outcomes. For example, the MNL model can overestimate recapture rates across flights when there are significant differences in departure times. For example, consider a market with three scheduled departures at 7:00 am, 8:00 am and 6:00 pm with the same demand volumes. If the early morning 7:00 am flight is closed, the MNL IIA will redistribute the demand for the 7:00 am flight to the remaining open flights at 8:00 am and 6:00 pm in proportion to their attractiveness. Thus, the demand will be redistributed equally to the open flights with spill to the OA (Other Airline)/do not fly option. However, we know intuitively that a customer who prefers the 7:00 am flight would consider the 8:00 am flight to be more attractive than the 6:00 pm departure. This issue can be addressed with a nested logit model by partitioning the flights into two buckets: morning flight departures and evening flight departures. By treating the 2 dayparts (parts of the day) as independent, overestimating recapture on the 6:00 pm flight is avoided. Similarly, nested logit models can be applied to price to distinguish major customer segments such as business and leisure. To illustrate further how a nested logit model works, consider the following example. Based on the assumption that flights that depart in the same time window are highly correlated with each other, departure time window bands can be defined (for example) as shown in Table 4.3. Each departure time window is assigned an index variable with a {0, 1} value. When a flight departs from a departure time window, di, the value is set equal to 1, otherwise it is set equal to zero. Figure 4.19 shows a nested choice model by outbound departure time. Figure 4.20 shows an alternate nested choice model where the first order nest is based on price. Results indicate that the nested logit model performs better than the multinomial logit model. It is widely used in flight scheduling to measure schedule profitability.

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Fig. 4.19 Nested model by outbound departure time Departure Time

$8:00am

$12:00pm

4:00pm

...

$400

$650

...

AA #61 DL #121 UA #222 8.15am 8.05pm 8.45pm $375 $300 $350 Nonstop Nonstop Nonstop

Fig. 4.20 Nested model by itinerary price Itinerary Fare

$200

Itinerary Itinerary Itinerary #1 #2 #3

An overview of demand unconstraining and forecasting techniques adopted by airlines (Azadeh, Marcotte, & Savard, 2014; Guo, Xiao, & Li, 2012; Strauss, Klein, & Steinhardt, 2018) with varying assumptions and methodologies all suffer from a fundamental limitation of only accessing an airline’s own booking data and not considering market trends across the competitive landscape. The transparency provided by shopping data, generated from travel agencies and OTAs shopping requests, provides insight into all airlines serving a market together with their selling fares and is invaluable to estimate market share, improve accuracy of demand forecasts and ultimately seat availability.

4.4.3.5 Forecasting All O&D’s Versus A Must Forecast List When forecasting demand by O&D, a question is frequently encountered: do we need to forecast all the possible O&Ds? The issue is, for example, the actual flown traffic from Waco, Texas to Dallas/Fort Worth to London may be very sparse, with a booking observed very infrequently. Instead of forecasting all the demand, which leads to fractional demand forecasts and high volatility in the variability of demand, the alternate is to create a must-forecast list. Typically, airline traffic data will indicate that 20% of the O&Ds contribute 80–90% of the total demand in the network. Lufthansa found the traffic to follow the 20:90 rule (Poelt, 2002). With this approach, only 20% of the significant markets, the must-forecast list includes connecting and local, are forecast. Demand is also forecast for the total leg class in the network which is the combined local and connecting demand by leg class. The must-forecast list connecting demand and local demand is subtracted from the combined local plus connecting demand (the total leg demand) forecast to create a

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pseudo-local leg forecast. An estimate of prorated weighted average revenue is also calculated for the pseudo-local demand. Pseudo Local Forecast ¼ Total Leg Demand – MustForecast List Connecting and Local Demand The pseudo-local forecast is a surrogate for the sparse O&Ds (not in the mustforecast list) for which average fare values can be derived from historical revenue accounting data. The input into the network optimizer in therefore the must forecast connecting service classes, the local demand by booking class and the pseudo-local demand by booking class.

4.4.4

Overbooking

Airlines require about three bookings to obtain one confirmed passenger onboard a flight. Overbooking is the process of selling additional reservations above actual aircraft capacity (seats) to compensate for the effects of cancellations, no-shows, duplicate bookings, and passenger misconnects. Overbooking allows an airline to fill up more seats on flights, thereby increasing the systemwide load factor (Dunleavy, 1995; Rothstein, 1971a, 1971b; Rothstein & Stone, 1967). No-shows are customers who hold a reservation at departure and do not show up for a flight. Go-shows are passengers who show up without confirmed reservation for a flight or those that show up with a confirmed reservation for which no reference is found in the carrier’s host reservations system. The latter category is referred to as no record passengers or NOREC’s. Mis-connects are passengers who missed their flight connections due to flight delays. Spoilage represents the number of empty seats on closed out flights. Oversales represent passengers who were denied boarding. The risks associated with oversales are passenger compensation, loss of goodwill and higher operating costs at the airports. The smaller the spoilage factor, the more effective the overbooking policy is. Overbooking is a balancing act with two objectives, minimizing spoilage and minimizing oversales, which should be satisfied simultaneously to maximize onboard revenues. An airline seat is perishable inventory. When a flight departs, an empty seat is lost forever and cannot be recaptured. Setting the optimal overbooking level to minimize spoilage is important. Denied boardings are also termed oversales or unaccommodated passengers. Typically, when an airline is in an oversale situation, volunteers are requested who are offered a denied boarding compensation voucher with a specific dollar face value that can be redeemed for future travel, usually within a 1-year period. The denied boarding costs vary by station and are influenced by customer demographics. For example, the average denied boarding cost at New York’s LaGuardia airport would be significantly higher than the denied boarding cost at former President Clinton’s home state capital of Little Rock,

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Arkansas. Minimizing the number of denied boardings minimizes the unaccommodated passenger costs and promotes goodwill for repeat business.

4.4.4.1 Operational Metrics Typical operational metrics that are used to determine the impact of overbooking to an airline are: 1. Voluntary denied boarding expressed as a number per 10,000 customers boarded. This data are not publicly available 2. Involuntary denied boardings expressed as a number per 10,000 customers boarded. The data are published by the Department of Transportation’s Bureau of Transportation Statistics on a quarterly basis for all U.S. airlines. 3. Collect denied boarding cost by airport. The cost for passenger re-accommodation on closed out flights varies significantly by airport. This data are not publicly available. The true cost of a denied boarding voucher is factored into the computation of optimal overbooking levels. Airlines estimate the value of the voucher by considering such factors as goodwill and average displacement cost of a passenger. Corporate policy dictates the minimum and maximum value of the voucher that will be offered to customers by airport.

4.4.4.2 Types of Overbooking Models The overbooking models in this section are discussed in the context of departure overbooking levels. There are two types of overbooking models: the economic overbooking model and the quality-of-service constraint model. Figure 4.21 illustrates the economic overbooking model with a quality-of-service constraint (expressed in oversales per 10,000 boarded) imposed on it. The American Airlines DINAMO system from 1986 used the economic overbooking model without any quality-of-service constraints.

$40,000 $30,000 $20,000 Spoil

$10,000 100

age C

102

ost

104

Quality of Service Constraint

Revenue & Cost

Total Revenue Ne

tR

ev

en

ue

st

le

Co

sa

r ve

O

106 108 110 112 Authorization Levels

114

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Fig. 4.21 Overbooking model with cost and quality of service constraints

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At the optimum, the overbooking level minimizes the number of spoiled seats and oversales simultaneously (Smith, 1982). To lower the risk of denied boardings and ensure an acceptable service level, the overbooking level may be lowered below the theoretical optimum as shown in Fig. 4.21. The service level may be represented as the maximum expected denied boardings on closed out flights. For an assumed capacity of 100 seats, the graph indicates the shape of the expected net revenue curve, the theoretical optimal overbooking level, and the impact of imposing a quality-of-service constraint. In the U.S., airlines typically do not impose a service level constraint in coach and to a large extent first and business class are managed manually using standard defaults by cabin size and day of week. European carriers are conservative when it comes to overbooking and some airlines even claim on their websites that they do not overbook flights. Penalties in the European Union (EU 261 Rule) are steep for involuntary denied boarding passengers. Hoteliers are overly sensitive to overbooking, and the term “overbooking” does not exist in their vocabulary even though some properties and chains practice it in conjunction with service level constraints. The sensitivity results from customers who do not always view hotel rooms as a commodity like an airline seat or a car rental. For example, an ocean view or mountain view room at the Westin on Kaanapali beach in Maui is not viewed as a commodity product. If a consumer can visualize the attributes of the room, they would be willing to pay a premium. Alternate accommodation for the discerning traveler is never the same experience. Not only is the term “overbooking” frowned upon in the hospitality vocabulary, the phenomenon of unaccommodated customers at hotels is dubbed “under departures” which implies that fewer customers than expected checked-out of the property that resulted in an oversold situation. There are several models that are in use today to compute overbooking levels. They can be categorized as the static models and dynamic models. The static overbooking models are widely used in practice. With the static models, the cancellations, and new bookings at predeparture points in time are not explicitly modeled. Instead, the departure overbooking is calculated, and a predeparture overbooking limit is periodically recomputed as the flight approaches departure based on whether a cancellation will be replaced by a new booking. Dynamic models consider the time dimension, cancellations, and new bookings to determine the overbooking level. Models for first class and business class typically use a service level constraint while coach class may use an economic overbooking model or a service level constraint model.

4.4.4.3 Forecasting Boarding Rate A primary input into the overbooking model is a forecast of the boarding rate (the show up rate at departure) and the standard deviation of the boarding rate. The boarding rate can be forecast using a time series model. The calculation of the boarding rate must include an adjustment for go-shows and mis-connects. No-recs

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are added to the count and mis-connects are subtracted to determine passengers boarded by cabin to calculate the show up rate. The show up rate for a flight cabin is calculated as follows: Show up Rate ¼

Passengers boarded in cabin Reservations holding on departure date

Passengers boarded in a cabin should account for upgrades to a higher cabin and not include non-revenue travel. A snapshot of reservations holding may be taken the night before flight departure or preferably a snapshot should be taken a few hours (usually 4 h) before flight departure. The mean absolute deviation of the boarding rate can be updated with a simple exponential smoothing model. Hence, σ 2s ¼ 1:25MADs error is normally distributed with a mean of based on the assumption that(the forecast ) zero and a finite variance N 0, σ 2e . Using PNR as a data source for no-show forecasting has its benefits (Kalka & Weber, 2000). They demonstrated using Lufthansa data that no-show forecast error could be reduced from 12.4 to 9.5%. Considering the various attributes on booked PNR’s, causal models can be developed to forecast no-shows (Dupuis, 2010; Garrow & Koppelman, 2004a, 2004b; Kambour, 2006). These models typically produce improvements in forecast accuracy of no-shows by 3–7%.

4.4.4.4 Oversale Rate Constraint The oversale rate limit is usually expressed as the maximum number of unaccommodated passengers for every 10,000 passengers boarded at the cabin level. This number is usually set between 10 and 20 denied boardings for every 10,000 passengers boarded. Sequentially increasing the authorization limit and determining the point at which the expected oversales exceeds the oversale rate constraint determines the overbooking level. The sequential iterative process can be made computationally efficient by incorporating a bisection search. 4.4.4.5 The Binomial Model A model that has been used but not widely implemented is the binomial model of cancellations and no-shows that are lumped together. The binomial distribution with parameters n and p is the distribution of the number of successes in n independent trials each with probability p. With the binomial model, n is the number of reservations holding at departure and p is the show up rate that is forecast for a flight. The overbooking limit can be calculated by iteratively raising the authorization limit and calculating the expected boarded and expected oversales. Iterations are stopped when the oversale rate constraint threshold is exceeded. The binomial probabilities for values of n, p and q can be calculated as follows:

4.4 Leg/Segment Revenue Management

pð i Þ ¼

131

n! pi qn–i i!ðn – iÞ!

E ðBoardedjAuthorization ¼ AuÞ ¼

c–1 X

ipðiÞ þ c

pðiÞ

i¼c

i¼0

E ðOversalesjAuthorization ¼ AuÞ ¼

Au X

Au X

ði – cÞpðiÞ

i¼c

For ease of computation, the normal approximation to the binomial can be used by setting the mean (μ) and variance (σ 2) of the normal distribution as follows. μ ¼ np σ 2 ¼ npð1 – pÞ The fundamental problem with the binomial model and the normal approximation to the binomial distribution is the issue that the binomial is a one parameter distribution. The observed variance of the show up process is typically higher than the estimate from the equation above. For example, for a show up rate of 0.85, the actual standard deviation of the show up process can range from 0.05 to 0.15. The larger the standard deviation of the show up rate, the lower the overbooking limit to manage the risk of oversales. Hence, using a binomial distribution or approximation to the binomial distribution results in lower estimates of the standard deviation of the show up rate which in turn results in aggressive overbooking limits that can result in higher oversales. In addition, the actual data may result in a variance to mean ratio that exceeds 1. For the same reason, another model that has been used by some airlines, the deterministic model, is not advocated since the overbooking controls are overly aggressive resulting in oversales. The deterministic model has a variance of zero, leading to more aggressive controls than the binomial. Overbooking Limit ¼

Capacity Showup Rate

4.4.4.6 Normal Distribution of Show Up Process By assuming that the show up process is a truncated normal distribution, for given values of the mean (μ) and standard deviation (σ) of the show up process, the key measures of expected boarded and expected oversales can be determined (Smith, 1982). The normal distribution of the show up process, shown in Fig. 4.22, is the most popular. A common mistake made is to not treat the show up process as a truncated normal since passenger demand greater than the authorization

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Fig. 4.22 Show up distribution

Capacity Authorization Level µ Show Up

(overbooking) level cannot be confirmed on a flight, these passengers do not receive denied boarding compensation and should not be considered as denied boardings. Let f(x) be the probability density function of the show up process with mean μ and variance σ 2. The area under shaded region between capacity (c) and the authorization level (Au) of the show up distribution in Fig. 4.22 is the expected oversales. Zc

ZAu xf ðxÞdx þ c

E ðBoardedjAuthorization ¼ AuÞ ¼ –1

f ðxÞdx c

ZAu EðOversalesjAuthorization ¼ AuÞ ¼

ðx – cÞf ðxÞdx c

These measures can be calculated from the spill model formulas presented in Chap. 3. For example, expected oversales is simply the difference between the expected spill at the authorization level subtracted from the expected spill at capacity.

4.4.4.7 Economic Overbooking Model An economic overbooking model determines the optimal overbooking level for a cabin that maximizes the net revenue on a flight. Let Cs denote the cost of a spoiled seat. The spoilage cost is the average cost of an empty seat in the cabin. The denied boarding cost is a nonlinear function and increases at an increasing rate as the number of oversold passengers increases. Let fos(x) denote the functional form of the cost of an oversale. This curve can be calibrated by station from historical data. The net revenue on a flight is given by Net Revenue ¼ EðTotal RevenueÞ – EðSpoiled Cost Þ – EðDenied Boarding CostÞ The terms on the right-hand side of the above equation are determined as follows:

4.4 Leg/Segment Revenue Management

133

E ðTotal RevenueÞ ¼ ðAuthorization Level Þðμs ÞðMarginal FareÞ The expected spoilage is given by E ðSpoiled SeatsjAuthorization ¼ AuÞ ¼ Capacity – E ðBoardedjAuthorization ¼ AuÞ E ðSpoilage Cost Þ ¼ E ðSpoiled SeatsjAuthorization ¼ AuÞ C s ZAu E ðDenied Boarding Cost Þ ¼ E ðOversalesjAuthorization ¼ AuÞ

x f os ðxÞdx x¼cþ1

From the above results, the optimal overbooking level can be determined with a sequential search or a bi-section search by evaluating different values of the overbooking level. Note that every time the overbooking level is increased by one, μs and σ s will also change. This implies that the resolution of the overbooking problem and the optimal fare mix in the cabin (the discount allocation problem) should ideally be solved simultaneously than sequentially. Empirical distributions can also be used to calculate the economic overbooking limit.

4.4.4.8 Calculating Predeparture Overbooking Levels Most of the literature on overbooking treats predeparture and departure overbooking as a single problem. The airline overbooking problem was first modeled as a discrete-time dynamic program (Rothstein, 1971a, 1971b). This was followed with a hotel overbooking model (Rothstein, 1974). The importance of explicitly modeling cancellations and no-shows was demonstrated empirically for hotel reservations (Bitran & Gilbert, 1996). Research on continuous time overbooking models is extremely limited (Chatwin, 1996a, 1999). Calculating the predeparture overbooking level requires information on the cancellation rate profile, which must be applied at the predeparture point in time when the overbooking level needs to be calculated and set. A simpler and effective approach than what is found in the literature is to use the reservations holding cancellation rate profile to determine predeparture overbooking levels. A simpler alternative that does not require the convolution of booking and cancellation rates is to construct fall off rates by cabin as shown in Fig. 4.23. The fall off rate is simply the reservations holding by reading day expressed as a percentage of the reservations holding at departure. It is usually calculated by cabin and day of week for each flight leg or segment. Fall off rates are far more stable and produce less volatility in overbooking levels than the convolution of booking and cancellation rates. It is widely used to determine the predeparture overbooking levels due to its simplicity. The maximum predeparture overbooking level is used as the overbooking limit for all days leading up to the peak because cancellations will not be replaced with new bookings. The predeparture overbooking level for a flight on reading day r is given by

4

Reservations Holding (%)

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Revenue Management of the Base Fare

Maximum Predeparture Overbooking Level

110% 100%

50%

0 0

6

13

20

27

34 41 48 55 62 Days to Departure

76

90

104

Fig. 4.23 Fall off rate for coach cabin

Predeparture Overbooking Levelr ¼ Departure Overbooking LevelxMaxi≤r ðFalloff Rate Profilei Þ The economic overbooking model (Smith, 1982) in DINAMO evaluated tradeoffs between denied boarding costs and the cost of a spoiled seat.

4.4.4.9 Static Models and Dynamic Models The models described above are called static models because the dynamics of cancellations and new bookings over time are ignored. These models determine the overbooking or authorization limit for a cabin based on the expected show up rate at departure and the reservations holding profile at the current predeparture point in time for a future departure date. Dynamic overbooking models (Chatwin, 1996b, 1998, 1999) accounts for booking arrivals and cancellations over time until the departure date. These models are more complex, difficult to calibrate and rarely used in practice. 4.4.4.10 Benefits of Overbooking Overbooking ensures that more customers can book on their first choice of air travel. If airlines could predict demand for flights with 100% certainty, they would not have to overbook flights. Unfortunately, this is not the case and overbooking is an accepted practice to protect revenue. On the positive side, overbooking benefits the airline as well as passengers. From a passenger perspective, overbooking improves the probability of fulfilling a passenger’s first request. This is because business passengers, who travel on unrestricted fares, frequently do not bother to cancel their reservations when their travel plans change. In some cases, passengers may make several alternate bookings when their travel plans are not firm, resulting in unclaimed seats if they are not cancelled. The airlines call these phenomena as cancellations and no-shows.

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135

Cancellations are valid until 1 day before departure, and no-shows happen when the customer who has a reservation does not board the flight on the departure day. Cancellation and no-show forecasts are required to set predeparture overbooking levels, and the no-show forecast is required to set the departure day overbooking levels. Both cancellations and no-show behavior vary widely by market, day of week and season and even by time of day. Sophisticated forecasting techniques are employed to determine the optimal overbooking levels. A secondary benefit for customers is airfares would be higher if airlines did not practice overbooking. Without the incremental revenue from filling expected unclaimed seats, airlines would have to raise prices to compensate for the empty seats. Ultimately forecasting is an art and there is always uncertainty associated with every forecast. Occasionally more passengers than expected show up at the gate for a flight. When this happens, airlines offer passengers who have the flexibility a seat on a later flight, and a voucher with a face value that can be applied towards the purchase of a ticket in the future. Studies at American have estimated true cost for an airline is 1/3rd the face value of the voucher.1 Robert Crandall was a proponent of the benefits of overbooking and he stated in Vantage Point, the American Way magazine (Crandall, 1991): Overbooking makes sense for everyone, customers and airlines alike. Without the revenue we get by filling seats “no-shows” would otherwise leave empty, we would have no choice but to raise prices. Thus “overbooking” helps us keep fares as low as possible. Even more important, it helps us use all the available seats effectively, thus allowing us to say “yes” rather than “no” when you call for a seat on the flight of your choice.

The Vantage Point article also showed this graph (Fig. 4.24) from the Department of Transportation, where Crandall remarked “Last year we denied seats to only 1,332 people, or only about 18 out of every one million passengers boarded.” From an airline perspective, overbooking improves load factor and simultaneously minimizes spoilage. Figure 4.25 illustrates the impact of overbooking for a flight. As shown in Fig. 4.25, the reservations holding profile indicates that in the absence of overbooking, there would be a 6% drop in load factor. Overbooking has a tremendous bottom-line revenue impact on an airline in filling up incremental seats and providing customers a higher probability of being accepted on their first choice for travel. Overbooking generates incremental revenues, and it is a well-acknowledged fact that it typically contributes approximately 25% of the total yield management revenue opportunity.

1

Discussion with Warren Lieberman, who did the study.

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Involuntarily denied boarding per 10,000 passengers in the third quarter of 1990 0.26

AMERICAN

0.43

DELTA

0.46

EASTERN

0.49

UNITED

0.85

NORTHWEST

1.21

CONTINENTAL

1.24

USAIR

2.69

PAN AM

3.30

AMERICA WEST TWA

4.56 4.82

SOUTHWEST

Source: Department of Transportation, American Way Magazine, Vantage Point

Reservations Holding

Fig. 4.24 Involuntary denied boarding, 3Q 1990

110 100

Maximum Predeparture Reservations Holding Aircraft Capacity = 100 seats Load Factor with Overbooking (95%) Load Factor without Overbooking (89%)

0 0 6 13 20 27

34 41 48 55 62 Days to Departure

76

90

104

Fig. 4.25 Impact of overbooking

4.4.5

Discount Allocation Controls

Until 1972, yield management research was limited to overbooking controls. In the early 1970s, British Overseas Aircraft Corporation (BOAC) introduced 21-day advance booking discount fares. Littlewood (1972) of BOAC proposed that discount passengers should be accepted if the discount fare was greater than the expected

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137

The Gamma Distribution

0.12 0.1 0.08 α = 3, β = 4

f(x)

0.06 0.04 0.02 0 0

10

20

30

40

50

x Fig. 4.26 Gamma distribution for various values of α and β

revenue of future higher paying full fare passengers. The two-class example proposed by Littlewood, now known as Littlewood’s Rule, is the foundation of discount allocation controls. For discount allocations, modeling the uncertainty in demand requires the demand for a booking class to be modeled as a normal distribution or a gamma distribution to compute protection levels. A common mistake is to model the distribution of demand as a normal distribution. The normal distribution is bell shaped (symmetric) and is characterized by its first two moments. However, demand is rarely symmetrical and assumes the shape of a gamma distribution that is skewed to the left as shown in Fig. 4.26 for various values of α and β. Also, the normal distribution can allow negative demand, but the gamma distribution is always positive. The gamma distribution function has the probability density function f ðxÞ ¼

βα xα–1 e–βx ;x ≥ 0 ΓðαÞ

where α is the shape parameter, β is the scale parameter and Γ is the gamma function. The gamma distribution, a two-parameter distribution characterized by its shape, α, and scale, β, is versatile and can be used to model demand very effectively. Based on the demand profile, the α and β parameters can adjust to model the distribution of demand accurately. In addition, the gamma has the important property that the random variable X is only defined for non-negative values x > 0 (unlike the normal) By matching the first two moments, we have

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Revenue Management of the Base Fare

μ ¼ αβ σ 2 ¼ αβ2 from which α and β can be calculated. α¼

σ2 μ2 β¼ 2 μ σ

In a multi-class environment, the objective of discount allocations is to obtain the optimal mix of full fare, discount, and deep discount passengers on board a flight to maximize total revenues. Discount allocation is effectively risk management to generate incremental revenues by turning away a lower paying passenger in the hope of filling the seat with higher paying passengers. The concept of discount allocations will be first explained in the context of a flight number with a single flight leg from Los Angeles (LAX) to New York’s JFK airport as shown below.

For example, when a deep discount request is made, the airline has the option to either accept the booking or reject the booking as illustrated in Fig. 4.27. Fig. 4.27 Discount allocation decision tree

Revenue (RB) Accept Request B Fare

Reject

Pr(D > x) * Revenue (RY)

4.4 Leg/Segment Revenue Management

139

A discount request can be rejected if the seat can be filled with a higher paying passenger. The optimal protection for booking class Y (RY) from booking class B (RB and RB < RY) can be described by the following relationship: PrðD > xÞRY ¼ RB or PrðD > xÞ ¼

RB RY

The value of x at the probability value of the revenue ratio defines the number of seats that must be protected for the higher-class Y from lower class B. The calculation of protection levels relies on the relative value of the fares and not the absolute values of the fares. For example, if RY ¼ $1000 and RB ¼ $700, then the revenue ratio is RB/RY ¼ 700/1000 ¼ 0.70, which is the same for any other combination of absolute values of RY and RB if the revenue ratio is preserved. The absolute fare values are by themselves not important. The calculation of the protection values is determined purely by the distribution of demand and the revenue ratios of the respective classes. This key observation is of tremendous significance especially in international markets where access to accurate information on the real value of the passenger flight coupon is difficult if not impossible to determine due to commissions, overrides and special provisions. However, the use of revenue ratios helps (Dunleavy, 1996). This model can be extended to multiple legs on a flight number. From an execution perspective, the inventory controls deployed by airlines are either leg-based controls with segment limits or segment class controls. The model can be extended to include upsell and recapture rates. When a request is rejected, the airline can quote a higher fare to the passenger who may accept the request. This is referred to as upsell. If the customer refuses the higher fare, the airline will next attempt to capture the passenger on an alternate flight at a discounted fare, even though the alternate flight is not the customer’s first preference. This is referred to as recapture. Figure 4.28 illustrates the decision logic with upsell and recapture. Revenue (RB) Accept Revenue (RY) Request B Fare

Reject

Accept Revenue (RB)

Upsell Fare Y (RY)? Reject

Accept Recapture? Reject

Fig. 4.28 Discount allocation decision tree with upsell and recapture

Spilled Passenger

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Revenue Management of the Base Fare

$450.00

$400.00

R(Y).Pr(D>x)

$350.00 $300.00 $250.00 $200.00 $150.00 $100.00 $50.00 $0.00 1

2

3

4

5

6

7

8

9

10

11

12

Demand

Fig. 4.29 Expected marginal seat revenue curve

When the demand is gamma distributed, the expected marginal seat revenue for the nth seat for a given class Y is given by: Z1 Expected Marginal Seat Revenue ¼ RY PrðD > nÞ ¼ RY

f ðxÞdx n

¼ RY ð1:0 – F G ðx ≤ nÞÞ ⌈ ( ) ⌉ n ¼ RY 1:0 – F G α, β Note that the rate of decrease of the expected marginal revenue depends on the mean of the demand forecast and the demand uncertainty. Since Pr (D > 1) ≥ Pr (D > 2) ≥ Pr (D > 3). . . ≥ Pr (D > n), the expected marginal revenue is a monotone non-increasing function, and the rate of decrease is not linear. Figure 4.29 illustrates an expected marginal revenue curve for a mean demand of μ ¼ 10, RY ¼ $400, and CV ¼ 0.45 assuming demand is gamma distributed. The DINAMO model used the logit approximation to the normal distribution (Swan, 1983) discussed in Chap. 3. For example, consider two booking classes Y and B and associated revenues of RY and RB (RY > RB) . Let μY and σ Y represent the mean and standard deviation of booking class Y. The number of seats, X, to be protected for the higher-class Y from B is given by: ( ) RY σ X ¼ μY þ Y ln –1 1:702 RB With multiple booking classes, there are two methods for computing the expected marginal seat revenue, both commonly referred to as EMSR. There are two variations in the literature—called EMSRA and EMSRB. While EMSRA relies on a pairwise comparison to compute the protection levels for the higher classes, EMSRB relies on calculating an average fare, weighted by demand of all booking classes above the fare from which seats need to be protected. Since demand is assumed to be

4.4 Leg/Segment Revenue Management

141

independent across the booking classes, additive means and variances are used for the joint distribution. In summary. EMSRA aggregates protection levels to successive pairs of classes by applying Littlewood’s Rule while EMSRB is based on aggregating demand and not protection levels. Simulation studies have shown that EMSRB (Belobaba, 1992; Smith, 1982) is superior to EMSRA (Belobaba, 1987, 1989). EMSRA is more readily applied to non-nested controls and EMSRB performs better in a nested inventory control environment. The revenue improvement of EMSRB is because the underlying joint demand distribution of higher classes is used to determine the protection level from the lower class while EMSRA uses a pairwise comparison to calculate protection levels. EMSRB also provides better estimates of expected traffic and revenue than EMSRA. For two booking classes, the EMSR approach produces optimal booking limits. When there are more than two booking classes, the booking limits are not optimal. In airline inventory systems, the lower valued inventory booking classes close first. Using this as a constraint, optimal booking limits with more than two booking classes have been addressed independently (Brumelle & McGill, 1993; Curry, 1990; Wollmer, 1992). Curry’s approach assumes demand follows a continuous distribution and extends to O&D itineraries. Wollmer’s approach assumes that demand follows a discrete distribution. Brumelle and McGill’s approach is based on subdifferential optimization and applies to both discrete and continuous distributions. The EMSRB approach relies on calculating an average fare, weighted by demand, of all classes above the fare from which seats need to be protected. For example, consider a cabin with four distinct fares, Y, B, M and Q. With this approach, to determine the total number of seats that need to be protected for Y, B and M booking classes from Q requires the calculation of the weighted average fare for Y, B and M and then calculating the number of seats to protect from Q. This assumes that the displacement rate is the same for the higher classes, Y, B and M, which is never true. If yield management controls are correctly set, the actual displacement rate (or spill rate) will increase from the highest fare to the lowest fare. In the American Airlines DINAMO system from 1986, the discount allocation model was an expected marginal seat revenue model using the logit approximation to the normal distribution (Swan, 1983), an extension of Littlewood’s Rule to multiple classes, which generated the joint protection levels for higher valued booking classes relative to the lower value booking class (Smith, 1982). This method was later called EMSRB (Belobaba, 1992) as a computationally viable heuristic to optimal booking limits.

4.4.5.1 Combined Overbooking and Discount Allocations Overbooking models generally ignore factors such as demand for a flight, booking class mix, class specific refund costs and cancellation rates. Though there are benefits to combining the overbooking and discount allocation models into a single decision support model, they are typically treated separately due to problem complexity. One of the first papers on the simultaneous overbooking and discount

142

4

Revenue Management of the Base Fare

allocation control was a model for a single flight leg with two types of passengers (Alstrup, Boas, Madsen, & Vidal, 1986) with predeparture cancellations and no-shows, denied boardings and downgrading of passengers. The model treats the booking process as a Markovian nonhomogeneous sequential decision process and is solved as a two-dimensional stochastic dynamic program. Chatwin (1998) proposed a model based on the assumption that lower booking classes book prior to higher valued booking classes. The combined overbooking and discount allocation model for a single flight leg with cancellations and no-shows was modeled as a Markov decision process (Subramanian, Stidham, & Lautenbacher, 1999). Chatwin (2016) defined a continuous-time model for an airline accepting bookings in multiple booking classes on a flight leg inclusive of cancellations and refunds. A robust optimization approach was proposed (Lan, Ball, Karaesmen, Zhang, & Liu, 2015) for the single leg problem with overbooking cost and revenue. The revenue benefits of the combined model from balancing load factor and optimizing the class mix was estimated at 1–3% (Fiig, Bondoux, Hjorth, & Larsen, 2016) A promising approach is the multi-leg airline overbooking mathematical model (Al-Bazi, Uney, & Abu-Monshar, 2019) under fuzzy demand conditions with a genetic algorithm (GA) to solve large problems efficiently. The approach considers passenger penalties, show up rate, demand, and the expected profit level. The promise lies in extending the model to represent both the show up rate and the future demand for flights. The combined problem has also been discussed in recent academic books (Gallego & Topaloglu, 2019; Talluri & van Ryzin, 2004).

4.4.6

Reservations Inventory Controls by Leg/Segment

The inventory control system in the host CRS serves as the execution component for revenue management recommendations. Inventory controls generated by a revenue management system should be updated on the airline’s host CRS inventory system. These updates can vary from once a day to several times a day if the flight is reoptimized. There are various inventory control models used by airlines for leg/segment inventory controls (Vinod, 2006). Most inventory control methods deploy a nested inventory control structure. Airlines that had deployed non-nested inventory controls have migrated to nested controls. Of the 400 airlines that operate today, fewer than 50 airlines deploy O&D controls which imply that the vast majority use nested inventory controls in their reservations system. The objective of nesting is to ensure that a lower valued booking class in the nested hierarchy is not available for sale when a higher valued class is closed for sale. Nested inventory controls can be applied at a flight leg or flight segment level of detail. There are 26 booking class codes and cabin designators established by IATA for distribution of availability status by booking class to global distribution systems.

4.4 Leg/Segment Revenue Management

143

Fig. 4.30 Non-nested controls

Y 20

B 25

M 30

V 40

Capacity = 100 seats Fig. 4.31 Parallel nested controls

Y 115

B 15

M 30

V 35

Q 25

Capacity = 100 seats

Non-nested Inventory Controls Figure 4.30 illustrates an example of non-nested controls with four booking classes. The booking classes are independent of each other. Non-nested booking classes are independent of each other and cause revenue dilution. A restricted lower valued booking class (e.g., V) may be open when a higher valued unrestricted booking class (e.g., Y) is closed. Non-nested controls are not practical unless demand for each booking class can be forecast with absolute certainty, which is not possible. Parallel Nested Inventory Controls Figure 4.31 illustrates an example of parallel nested controls with five booking classes and all the restricted booking classes (B, M, V, Q) of different value are nested into the unrestricted higher valued booking class Y. Parallel nested controls are an improvement over non-nested controls since all the lower valued restricted booking classes are nested in the higher valued unrestricted booking class (e.g., Y) which guarantees that Y has the same or higher availability than the lower valued restricted classes. The primary advantage of parallel nesting is that the last seat sold will be the highest valued unrestricted booking class.

144 Fig. 4.32 Serial nested controls

4

Revenue Management of the Base Fare

Y 115

B 95

M 75

V 45

Q 25 Capacity = 100 seats

Serial Nested Inventory Controls Figure 4.32 illustrates an example of serial nested controls with five booking classes and all the booking classes are nested based on value. Serial nesting guarantees that a lower valued booking class will never be open when a higher valued booking class is closed for sale. It is frequently used in conjunction with segment close indicators or segment limits. Mixed Nested Inventory Controls Figure 4.33 illustrates an example of serial nested controls with eight booking classes Mixed nesting controls are frequently used by airlines who wish to control consolidator, cruise line or promotional traffic with a guaranteed allocation. In this scenario this low yielding traffic (Z, N) is parallel nested into Y class. Hybrid Nested Inventory Controls Figure 4.34 illustrates an example of hybrid nested controls with eight booking classes. Hybrid nesting is a variant where there are two or more independent nesting structures in a cabin that are independent. In the example, both Y and N booking classes are at the same level in the hierarchy for a cabin. This is sometimes used to manage demand from different channels independently. It suffers from the problem that when a booking class hierarchy is sold out, seats cannot be borrowed from the second booking class hierarchy without manual intervention.

4.4 Leg/Segment Revenue Management Fig. 4.33 Mixed nested controls

145

Y 115

N 15

B 100

L 5

M 80

Z 20

V 65

Q 40 Capacity = 100 seats

Sometimes, nested controls are used in conjunction with segment limits, segment close indicators and minimum/maximum controls. Note that the N booking classes, k1, k2, . . ., kN, are in a hierarchy based on decreasing average fare value and represent an ordered set. k1 ≥ k2 ≥ k3 . . . kN

4.4.6.1 Calculating Seat Availability There are two methods, threshold nesting and net nesting, for calculating seat availability. Threshold nesting is also referred to as theft nesting, and net nesting is also referred to as standard nesting. With threshold nesting, a booking in any booking class impacts all the booking classes in the class hierarchy, thus maintaining the protection level across all classes, except for those that are closed, regardless of what booking class was sold. The calculation of seat availability in a cabin in booking class i is as follows: Seats Availablei ¼ Authorizationi – Total Seats Sold; i ¼ 1, 2, . . . , N Authorizationi is the booking limit for class i and Total Seats Sold is the total seats sold across all booking classes in the cabin.

146

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Revenue Management of the Base Fare

Fig. 4.34 Hybrid nested controls Y 115

N 15

B 100

Z 20

M 80

L 5

V 65

Independent Structures in Coach Cabin

Q 40 Capacity = 100 seats

Total Seats Sold ¼

N X

Seats Sold k

k¼1

With net nesting, inventory action is taken in the requested class and all classes above the requested class in the hierarchy, up to the base class (usually Y). The calculation of seat availability in a cabin is as follows: Seats Availablei ¼ Authorizationi –

k ¼N X

Seats Sold k ; i ¼ 1

k¼i

" Seats Availablei ¼ Min Availabilityi–1 , Authorizationi –

k ¼N X

!# Seats Sold k

;i

k¼i

¼ 2, . . . , N Authorizationi is the authorization limit for class i, Seats Soldkis the seats sold in class k and Availabilityi – 1 is the seat availability in the class immediately above i, in the class hierarchy.

4.4 Leg/Segment Revenue Management

147

Table 4.4 Net nesting versus threshold nesting seats available comparison Booking class Y B M H V Z Q

Authorization level 420 385 320 260 210 180 60

Seats sold 5 10 15 35 40 55 35 195

Seats available Threshold nesting 225 190 125 65 15 –15 –135

Seats available Net nesting 225 195 140 95 80 80 25

Total seats sold Flight Leg: DFW-SFO; Flight Number 100; Coach Capacity ¼ 300 seats Initial Conditions: Seats Sold Y ¼ 5, B ¼ 10. M ¼ 15. H ¼ 35, V ¼ 40, Z ¼ 55, Q ¼ 35 Table 4.5 Comparison of net and threshold nesting methods 1 2 3 4

Net nesting Conservative Does not guarantee last seat availability to the higher class Preferred when only a few departure days in the future are re-optimized daily Preferred when uncertainty in demand for early booking lower valued classes is high

Threshold nesting Aggressive Guarantees last seat availability to the highest class Preferred when forecasting and optimizations of the network are frequent Preferred when uncertainty in demand for higher valued classes is high

As the calculation indicates, net nesting requires booking counts by booking class while with threshold nesting, only the total seats sold in a nested hierarchy is required. Table 4.4 illustrates a snapshot of seat availability based on threshold nesting and net nesting. While the same availability can often be achieved with either calculation based on how the bookings come in, there are several differences between net and threshold nesting calculation methods. Table 4.5 summarizes the key differences between the two approaches for calculating seat availability. A variation of net nesting is bottom-up nesting to address the out of sequence booking problem with net nested controls. Functionally, these top-down or bottomup controls are available on some reservations systems and inventory packages like CASH++2 where inventory is taken from when out of sequence selling has consumed protected inventory in a higher class. Another type of control is RS13,3 which has many parameters for setting nested controls. It is manually intensive, but most parameters in RS13 can be recommended by revenue management.

2 3

Developed jointly by Alitalia, Philippine Airlines and Pakistan International Airways. Developed by British Airways.

148

4

LAX

Revenue Management of the Base Fare

JFK Flight No 1

LHR Flight No 1

Fig. 4.35 A single flight with multiple segments

4.4.6.2 Segment Close Indicators and Segment Limits Segment limits are numerical controls that permit itinerary control (at a segment level) when applied together with leg-based controls. For airlines operating flights with multi-stop flights on a single flight number, segment close indicators (SCIs) may be advantageous in restricting sales for selected segment classes that are lower valued when demand exists for higher valued segment classes. A segment close indicator indicates that the booking class for the segment is closed. Consider the single flight with multiple segments shown in Fig. 4.35. The flight from DFW to LHR connects over JFK. It may be advantageous to close DFW-JFK V booking class and keep the DFW-LHR V booking class on the DFW-JFK flight leg open. This is accomplished by establishing a segment close indicator for the DFW-JFK V booking class. Hence, SCIs are a mechanism to provide itinerary control for O&D’s within a flight number. However, SCIs can only be turned on and off and these controls are not dynamic. As a result, if cancellations occur, the SCI is not automatically turned off for the DFW-JFK V booking class, which could result in spoilage. Similarly, if the DFW-LHR V demand increases significantly and there is no SCI in place for DFW-JFK V, it will cause revenue dilution. This can be overcome by having the revenue management system send inventory control updates on a frequent basis to the host CRS. An alternative to SCIs is to use segment limits, which provides the capability to control segment class availability on a numeric basis. This numeric control provides the capability to limit sales to a given segment class (e.g., DFW-JFK V) to a numeric limit. Segment limits allow the inventory control system to re-open or close the class again because of changes in booking activity. Segment limits are also required on flights when a carrier has restricted fifth freedom traffic rights (see Appendix A). The restriction can take the form of a limit on total passengers carried on the fifth freedom segment by individual flight or by flight-month. Other variations include nested segments and the application of a minimum and maximum authorization level for each segment class. Segment limits may either be nested or non-nested. Most reservations systems that use segment limits use non-nested segment limits. This has the benefit of allowing a user to close a class in the middle of the nesting structure without closing the classes below (a useful option on those occasions when fare actions change the relative value of fares). However, nested segment limits have the same obvious benefits as nested leg controls.

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4.4.6.3 Point of Sale Controls Point of sale controls support the effective control of published tariffs and off-tariffs at multiple levels from region and country down to an individual travel agency in the indirect channel. For published tariffs, point of sale control can provide preferential availability on demand when there are currency fluctuations at the location where the booking is made. For off-tariff fares, point of sale controls ensure that preferential availability is provided based on the value of the negotiated fare. This addresses the wide dispersion of fare values negotiated with individual travel agencies for the same market and same booking class. This feature can also be used to control net fares negotiated with online channels and web supermarkets, based on value. The unique ability to restrict sale of a product at a specific point of sale, is a way to regain control of distribution. Point of sale controls are of strategic importance for an airline, regardless of the type of inventory controls used (leg/segment or O&D controls). Figure 4.36 illustrates how POS allocations can be set in a serial nested inventory control environment in a reservations system. This is specific to the BABS (British Airways Booking System), which used RS13. In this scenario, sub-classes (M1, M2, M3, . . .) can be established based on a pre-defined point of sale table with parallel nested allocations for each POS sub class attached to a booking class. The sub-classes serve as surrogates for the POS that is given a specific parallel nested allocation into the booking class. This type of structure with sub-classes does not impact normal reservations processing, and no changes are required to the passenger name record Fig. 4.36 Example of POS controls with leg/segment inventory controls

Y 115

B 95

M 75

V 45

Q 25 Capacity = 100 seats

M1 15

Manila, Philippines

M2 25

Singapore

M3 20

Bangkok, Thailand

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(PNR) since the base class M, in the example, is the level at which inventory is reconciled both on demand and during nightly file maintenance.

4.4.6.4 Shared Cabin Inventory The objective of a higher valued cabin (e.g., first class or business class) sharing inventory with a lower valued cabin (e.g., coach) is to improve the overall load factor for a flight. If demand for the higher valued cabin is forecast to be low on a future flight departure, then some of the higher valued cabin seats may be sold to coach passengers at the time of the reservation. The airline can then at its discretion decide on the specific valued customers who should be upgraded to first class to make room for the additional coach reservations that were accepted because of low demand in the higher valued cabin. If there is an unexpected surge in demand for the higher valued cabin, then the passenger requests must be honored if there are seats available in the cabin. Figure 4.37 illustrates the shared cabin inventory concept between first class and coach cabin. In the shared cabin inventory environment, when reservations are accepted for a flight, initial sales are made directly against the premium and coach cabin inventories. After the exclusive coach cabin seats are sold out, additional sales are made on a first come first served basis from the shared seat inventory. Sales from the shared inventory decrement both the higher valued cabin and lower valued cabin seats available simultaneously. When there are more than two cabins on a flight, both complete and partial sharing of inventory is feasible. Consider the 2 shared cabin matrix definitions shown in Table 4.6 to illustrate this concept. The shared cabin matrix defines a from-with share relationship. Row-wise, the sharing is from. Column-wise, the sharing is with. In the case with complete sharing, F can share seats with both Y and J cabins and J can share seats with Y. In the partial sharing example, the sharing is strictly one level up. Hence, F can only share seats with J and J can only share seats with Y. There is no sharing between

Exclusive Seats

Shared Seats

First Class Cabin Seats

Coach Cabin Seats

Fig. 4.37 Shared product inventory Table 4.6 Complete sharing and partial sharing

Complete sharing Cabin F F X J X Y X

J

Y

X X

X

Partial sharing Cabin F F X J X Y

J

Y

X X

X

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F and Y. Partial sharing is more conservative and in a potentially oversold situation will tend to result in fewer oversales and result in lower load factor.

4.4.6.5 Funnel Flights/Overlap Flights and Inventory Control Funnel flights came into existence in the 1980s and disappeared in the late 1990s in the U.S. Funnel flights are still very prevalent in China and other developing countries who want to promote an airport in an interior city as an international gateway. The practice is deceptive to the customer. A funnel flight represents two operating flights of an airline which have a common connecting airport. With funnel flights, in a city pair availability display, the funnel flight appears as a direct flight enhancing GDS screen placement. A funnel flight is also known as a change of gauge, since there is a change of aircraft at the connecting airport while the flight is categorized as a nonstop flight since the flight number on both segments is the same. This is an example of a funnel flight. Consider the operating flights from Albuquerque, New Mexico (ABQ) to Dallas/Fort Worth (DFW) and DFW to London, Heathrow (LHR). The operating flight ABQ-DFW flight number 100 connects to operating flight DFW-LHR flight number 200. A funnel flight consists of a pseudo equipment code that is using all the associated operating legs that are mapped to the funnel flight. A flight ABQ-LHR with a unique flight number (e.g., 300) is created. The city pair availability display on an agency desktop will display ABQ-LHR as a nonstop and seats sold on the funnel flight number 300 will decrement availability from the two operating flights and the funnel flight. There are several impacts to funnel flights. Airline scheduling systems will treat funnel flights as marketing codeshare flights internal to the airline. They impose a computational burden on inventory control. When a funnel flight is booked, seats sold counts on the individual operating flights associated with the funnel flight must be updated. Departure control systems (DCS) do not create virtual coupon records (VCR) for the operating flights; there is only one VCR for the funnel flight. In the event of a flight disruption on one of the operating flights, the passenger will not have a ticket to be exchanged and the airline will require a business process exception to address passenger re-accommodation. Funnel flights impact revenue management systems and a mapping of the funnel flight to the operating flights needs to be maintained. While forecasts will be generated for the operating flights and the funnel flight, inventory controls are based on the aircraft capacity of the operating flights since a change of gauge is frequent and funnel flight inventory controls are derived from the inventory controls of the operating flights.

4.4.7

Performance Measurement

Performance measurement and feedback is an integral component of any revenue management system to improve long-term performance, adapt and improve the revenue management models to ensure continuous improvement. Performance should be measured over time to facilitate month over month and year over year

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comparisons to detect systematic weaknesses in the revenue management process and identify corrective actions that need to be taken. There are two types of metrics: Standard key performance indicators and the revenue opportunity model.

4.4.7.1 Standard Performance Metrics Standard performance metrics can be broken down into post departure and predeparture, shown in Tables 4.7 and 4.8, respectively. Table 4.7 Post departure performance measures Corporate metrics Load factor

Yield Revenue per available seat mile/kilometer Cost per available seat mile/ kilometer

Market share

Oversale (denied boarding) rate

Spoilage Spoilage rate Oversale cost

Load factor on closed flights

Closing rate Model metrics Forecast Error

Description The ratio of onboard traffic to available seats expressed as a percentage. An alternate definition is the ratio of revenue passenger miles (km) to the available seat miles (km) expressed as a percentage Passenger revenue per revenue passenger mile (km) This is considered the single most important measure and is the ratio of passenger revenue to available seat miles (km) The cost per available seat mile or kilometer explains the efficiency of the airline in a nutshell. It is calculated by dividing the operating costs of the airline by available seat miles (km). The lower the CASM (CASK), the more profitable and efficient the airline Represents an estimate of the proportion of total traffic in a market. Can be estimated from marketing information data tapes (MIDT) and passenger shopping data Calculated separately for voluntary and involuntary denied boardings. It is the ratio of number of denied boardings to the number of passengers boarded expressed in denied boardings per 10,000 passengers boarded Represents the number of empty seats on closed flights Ratio of number of spoiled seats to the number of passengers boarded expressed as a percentage Cost of customers who were denied boarding. Should be tracked by airport. Components of denied boarding costs are voucher costs, meals, ground transportation and goodwill. Expressed in unit of currency per person by airport This is the correction factor for incorrect overbooking. Measurement is based on open/close status across the life of the flight by booking class and calculated as a weighted average by flight leg and base cabin Probability that demand for a booking class exceeds the available seats in the class Description Errors associated with demand forecasting, cancellation rate forecasting, boarding rate forecasting. Common measures used are: Mean Absolute Deviation, Standard Error, Bias, Weighted Mean Absolute Percent Error and Mean Squared Error

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Table 4.8 Predeparture performance metrics Corporate metrics Booked load factor

Expected load factor

Expected revenue

Expected yield Expected revenue per available seat miles/kilometers Spill rate by booking class

Closing rate Model metrics Forecast errors

Description The ratio of onboard traffic to available seats expressed as a percentage. An alternate definition is the ratio of revenue passenger miles (km) to the available seat miles (km) expressed as a percentage The expected load factor of a flight at departure based on current bookings, forecast of remaining demand and inventory controls Expected revenue of a flight at departure based on current bookings, forecast of remaining demand and inventory controls Expected passenger revenue per revenue passenger mile (km) The ratio of expected passenger revenue to available seat miles/kilometers Ratio of spilled passengers to unconstrained demand to date for a future departure. Statistic must be computed by leg class or service class depending on the inventory control method. If the spill rates are not ascending from highest valued class to the lowest, it identifies a problem with how discount allocations are set Probability that demand for a booking class exceeds the available seats in the class (predeparture closing rates) Description Errors associated with demand forecasting, cancellation rate forecasting by reading day interval. Common measures used are: Mean absolute Deviation, Standard Error, Bias and Mean Squared Error

The predeparture performance of a flight is monitored to take corrective actions when a flight’s expected performance is in doubt. Table 4.8 summarizes some of the key measures associated with corporate performance and model performance.

4.4.7.2 Revenue Opportunity Model The revenue opportunity model (ROM) is used by airlines to isolate revenue management performance from other factors that influence revenue generation. The leg/segment revenue opportunity model was developed at American Airlines in 1987 to understand how revenue performance could have been improved in the prior month if perfect information were available. The ROM evaluates the performance of revenue management controls when applied on historical departures. It Identifies the optimal inventory controls, with perfect hindsight, to produce the maximum revenue and determines whether the actual controls used were relatively good or poor. It answers the “what-if” question by providing insight into what could have been done differently to improve revenue performance.

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Fig. 4.38 Single flight leg

The discount allocation revenue opportunity model determines the percentage of the total revenue opportunity that was gained through the process of revenue management. Consider the single flight leg shown in Fig. 4.38. The discount allocation revenue opportunity model is shown on Table 4.9. The actual onboard passengers are a direct consequence of accepting the inventory controls recommended by revenue management. No controls assume that revenue management controls were not applied, and passengers were accepted first come first served from the lowest to the highest booking class. Perfect controls assume that with perfect hindsight, the most valuable passengers would have been accepted. Unfortunately, perfect controls cannot be achieved in practice due to the underlying uncertainty in the demand forecast and the uncertainty of show ups for a flight. The process of revenue management effectively manages the risk associate with this uncertainty and maximizes expected revenues. The corresponding revenue metrics for the three scenarios are: Revenue Opportunity ¼ $12, 150 – $7100 ¼ $5050 Revenue Earned ¼ $9352 – $7100 ¼ $2252 Percent Revenue Gained ¼

Earned Revenue × 100 ¼ 44:59% Revenue Opportunity

An alternate way of viewing the performance statistics is shown in Fig. 4.39 which provides a breakdown of the total revenue opportunity into spoilage, dilution and revenue gained through the yield management process. The Revenue Customer Opportunity metric is calculated from the optimal mix achievable up to the actual boarded count by flight leg. Discount allocation spoilage represents the spoilage that results despite excessive demand for a flight that is a direct result of over-estimating show up rates and/or demand for higher valued booking classes. Dilution ¼ Revenue Customer Opportunity – Actual Revenue Spoilage ¼ Maximum Revenue Opportunity – ðActual Revenue þ DilutionÞ For leg/segment carriers, the revenue opportunity measures must be calculated by flight leg, flight segment or at the flight number level of detail. For O&D carriers, the revenue opportunity measures must be calculated at the network level. To compute the measures for a flight number with multiple segments or network will require that a deterministic product mix linear program be solved (Chandler & Ja, 2007).

Average fare ($) 179 169 139 119 99 79 59

Unconstrained demanda 7 11 17 23 31 36 52 177

Passengers RM controlsb 5 8 11 14 17 14 19 88 No controls 0 0 0 0 12 36 52 100

Flight: 100 AUSDFW; Capacity: 100 seats a Post departure unconstrained demand from traffic based on inventory open/close data b Actual observed passengers(traffic) onboard

Booking class Y B M H V Z Q Total

Table 4.9 Calculating discount allocation revenue opportunity Perfect controls 7 11 17 23 31 11 0 100

Revenue Onboard ($) 895 1352 1529 1666 1683 1106 1121 9352

Minimum ($) 0 0 0 0 1188 2844 3068 7100

Maximum ($) 1253 1859 2363 2737 3069 869 0 12,150

4.4 Leg/Segment Revenue Management 155

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Fig. 4.39 Components of the revenue opportunity model

4.4.8

Revenue

Minimum Revenue

Maximum Revenue

Revenue Customer Opportunity

Dilution

Revenue Gained

Minimum

Actual Revenue

Revenue Opportunity

Spoilage

Critical Situation Identification

Revenue management is a mission critical application. Data processing and execution of the models should occur daily so that all information related to flight and market performance is available for revenue management analysts when they come to work in the morning. Revenue management analysts manage flights on an exception basis by flight entity and market entity. Flight entities and market entities are groupings of flights and markets that are assigned to one or more revenue management analysts. Consider a network carrier with 5000 flight departures per day or 1,650,000 (330 future days × 5000 flights/day) flights under their direct control to manage inventory controls. Reviewing all flights regardless of criticality is an impossible task, and exception processing is the norm for managing flights. Key performance indicators (KPIs) such as booking build up, expected booked load factor, overbooking levels as a percentage of capacity, flights with low bookings, group pickup as a function of days to departure, special events, etc. are defined in the system with pre-defined thresholds for these KPIs. When a KPI threshold is exceeded (e.g., booked load factor of 91% 21 days before departure), an exception is created, and the flight is reviewed by a revenue management analyst. Revenue management analysts also coordinate activities with the airline’s CRC (Central Reservations Control) function to address schedule change, passenger reaccommodation, extra sections and queue management for resolving waitlist, upgrades, etc.

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157

An often-debated question is the value of user adjustments. There is anecdotal evidence that user intervention generates incremental revenues (Bach, 1999; Mukhopadhyay, Samaddar, & Colville, 2007; Zeni, 2003). Do user overrides to inventory controls, either directly, or indirectly by modifying the demand forecasts, improve revenue? User adjustments can add value in situations such as special events, unexpected fare sales by competitors, entry into new markets where there is no historical booking data and entry of new competitors in a market. Under normal operating conditions, user overrides to system generated recommendations should be kept to a minimum to avoid loss in revenue. However, user intervention is required when major or catastrophic events occur such as the COVID-19 pandemic of 2020 when historical data cannot be used to predict future demand.

4.5

Origin and Destination (O&D) Revenue Management

Leg/segment revenue management was followed by Origin and Destination inventory controls for network carriers to control the flow of connecting traffic through their hub airports based on the value of the booking (Vinod, 1995, 1996a, 1996b, 2006). Looking beyond leg/segment inventory management, advances in revenue management include network approaches (DeSylva, 1982; Dror & Ladany, 1988; Glover, Glover, Lorenzo, & McMillan, 1982), stochastic dynamic programming (SDP) and Markov decision problem (MDP) approaches (Alstrup, Andersson, Boas, & Madsen, 1989; Bitran & Mondschein, 1995; Lautenbacher & Stidham, 1999; Subramanian et al., 1999; Wang, 1983), stochastic gradient algorithm and approximate dynamic programming (Bertsimas & de Boer, 2005; Bertsmias & Popescu, 2003), genetic algorithms with very limiting assumptions (Pulugurtha & Nambisan, 2003) and ‘bid pricing’ approaches (Lee & Hersh, 1993; Simpson, 1989; Talluri & van Ryzin, 1996, 1998, 1999; Vinod, 1995; Vinod & Ratliff, 1990; Williamson, 1992). An origin and destination revenue management system consists of the core components shown in Fig. 4.40. A successful O&D revenue management business process should ensure that there is a continuous feedback process to improve reliability of the models over time. Origin and destination (O&D) control is also frequently referred to as itinerary control. An itinerary describes a passenger’s one-way origin and destination pair, Host CRS / DCS Reservations Booking/PNR Data

Continuous Feedback O&D Demand Forecasting

Overbooking

Post Departure Data Historical Data

Post Process Nested Controls Nested Bid Price Controls Curve Update Host CRS Inventory Controls

O&D Network Optimization

Critical Situation Identification

Fig. 4.40 Core components of an origin and destination revenue management system

Performance Reporting

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Fig. 4.41 An airline network with connecting traffic

LHR o1

LAX Flight No 1

Table 4.10 Itinerary fares in a network

Itinerary LAX-JFK LAX-LHR LAX-FCO JFK-LHR JFK-FCO

Booking classes Y ($) 715 1426 1712 1100 1119

tN

gh

Fli

JFK Fl

igh

tN

o

B ($) 375 475 650 424 565

2

FCO

K ($) 225 424 575 335 465

including connect points and time of day. The control of reservation inventory by origin and destination is complex even for an airline with a small route network with, say, even two hundred departures a day and 20% connecting traffic. To control at the O&D level, the value of a passenger determines reservation availability based on several factors such as itinerary, departure date, base compartment (e.g., cabins F, J and Y), booking class (e.g., Y, B, M, Q, etc.), published fare, off-tariff or private/ confidential fare and point of sale. The importance of O&D controls is illustrated below with a series of simple examples where the level of detail and complexity of the inventory control technique employed in the reservations system is gradually enhanced. Consider the expanded network shown in Fig. 4.41 where a new service, flight number 2, has been introduced from JFK to FCO (Fumicino, Rome, Italy) with its associated fares by itinerary as shown below. The revenue potential depends on the precision of the reservation controls. With a series of examples, we will now illustrate the value of the last seat for the LAX-JFK flight leg under varying degrees of sophistication of the inventory controls. Table 4.10 shows the fares in the network (Fig. 4.41). No Controls The simplest case to consider is one where there are no controls, and the entire inventory is made available on a first come first served basis. When there are no inventory controls, the value of the last seat is $225. Leg Class Controls With leg class inventory controls on a reservation system, inventory is controlled based on the relative value of the various booking classes without regard to the origin and destination. In this scenario, the last seat will be sold to the highest valued

4.5 Origin and Destination (O&D) Revenue Management

159

booking class (Y) flowing over the leg LAX-JFK. The itinerary booking classes in Y that flow over the LAX-JFK leg are LAX-JFK Y ($715), LAX-LHR Y ($1426) and LAX-FCO Y ($1712). Since all Y class passengers regardless of itinerary are controlled by the same booking class code (i.e., Y), the last seat can be sold to the lowest paying Y passenger flowing over the LAX-JFK flight leg. Hence, the value of the last seat is $715. Segment Class Controls With segment class controls on a reservations system, inventory is controlled based on the relative value of the various booking classes by segment. For the network described above, the three segments that will be controlled by segment class are LAX-JFK, JFK-LHR and LAX-LHR. In this scenario, the last seat will be sold to the highest booking class over the entire segment (i.e., flight number). Hence, the value of the last seat is given by $1,426. Itinerary Class Controls With itinerary class controls, inventory can be controlled based on the relative value of the various itinerary classes flowing over a specific flight leg. Trade-offs can be made between accepting and rejecting local, through and connecting itineraries. In this scenario, the last seat will be sold to the highest valued itinerary class over the entire network that flows over the LAX-JFK flight leg. Hence, the value of the last seat is $1712.

4.5.1

First, Second and Third Order Network Effects

Origin and destination revenue management methodology is to determine the right mix of short-haul, medium-haul and long-haul demand that should be accommodated on an airline’s global route network to maximize total revenues subject to capacity constraints. By optimizing the network, instead of a flight leg or flight segment, the model simultaneously evaluates trade-offs of the network effects simultaneously. For example, consider the 3 × 3 hub-and-spoke network shown in Fig. 4.42. If there is a surge in demand from SEA to DFW because of a promotional fare filing, Fig. 4.42 Sample 3 × 3 airline network

SEA

SFO

LAX

BOS

DFW

LGA

MCO

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Table 4.11 First, second and third order network effects First order effects (+) SEA-DFW

Second order effects (–) SEA-BOS SEA-LGA SEA-MCO

Third order effects (+) SFO-BOS SFO-LGA SFO-MCO LAX-BOS LAX-LGA LAX-MCO

the first, second and third order impacts on traffic flows are summarized in Table 4.11. The narrative for the positive and negative higher order network effects is as follows: 1. First Order Effect: When a promotional fare is introduced from SEA to DFW, the local SEA-DFW market will see a surge in demand (+) 2. Second Order Effect: Because of the increased demand from SEA to DFW (local market), availability will be restricted to the connecting markets that have the SEA-DFW leg in common (–) 3. Third Order Effect: Because availability is restricted for the connecting markets originating out of SEA (the second order effect), there will be greater availability for markets that do not originate in SEA and hence they will see increased traffic (+).

4.5.2

Virtual Nesting

The first generation of O&D revenue management was called virtual nesting, a nested inventory control technique that provides approximate origin and destination control based on the value of the passenger. This is accomplished by clustering the various itinerary booking classes that flow over a flight leg into a manageable number of virtual buckets on the inventory detail record based on customer value. The first generation of virtual nesting deployed at American Airlines in 1987 maintained a static virtual nesting table in Sabre PSS (Smith, 1986). A bucket, which consists of several itinerary booking classes mapped into it, is used to control inventory instead of a booking class. Customer value, known as the Cumulative Effective Revenue (CER), is determined by an algorithm (Smith & Penn, 1988) that considers the traffic flows in the network. The value of an itinerary class is the fare net of upline and downline displacement costs. All passenger itineraries flowing over each flight leg in the network are clustered into a fixed number of buckets. The number of buckets is dependent on the space available on the inventory detail record (IND) on the reservations system. If many buckets are required to produce more granular controls, then an expansion of the IND records will be required, which could be a major investment. The clustering

4.5 Origin and Destination (O&D) Revenue Management

161

LHR

LAX

g

Fli

JFK Flight No 1

Displacement Cost = $300

1 No Displacement Cost = $400 ht

Fl

igh

Displacement Cost = $350

tN

o

2

FCO

Fig. 4.43 Sample network with displacement costs by flight leg Table 4.12 Calculation of cumulative effective revenue O&Ds over LAX-JFK LAX-JFK LAX-JFK LAX-JFK LAX-LHR LAX-LHR LAX-LHR LAX-FCO LAX-FCO LAX-FCO

Booking class Y B K Y B K Y B K

Fare ($) 715 375 225 1426 475 424 1712 650 575

Cumulative effective revenue ($) 715 375 225 1026 75 24 1362 300 225

process to index itinerary classes into buckets is accomplished with a dynamic programming model that minimizes the variance of customer values within a bucket and simultaneously maximizes the separation between buckets (Vinod, 1989). The buckets are serially nested to ensure that as sales build up for a flight, the lower valued itinerary classes are automatically shut off when the corresponding bucket is closed. A virtual nesting table stores the bucket index of the itinerary class for each leg on the reservations system. Hence, virtual nesting provides the capability to control a large number of itinerary classes on a reservations system with a few virtual buckets. The example is based on the sample network shown in Fig. 4.43. The table below illustrates the indexing process for the LAX-JFK flight leg with a capacity of 200 seats and four buckets. The flight leg LAX-JFK has four virtual buckets in the example. There are nine service classes that need to be indexed into the virtual buckets. The authorization levels for each bucket are determined by revenue management. The seats sold count is maintained by bucket in this example since availability calculation is based on net availability. The displacement costs are determined from the network optimization model. Note that the overbooking level and class authorization levels were determined based on assumed inputs. Tables 4.12 and 4.13 illustrate the indexing of all itineraries for a flight leg LAX-JFK shown in Fig. 4.43. The Cumulative Effective Revenue (CER) represents the value of the reservation request after factoring in upline and downline

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Table 4.13 Virtual nesting indexing of service classes to flight leg O&Ds over LAX JFK LAX-FCO LAX-LHR LAX-JFK LAX-JFK LAX¼FCO LAX-JFK LAX-FCO LAX-LHR LAX-LHR

Booking class Y Y Y B B K K B K

Fare ($) 1712 1426 715 375 650 225 575 475 424

CER ($) 1362 1026 715 375 300 225 225 75 24

Virtual nesting bucket index 1 1 1 2 2 3 3 4 4

Table 4.14 Inventory detail record by flight leg Virtual nesting bucket index 1 2 3 4

Authorization level 225 180 135 50

Seats sold 22 33 55 30

Seats available 85 62 50 20

Flight Leg: LAX-JFK

displacement cost. For a service (s) class (c) on leg m, the CER is derived from the leg displacement costs as follows: X CERm Displacement Cost j SC ¼ Faresc – jES j 6¼ m where Faresc is the fare for service s and class c, Displacement Costj is the displacement cost for leg j determined by the network optimization model. Virtual nesting indexing is described in the context of the LAX-JFK flight leg. O&Ds are next sorted by CER value and indexed into a bucket to create the virtual nesting table. The example assumes that the total number of buckets available for coach cabin is 4. The inventory detail flight record is shown in Table 4.14. Availability calculation is based on net nesting and the authorization levels for each bucket are calculated using one of the two variations of expected marginal seat revenue calculation.

4.5.2.1 Dual Indexing When virtual nesting was deployed for American Airlines on Sabre PSS, an important consideration was dual indexing. With dual indexing a connecting market can be indexed distinctly on the first leg versus the second leg based on the reservation value. For example, consider the AUS-BOS market connecting over the DFW hub. AUS-BOS B (booking class) may be indexed into bucket 2 (higher in the hierarchy)

4.5 Origin and Destination (O&D) Revenue Management

163

on the AUS-DFW flight leg while AUS-BOS B may be indexed into bucket 4 (lower in the hierarchy) on the DFW-BOS flight leg. The indexing assignment is a function of relative value of the AUS-BOS B origin and destination, relative to all the other market classes, on the AUS-DFW and DFW-BOS flight legs, respectively. Multiple indexing is not required for U.S. majors. Dual indexing is adequate because the economics of a hub and spoke network has on the average 30% locals and 70% connecting traffic. This translates into an average length of haul per passenger of 1.7 (less than two legs).

4.5.2.2 Dynamic Virtual Nesting The key difference with dynamic virtual nesting is that the determination of the bucket index for a specific itinerary is dynamic and determined at the time when availability is requested. Hence, it is not required to store the indexing scheme on the host CRS. Each flight leg will have a CER range for each bucket and the virtual nesting range for each leg could vary from leg to leg, called dual indexing for two legs of an itinerary and multi-indexing when more than two legs are required for the itinerary. This is based on the value of the itinerary CER and the CER ranges established for the flight legs as part of the revenue management nightly batch processing. When an availability request is received, the CER is calculated to determine the bucket index which has the seats available for the bucket. 4.5.2.3 Virtual Nesting Indexing Mapping of market classes to buckets can be accomplished by uniformly mapping the market classes to buckets based on historical traffic or a combination of historical traffic and anticipated remaining demand forecast by bucket. This can be improved with an optimal mapping algorithm that minimizes the displacement cost within a bucket. Instead of mapping O&D classes to buckets based on the equal traffic heuristic, an important consideration is the minimization of displacement of higher valued passengers by lower valued passengers within the same virtual nesting bucket on the AUS-DFW flight leg. For example, consider the network shown below. Consider the network shown in Fig. 4.44. With virtual nesting, it is quite likely that an AUS-BOS V fare of $399 and an AUS-MIA M fare of $299 are mapped into the same bucket on the AUS-DFW flight leg. Now, if there is only one seat available in that bucket, the higher valued request may be turned away because the lower valued request made the reservation first. While the problem cannot be avoided as long as inventory is controlled with a finite number of virtual nesting buckets into which O&D fare values are mapped, the problem can be minimized. To address the displacement issue, of higher valued bookings being displaced by lower valued bookings within the same bucket, an optimal indexing algorithm for virtual nesting controls was developed (Vinod, 1989). The indexing problem can be solved as an integer program. However, a dynamic programming approach is more efficient to solve this problem. The optimal indexing algorithm described has numerous applications in revenue management across industries. It was originally developed to improve the equal traffic by bucket

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4

Revenue Management of the Base Fare

BOS o2

tN

AUS

h lig

F

DFW Flight No 1

Fl

AUS-BOS V Fare = $399 AUS-MIA M Fare = $299

igh

tN

o

3

MIA

Fig. 4.44 Sample network to illustrate indexing

heuristic-based indexing that was first deployed at American Airlines in 1990 as part of virtual nesting-based origin and destination inventory controls implementation in the American Airlines partition in Sabre. The indexing recommendations from the optimizer are stored in the host CRS for real time O&D control. The optimal indexing model improves revenues by around 0.5% in the airline network. This indexing model has been used for booking class realignment. This is the mechanism to map fare basis codes to booking class codes based on fare values (discussed in Chap. 2). The indexing model has also been used for defining rate categories (also called rate pools) for hotels and rental car revenue management implementations. This is the unit of inventory that is forecast and optimized. For example, in the hotel context, the rate categories must be defined for demand forecasting and revenue mix optimization. The rate categories also serve as a surrogate inventory control point within the reservation systems for the purposes of real time inventory control. The Indexing Model Consider the market class fares associated with a cabin over a specific period as an ordered set. This ordered set is created by taking the actual flown traffic, where flow traffic is at a service class, fare basis code level, and aggregated to a market class level by computing an average fare, weighted by traffic. The ordered set exhibits the following property. x1 < x2 < x3 . . . xM This can be accomplished by sorting the individual fares in ascending order of the fare values. Also, let w1, w2, . . ., wM represent the corresponding forecast arrival demand (or flown ticketed data as a surrogate) for the fares within the ordered set. Independent of the desired number of partitions (clusters), each partition is contiguous, i.e., if xk > xi and xi and xk are grouped together, then xi + 1, xi + 2, . . ., xk – 1 would also belong to the same partition. This is the underlying string property of the problem.

4.5 Origin and Destination (O&D) Revenue Management

165

Now, given an ordered set, the number of possible contiguous partitions of length n (the number of virtual buckets) for M individual fares associated with a given cabin is given by the number of ways M – 1 partitions may be selected taking n – 1 at a time. This number is indeed quite large. For example, if 20 individual fares must be grouped into 7 virtual buckets, the number of possible partitions is given by (19!)/ (6!) (19 – 6)! ¼ 27,132. It is therefore apparent that an explicit enumeration procedure would not be economical. The effectiveness of the enumeration procedure may be improved significantly by utilizing the string property to obtain the recursive relationship of dynamic programming. Exploiting the string property in a dynamic programming framework results in an efficient algorithm that can solve large problems (M ¼ 1000, n ¼ 10) in less than 1 s CPU time on a medium class server. Let cij ( j ≥ i) be the within group sum of squares for a partition consisting of fares i through j only. Hence, cij ¼

j X

( )2 wk xk – xij

k¼i

where j P

wk xk xij ¼ k¼ij P wk k¼i

Obviously, cii ¼ 0 Let Fn(i) be the sum of sum of squares in the partitions x1, x2, . . ., xi (i ¼ 2, . . ., m) into n clusters or buckets. At the optimum, Fn(i) will be the optimum value (i.e. the minimum within group sum of squares) for a problem consisting of fares 1 through i only with at most n partitions. Then, F n ð0Þ ¼ 0; n ¼ 0, . . . , m F i ðnÞ ¼ 0; i ¼ n, . . . , m F 0 ðiÞ ¼ 1; i ¼ 1, 2, . . . , M The dynamic program for minimizing the objective function may be written in the recursive form ⌈ F n ð jÞ ¼ Minimum cij þ F n–1 ði – 1Þ; j ¼ 1, 2, . . . , M

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4

Revenue Management of the Base Fare

1 ≤ i ≤ j; n ¼ 1, 2, . . . , j Fn(M ) provides the value of the optimum solution with at most n partitions. The solution is stepwise optimal. The recursive relationship gives the optimum value for each value of n, which is the number of partitions. If the number of partitions or clusters is pre-specified to be n, then the index n in the recursive relationships will range between 1 and Min(n, j). The optimum partition may be obtained by the process of backtracking. Note that to use the recursive relationship, the value of cij must be first computed. There are M(M + 1)/2 such values, of which M are zero. The remaining M(M – 1)/2 values of cij can be readily computed.

4.5.2.4 Utilization of Buckets The typical utilization of virtual nesting buckets is shown in Fig. 4.45 below with eight virtual nesting buckets. The frequency distribution is typically fat in the middle and tapers from the mean. Unlike the equal traffic heuristic, when the traffic flow is mapped to the optimal indexing structure, the utilization of the buckets will be higher in the middle and less toward the buckets at the ends. 4.5.2.5 Fares Versus Cumulative Effective Revenue In general, fares should not be used for indexing markets in a virtual nesting environment. The CER must be computed and used instead of the market class fares in the indexing model. The CER is the O&D fare minus the displacement cost of upline and downline legs (excludes the leg being considered for indexing). If the input data are the historical flown ticketed data, then the fare can be substituted with the CER values. On a typical short haul leg (e.g., AUS-DFW) there could be as many as 300–500 origin and destination classes that need to be indexed. It is important to use the historical flown ticketed data to determine the weighted average, to eliminate outliers from being indexed into a bucket by itself, prior to running the indexing model. The model provides the optimal mapping of origin and destination classes to a set of virtual buckets. The number of virtual nesting buckets is dictated by the capabilities of the reservations systems. In the case

Utilization (%)

Fig. 4.45 Bucket utilization with optimal indexing

1

2

3

4

5

6

Virtual Nesting Buckets

7

8

4.5 Origin and Destination (O&D) Revenue Management

167

of American Airlines, the Sabre partition was established with eight virtual nesting buckets.

4.5.3

Continuous Nesting (Bid Price Controls)

The first O&D yield management system based on virtual nesting controls was deployed in 1987 for American Airlines (Smith, 1986; Smith et al., 1992; Vinod, 1992). While a few airlines deployed virtual nesting in the 1990s such as United Airlines, Delta Air Lines, KLM Royal Dutch Airlines and Scandinavian Airlines System most network carriers began adopting continuous nesting controls (also known as bid price controls) (Vinod, 1995) during this time. American Airlines and US Airways migrated to Sabre’s bid-price based O&D system in 1998. The bid price is the opportunity cost of not having an incremental seat on a flight. Adoption of bid price controls by network carriers started in the late 1990s and continues to this day. The idea for continuous nesting using bid prices was based on the observation that group requests were accepted and rejected based on the computation of the minimum acceptable fare (Vinod & Ratliff, 1990). With continuous nesting the concept of group control was applied to individual requests with bid prices generated by a stochastic network optimization model. The control of reservation requests by origin and destination is a vast improvement in inventory control technology (Feldman, 1995; Gallacher, 1996) over leg/segment controls. Since inventory is controlled at a finer level of detail than virtual nesting, O&D control by the method of continuous nesting generates incremental revenues over and above the traditional leg/segment and virtual nesting inventory control techniques. Today, the continuous nesting approach for origin and destination inventory control is the most widely adopted method in the airline industry today. Figure 4.46 illustrates the fundamental difference between continuous nesting (bid price controls) and the traditional allocation-based leg, segment, and virtual nesting controls. The objective is to determine a bid price for each flight leg by base compartment (first class F, business class J and coach class Y) based on current reservations holding, forecast of remaining demand by itinerary class, variation in remaining demand forecast and remaining capacity. Continuous nesting controls are called bid price controls. The bid price is the opportunity cost of not having an incremental seat on a flight leg in the network. Alternately, the bid price can be defined as the incremental total revenue that can be derived if one excess unit of capacity is available. The excess revenue would be obtained by accepting a different mix of passengers by service (origin, destination, routing) and class in the network. The bid price is also frequently interpreted as the minimum acceptable fare on a flight leg. Since the model to calculate bid prices considers demand uncertainty, revenue management practitioners frequently refer to the bid price as the probabilistic shadow price, displacement cost, and probabilistic dual cost.

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4

Fig. 4.46 Minimum acceptable fare versus cumulative bookings

Revenue Management of the Base Fare

Minimum Acceptable Fare

Y1 = Authorization level for highest valued virtual nesting bucket in coach cabin Y1 Y2 Y3 Y4 Y5 Y6

Y6 = Authorization level for lowest valued virtual nesting bucket in coach cabin Cumulative Bookings

Since inventory can be controlled at the individual itinerary and class level, continuous nesting provides greater revenue benefits and reduces the load on the host CRS by eliminating virtual nesting buckets. In addition, continuous nesting offers several additional advantages such as inventory control by point of sale (POS) or point of commencement (POC), inclusion of direct variable costs in the evaluation and the inclusion of strategic value to handle currency fluctuations and promotions. Bid price controls with a bid price and gradient provide the flexibility of detailed inventory control by service class without introducing a significant overhead to reservations processing. The total bid price for a service is simply the sum of the individual bid prices of the flight legs that make up the service. If the total bid price is less than the fare, the request is accepted, otherwise, it is rejected. Financial availability is determined by the net contribution calculation, given by: X γj Net Contribution ¼ Faresc – j2S

Faresc is the fare for service s and class c, γ j is the bid price for flight leg j; s 2 S, where S is the collection of services in the network. If the net contribution is positive, the reservation request is accepted, otherwise, it is rejected. In addition, the incremental bid price or gradient is used to adjust the bid price when bookings and cancellations occur. The gradient can be interpreted as the change in bid price for a unit change in available seats. With each new booking, the bid price is increased by the bid price gradient. With each cancellation, the bid price is decreased by the bid price gradient. If a request is accepted, the bid price is updated by adding the gradient as follows: γ þj ¼ γ j þ Δ j * Seats Booked j

4.5 Origin and Destination (O&D) Revenue Management Table 4.15 Bid price curve example

Number of bookings ⋮ 80 81 82 ⋮

169

Bid price ($) ⋮ 110 112 115 ⋮

Leg: AUSDFW Flight No. 100

where γ j is the current bid price of leg j, γ þj is the new bid price on leg j, Δj is the gradient on leg j and Seats Bookedj represents the number in party for the booking request. Similarly, if a cancellation occurs, the bid price is subtracted by the gradient as follows: γ þj ¼ γ j – Δ j * Seats Booked j Note that this assumes that the relationship of the gradient with the change in capacity is linear. This, however, is an approximation that can only be applied for small changes in available seats. A better alternative is to generate a bid price vector (also known as a bid price curve) from the network optimization model based on the assumption that primal feasibility exists as bookings are accepted. By storing the bid price vector on the flight inventory record, the linear assumption can be relaxed. An argument can be made that frequent re-optimizations of the network obviates the problem, and the bid price/gradient approach is comparable in performance to the bid price vector. However, there will always be latency between network re-optimizations from revenue management which makes the bid price curve more appealing. Table 4.15 illustrates this point for a flight leg/departure date. Actual implementation on a reservations inventory control system will be based on seats available with the associate bid price. As observed, continuous nesting provides a level of granularity beyond virtual nesting controls. Every service class availability request is considered unique, unlike virtual nesting where availability was the same for all service classes mapped into a virtual bucket. Continuous nesting is a radical departure from the allocation driven world of inventory controls to a price driven inventory control framework. The figure indicates the relationship between seats sold and the minimum acceptable fare (MAF). The step lines in Fig. 4.46 indicates traditional nested bucket allocations while the curve indicates the true nature of the relationship between MAF and seats sold. As reservations continue to build, the minimum acceptable fare increases. The dynamic nature of continuous nesting controls (i.e., changing nature of the bid price as a function of reservations holding) can be accomplished by adjusting the bid price by the bid price gradient for every sell and cancel that occurs. Note, however, that the gradient is a linear approximation for small changes in reservations holding, and volume of reservation activity-based network re-optimizations are required to adjust the bid price and gradient in the network to ensure maximum effectiveness.

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4

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Since inventory can be controlled at the individual itinerary and class level, continuous nesting provides greater revenue benefits and reduces the load on the host CRS by eliminating buckets. In addition, continuous nesting offers several additional advantages such as inventory control by point of sale, inclusion of direct variable costs in the evaluation and the inclusion of strategic value to handle currency fluctuations and promotions.

4.5.4

Network Optimization Models

The objective of the network optimization model is to determine the optimal inventory controls, taking into consideration the first order, second order and third order effects in the network, that maximizes the total expected revenues in the network subject to available capacity. To consider the marginal trade-offs between competing service classes in a network explicitly, a network optimization model is required to calculate the optimal bid prices. Earlier models (Glover et al., 1982) assumed that demand was deterministic resulting in aggressive sub-optimal inventory controls. A refinement to the deterministic model where demand variability and non-linear diminishing marginal returns property of booking class inventory allocations were modeled using a stepwise linear approximation to the demand curve (Vinod & Ratliff, 1990). The stepwise approximation split the allocation decision variables into discrete units which provided a discrete approximation to the EMSR curve. This method had the disadvantage of solving for an exceptionally large number of decision variables to account for the linear approximation. The number of constraints did not increase. An algorithm that directly models the relationship between price, supply, and the uncertainty in demand, without resorting to a linear approximation, to calculate the probabilistic bid price and bid price vector was developed (Vinod, 1992) for the Holiday Inn Revenue Optimizer (HIRO) project and later scaled for airline O&D carriers. The approach used a Lagrangian relaxation of the problem that was solved with the sub-gradient method. This approach had its origins in crew pairing at American that used a problem size reduction technique to solve extremely large (integer) set partitioning (SPLP) and set covering (SCLP) linear programs (the American Airlines Siper-80 (MD-80) fleet had over 300+ aircraft and the number of possible crew pairings is unknown, probably greater than two trillion). Using a Lagrangian relaxation of the original formulation (Fisher, 1981), the sub-gradient model was used to quickly iterate a few times, determine approximate dual prices to reduce the problem size (eliminate pairings which are columns in the SPLP/SCLP) and solve the problem to improve the current value of the objective function. This was an iterative approach to reduce excess pay and credit for pilots and flight attendants up to the desired target and there is no guarantee it can be achieved. Refinements to the network optimization algorithm to speed up convergence were proposed by Fite (1993) for Club Med. The network optimization model to determine the bid prices can be formulated as either a deterministic or stochastic linear program that explicitly models the demand

4.5 Origin and Destination (O&D) Revenue Management

171

distribution. A non-linear programming formulation is described below where demand is assumed to be gamma distributed. A standard deterministic model for the network optimization model for a single cabin for a future departure date called the optimization date is as follows: XX Max Rsc X sc s

c

XX I sc X sc ≤ C j ; j ¼ 1, 2, . . . , N s:t: s

c

0 ≤ X sc ≤ Dsc where: Rsc Xsc Isc Dsc Cj N

Fare value of service s and class c Non-nested allocation for service s, class c Zero/One Indicator variable. Assumes value of 1 if service class s flows over flight leg j; else equals 0 Remaining demand forecast for service s and class c Remaining capacity for flight leg j in the network ( j ¼ 1, 2, . . ., N) Number of flight legs in the network

Solving the deterministic network problem can lead to aggressive inventory controls since demand is assumed to be known with certainty. When demand for each origin, destination class is represented by a probability density function f(x), the formulation is as follows.

Max

XX Z s

c

x

Rsc X sc f sc ðxÞ þ

XX ð1 – F sc ðxÞÞRsc X sc s

0

c

XX s:t: I sc X sc ≤ C j ; j ¼ 1, 2, . . . , N s

c

0 ≤ X sc ≤ Dsc

R fsc(x) is the probability density function for a specific service class (sc), is the integral between specific limits of integration and Fsc(x ¼ x0) is the cumulative density function defined as:

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4

PrðX ≤ x0 Þ ¼

Revenue Management of the Base Fare

Zx0 f sc ðxÞdx: 0

Assuming that demand for a service class is gamma distributed, the value that each Xsc assumes is determined as follows: X sc ¼ x0 ¼ F –1 sc ðpÞ P ui i ; p > 0; and 0 otherwise p ¼ F sc ðx0 Þ ¼ 1 – Rsc Note that the term ∑iui is the sum of the duals (shadow prices) for the legs over which the specific service class (sc) flows in the current iteration. By assuming that demand is gamma distributed, G(α, β), the term F –1 sc ðpÞ can be evaluated with a fast table lookup with three-way interpolation. The alternative is to evaluate the cumulative density function of G(α, β) directly, but it will be computationally slow given the millions of evaluations required for each iteration of the network optimization model. Rx0 The term Rsc xf ðxÞdx can be derived from first principles, like the derivation of 0

the gamma spill model illustrated in Chap. 3. Hence, (

Zx0 xf ðxÞdx ¼ Rsc / β F sc

Rsc

x0 α þ 1, β

)

0

which can be readily evaluated. There are numerous techniques that can be used to solve the above formulation. One method is to first reformulate the problem as a Lagrangian relaxation model and solve it by the sub-gradient method (Fisher, 1981). The Lagrangian problem is given by Min fMax

XX Z s

c

x

0

Rsc X sc f sc ðxÞ þ

XX ð1 – F sc ðxÞÞRsc X sc – s

c

( ! XX I sc X sc – C j g; j ¼ 1, 2, . . . , N u s

c

4.5 Origin and Destination (O&D) Revenue Management

Initialize bid prices by flight leg for the network

Prorate revenues based on bid prices

Use EMSRB based equation to update bid prices by flight leg

No

173

Calculate network revenue for iteration i (Zi)

Zi – Zi–1 < ε Yes STOP

Fig. 4.47 Leg decomposition model

0 ≤ X sc ≤ Dsc Given an initial vector of duals u0, a subgradient method can generate a sequence u by the following rule. k

( ) ukþ1 ¼ uk – t k Axk – b where xk is the solution to the Lagrangian problem in iteration k and tk is a positive scaler step size and (Ax – b) is used to represent the constraint set in the original problem. An iteration is considered an improvement if: the current value of the Lagrangian cost in the current iteration is lower than the incumbent, or if the total constraint violation error is lower than the incumbent, or if the maximum violation across all constraints is lower than the incumbent. This method requires a stopping criterion, which can be based on a pre-defined threshold of the difference between the primal cost and the dual (Lagrangian) cost. The nonlinear program formulation solves for the bid prices (the dual variables of the capacity constraints) but does not make any provisions for nesting and the service class allocations produced by the model are non-nested allocations. This formulation, like EMSRA, treats the service classes as a series of separate, partitioned allocations and ignores the fact that when nesting is considered, service classes jointly share allocations and higher valued service classes can access inventory below them in the nested structure. Deployment of continuous nesting in a host CRS assumes that the service classes share allocations. All service classes that are open share the remaining capacity. This model, like EMSRA, underestimates the traffic and network revenues. This in turn leads to incorrect bid prices since the calculation of bid prices in the formulation is dependent on the objective function. The discrepancy in the traffic estimate was confirmed for the normal distribution of demand (Talluri & van Ryzin, 1998). To take nesting into account and address the underestimation in traffic with the above formulation, an alternative approach is to apply EMSRB in the context of an iterative leg decomposition model (Smith, Rao, Tsioutsias, & Zhang, 1997). Figure 4.47 outlines the leg decomposition model.

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4

Revenue Management of the Base Fare

The leg decomposition model assumes an initial set of bid prices by leg for the network to initiate the iterations. With the leg bid prices, for each leg in the network, the flow traffic revenue is converted to an equivalent leg value using bid price-based proration. γj Rscj ¼ Rsc P γk k2sc

where Rscj is the prorated revenue of service class sc flowing over leg j, γ j is the bid price of the jth leg and ∑k 2 scγ k is the sum of the bid prices for service class sc. From the individual prorated revenues, the weighted average is calculated by class for each leg and the EMSRB based equation is used to calculate the new bid prices for each flight leg in the network. ( j ) (P P ( j ) ) PP j c Dsc Rsc I Rsc – γ j :Pr s c Dsc I Rsc – γ j ≥ C j s ( j ) PP γj ¼ s c Dsc I Rsc – γ j ( ) ( ) where I Rscj – γ j is either equal to 1 when the net contribution Rscj – γ j is positive or equal to zero when the net contribution is negative or zero. Note that the second numerator (P Pterm in ( the ) ) j D R – γ j ≥ C j is the closing rate for leg j, based on all service Pr sc s c sc class demands that are open to fill the remaining capacity Cj. At optimality, for each leg, the bid price is equal to the value of the last seat sold. The above equilibrium equation for each leg states that, at optimality, the bid price is equal to the value of the last seat sold. At the end of each iteration the network revenue is calculated assuming demand is gamma distributed. If the improvement in network revenue between successive iterations or groups of iterations is below a pre-specified threshold, the iterations are stopped. Otherwise, the iterations continue until the stopping criteria have been met. This approach scales to large problems like the previous model and has the advantage of inventory sharing among all open service classes. This is consistent with what happens with bid price controls for real time management of seat inventory. Simulation studies by the authors of the leg decomposition model have shown revenue improvements over the previous model. Surprisingly, the positive results from this study contradicts earlier studies (Williamson, 1992). Perhaps these results were positive because of the bid price-based proration that was used rather than mileage or fare-based proration. Unlike the non-linear programming approach which produces allocations by service class as a biproduct, the leg decomposition model does not. The nested allocations on the host CRS are required for airlines that do not control all their O&Ds with bid price controls. The allocations are used to compute a weighted average fare by booking class to compute the nested controls that is a post process

4.5 Origin and Destination (O&D) Revenue Management

175

(see Sect. 4.5.8) of the optimization model. If all markets are on O&D control, the service class allocations are not required. The most recent models to determine bid prices consider upsell and recapture. An alternative to the non-linear programming approach or leg decomposition approach is to calculate bid prices was proposed by Talluri and van Ryzin (1996) based on the observation that the network optimization problem is a sequential decision-making problem under uncertainty. With this approach the recursive technique of dynamic programming (Bellman, 1956) can be used to determine the maximum expected revenue given the remaining capacities and time to departure. The method is appealing but has some unique challenges. First, forecasting the temporal arrival order of bookings across multiple booking classes is challenging (Weatherford, Bodily, & Pfeifer, 1993). Second, the dimensionality problem of the dynamic program needs to be addressed. Scalability of a dynamic programming formulation (Chapius, 2008) is an issue. Solving a large network of a U.S. major may prove intractable in real time. For example, an origin and destination yield management application should recalculate the network optimal inventory controls for approximately 60–90 days into the future daily and be able to reoptimize the network on an ad hoc basis when initiated by a revenue management analyst. User initiated or reservation activity driven ad hoc re-optimizations of the entire network can average between 5 and 50 per day. Each network optimization should be for the entire schedule for a day (to avoid edge effects, which is the revenue adjustment for service classes that are only partially in the network being optimized), which even conservatively may consist of 300,000 – 1,000,000+ service classes. However, if these two issues can be addressed, this is a very promising approach. The formulations described earlier ignore same flight upsell and cross flight recapture demand interactions across flights and classes due to closures. Recent research on customer choice models demonstrates the value of addressing same flight sell up and cross flight recapture (Gallego, Ratliff, & Shebalov, 2015; Vulcano et al., 2012). Significant revenue benefits have been reported with simulation studies of using a choice-based simulation approach (Vulcano, van Ryzin, & Chaar, 2010) over other methods. The firm conclusion is that modeling the actual demand process with attributes such as time of day, elapsed time, number of connections, brand and price with a choice model is a first order effect and has significant advantages.

4.5.5

Calculation of Seat Availability

To illustrate how seat availability is calculated, we will consider the same network shown in Fig. 4.43. Table 4.16 illustrates the optimal bid prices for each leg in the network for assumed values of the O&D class demand forecasts, demand uncertainty and available space (overbooking level—net reservations holding) for the coach cabin. Note that the gradient is the incremental bid price and is valid for small increments in demand before the network must be re-optimized to obtain a better estimate of the gradient under current network conditions.

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4

Table 4.16 Network bid prices

Leg LAX-JFK JFK-LHR JFK-FCO

Revenue Management of the Base Fare

Bid price ($) 300 400 350

Gradienta ($) 12 18 14

a

The gradient is the incremental bid price for the next seat from the bid price curve

Table 4.17 Availability by O&D, class Service LAX-JFK LAX-JFK LAX-JFK LAX-LHR LAX-LHR LAX-LHR LAX-FCO LAX-FCO LAX-FCO JFK-LHR JFK-LHR JFK-LHR JFK-FCO JFK-FCO JFK-FCO

Class Y B Q Y B Q Y B Q Y B Q Y B Q

Fare ($) 715 375 225 1426 475 424 1712 650 575 1100 424 335 1119 565 465

Total bid price ($) 300 300 300 700 700 700 650 650 650 400 400 400 350 350 350

Net revenue ($) 415 75 –75 726 –225 –276 1062 0 –75 700 24 –65 769 215 115

Availability Open Open Closed Open Closed Closed Open Closed Closed Open Open Closed Open Open Open

It is important to note that to calculate availability, bid prices are assumed to be additive based on the assumption that the objective function is continuous, convex, and differentiable (no discontinuities). This may not be the case (Talluri & van Ryzin, 1998). However, the additive bid price assumption is a reasonable assumption for determining availability based on the reservation value of a request by origin and destination. Table 4.17 illustrates the fares, total bid prices, net revenue, and availability for the various service classes in the network. In summary, with bid price controls, the inventory is maintained at the leg/cabin level with the associated overbooking level, bid price and gradient. Threshold nesting is implied with continuous nesting controls. When a booking request is received, a reservations availability check is first performed. Some airlines refer to reservation availability as physical availability. This is a simple check to see if seats sold is less than the authorized capacity for the cabin. If reservations availability is positive across all legs of the O&D, the pertinent fare is retrieved from the market booking class adjustment table to calculate the financial availability using the bid prices. If the financial availability is positive, the sell request is processed, and inventory is updated on the pertinent flight leg inventory detail records at the cabin level. In addition, with continuous nesting, reservation requests can be controlled by POS at a granular level. The actual point of sale control can be at distinct levels—

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from the highest (e.g., country or region) to the lowest (e.g., individual travel agency). With seamless availability (direct connect availability) the ARC/IATA number of the travel agency is known with the availability request transaction (Vinod, 1997). Hence, the availability display can be customized at the required level of point of sale by storing the appropriate market adjustment in the market class fare adjustment (MCFA) table (also called the Market Adjust Table (MAT)) in the host CRS. For example, a travel agent in Paris can be given preferential availability for the same product offering over a travel agent in London with a simple adjustment of the market booking class adjustment table in the host CRS. With virtual nesting, there are limitations since this can only be accomplished by indexing the Paris travel agent in a higher bucket than the London travel agent and the number of buckets available on the host CRS are finite. The control of inventory by O&D will require an upgrade to the inventory control package on the host CRS. The major advantages of bid price controls are: 1. Continuous nesting generates incremental revenues over and beyond virtual nesting controls. Various simulation studies have shown that the incremental revenues range from 0.5 to 1.5%. 2. Unlike virtual nesting, the issue of blockage does not arise. This problem can be reduced with dynamic virtual nesting versus static virtual nesting. 3. Precision control of inventory based on the value of the individual reservations request. 4. Precision controls are very dynamic since the bid price is updated by the bid price gradient. 5. Inventory controls are intuitive. The bid price is the minimum acceptable fare to accept a reservation. 6. Unlike virtual nesting, unlimited point of sale control capability by introducing entries into the MCFA table in the host CRS. 7. The load on the reservations system is significantly lower than virtual nesting since availability is calculated on request unlike virtual nesting where availability is pre-stored with indexes in the virtual nesting tables (core and default). The major disadvantages of bid price controls are: 1. The bid price gradient is assumed to be linear over an infinitesimal time interval. Coupled with the dynamic nature of the inventory control mechanism, this dictates the need for frequent re-optimizations of the network to ensure inventory controls are accurate. This issue can be overcome partially with the bid price vector. 2. Significant investment is required to upgrade the mainframe inventory control package in the host CRS. A less expensive alternative is to deploy a real time cooperative Unix based availability processor that is attached to a host CRS that maintains all the logic for O&D control (Hobt & Shrimpton, 1996; Smith & Green, 1993; Vinod, Nilson, & Hobt, 1997). Inventory is synchronized with every sell and cancel transaction with a two-phase commit. Regardless of the

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approach taken, a major investment is needed. Ultimately, the preferred direction is up to the individual airline Information Technology organization. 3. Organizational and business process changes are required before cutover to an origin and destination yield management environment. It also provides a unique opportunity for an airline to merge the pricing and yield management organizations (Vinod, 2003). Bid price-based continuous nesting controls were first deployed at Holiday Inn in the U.S. (the HIRO—Hurdle Rate, Inventory, Reservation & Optimization initiative) in 1992. It was the first large scale implementation of bid price controls (de Cardenas, Hobt, & Vinod, 1992). As part of the initiative, the Holidex reservations system was upgraded from rate-based allocations to continuous nesting (bid price) controls (Hobt, de Cardenas, & Vinod, 1992) to accept and reject reservation requests by rate, length of stay and the arrival date by property. In addition, a single image database of rooms inventory with two-way connectivity between the property management system and Holidex was established when each property went live with HIRO. SAS was one of the first airlines to migrate to O&D controls with their hybrid version of flexible revenue ranges for each bucket, with the bid price-based availability calculation in 1993. American Airlines migrated from virtual nesting controls to continuous nesting controls in 1998. US Airways migrated from leg/segment controls to continuous nesting controls in 1998. In Europe, Air France and Alitalia were some of the first airlines to migrate to origin and destination yield management in the late 1990s.

4.5.6

Fare Qualification Rules in Passenger Valuation

Passenger valuation has been at the heart of revenue management with the advent of O&D inventory controls with continuous nesting (Vinod, 1995) or virtual nesting controls (Smith, 1986; Smith et al., 1992; Vinod, 1989). To control seat availability by O&D requires market values, an estimate, by booking class at varying degrees of sophistication (inclusive of fare qualification rules) to ensure that the market value used for availability determination is as close as possible to the ticketed fare. Both techniques require the calculation of the net contribution for each availability request of a one-way itinerary. The Faresc described earlier is the Qualified Faresc. X Net Contribution ¼ Qualified Faresc – γj j2S

The Qualified Faresc is the most restrictive market value for service s and class c, γ j is the bid price based on current bookings for flight leg j; s 2 S, where S is the collection of services in the network. The deployment of origin and destination inventory control on a reservation system can use a simple average of the service

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Table 4.18 Fare qualification rules applicable for availability determination Fare qualification rule Frequency Trip type Advance purchase Blackout dates POINT OF SALE PCC/OAC Selling date range, day of week or time window Travel day of week and date range Days open prior to departure Days closed prior to departure Included flight number ranges, flights Excluded flight number ranges, flights Flight routing restrictions

Joint carrier restrictions Inhibits Operating partners Originating airline or GDS Marketing carrier

Description Day-of-week qualifier for departure date of the service Nonstop, direct, online connection, interline connection Number of days before departure of the service that the booking must occur Date ranges on which the booking class item may not be used Where the booking is made, includes all levels from region to agency id Originating request from a pseudo city code (PCC) or office accounting code (OAC) Date range, day of week or time window when the transaction may occur Specific day of week or date range within which the departure date of the service must occur Number of days before the service departure that item is applicable Number of days before the service departure that item ceases to be applicable List of flight numbers or flight number ranges on which this item is applicable List of flight numbers or flight number ranges on which this item is not applicable Connect point requirements for a service: any airport specified, all airports specified, board point, specific connect point, sequence of connect points Carriers that may be included in an interline connection Restrictions on use, e.g., booking only, host agent only, etc. Applies to the specified operating partners Applies for originating airline or GDS Applies to the marketing carrier

class fare or have a more detailed representation based on the fare qualification rules that can be determined when an availability request is made. For example, fare restrictions such as minimum stay and maximum stay are not applicable since they cannot be determined when an availability request is being made. The applicable fare qualification rules are the ATPCO fare categories and footnotes and are summarized in Table 4.18. Using fare qualification rules ensures that the market value used for determining availability is as close as possible to the ticketed fare. While the values will never be the same, the objective is to ensure that they are close.

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Alternatives for Creation of Market Values

The traditional approach to creating market values is an offline process, wherein historical data from revenue accounting and future fares are combined to determine the market values. This is done by matching the first few characters of the historical fare basis code from revenue accounting to the future fares. Historical revenue accounting data are required to weight future fares and determine an average value, taking into consideration the fare qualification rules required by the airline. Point of commencement (POC) of an itinerary determines the fare direction when the market values are created. For example, if a customer departs from DFW to FRA in September and returns in December, the fare direction is DFW-FRA. Hence the market values used to determine availability for both DFW FRA in September and FRA-DFW in December should be based on the fare direction DFW-FRA market values computed for September. This also illustrates the importance of journey data and knowing the POC for booking class availability determination on both the outbound and inbound segments. The preferred alternative is to eliminate the static storage of the market value table in the host CRS inventory system and calculate the market values dynamically in real time every time availability is requested. Large network carriers may have 40–80 million market value entries in a pre-NDC world that are updated daily. With NDC, fares are bound to increase with time-of-day specific fares, date specific fares and routing specific fares which could result in a much larger market value table that can exceed a billion entries. Updating a market value table with a billion rows daily is difficult and introduces a failure point that should be avoided. This will require an ultra-fast pricing engine that can price out all RBDs for an O&D assuming the booking classes are open. This on-demand computation of the market values can then be used for availability determination. This approach has the added benefit that the market value is essentially the fare and hence the gap between market values and the ticketed fares will no longer exist.

4.5.8

Post Process Nested Inventory Controls

A post processing requirement for the network optimization model is to create the requisite multiple serial nesting controls or variant supported in the host CRS. The nested inventory controls can be created from the idealized nesting structure by sorting the service classes flowing over each leg by the reduced cost and determining the changeover point in the sorted list to determine the nested allocations. This serves two purposes. First, it supports a phased migration to O&D controls from leg/segment controls. Second, the existing host CRS infrastructure can be used to post AVS messages from the host CRS to the GDSs as an alternative to the bid price based AVS message generation.

4.7 Industry Impact of O&D Revenue Management

4.6

181

Inventory and Legacy Systems

Advancements in revenue management are only meaningful if inventory control recommendations can be implemented on the airline’s host CRS to be executed in real time against availability and sell transaction requests. Enhancing legacy TPF/ALCS based reservations systems (ALCS is running TPF in a MVS environment) to support O&D controls is an expensive proposition. These initiatives are multi-year projects and begin with the expansion of the inventory records to support additional fields for O&D controls. Besides the expansion of the inventory records and developing the O&D availability processing logic on the reservations system, a vast amount of time is also spent on testing since many applications rely on inventory and each of these needs to be tested after the inventory record expansion. Nevertheless, several legacy reservations systems were upgraded to support inventory controls by origin and destination. For example, the Sabre PSS was upgraded to support virtual nesting controls in 1987. It also went through a major upgrade in 1997 to support bid price controls for American Airlines, Canadian Airlines International and US Airways. Other legacy reservations systems that upgraded to O&D controls in the 1990s include United’s Apollo, EDS’s Shares, Delta’s Deltamatic, and Northwest’s NWA PARS. Among the European carriers, SAS deployed O&D controls in 1993 (Petersen, 1996). Alitalia’s ARCO host CRS was upgraded to bid price controls in 1997. To overcome this limitation of enhancing legacy reservations systems, Sabre partnered with Air France and developed the Availability ProcessorTM, a Unix-based satellite co-operative processor that can be channel attached to a host CRS for O&D availability and sell processing for O&D control in 1997 (Hobt & Shrimpton, 1996; Smith & Green, 1993; Smith, Vinod, & Green, 1997; Vinod, 1997, 2005b; Vinod et al., 1997). Today, the use of satellite processors for availability and sell processing in an open systems environment is common. Examples are SabreSonic Inventory which is an enhanced version of the Availability ProcessorTM, Altea Inventory and PROS RTDP. Google/ITA has a read-only availability proxy, called DACS (Dynamic Availability Calculation System) to process availability requests for their shopping engine.

4.7

Industry Impact of O&D Revenue Management

Besides American in 1987, virtual nesting was adopted by a few carriers including Scandinavian Airline System (SAS), KLM Royal Dutch Airlines, United Airlines and Delta Air Lines. The more popular method for O&D inventory control was continuous nesting (Vinod, 1995; Vinod & Ratliff, 1990), also known as bid price controls, because of its ability to discern and control each individual reservation request based on value by point of sale. This capability was developed in Sabre PSS in 1997 for American Airline’s migration to bid price controls from virtual nesting controls in 1998. Canadian Airlines International and US Airways also migrated to bid price controls on Sabre PSS in 1998. The challenge faced by many airlines was

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to have the O&D control capability on the legacy reservations system without incurring high development costs associated with the expansion of the inventory records and associated changes in availability and sell processing. To support advances in yield management, several legacy reservations systems were upgraded to support inventory controls by O&D. Examples include United’s Apollo, EDS’s Shares, Delta’s Deltamatic and Northwest’s IPARS system.

4.8

Branded Fare Products

To offset declining yields, airlines have focused on a range of initiatives to charge customers for extras based on the assumption that customers will be willing to pay for certain attributes for a trip. Attributes commonly promoted are pre-reserved seats, advance seat assignment, frequent flyer lounge, checked bags, premium check-in, meals and Wi-Fi. This led to the creation of branded fare families in 2006, pioneered by Air New Zealand, Air Canada, Qantas, and others. Besides the incremental revenue generation opportunity, airlines bundle fares and booking classes into memorable brands for powerful marketing and advertising campaigns. A cryptic eight-character fare basis code, say for example, QEL14APW (“Q” is the RBD, “E” is excursion fare, “L” is low season, “14AP” is 14-day advance purchase and “W” is a weekend fare) is meaningless to a traveler, but a brand named “Freedom” can be marketed to mean something to a customer. With branded fares, airlines can offer transparency to customers based on the value of the service rendered and promote the sale of ancillary services (Vinod, 2008). Historically, airline seats have always been sold from the bottom up, from the lowest qualified fare. With branded products, an airline can sell from the middle or the top, based on a consumer’s preference for the attributes associated with a branded product Airlines employ a variety of approaches that fall into three general categories (Vinod & Moore, 2009). The original branded fare families were bundled, where services were offered based on the fare paid. Hence, if a specific brand was purchased, the customer received all the attributes associated with the branded fare. The second is the fully unbundled or à la carte pricing, where the fare only includes standard transportation, and each service option is available for an additional fee. Product unbundling allowed airlines to sell individual ancillaries that were not included in a branded fare product. This is like the LCC model promoted by carriers like Ryanair. Third, is the hybrid model where bundles of ancillary services are offered at a price lower than the combination of individual items. In addition, even with the purchase of a lower valued branded product, customers can pay for specific ancillary services such as advance seat selection. Besides network carriers, some LCCs have adopted this model as well. The promotion of service differentiation with branded fare products and sale of ancillaries are collectively referred to as “merchandising”.

4.8 Branded Fare Products

4.8.1

183

Branded Fare Family Example

Each branded product is a distinct segment based on soft qualifiers such as access to the lounge, priority baggage, premium check-in, fare refundability, advance seat selection, frequent flyer miles based on mileage or dollars spent and change fees. Each category has a range of one-way fares, with only one selling fare in each category at a given point in time. In addition, even with the purchase of lower valued branded product, customers can pay for specific ancillary services such as advance seat selection. ATPCO in conjunction with IATA have developed a service fee solution. The fee types are OB (ticketing fees), OC (optional service fees) and OA (booking fees). Table 4.19 illustrates an example of branded fare families.

4.8.2

Fare Family Attributes

While the list of attributes associated with a brand may seem endless, Table 4.20 provides a list of the most important attributes that airlines consider when creating brands.

4.8.3

Challenges

Most airlines map unique booking classes (RBDs) to brands which poses a problem. A typical airline has on average 10 fare levels and 3 brands, then 30 unique RBDs are required, which exceeds the limit of 26 used for GDS distribution. Hence, when an availability matrix of fare levels to brands is displayed in a shopping response, there could be gaps in the matrix. An option is to map fare basis codes to brands on an exception basis, which is cumbersome, and shopping and pricing systems need to be able to interpret this mapping to create a display. An option is to create an industry standard wherein the last character of the fare basic code could be used to indicate Table 4.19 Branded fare families example Basic economy Basic seat Seat assignment on departure day No frequent flyer miles accrued

Economy Comfortable seat In seat power for laptop Pre-reserved seats 50% miles accrued

Premium economy Larger seat Extra leg room Premium cuisine Wine and spirits Personal entertainment Pre-reserved seats Baggage allowance Refundable fare 100% miles accrued

Business class Window or aisle seat Priority boarding Larger seat Extra leg room Premium cuisine Wine and spirits Lounge access Baggage allowance Refundable fare 125% miles accrued

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Table 4.20 Significant attributes for creating brands Brand attribute Change fees Refundability Advance purchase and minimum stay requirements Seat assignment Baggage allowance

Upgradability Mileage accrual Lounge access Premium airport services

Description Fees/penalties for making itinerary changes Ability to refund a ticket before and during travel and associated fees/penalties Traditional fare restrictions that control fare availability based on time of purchase and duration of stay Ability to pre-reserve a standard or premium seat before travel Weight or piece allowance. Works better for piece concept, less so for weight concept due to self-service fulfillment issues Ability to upgrade to a higher class of service using cash or frequent flyer miles Ability to accrue status and/or award miles (reduced/ increased based on fare purchased) Access to host airline and/or third-party lounges (for business and first class only) Ability to use premium airport facilities for expedited checkin and priority boarding, access to free limo transfer

the brand for shopping and pricing. Unfortunately, no such standard exists today, and this approach is deployed on some airline websites as a customization by the shopping vendor. This however creates a channel disparity since only some of the fares available on the airline website are available on the GDS. To determine the composition of a branded fare product, two assumptions are required. First, customers will choose a branded fare product that maximizes their utility. Second, price consistency is required to ensure that the price of the higher brand is less than the lower brand inclusive of the prices of ancillary items included in the higher brand. The problem is challenging since we need to find the profit maximizing set of ancillary services to include into each branded fare product. With branded fares, determining the tariff structure based on a customer’s willingness to pay for a group of attributes that are associated with a branded fare product is challenging. The tariff structure should maintain fare differentials between branded products such that it eliminates dilution and promotes upsell to higher valued branded products.

4.9

Connectivity

Any book on revenue management would be incomplete without a discussion on air connectivity. Air connectivity products automate the workflow to enable the GDS connected travel agents to request and sell an airline’s product. Airlines support various air connectivity products at varying degrees of sophistication using teletype, EDIFACT and XML messaging standards to receive and respond back to requests.

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Given the large number of bookings that originate in a GDS, the importance of connectivity cannot be understated. When an airline makes an investment in O&D, the level of participation with all GDSs is of critical importance. Specifically, an airline should participate in married connections, seamless sell, seamless availability, journey and married to journey.

4.9.1

AVS/AVN

Availability status messages (AVS) are teletype messages used to advise airlines and GDSs of the availability by flight number and booking class to sell or not to sell seats on the airline. AVS messages can be inbound from other airlines and GDS to the host airline or outbound from the host airline to other airlines and GDSs. Types of messages include flight closed, request possible waitlist open, waitlist closed, flight not operating, flight re-opened. These messages can be an open/close status (AVS) or a numeric status (AVN) based on number of seats available. AVS and/or AVN messages from airlines are stored in the GDS based on the agreement. For the host airline, all flight/segment/ departure date/classes are considered open for sale unless a close message is sent (closed segments can be reopened with an open message) by the airline. All availability and sell processing occur in the GDS. The GDS sends a message via teletype after the sell activity is recorded in the GDS. The exception to this is the airline posts AVS status in a “request” status. This requires the travel agent to sell pending confirmation from the airline.

4.9.2

Basic Booking Record (BBR)

Basis booking record (BBR) is the most rudimentary level of participation of an airline in a GDS. It is a teletype sell request sent at end transaction, where the airline confirms upon receipt of the teletype message. This product allows travel agencies to book participating airlines with basic internal systems. The city pair availability request shows just the class of service available with no numeric value and the travel agent will not know if the seat is confirmed until the airline responds back after end transaction. Travel agency incentives do not apply to BBR bookings.

4.9.3

Direct Access Interactive (DAI)

GDS agents can request availability and sell against an airline’s host inventory resident on the host CRS. The GDS communicates with the host CRS through a pool of airline LNIATAs (LiNe Interchange Address Terminal Address) allocated as connections to that GDS. Transactions appear to the airline as if they are airline agent entries. This processing requires the travel agent to request access to the airline’s inventory system.

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Seamless Sell and Seamless Availability

Seamless connectivity between the host CRS and the GDSs that provide a significant percentage of total bookings is a requirement for origin and destination control. GDSs offered seamless sell transaction processing before seamless availability was offered. Seamless sell is also called Direct Connect Sell (DCS) and seamless availability is also called Direct Connect Availability (DCA).

4.9.4.1 Direct Connect Sell (DCS) Direct connect sell (DCS) is also referred to as seamless sell and is a required level of participation with a GDS for O&D control. In this scenario, availability is displayed via either AVS data stored on the GDS database or through Direct Access. Sells are made against the airline inventory system. DCS enables a GDS travel agent to sell an airline segment from the GDS as if the agent were selling directly from the host CRS. The GDS instantly and transparently checks the airline’s internal database to provide the GDS agent with last seat availability. DCS allows full usage of host CRS O&D controls to compute availability before the booking is made. Bookings on a DCS airline participant have the record locator returned after end transaction (ET) to provide the agent with a positive acknowledgment and a record of the booking in the GDS. Messages received under this method of connectivity are received in an EDIFACT standard data packet containing full POS (point of sale) information at the agent level. DCS is a requirement for O&D control. 4.9.4.2 Direct Connect Availability (DCA) Direct connect availability (DCA) is also referred to as seamless availability and is a required level of participation with a GDS for O&D control. DCA enables a GDS travel agent to receive true availability in the city pair availability (CPA) display based on the availability processing logic that is resident in the host CRS. An airline’s last seat availability is integrated into the standard GDS CPA display. A DCA sales indicator is appended to segments within the CPA display, thus differentiating the airline product. DCA does not require special formats or additional keystrokes. With DCA, a GDS subscriber can view an airline’s availability as seen by an airline reservation agent. With no special formats or additional keystrokes, an availability request is received from the GDS as if the request originated from the host CRS. The GDS “seamlessly” retrieves availability from the host CRS database to provide the GDS agent with up-to-date true last seat availability. The primary advantage of using direct connect availability (DCA) is to improve presence of what is available for sale since the airline’s availability package is being queried directly for display on the GDS. For the actual sell process, to receive a confirmed booking, the process is then the same for DCS and DCA, wherein the airline’s availability processing logic is applied for determining what can be sold. The airline’s host CRS will receive messages, under this method of connectivity, in an EDIFACT standard data packet containing full POS information at the travel

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agent level. Direct connect availability (DCA) cannot be supported without the presence of direct connect sell (DCS). DCA is a requirement for O&D control. Seamless availability extends the host CRS’s inventory management system through the GDSs to the subscriber. In addition, seamless availability interacts with low fare search products offered by the GDSs. The host CRS will receive messages, under this method of connectivity, in an EDIFACT standard data packet containing full POS information at the agency level. Hence, if the MCFA table in the host CRS supports POS based fare adjustments, inventory can be controlled at the appropriate POS level. In the absence of seamless availability agreements, availability on a GDS is determined based on AVS/AVN stored on the local GDS database. AVS and AVN controls are either leg based, or segment based. With seamless connectivity, a GDS agent can query host CRS availability directly, hence providing the capability for true last seat availability and full origin and destination controls.

4.9.5

Market Restricted Flights

To minimize the impact of availability requests that need to be processed by an airline’s host CRS, some airlines resort to posting market restricted (MR) flights to a GDS. They are referred to as “MR Flights.” When a flight is market restricted, any O&D availability request that includes the market restricted flight will be sent to the host CRS for true last availability based on the O&D inventory control logic resident on the host CRS. Conversely, if a flight is not market restricted, the availability response may be based on AVS or AVN status that is stored locally on the GDS. With a few exceptions, most airlines hosted on Amadeus pay an additional polling fee, which can vary by territory, when the availability request is sent to the airline’s inventory system for true last seat availability, in addition to the traditional segment booking fees charged by GDSs. Polling fees can be avoided if availability is returned based on AVS/NAVS, which defeats the purpose of O&D controls. Revenue management systems can recommend flights that should be placed on market restricted (MR) status based on booked load factor, days to departure and expected proportion of connecting traffic. There are some fundamental differences in the flow of availability transactions for airlines when they originate from an Amadeus travel agency versus Sabre and Travelport. Also, the impact of incremental polling fees is greatest for airlines hosted on Amadeus for reservations. The polling has implications for airlines on O&D inventory control. Figure 4.48 illustrates the availability request and response workflow for airlines that are not Amadeus carriers. The highest level of participation for connectivity between an airline and a GDS is seamless availability or direct connect availability, which is interactive. For O&D control, the availability processing logic resident in the host CRS must be invoked to determine true last seat availability. However, airlines not hosted on Amadeus are required to pay the polling fee for requests originating from an Amadeus travel agency, to query the host CRS for true last seat

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Fig. 4.48 Availability request/response for non-Amadeus carriers

Fig. 4.49 Availability request/response for Amadeus carriers

availability. The alternative is to return availability based on AVS/NAVS, which dilutes the value of O&D controls. Figure 4.49 illustrates the availability request and response workflow for airlines that are Amadeus carriers. In this scenario, since Amadeus is the receiving system (identifier 1A) for airlines hosted by Amadeus for reservations, all channels inclusive of Sabre and Travelport are subject to a polling fee that the airline must pay unless they resort to an AVS/ NAVS response for availability. Airlines use the MR flight status to reduce the polling fees paid to Amadeus. Flights are deemed MR by an airline’s revenue management system, based on the connecting traffic carried on board these flights, which requires accurate POS based

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O&D availability. The number of flights that are posted as market restricted is a function of the percentage of total reservations provided by a GDS and the underlying transaction cost structure for seamless availability. O&D revenue management must recommend a set of flights by date that must be on market restrict status based on two key factors—the bid price relative to the fares flowing over the segment and the expected number of connecting passengers forecast over the segment. The MR capability is also useful for airlines when they cutover from leg/segment controls to O&D controls with a phased migration.

4.9.6

Married Segments

Married segment logic in the airline’s reservations inventory system is a requirement for O&D control. Married segment logic allows segments to be married or “joined” as a connection from an O&D based availability display and treated as a single unit through the booking, pricing, and ticketing process. This protects market-specific inventory from unauthorized, partial cancellations. This logic ensures that inventory determined to be available on an origin and destination basis is sold as an origin and destination with the applicable fare. A common practice when seats are not available on a segment is to make a reservation for a connecting service that includes the segment followed by the subsequent cancellation of the upline or downline segment to fulfill the original request. When flight segments have been married as an origin and destination service, cancellation of any segment in the service will result in all segments within the service to be canceled. Sell from availability transactions involving connecting flight segments should be married to indicate the origin and destination sold. Similarly, when segments are sold from the availability display of a GDS, all connecting segments in the service should be sold as an origin and destination to conform with the way their value and availability levels have been calculated. Participating airlines decide which segments to marry and the control of these segments is applied with the sell request seamlessly. Up to three segments can be married and any reservation modification or partial cancellation must be authorized by the airline. Travel agencies realize that the sell is valid and legitimate, thus knowing that neither airline action nor debit is forthcoming due to inaccurate or illegal booking of flights.

4.9.7

Journey Data

Journey is a requirement for O&D control. Journey data provides additional information to the airline about the rest of the passenger itinerary. The airline can evaluate individual segments relative to the entire journey. Journey Data can be sent with Direct Connect Availability, Direct Connect Sell and Direct Access Interactive messages. Journey data enables the evaluation of a flight segment relative to a passenger’s entire journey, ensuring the product is priced based on its true value. Journey data when used in conjunction with married segment control generates incremental revenues for the airline.

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Married to Journey

Married to Journey Data is an optional feature that allows segments not sold at the same time to be married together. Seamless Availability, Seamless Sell, Married Segments and Journey Data must be activated by the airline to access this feature. Married segment control allows participating airlines to marry newly sold segments in a single sell entry. However, some airlines find this restrictive, since they are unable to marry newly sold segments to one that was previously sold. Married to journey provides the distinct capability to marry previously sold segments with a new sell request. GDS connected travel agencies realize in a world of airline partnerships and market partnerships that their booking is seen in its entirety and valued as such. The airline can see the whole picture in their evaluation to confirm the request. Journey data are a requirement for O&D control.

4.9.9

Interactive Seat Maps

Interactive seat maps allow real time display of individual seat availability on a flight by cabin, as well as easy integration into graphical seat maps. This feature minimizes calls to an airline reservations center allowing travel agents to focus on revenue producing requests.

4.9.10 Interactive Pre-reserved Seats Interactive pre-reserved seats allow for the immediate transmission of a seat assignment request and airline confirmation with a specific seat. An agent can request the seat from an airline with or without a seat map. Both the Interactive Seat Maps and Interactive Pre-Reserved Seats provide the foundation for airline merchandising programs such as Paid for Seats.

4.9.11 Point of Sale Point of sale (POS) is defined as the location at which bookings are sold. It provides information about the location of the agency. POS automatically comes with direct connect EDIFACT products like DCA and DCS. It is optional with teletype products. Extended POS data provides additional fields, and the sequence of elements is based on industry standards. Unlike an online direct airline distribution request for availability, information about the user inquiring can be immediately given when requesting availability. For seamless O&D availability requests, airlines respond with availability for booking classes based on the logic resident in the host CRS inventory. It could be based on the POS or point of commencement. If flights are currently booked and

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Region

Country

...

...

City Group

City

Travel Agency Group

...

...

... Travel Agency

Fig. 4.50 Sample POS hierarchy

active in the agency’s AAA (agent assembly area, an area in memory in which items of data are stored temporarily during the shopping or booking process), journey data will be used to determine availability for the new segment based on the entire itinerary. The POS is a many-to-one hierarchy where rules can be applied at multiple levels to control availability. The hierarchy begins with region, country, city group, city, agency group down to the ARC IATA number of the individual travel agency. An example of a POS hierarchy is shown in Fig. 4.50. If the POS is in different countries, then the unit of currency could be different. In international markets, currency fluctuation and differentiation of third, fourth, fifth and sixth freedom traffic (defined in Appendix A) are important to control traffic flow in the airline network.

4.9.12 Point of Commencement Point of commencement (POC) is offered with direct connect availability and journey data. POC uses a journey’s starting point information in addition to POS information to determine availability. The GDS definition for POC is based on the PADIS/IATA standard and it constitutes the ORG (origination) of the chronological first segment (obtained by sorting segments by departure date and time) in the PNR or shopping request, regardless of the marketing airline for that segment. However,

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from an airline perspective, this information is not always useful. O&D carriers expect the inventory system to determine the chronological first host airline segment from journey data when an interline itinerary is involved. For O&D control, airlines prefer POC to prevent abuse by travel agents and OTAs who can switch between different points of sale to benefit from more favorable seat availability.

4.10

Regaining Control of Off-tariff Fares with O&D Controls

Point of sale-based availability adjustments based on rules in the reservations inventory system can be made for off-tariff fares negotiated with multiple travel agencies at different price points for the same product. For example, consider a market where the off-tariff fare that is negotiated between two travel agencies and the airline are $600 and $650 in the same roundtrip in booking class K. Since availability with POS can be controlled at the individual ARC/IATA number of the travel agency, availability of the K booking class can be controlled based on the value of the negotiated fare. If the total bid price is greater than $600 but less than $650 for the roundtrip, the negotiated fare of $650 can be accepted while the negotiated fare of $600 can be denied. By addressing revenue dilution with precision, the wide dispersion of fare values negotiated with individual travel agencies for the same market and booking class can be effectively controlled. O&D control provides a simple method for the airline to regain control of the plethora of off-tariff fares that were negotiated by sales agents with travel agencies.

4.11

Availability versus Inventory

Inventory is part of an airline’s host CRS and maintains the booking counts. What is observed by a travel agent at a POS is different from “seats available” by booking class in the airline’s inventory system. The seats available viewed from the inventory system by an airline analyst is the unconstrained view and is quite different from what is observed at a point of sale. For a market, availability by booking class at a point of sale may be subject to business rules. In addition, available seats by booking class as observed at a point of sale during a city pair availability request is usually limited to a numeric value of seven and sometimes limited to four or nine.

4.12

Maintaining Integrity of O&D in Inventory

Circumventing inventory controls in the quest for a lower fare is performed by travel agents and savvy travelers. Travel agents run the risk of receiving agency debit memos (ADM) from airlines when a ticket has been issued that is not in compliance with the fare rules. There are several scenarios to consider. For effective origin and destination inventory control, married connection processing must be supported on the host CRS inventory control system and the

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participating GDSs. Married connection processing logic is required on the reservations system to permanently join segments sold as a connection from an origin and destination-based availability display. This logic ensures that inventory determined to be available on an origin and destination basis is sold as an origin and destination with the applicable fare. A common practice when seats are not available on a segment is to make a reservation for a connecting service that includes the segment followed by the subsequent cancellation of the upline or downline segment to fulfill the original request. When flight segments have been married as an origin and destination service, cancellation of any segment in the service will result in all segments within the service to be canceled. Sell from availability transactions involving connecting flight segments should be married to indicate the origin and destination sold. Similarly, when segments are sold from the availability display, all connecting segments in the service should be sold as an origin and destination to conform with the way their value and availability levels have been calculated. Journey control processing is also an integral component of the host CRS inventory control system. Journey control enables the prevention of agency abuse by providing the ability to include previously booked segments as part of the total itinerary for availability determination. Journey control data can be transmitted during an interactive availability or interactive sell session and follows the actionable travel segments (TVL). All the segments or a specified number of segments requested by an agent in the AAA/PNR will be included in the journey data. It does not support past date/flown segments or cancel segment processing and the number of journey data segments sent to the travel agent before and after an availability or sell query will depend on the carrier. For availability processing with journey data, inventory must have the capability to consider booked segments as part of the total itinerary for availability. It is important to note that no response is permissible for the existing journey segments. The data come in a sorted order, sorted by date and departure time. During availability and sell processing, the host CRS inventory system will automatically associate the booked journey control data segments to either end of the requested segments to extend the itinerary for a more comprehensive evaluation. Prior to evaluation, journey segments added to the requested segments will be validated as desired connections with a simplified rule set for maximum connect times and inter-airport transfers. Simplified rules will be applied for validation such as applying a maximum connection time criteria and accounting for inter-airport transfers. These transfers are strictly multi-airport only, not ARUNKs transfers. Open jaws on an itinerary show up as ARNK, pronounced “ARUNK” means “arrivals unknown”.

4.12.1 Codeshare Availability Codeshare flights are very prevalent today. The availability status for a significant number of codeshare segments continues to be controlled by AVS. AVS stop sell teletype messages from the operating carrier to the marketing carrier are frequently

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delayed before they are made effective on the marketing carrier’s reservations system. This availability latency provides opportunities for the savvy traveler to book the marketing flight for a lower fare. The savings associated with booking marketing flights on international itineraries can be quite significant. This is perfectly legal. This problem can be averted by resorting to cascading codeshare availability or the bid price exchange (for O&D carriers only) for true last seat availability.

4.12.2 Out of Sequence Bookings Bookings made out-of-sequence by travel agents can result in revenue dilution. Out of sequence bookings are made to secure a booking on a peak demand segment, with the objective to complete the itinerary at a later stage. Consider the market LondonBombay (LON-BOM) where the point of sale is LON. Since BOM-LHR in December is peak season, a travel agent may book the return segment first to secure a booking in a cheap booking class. Later the agent may add LHR-BOM, the outbound segment, in an off-peak season (September). The directional market, the true O&D directional market, which in this case is LON-BOM, indicates the market value to be used for availability determination with O&D controls. This is consistent with the fare filing process; the rules (advance purchase, minimum stay, season, etc.) are always dictated by the outbound departure date which should be adhered to when a market value is retrieved for availability determination. To offset the revenue loss, inventory must dynamically close an open booking class on the outbound segment to equalize the potential loss in revenue. This is called revenue equalization. The objective of O&D inventory controls is to ensure that SOTO (sold outside, ticketed outside) and SITI (sold inside, ticketed inside) bookings are priced the same. Leveraging journey data and applying complete trip evaluation, potential revenue loss from out-of-sequence bookings by travel agents can be corrected. Very few O&D carriers4 have implemented revenue equalization logic in their host CRS inventory control systems.

4.12.3 Integrity of O&D Controls and Mixed Classes Fares are filed by market. Associated with a fare are fare qualification rules and a booking class, also known as a revenue booking designator (RBD), which is a customer segment in the market that the airline wants to attract by filing fares. While itineraries are priced for one-ways or roundtrips, availability is determined directionally. When a roundtrip is requested, availability is determined for each direction before it is booked and priced. For airlines using O&D inventory control, mixed booking classes may result in an itinerary when the lowest fare is determined during shopping and pricing. This is

4

Discussion with Ramesh Venkat, SVP Revenue Management at Emirates in 2012.

4.12

Maintaining Integrity of O&D in Inventory

Outbound Departure Date Monday, October 5

195

Connecting Flight

Flight No 101 DFW

AUS Flight No 202

Flight No 201

Nonstop Flight

SEA

Inbound Return Date Wednesday, October 7

Fig. 4.51 A passenger itinerary example

contrary to what one might expect as a revenue management analyst. For example, if a customer books a trip from AUS-SEA connecting over DFW, O&D availability for AUS-SEA (through availability, also referred to as “thru availability”) will be a set of booking classes that are either open or closed. Hence, one would expect that the AUS-DFW flight connecting to the DFW-SEA with a different flight number will have the same booking class on each leg of the outbound itinerary. In reality, frequently, the booked itinerary may have mixed booking classes; AUS-DFW could be a Y class and DFW-SEA could be a B class. This will be reflected in the revenue accounting data, which may show that ticketed PNRs may sometimes have different RBDs on the different segments. The occurrence of mixed booking classes in an itinerary is a direct result of how airline pricing works. This negatively impacts the integrity of O&D inventory controls. Seat availability is based on a single booking class for the connecting O&D and airline pricing is influenced by the type of availability rules that must be applied. Consider the passenger itinerary shown in Fig. 4.51. When an airline deploys an O&D revenue management solution, the forecasts generated are by origin, destination, one-way itinerary and RBD. For shopping and itinerary pricing purposes, a carrier may specify no journey, journey—flow and journey—local. No journey is applicable to leg/segment carriers and journey flow and journey local are applicable for O&D carriers. Mixed classes are explained in the context of the passenger itinerary shown in Fig. 4.51.

4.12.3.1 Leg/Segment Carriers: Why Do Mixed Classes Occur on the Ticketed PNR? For airlines that operate on a leg/segment environment, a direct connect availability (DCA) city pair availability request will return booking class availability for each leg of the request. Consider the city pair availability (CPA) request and response from Austin (AUS) to Seattle (SEA) shown below. 120AUGAUSSEA 1ZZ 101 F7 Y7 B7 M7 H7 V7 Q3 2ZZ 201 F4 Y7 B4 M0 H0 V0 Q0

AUSDFW 0815A DFWSEA 1030A

0920A 73H 0 DCA 1230P 777 0 DCA

What happens next is that an agent can make a booking in Q class for the first segment (AUS-DFW) of the outbound and a B class for the second segment (DFW-SEA) of the outbound itinerary.

196 122AUGSEAAUS 1ZZ 202 F7 Y7 B7 M0 H0 V0 Q0

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SEADFW 0815A

0920A 73H 0 DCA

For the return (inbound) itinerary, the agent can book in B class. Once the booking is made it should be priced and ticketed. When an itinerary is priced, a low fare search algorithm is invoked to ensure that the lowest available (bookable) fare is made available to the customer. Assuming for the moment that all fares filed are roundtrip fares, there are different alternatives for fare construction subject to fulfilling the fare qualification rules (e.g., advance purchase, minimum stay, season, etc.) and fare combinability rules. For the outbound we have the following options: 1. A through-fare (also referred to as “thru fare”) from AUS to SEA. A thru fare is a market fare for AUS-SEA and has a fare basis code associated with it. 2. A local fare from AUS to DFW and a local fare from DFW to SEA. In this situation the fare break point is DFW and there are two fares involved—one from AUS to DFW and the second from DFW to SEA with unique fare basis codes for AUS-DFW and DFW-SEA, respectively. For the inbound we have a single option—which is a thru fare from SEA to AUS. The fare path is SEA-AUS and it is mapped to a single flight, so only the thru fare applies. For the roundtrip, subject to fulfilling fare combinability rules, the lowest fare can be one of the following: 1. Sum of locals (SOLO) on the outbound and a thru fare on the return, it is rare to see cases when the SOLO results in the same RBD on AUS-DFW and DFW-SEA is cheaper than the thru fare for the same RBD. If it does occur, it is because of irrational pricing practices influenced by the presence of LCCs. As a rule of thumb, the thru fare will always be priced lower than the SOLO. But when there is pricing pressure from LCCs in the local markets, the thru fare can be more expensive than the SOLO fares. It is more likely we could have a situation when an AUS-DFW M class and a DFW-SEA B class is cheaper than the lowest available thru fare. This assumes that the cheaper H, Q and V fares available on AUS-DFW are not available due to fare restrictions. On the ticketed PNR, the outbound will have mixed classes M/B on the respective segments. Further assuming B is the cheapest fare on the return, the ticketed PNR will have a B class on the return. 2. The thru fare on the outbound and the thru fare on the inbound (both CAT10 combinable) results in the lowest fare. If M on the outbound and B on the inbound are combinable and produces the lowest fare, then the ticketed PNR has only two booking classes, one for the outbound and one for the return, respectively. Regardless of the city pair availability display, it is likely that the itinerary can be rebooked into different booking classes influenced by low fare search. This is why

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experienced travel agents book outbound and inbound itineraries in Y by default with the knowledge that when the itinerary is priced, low fare search may rebook the customer into a different booking class that results in the lowest fare.

4.12.3.2 Implications for Leg/Segment Controls For itinerary shopping and pricing purposes, leg/segment carriers are classified as non-journey carriers. Hence if the O&D request is a connecting O&D, low fare search will find the absolute lowest priced itinerary after considering SOLO and thru fare options subject to fare combinability rules. Hence the incidence of mixed classes on ticketed PNRs will be very prevalent. For offline revenue management decision support, in a leg segment environment, mixed classes are insignificant since revenue management demand forecasts are based on flight leg or segment booking class forecasts. In addition, the revenue mix optimization model to determine inventory controls are based on optimizing the leg or segment and not the network. An important point to note is that if itineraries are classified as local versus connecting from revenue accounting data (to justify migration to O&D) based on whether there is one or more booking classes on a one-way itinerary, we would tend to underestimate the magnitude of connecting traffic. Thus, when migrating to O&D from leg/segment, classification of local versus connecting traffic should be based on schedule minimum connect times (MCT) for domestic to domestic and domestic to international connections. 4.12.3.3 O&D Revenue Management Carriers: Why Do Mixed Classes Occur on the Ticketed PNR? For airlines that operate on a O&D inventory control environment, we shall consider the same example. In this scenario, a direct connect availability (DCA) city pair availability request will return booking class availability for each leg of the request. Consider the same itinerary shown in Fig. 4.51 from Austin (AUS) to Seattle (SEA). The city pair availability request can result in the response shown below: 120AUGAUSSEA 1ZZ 101 F7 Y7 B7 M3 H1 V0 Q0 2ZZ 201 F7 Y7 B7 M3 H1 V0 Q0

AUSDFW 0815A DFWSEA 1030A

0920A 73H 0 DCA 1230P 777 0 DCA

Note that the key difference is that the same numeric availability is displayed on both legs of the O&D request. This is because the computation of availability is for the O&D, AUS-SEA, based on a market value for each RBD and the associated bid prices for the two legs AUS-DFW and DFW-SEA. What happens next is that an agent can make a tentative booking in H class for the first segment of the outbound and a H class for the second segment of the outbound itinerary. For the inbound itinerary, we shall assume it is a nonstop from SEA to AUS.

198 122AUGSEAAUS 1AA 202 F7 Y7 B7 M0 H0 V0 Q0

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AUSDFW 0815A

0920A 73H 0 DCA

For the return (inbound) itinerary, B class can be booked. For carriers on O&D inventory control, there are two distinct ways in which an itinerary can be priced based on what the airline has specified. O&D carriers are called journey carriers and the two options are journey—flow and journey—local. Journey—flow is more restrictive than journey—local. Even with a single fare, there can be a mixed class because of multiple cabins being used. A regional commuter flight with one cabin (coach) connecting to a longhaul flight with two cabins (first and coach) will result in mixed classes on the itinerary. Journey carrier—flow is based on thru availability of RBDs. Hence itineraries can be priced based on the thru fare or SOLO. If Y and B are available for the O&D (thru availability), then the itinerary can be priced based on thru fares Y and B and SOLO fares with the combinations YY, BB, YB, BY to find the lowest fare. Journey carrier—local gives the benefit of the doubt when SOLO pricing produces a lower priced itinerary and the RBDs in the SOLO pricing structure must be mixed classes unless the RBD has thru availability. Hence, for example, if only B class has thru availability, a SOLO fare BB is acceptable; otherwise, the fare should be based on mixed classes. Again, when itineraries are priced based on SOLO, fare combinability rules need to be respected. Journey-local allows cheaper fares to be priced thus the set of fares is a superset of journey-flow.

4.12.3.4 Implications for O&D Carriers For itinerary shopping and pricing purposes, O&D carriers are classified as journey carriers and they can be journey—flow or journey local. In the journey—flow scenario, if the O&D is a connecting O&D, low fare search will find the lowest priced itineraries based on thru availability only, subject to fare combinability rules. SOLO processing is always an option but only applies to RBDs that have thru availability. In the journey—local scenario, if the O&D is a connecting O&D, low fare search will find the absolute lowest priced itineraries based on SOLO and thru fare options subject to fare combinability rules. For example, if B class has thru availability, a SOLO fare BB is acceptable; otherwise, the fare should be based on mixed classes. Again, when itineraries are priced based on SOLO, fare combinability rules need to be respected. Journey-local is a superset of journey-flow itineraries. For offline revenue management decision support, in an O&D environment, the question arises: how should mixed classes be handled by revenue management decision support and inventory control? The processing is simple based on the following rule: If a connecting itinerary is priced based on SOLO, the connecting itinerary (say single connect) should be treated as two local itineraries with their respective RBDs for the two segments that made up the valid connection. Hence, all revenue management processing—demand forecasting and revenue mix optimization will only have a unique RBD. Due to the large number of mixed classes that can exist, mixed classes are normally not considered for forecasting and optimization.

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Significance of Seat Availability for Online and Offline Distribution Channels

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For inventory control purposes, the market value table, used for look-up of the approximate fare for determining seat availability with bid price controls, should have only a single booking class code associated with each market whether it is a local market or a connecting market due to the large number of mixed classes that can arise. The impact of using mixed classes in inventory control is more detrimental than mixed class processing by revenue management decision support since availability and sell transactions will be out of sync and result in price jumps and UCs (Unable to Confirm at Sell). Hence, in an O&D environment, mixed class-based classification of data are not recommended for revenue management decision support or real time inventory control. When an airline migrates to O&D controls, the following are recommended: 1. Journey—flow is activated for shopping and pricing 2. Fares are filed by market (local and connecting) for all the significant customer segments (RBDs)

4.12.3.5 Thru Fare Precedence Another availability rule sometimes used by airlines is called thru fare precedence. The objective of thru fare precedence is to use the minimum number of fare components during shopping and pricing. For example, in a nonstop market there are two fare components, one for the outbound and one for the inbound. In a single connect market, there can be two, three or four fare components for the itinerary. Thru fare precedence has the objective of pricing the itinerary with the fewest number of fare components for shopping and pricing.

4.13

Significance of Seat Availability for Online and Offline Distribution Channels

Seat availability returned from an airline’s reservations inventory system is the final product of the revenue management process to facilitate selling. It is what the customer shopping on an airline website, airline reservations agent or a travel agent sees when a request for seat availability is made. Connectivity defines the level of participation between an airline and a GDS to send and receive messages between the agency desktop and the host CRS where the airline’s seat inventory is managed. Level of connectivity between the host CRS and the GDS is an important consideration for accurate availability. The highest level of participation between an airline and a GDS is Direct Connect Availability (DCA), also called seamless availability. With direct connect availability, a travel agent that subscribes to a GDS can query the host CRS for true last seat availability. Figure 4.52 shows the evolution of connectivity for processing availability requests. Seamless availability is a requirement for carriers that manage inventory by origin and destination. Besides the city pair availability requests submitted by travel agents, the greatest demand for availability by booking class is for shopping requests. Shopping

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Teletype

Direct Access

Direct Connect

Availability Status (AVS) Numeric Availability Status (NAVS) Availability by leg or segment Not based on POS Latency can result in price jumps

Terminal emulation parsing Accurate host airline availability Agent submits host CRS native commands Supports POS and O&D availability

Interactive sell and availability True last seat availability Availability by O&D and POS Married segnent & journey High message volumes to host CRS

Fig. 4.52 Evolution of GDS connectivity for availability processing

algorithms process schedules, fares, and availability in real time to determine a set of itineraries that can be booked. In the distribution landscape, there are four primary channels of distribution as shown in Fig. 1.2. The direct channels transact directly with the airline’s host CRS and the indirect channels transact with a GDS for schedules, fares, and availability. There has been a shift from indirect to direct bookings over the past two decades, and the indirect channels contributed about 50% of worldwide bookings in 2019. Low fare efficacy is a critical component for a distribution channel to retain and enhance market share. If the lowest priced itinerary for one distribution channel is consistently superior to a competing distribution channel, it will over time lead to market share erosion as customers switch to the channel that consistently displays lower fares (Vinod, 2021d). This unparalleled transparency of schedules and fares over the Internet has propagated a bargain hunting mentality among online leisure travelers, resulting in a disproportionate growth in availability processing due to the increased shopping activity. A shopping request can return several options from which the customer or agent selects an itinerary and makes a booking. Over the past decade there has been significant growth in shopping volumes. Travel agents (GDS subscribers) who used to request up to 19 itinerary choices can now request 100–500 itinerary choices. OTAs return an even larger number of low fare search options. The look-to-book ratio is simply the ratio of shopping requests to actual bookings. While the look-tobook ratio for a traditional travel agency channel may range from 12: 1 to 20: 1, the look-to-book ratio for the OTAs (e.g., Expedia, Ctrip, Svenska Resegruppen AB, etc.) could be significantly higher and range from 300: 1 to well over 2000: 1 for certain markets. Other factors that contribute to higher look-to-book ratios are calendar shopping, comparison shopping by consumers across websites, use of robotics to “mine” websites for competitive information and the increasing sophistication of low fare search engines that return more itinerary options to customers that necessitates the determination of additional seat availability. Robotics are easy to build and are used extensively to obtain competitive information across websites. They also serve as the predominant information source for rental car and hotel companies due to the absence of a mechanism for centralized information sharing on selling rates. Calendar shopping also has a significant influence on shopping volumes. Figure 4.53 illustrates the results of a typical calendar shopping request. In this example, alternate dates are searched ±3 days of the requested departure and return dates. While a single low fare search request must evaluate numerous itineraries to arrive at

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Selected departure date: Thursday, November 29 Selected return date: Thursday, December 06 Total fare for selected itinerary: $609 Lowest fare available: $534

Departing Mon, Feb 19 Departing Tue, Feb 20 Departing Wed, Feb 21 Departing Thu, Feb 22 Departing Friday, Feb 23 Departing Sat, Feb 24 Departing Sun, Feb 25

Returning Fri, Mar 16

Returning Sat, Mar 17

Returning Sun, Mar 18

Returning Tue, Mar 20

$588

Returning Mon, Mar 19 $609

Returning Thu, Mar 22

$609

Returning Wed, Mar 21 $609

$574

$574

$534 $534

$538

$568

$534

$568

$588

$588

$588

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$588

$588

$588

$588

$574

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$609

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$538

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$538

$574

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$534

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570

570

$574

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$538

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$609

Fig. 4.53 Calendar shopping example

the lowest fare, the computational complexity for a calendar shopping with ±3 days returns 49 responses for every departure and return date combination. Alternate Cities is an option that extends the search space within a mileage radius (e.g., 100 miles) of the preferred origin and destination. For example, a shopping request from New York to Los Angeles with the Alternate Cities option will extend the search space to include all airports in the New York (e.g., JFK, LaGuardia, Newark, Islip McArthur, and Stewart-Newburg) and Los Angeles (Los Angeles, Ontario, Santa Ana, Burbank, Long Beach) metropolitan areas. This adds to the search complexity to evaluate more itineraries for availability and fare rules to determine the lowest fare.

4.13.1 Approaches to Determining Availability Seamless availability provides the highest-level of participation between an airline and a GDS. With this level of connectivity, a travel agent has access to true last seat availability of the airline. This is because every request for availability is an interactive real time query to the availability package resident on the host CRS where an airline’s seat inventory is maintained. Availability returned from a seamless availability request is perfectly accurate since it relies on the airline’s host CRS to provide the availability response based on the specific logic deployed on the host CRS. This includes the availability processing logic for determining availability combined with any operational rules that have been deployed to inhibit availability based on certain conditions. Examples are inhibiting availability for a specific class by point of sale (POS) and flight for a specific channel. Only when a seamless availability transaction is subject to a timeout restriction (normally 3 seconds) is the availability based on availability status

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(AVS) which is by leg class or segment class. AVS may be applied as a status (open/ closed) or numeric and is generated by an airline’s host CRS and distributed to Global Distribution Systems. Seamless availability also integrates with low fare search products such as Trip SearchSM and Bargain Finder MaxSM. However, the high look-to-book ratios from online channels preclude seamless availability transactions to be submitted to the host CRS for every shopping request because the legacy host CRS mainframes are capacity constrained and cannot cost effectively scale to meet the online shopping demands of the end consumer. Hence, airline distribution is going through an availability crisis that continues to grow with the growth in shopping volumes from the online channels. AVS has limitations because it is by leg class or segment class and not stored by POS. To address this, there were attempts to promote an O&D AVS status message by POS, but it was not adopted due to the sheer volume of teletype messages that would have to be generated by the airline’s host CRS and accepted by the receiver. Besides, the issue of latency associated with AVS continue to exist. With the limitations of AVS and the response time issues associated with directly querying the host CRS for every shopping request, online channels have turned to cache technology using previously stored availability results rather than query the host CRS. The cache is periodically refreshed based on an algorithm that is a function of age and usage. When an item is not found in cache, the availability response can be based on pre-stored AVS or by submitting the query to the host CRS to refresh the cache.

4.13.2 Impact of Cached Availability on the Revenue Management Value Proposition Cache is a double-edged sword. On the one hand it takes the processing load off an airline’s reservations system, but it also produces poor availability. Contrary to common belief, cached availability can negatively impact on airlines that control inventory by leg class, segment class or by O&D. The standard approach employed by most online channels is to cache availability by segment class. On the surface, for an airline that operates in a leg/segment inventory control environment, this may seem reasonable, but it is deceptive because it does not address the following conditions that may be specific to an availability request. 1. Internal mapping of booking classes to distinct geographic regions, a common feature adopted by international carriers to address market strategy, currency, and fare differences. 2. Most airlines deploy business rules associated with inventory in their host CRS that would be evaluated when availability is requested based on operational considerations. The permutations and combinations of possible availability responses based on business rule conditions cannot be captured when inventory is cached by segment class.

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Significance of Seat Availability for Online and Offline Distribution Channels

203

Today, there are over 40 airlines worldwide that constitute U.S. majors, and international flag carriers that control inventory by origin and destination. In this scenario, cached availability by segment class negates an airline’s investment in O&D revenue management since availability is not displayed based on the value of the O&D request but displayed by segment class regardless of the origin and destination requested and its associated value. When segment class availability is cached, an online channel is returning local (segment) availability for every origin and destination request. When the O&D consists of two or more connecting segments, it effectively destroys the value of O&D control established by revenue management and displays segment availability for the segments in the O&D. To address the limitations of cached availability, an improvement is to develop an availability cache by origin, destination and point of sale. While accuracy results measured in confirmations of a booking request indicates that this is a significant improvement, it also has limitations leading to availability errors. To operate in an O&D environment, a fundamental requirement for the sophisticated airlines is the ability to display accurate availability based on the value of the actual reservation request, regardless of the distribution channel. The market value of the requested service used for availability translates into the following distinct requirements. They are: 1. Point of sale control. These could be specific fares by individual POS or merely POS adjustments made to a base fare on the pre-defined POS hierarchy shown in Fig. 4.50. At issue is also the control of off-tariff fares for distinct travel agencies who have negotiated fares with varying fare amounts by market that are booked in the same booking class. 2. Fare qualification rules based on ATPCO fare filing categories to ensure that the market value used for availability determination is as close as possible to the ticketed price. While there will always be a difference, the objective is to close the gap. Fare qualifications rules are described in Table 4.18. 3. Capability to enforce business rules to display availability or inhibit availability for a specific booking class at distinct levels (e.g., system, market, flight) by channel. This is a requirement that every airline has, regardless whether they are operating by leg/segment or O&D. 4. Proration of interline itineraries to determine the value of the online segment for availability determination. This is a unique benefit of O&D control that prevents revenue dilution on interline itineraries by ensuring that availability is based on the expected revenue share of the online segments of the total itinerary. In summary, cached availability has its limitations, resulting in an incorrect response to an availability request. There are two types of availability errors that occur when the cache does not reflect the true availability resident in the host CRS. They are: Type 1 Error is an “open-wrong” error which occurs when the cached availability for a booking class is open while the class is truly closed in the host CRS. This error can result in an unable to confirm at time of sell (“UC”). A type 1 error can also result

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in the customer experiencing a price jump which implies that the minimum available fare displayed is lower when only a higher fare is truly available. Type 2 Error is a “closed-wrong” error which occurs when the cached availability for a booking class is closed while the class is truly open in the host CRS

4.13.3 Proxy Based Availability as an Alternative to Cached Availability With the growth in air shopping volumes over the past decade, the demand for availability responses from airline inventory systems has never been greater. To minimize the load on the inventory system, an availability proxy can be deployed that replicates the availability processing logic resident on the airline’s inventory system (Vinod, 2007a, 2007b). Figure 4.54 illustrates a logical architecture for deploying an availability proxy for O&D carriers that use bid price controls. Similarly, availability proxies can also be deployed for leg/segment carriers where the updates from the host CRS are seats available counts by booking class. The proxy can be deployed in the airline’s data center or in the cloud to meet demand. This example assumes that airline direct online and offline users are connected to the host CRS for availability. However, if the host CRS is a legacy system that cannot support higher transaction volumes, all requests can be directed toward the availability proxy. The benefits of an availability proxy are based on the recognition that cached availability can never store availability at a detailed level to eliminate availability errors. The availability proxy replicates the availability processing logic resident in a host CRS without submitting the availability requests directly to the host CRS. By providing a mirror of the availability processing logic in a proxy environment ensures that availability transactions can be offloaded from the host CRS without

Fig. 4.54 Availability proxy deployment scenario for O&D carriers

4.13

Significance of Seat Availability for Online and Offline Distribution Channels

205

Table 4.21 Reservations inventory versus availability proxy Inventory System of record for inventory. Maintains booking counts by flight/cabin, leg class and segment class Maintains optimal inventory control recommendations from revenue management and updates the controls for every sell and cancel Determines true last seat availability based on an airline’s specific availability processing requirements for leg/segment or O&D controls Processes sell and cancel transactions. Booking counts are updated for every sell and cancel transaction Generates AVS/AVN messages based on leg class or segment class inventory status

Availability proxy Does not maintain booking counts but relies on a combination of availability status and inventory controls (e.g., bid price) from the airline’s reservations system Typically receives the inventory controls and booking class status from a host CRS

Determines true last seat availability by replicating the availability process on the host CRS Does not process sell transactions. Hence does not update inventory counts for every sell and cancel Does not generate AVS/AVN messages based on class inventory status

losing accuracy in availability responses. The availability proxy framework is based on the core inventory processing logic resident in a host CRS, yet it is not an inventory system. Key similarities and differences between an availability proxy and inventory sub-system are summarized in Table 4.21. An availability proxy typically utilizes the same code base as the host CRS inventory system, but it is a read only proxy that can be distributed worldwide, to minimize network latency, where the demand is the greatest. With the availability proxy, a participating carrier sends real time messages on changes in inventory—bid prices or bid price curve for O&D revenue management carriers and seats available for leg/segment revenue management carriers. The availability proxy offloads the expensive, real time host CRS availability processing on to an open systems platform by replicating the availability and inventory business logic resident in the airline’s host CRS. The availability proxy relies on the host CRS to provide real time updates on changes to the inventory controls and availability status. With this approach, all availability and shopping transactions from various points of sale will be processed directly by the availability proxy for true last seat availability and do not have to rely on cached availability or AVS. The exception takes place when a time out occurs on an availability transaction at which time cache or AVS would serve as the fallback. This approach of mirroring an airline’s host CRS inventory environment has also been adopted by companies like ITA Software (now part of Google) that provide a shopping service for the consumer direct channel.

4.13.4 Distributed Availability The availability proxy of an airline’s inventory system ensures that users who demand availability the most such as OTAs or Google Flights have access to

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accurate true last seat availability in real time. The availability proxy is always more accurate than cached availability since it reflects the range of business rules resident in the airline host CRS for availability determination. Leveraging the cloud for deployment also has its unique benefits (Kavis, 2014). Some airlines may have proprietary logic for seat availability determination. In this scenario, a black-box availability proxy can be deployed with collaboration from the airline. This allows a partner carrier to deliver inventory data and rules 24/7 to achieve a higher level of accuracy in seat availability. Partner carriers can also deliver software updates based on their software release schedule. Distributed availability provides a scalable solution, usually in a private or public cloud, to support the growing demands of air shopping worldwide. With cloud computing, an airline has an endless pool of resources at its disposal. The software solution can be configured to increase or decrease the amount of compute resources required dynamically to handle peak volumes. Further, deploying the solution in the cloud at multiple locations worldwide reduces network latency.

4.14

Alliances and Partnerships

A discussion on revenue management would be incomplete without a discussion on airline alliances, joint ventures, and partnerships.

4.14.1 Origins of Codeshare The origins of codeshare can be traced back to Richard Adams Henson, a test pilot and flight school operator, who founded the commuter airline concept. He started the Hagerstown Commuter airline and provided commuter service to Washington D.C. In 1967, Henson partnered with Allegheny Airlines creating the first codeshare agreement with a major airline before the term “codeshare” was invented. The term “codeshare” was coined in 1989 by American Airlines and Qantas Airways. Their collective codeshare agreement linked several U.S. cities to Australian cities. Codeshare gained momentum in the early 1990s.

4.14.2 Origins of Global Alliances The creation of global alliances can be traced back to Northwest Airlines based in Eagan, Minnesota, close to Minneapolis-St. Paul International Airport (MSP) and KLM Royal Dutch Airlines based in Amsterdam (AMS), Netherlands, when they launched a major codeshare agreement in 1989. It was the first significant bi-lateral alliance and as part of the agreement, KLM also invested 25% in Northwest Airlines. Such investments in partner carriers are common today. At that time Northwest Airlines was the fourth largest airline in the U.S. behind American Airlines, United Airlines and Delta Air Lines. KLM was in a similar situation in Europe behind

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British Airways, Lufthansa, and Air France. Northwest and KLM had limited opportunities for growth and expansion. The world’s first Open Skies agreement was signed between the U.S. and the Netherlands in 1992. The Open Skies agreement accelerated the growth of the bi-lateral alliance. Neither Amsterdam (KLM’s hub) nor Detroit (Northwest’s primary hub besides Memphis) were major international gateways at that time. While most airlines were exploring codeshare opportunities, Northwest and KLM created a 50/50 joint venture in which they agreed to share bottom line profits and losses on their transatlantic routes. After the Open Skies agreement was signed, Northwest and KLM applied and received anti-trust immunity (ATI) from the U.S. government for all joint activities in their partnership. Airline partnerships that operate under antitrust immunity stipulate a higher degree of cooperation and can create an environment that is conducive to collusive behavior (Bilotkach, 2019). With ATI they planned to operate as a single airline. They exchanged pricing and revenue management analysts so that fares from the Netherlands to the U.S. for both carriers could be filed by Northwest and KLM analysts located in Amsterdam. Similarly, fare filings for both carriers for the U.S. point of sale were set by Northwest Airlines in Minneapolis. On both sides of the Atlantic, Northwest and KLM used Sabre’s AirPriceTM fare management system for filing joint fares. Their strategy was incredibly significant since it allowed them to grow collaboratively which they could never have accomplished individually. Airline Alliances, Open Skies and DOT approval of ATIs after careful evaluation has been a major policy success story for consumers and the airline industry (Dean & Shane, 2010). In the modern era for alliances, not all partnerships are or should be equal. Benefits should be roughly proportional to the ASMs they operate. Smaller airlines in an alliance are vital for marketing reasons, to provide the “dots-on-the-map” (Wickson, 2017) to demonstrate reach, even though opportunities for growth may be limited.

4.14.3 The Modern Alliances The first global airline alliances were formed in the 1990s. Airlines have come to grips with the power, global reach and market penetration of coordinating schedules, pricing, ticketing, share flights with codeshare agreements, through check-in, and frequent flyer program exchange. The move toward creating worldwide airline alliances is a continuation of the trend toward consolidation of the airline industry that started in the mid-1980s after the deregulation of the U.S. domestic market. The major alliances today are Star Alliance, Sky Team and One World. Alliances operate under the fundamental unwritten maxim “united we stand, divided we perish”. With the common understanding that international mergers between airlines is not attainable due to sovereignty and nationalist issues, alliances have prevailed. Alliances can produce 50% or more of the actual revenue benefits of an actual merger by code sharing of flights. Code sharing allows an airline to reach

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Virtual Airline

Value to the Alliance

Consolidated Maintenance and Engineering Back Office and Purchasing Operations Information Technology Consolidation Integrated Alliance Network, Route Planning, Scheduling

Alliance Revenue Management

Consolidates Sales, ATO, CTO Operations Seamless Customer Service

Through Check-in Frequent Flyer Exchange Codeshare

Time to Value

Fig. 4.55 Creation of a virtual airline

markets without investing in additional aircraft and to offer reciprocal benefits. This is accomplished by displaying its two-character airline code on flights operated by an alliance partner on global distribution systems accessed by travel agents to book flights. When an alliance is established between two or more carriers after approval from their respective governments, the carriers have antitrust immunity thereby permitting them to effectively manage their international operations as one carrier. This includes sharing capacity, sell tickets on each other’s flights, share sensitive marketing data, and set prices on routes where they now compete. However, antitrust immunity for setting prices and sharing capacity is not always granted in which case the incremental revenue benefits of an alliance will not be fully achieved. There is a general belief in the airline industry that partnership in an alliance is a fundamental requirement to first, survive; second, remain competitive and third, capture market share. Airline alliances also have a significant impact on airline pricing and revenue management (Boyd, 1998; Shumsky, 2006; Vinod, 1999, 2005d). Coordinated pricing and revenue management decisions drive incremental revenues for the alliance. Figure 4.55 illustrates the potential evolution of partners in an alliance to create a true virtual airline to achieve the maximum benefit. Once an alliance has been established, the Information Technology (IT) teams bear the burden of streamlining inter-operability (de Pommes, 1998) of disparate airline IT platforms to realize the full potential of the commercial agreements. The prioritized list of IT initiatives on an accelerated timeline will include codeshare, ticketing, seamless customer handling with through check-in, and transparency of frequent flyer programs.

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4.14.4 Codeshare Flights Code sharing is an important part of building an airline alliance. Codeshare flights are flight segments that are operated by one carrier but marketed for sale by one or more carriers. Codeshare flight management has been prevalent in the airline industry even before the formation of alliances. However, the extent of code sharing is an order of magnitude higher between airlines in an alliance. Code sharing provides an airline with the capability to increase their level of service beyond limits established with bilateral agreements. Amenities and services, such as earning miles, through check-in, and premium status are supported in code sharing arrangements. Code sharing extends the reach of the operating carrier without investing in operating costs such as additional aircraft, crew, and ground resources. Consider the hypothetical interline network shown in Fig. 4.56. American Airlines (AA) is the operating carrier from PHX-DFW and DFW-LHR while British Airways (BA) is the operating carrier from LHR to FRA. AA is the marketing carrier from LHR to FRA with flight number AA 6120. Similarly, BA is the marketing carrier from PHX to DFW (BA 3111) and from DFW to LHR (BA 3050). Hence, AA is sharing its operating flight with BA from PHX to DFW and DFW to LHR allows BA to put its own flight number on the same physical aircraft and sell its seats. Government regulations require the disclosure of the operating carrier. There can also be multiple marketing flights associated with an operating flight. The other advantage of codeshare flights it that it allows a carrier to show connecting services as online versus interline. Customers prefer online connections and online connections are also favored in most GDS and online displays. DEI is a general term in the Standard Schedule Information Manual (SSIM) standing for “Data Element Identifier.” Specific data elements are referred to as DEI 9 or DEI 101. Operating flight to marketing flight mapping is maintained in the DEI.

4.14.4.1 Types of Codeshare The three most prevalent codeshare agreements between an operating carrier and a marketing carrier are hard blocks, soft blocks, and free sale. With hard blocks, the operating carrier allocates a fixed number of seats on a flight for the marketing carrier who has purchased the seats. Unsold inventory cannot be returned to the operating carrier. The marketing carrier will be responsible for managing the purchased seats. For the operating carrier, the hard block of seats will be treated as seats sold in the inventory system. With soft blocks a block of seats provided by the operating carrier to the marketing carrier with a release date for unsold inventory, typically 7–14 days

PHX

AA 111 BA 3111

DFW

Fig. 4.56 Codeshare flights

AA 50 BA 3050

LHR

BA 120 AA 6120

FRA

Operating Carrier Flight Numbers Marketing Carrier Flight Numbers

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before departure. This creates some level of exposure for the operating carrier when seats are released from the marketing carrier before departure. Free sale is the most common form of codeshare. This is a “sell and report” environment where each codeshare partner is responsible for the control of inventory for flights that they operate.

4.14.4.2 Codeshare Availability for Free Sale For free sell codeshare, there are various options for codeshare availability Standard codeshare availability is based on AVS. The operating carrier sends a stop sell message to the marketing carrier. Until a stop sell message is received by the marketing carrier, the marketing carrier can continue to sell. True Availability is specific to situations when both carriers are hosted on the same reservations system. In this scenario, the availability request can go to each inventory partition of the operating carriers for true last seat availability. Cascading Availability is an inefficient process wherein the availability request is cascaded to the operating carrier for true last seat availability. This is also referred to as seamless codeshare availability. Cascading availability relies on the DEI record which provides the flight number mapping between the operating carrier and the marketing carrier. Bid Price Exchange is the capability for the operating carrier to share the current bid price in real time for a flight leg/date with the marketing carrier. This is required for carriers using O&D control. Bid price exchange guarantees metal-neutral availability. Bid price exchange is preferred over cascading since an O&D involving codeshare flights can be evaluated based on the value of the reservation and the actual bid prices on the associated legs of the itinerary. As airline alliances have matured, there is renewed interest in joint ventures and partnerships. Examples are Emirates/Qantas and LATAM Airlines Group. Cross border joint ventures with major equity investments are becoming common. Delta Air Lines, for example, has forged joint ventures with Virgin Atlantic, Aero Mexico, Virgin Australia and LATAM Airlines Group. The equity stake ensures long-term commitments from the partnership.

4.15

Alliance Revenue Management

Collaboration between airlines in an alliance has a significant impact on revenue management, influenced by the availability of seats and optimal traffic flow across the alliance. Complexity in alliance revenue management stems from the fact that several disparate elements of an airline’s pricing and revenue management process all need to come together in the context of the alliance. Fine tuning the alliance revenue management process between airlines should also surpass cultural boundaries and alignment of core objectives. Various models based on static and dynamic mechanisms have been proposed (Boyd, 1998; Vinod, 1999, 2005d; Wright, Groenevelt, & Shumsky, 2010). The primary drivers of alliance revenue management are:

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Alliance Revenue Management

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Alliance Pricing Alliance pricing, the ability to file joint fares between partner airlines, is not always feasible because of anti-trust laws enacted by many countries to prevent price collusion and perceived protection for the flagship carrier from unfair foreign competition. Anti-trust immunity is required to file joint fares. Centralized Versus Decentralized Revenue Management Centralized revenue management for an alliance is conceptual, in practice revenue management will always be decentralized. Hence, the demand for enhanced interoperability between airline revenue management and inventory control systems. In a decentralized environment, ideally all airlines in an alliance should be on the same revenue management system, using either leg/segment controls or O&D controls. O&D is the preferred approach since the true O&D request of a passenger may span multiple segments operated by several carriers. However, this can never be realized across all carriers in an alliance since there are small and large carriers in an alliance with different inventory control requirements and investment priorities. Besides forecasting demand for an airline’s markets, demand forecasts for codeshare flights are also required to facilitate inventory control. Overbooking Policy A common overbooking policy for denied boarding compensation is required across alliance partners. Special Prorate Agreements Bilateral codeshare agreements with provisions for special prorate agreements need to be negotiated that can provide mutual benefit for the partners in the alliance. Class Mapping A booking class hierarchy is unique to an individual airline. Equivalent booking class mapping is required in a free sale codeshare agreement. The booking classes from the marketing carrier are mapped to the booking classes of the operating carrier, for determining availability. Inventory Control Ideally a common currency is required for inventory control in an alliance. However, airlines are hosted on different reservations systems and there will be differences in how seat inventory is controlled. Besides, some airlines may be on leg/segment controls and others on O&D controls. To minimize discrepancy in availability on codeshare flights, partner airlines need to invest in cascading codeshare availability at a minimum. For O&D carriers, a bid price exchange from the operating carrier to the marketing carrier is required for true last seat availability. If multiple airlines in an alliance are hosted on the same reservations system, married segment controls between alliance partners are feasible.

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Central Reservations Office (CRO) Displays At airline call centers, ATOs and CTOs there is an opportunity to influence displays like city pair availability and itineraries returned from shopping requests by sorting the display order from lowest to highest bid price to redirect demand from high load factor flights to low load factor flights. GDS Connectivity Connectivity plays a critical role in alliance revenue management. Direct connect (seamless) availability, direct connect (seamless) sell, point of commencement and journey controls are essential. The GDSs also offer features to maximize traffic flow in an alliance and build brand loyalty. Preferential displays for alliance partners were first introduced by the GDSs in 1998. Integrating partner displays maximizes traffic flow within the alliance. A travel agent can filter availability displays to include only partners in the alliance. Commissions to Promote Traffic Flow Airlines frequently provide variable commissions to travel agents between partners to promote route traffic flow. This requires coordination between the partner airlines to ensure availability for the route being promoted with higher commissions. Seamless Customer Experience Other requirements for a seamless customer experience include through check-in, synchronization of seat assignments and seat maps across alliance partners, PNR exchange, frequent flyer accruals and redemptions. Alliance Flight Scheduling To develop an integrated schedule for an alliance, data sharing is required to develop a demand model for the alliance. This requires the ability to access and aggregate the full PNR (all segments across all alliance partners from all channels) data along with industry data during the schedule creation process. Coordinating flights within an alliance can result in higher connecting passenger flow traffic through a hub. This in turn can negatively impact availability for local demand resulting in spill. This has a negative revenue impact on the domestic partner in the alliance. With hub-to-hub flights operated by both partners in an alliance, the primary goal is to maximize the number of connections to provide the maximum number of connecting opportunities for services between spoke cities. In Fig. 4.57, flight number 1 is more desirable than flight number 2 since it offers more connecting opportunities. Since hub-to-hub flight schedules may be fixed owing to time zone differences, partner airlines should develop the spoke city schedules around the hub-to-hub flights to maximize connecting opportunities and traffic flow.

4.16

What Revenue Management Capability Does My Airline Need? Spoke Cities

Airline A Hub

Airline B Hub

213 Spoke Cities

Time

Fl i

Fli

gh

gh

tN

um

be

r1

tN

um

be

r2

Hub-to-Hub (trunk) Flights

Fig. 4.57 Schedule retiming to maximize connection opportunities

Recovery from Irregular Operations Membership in an alliance means access to a larger pool of aircraft and frequency of flights. This implies that the ability of an alliance network to recover from irregular operations is theoretically better than that of a single airline. This will require training, coordination and communications between flight operations control centers.

4.16

What Revenue Management Capability Does My Airline Need?

A question that many airline executives have asked before they arrived at a decision is “what revenue management capability does my airline need?” A careful evaluation must be conducted to determine the specific pricing and revenue management requirements for an airline (Vinod, 2003). A decision to implement a revenue management process is dictated by the strategic initiatives that are underway. When a decision is made to make an investment, the airline must carefully evaluate the business requirements and the strategic directive of the airline to chart out a path that is most appropriate to fulfill the business needs and generate maximum payback on investment. Rarely is the decision purely a trade-off between a leg/segment solution versus an origin and destination yield management solution. The business decision is based primarily on the airline network and dynamics of the route network to effectively manage seat inventory in a competitive environment (Vinod, 2003).

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With O&D inventory controls, an airline has the option to place specific flights on a market restricted status on a GDS for selective polling. These flights are controlled by origin and destination of the city pair display. For example. if a specific flight from JFK-LHR is placed on market restricted status, and the city pair display includes a connection from LHR to Rome (FCO), then the entire origin and destination JFK-LHR-FCO will be on O&D control and availability will be displayed based on the value of the requested origin and destination, JFK-LHRFCO. In this scenario, an airline can place a small percentage of future flight departures on market restricted status for O&D control. The remaining flights will continue to be controlled using the traditional nested inventory controls. The rationale is to limit distribution costs, which are typically transaction based, while still receiving most of the revenue benefits. The revenue benefit is achieved based on the fundamental premise that 20% of the flights that are placed on market restrict status will contribute 80% of the incremental revenues. For example, a specific future flight departure from LHR-BKK which may only be projecting a low expected load factor will not be placed on market restrict status due to the absence of the revenue opportunity. Except for U.S. majors, international airlines prefer to exercise the market-restricted option offered by GDSs rather than participate in 100% polling. SAS was one of the first European carriers to post market-restricted flights to Amadeus when they migrated to O&D inventory control in the 1990s. The size of an airline measured in flight departures per day, revenue passenger miles or available seat miles has little or no bearing on the type of revenue management solution capabilities that an airline should seek. The capabilities required are typically dictated by the demographics of the traffic flow in the airline route network, connecting traffic as a percentage of total onboard traffic, traffic rights, interline traffic and point of sale. The notion that O&D is only applicable for large airlines is untrue. Nearly every airline in the world would benefit from an O&D solution because it allows an airline to control the number of flights that will be polled from a GDS and controlled by O&D. However, the total cost of ownership (TCO) must be clearly weighed against the benefits before a decision is made. An airline may choose to control only a small percentage or a large percentage of flights by O&D. The number of flights posted as market restricted is always limited due to two reasons: first, it helps keep the GDS transaction costs under control and second, the market restricted flights should contribute a large percentage (say 80%) of the incremental revenues. The remaining flights will continue to be controlled with the traditional nested inventory controls. In addition to TCO, key business factors that support migrating to an O&D environment are: Percentage of Connecting Traffic Any airline that has at least 15% connecting traffic will derive incremental benefits from O&D controls. A vast majority of the major U.S. airlines such as American Airlines, United Airlines and Delta Air Lines have a hub and spoke network with multiple hubs for connections and the traffic composition on each leg is 30% local and 70% connecting traffic. Even low-cost carrier Southwest Airlines, a point-to-

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What Revenue Management Capability Does My Airline Need?

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point carrier, has 25% connecting traffic. Several prominent international flag carriers such as British Airways, Swiss International Airlines, Singapore Airlines and Cathay Pacific have a single major domestic hub for connecting traffic (domestic to international, international to domestic and international to international). Lufthansa has hubs in Frankfurt and Munich. Alitalia has hubs in Rome and Milan. Air France-KLM by virtue of their merger has hubs at Charles de Gaulle and Amsterdam. Carriers like Gulf Air used to operate multiple hubs (Bahrain, Abu Dhabi, Doha, and Muscat) in the Middle East before the ownership structure changed. In addition, an airline with rights to fifth freedom and access to sixth freedom flights (Appendix A) will benefit from origin and destination revenue management. Cathay Pacific’s hub in Hong Kong’s Chek Lap Kok airport and Singapore Airlines’s Changi Airport are examples where connecting traffic only constitutes sixth freedom traffic. Proportion of Interline Traffic An airline network with significant interline traffic can potentially benefit from O&D controls if agency abuse can be controlled. This is because the availability calculation at the time of the reservation request can be based on the revenue share of the requested service. For example, consider a service with two segments and the second segment is an interline carrier. Now, availability on the first segment can be based on the revenue share of the total service (segment 1 and 2). The revenue share for the first segment can be determined in several ways based on bilateral agreements such as straight rate prorate, square root of the miles, fixed amount, etc. Alternate prorate rules include the straight rate prorate based on IATA cost weighted mileage factors (CWMF), bilateral prorate agreements and existing codeshare agreements. There are implications on availability processing for each approach. Inventory Control by Point of Sale Point of sale capability enables a reservations system to display availability to a travel agent based on the location at which the sale occurs. Control of inventory by point of sale generates incremental revenues beyond the baseline revenue. Point of sale can generally be defined at various levels, ranging from a wide geographic region spanning countries, individual country, regions within a country, city, groups of travel agencies and the individual travel agency. This level of point-of-sale qualification is provided in a seamless availability transaction to make this control possible. Off-tariff Control Off-tariff or unpublished fares are very prevalent in the Asia Pacific region. A large percentage of bookings made in Japan, Korea, Hong Kong, Taiwan, Thailand, etc. are bookings made with off-tariff fares. Off-tariff fares are negotiated fares between an airline and a specific entity such as a travel agent or a tour operator and not available for public distribution. While off-tariff fares are low yield fares, they are frequently booked in a surrogate booking class regardless of the value of the fare. With O&D, off-tariff availability can be controlled based on the ARC/IATA number

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of the travel agency and the associated value of the fare when an airline subscribes to the seamless availability and seamless sell services offered by the GDS. All GDSs support a negotiated fares database and airlines use ATPCO (Airline Tariff Publishing Company) as the primary source for distribution of these fares for private viewership. Booking Class Realignment Realignment of booking classes must be addressed early in the process of committing to origin and destination yield management. The objective of booking class realignment is to evaluate the current fare structure by market and ensure consistency in the mapping of fare basis codes to the corresponding booking class codes. The number of booking classes required to satisfy the business requirements is usually dictated by several factors such as market segmentation, competitive fares offered, host CRS nesting structure, class codes used to status the GDS, consolidator traffic and point of sale.

4.16.1 Phased Adoption To ensure a smooth transition and change management process, a phased approach is the ideal way to introduce O&D to an airline. O&D revenue management must determine the network optimal bid prices for O&D control as well as compute the traditional nested inventory controls that must be posted back to the host CRS. When O&D revenue management is first deployed, it is imperative that all flights be managed with the traditional nested inventory controls. This can be accomplished very easily by not posting any flights as market restricted. In the absence of marketrestricted flights, no flights will be polled from a GDS for O&D control. Over time, a few flights can be put on market restrict status and this percentage can be gradually increased over time as acceptance of the new business process grows within the airline. Investing in O&D revenue management also requires a reinvention of the business process to control flights from a more global perspective. While focus on controlling flights on an individual basis might remain unchanged, several new tasks may have to be incorporated into the job function of a yield management analyst such as: 1. Monitoring pricing actions and its subsequent impact on demand forecasts 2. Forecasting and adjusting demand at the origin, destination, class level 3. Reviewing and filing fares by point of sale for preferential availability at different parts of the world 4. Reviewing and adjusting the market restricted flight list for O&D control 5. Actions based on competitive closings in key markets With O&D inventory controls there is a renewed interest in coordinating pricing and yield management decisions. Pricing actions impact the demand forecasts

4.17

Revenue Management for Groups

217

generated by revenue management. Based on the calibrated price elasticity of demand, the demand must be modified to reflect the new price. By coordinating pricing actions with the yield management process enables the pricing analyst to proactively evaluate alternate pricing scenarios that can be evaluated by the yield management revenue analyst to determine the upside or downside potential before implementing the pricing actions. An airline’s revenue is largely determined by its combination of flight schedule, fare structure, and yield management practices. Other factors, such as frequent flyer program, in-flight comfort, and safety also affect corporate revenues, but the traveling public often does not perceive a difference between airlines. Because each of the flight scheduling, pricing, and yield management functions constitute major planning processes, many airlines routinely make these marketing decisions independently. Each group makes reasonable business decisions, but the independent decision-making silos may yield less than expected financial performance and the airline misses the opportunity to maximize profit if the plans are not synchronized. Without the input of the flight-scheduling group, the pricing group may choose to lower fares in response to soft demand. However, the flight-scheduling group may be planning to reduce capacity on the same routes for the same reason Likewise, if the revenue management group reduces the availability of the discount classes because demand was high for this flight last year while the pricing group decides to raise discount fares 10% for the same reasons, the airline will not realize the expected profit. To ensure seamless decision making across organizational boundaries, the importance of role-based workflows cannot be overlooked. When considering the use of O&D revenue management an airline should evaluate the short-term and long-term financial return on the investment. For a typical airline, most of the yield management resources are focused on the shortterm goal of maximizing revenue with a given schedule and fare structure. However, yield management planning for the long-term often has significant financial impact as well. Long-term yield management planning must consider the entire marketing plan, encompassing flight schedules, fares, group, and off-tariff sales practices, distribution methods, passenger handling policies, frequent flyer program, and advertising. Because so many functions are involved, long-term planning must be conducted at the corporate level.

4.17

Revenue Management for Groups

Group traffic is an integral component of the total traffic in an airline network. Sales agents have targets by region and are responsible for negotiating and capturing group traffic for the airline. While group bookings may fill up empty seats on flights, they also have the potential to displace higher paying individual passengers and diluting revenues. There is a common belief that group yields are traditionally lower than the individual passengers on a flight since groups negotiate fares months in advance of the actual departure rate. However, by applying the core concept of the group

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indifference curve for individual passengers, this does not have to be the case (Yuen, 2002, 2003). Negotiating group fares and managing group traffic in an airline network is of critical importance to ensure that network revenues are preserved (Busuttil, 1995; Yuen, 1998). In revenue management terminology, a “group” constitutes a large party that negotiates a special fare with the airline for a block of seats. The definition of group size is procedural and dictated by the business process for managing group requests at an airline. Typically, it is greater than or equal to nine. Group traffic, expressed as a percentage of total flown traffic in an airline network, varies significantly by geographic region and even by country. Mature markets like the U.S. sees fewer group requests while less mature markets like China and Asia see more group activity. Procedures vary widely from airline to airline to accept group requests. While there is not a universally accepted best practice on group management, there are some common traits between airlines. Limited autonomy is usually given to the field sales offices to accept and reject group requests since a centralized group desk does not want to be inundated with requests for space. Once the sales agent accepts a group, the group desk logs the pertinent information about the group. Expert systems have also been used to automate responses to standard group requests. Air France reported that 70 percent of group requests were automated with a rules-based expert system (Descroix, 1989). Typically, when the group request exceeds certain thresholds such as group size, booked load factor and days to departure, the request is sent to the airline group desk where the information on the group is inserted into the group queue and sent to revenue management analysts for evaluation. This is an iterative process between the group desk and revenue management analysts. The group desk communicates with the field and with revenue management and serves as the arbitrator for every large group request.

4.17.1 Types of Groups Groups fall into four distinct categories by increasing order of complexity for a sales agent or airline group desk to respond to a request for a quote. They are ad hoc groups, planned or series groups, convention groups and allotments. Ad hoc group requests are the most common and represent a request by a group for a price quote, one-way or roundtrip, for a specific origin and destination. Planned or series groups request seats on future flight departures over a date range. The requests may originate from cruise lines, tour operators, travel agencies and airline group sales managers. For example, a tour operator may request 40 seats on the 8:00 am departure from Dallas to Miami on Wednesdays and Saturdays to transport customers who have purchased tickets for a cruise. Convention and special event groups negotiate group rates to fly customers into a city where the convention is held. In this case, customers may originate from several

4.17

Revenue Management for Groups

219

origin points to a single destination and depart after the event to their respective destinations. Allotments are requests from tour operators who require a block of seats from an airline. When an allotment is secured, the airline marks the allotment inventory as sold. Tour operators also negotiate the rate for the allotment as well as the release date before departure for unsold seats back to the airline.

4.17.2 Group Evaluation Groups negotiate fares and generally book several weeks and even months in advance. The negotiated fare that is quoted depends on several factors such as current bookings on the requested service for the requested departure dates, size of the group, expected group attrition rate and number of complementary seats requested (Vinod, 2013b). A group evaluator decision support capability can facilitate in deciding whether to accept or reject the group booking request. This is accomplished by quoting a minimum acceptable fare for the requested group after taking into consideration the expected displacement cost of individual passengers, projected group attrition forecast, the size of the group, the peripheral profit (revenue minus actual cost) offered and the number of complimentary seats requested by the group. The minimum acceptable fare or break-even fare for the group must be determined after considering upline and downline network effects. The group indifference curve is shown in Fig. 4.58. The indifference curve illustrates the characteristics of profitable and unprofitable groups. If a group pays above the break-even fare, it represents incremental profit. The incremental profit multiplied by the total seats requested may be used as a more effective yardstick to reward incentives to sales managers than seats sold.

Group Indifference Curve Minimum Acceptable Fare

$700 $600 $500 $400 $300 $200

$100 $0

0

10

20

30

40

Group Size

Fig. 4.58 Group acceptance indifference curve

50

60

70

220

4

Revenue Management of the Base Fare

The minimum acceptable fare can be calculated for both leg/segment and O&D inventory control environments, though in an O&D environment, calculation of the minimum acceptable fare considers the first, second and third order effects in the network.

4.17.3 Allotment Planning The allotment planning process has its origins in Japan due to the unique relationship between travel agents and airlines. In Japan, a few large travel agencies control over 80% of all airline bookings in Japan. Allotment inventory is also prevalent with tour operators in Europe who negotiate allotments with airlines. These travel agents negotiate rates with carriers for a significant amount of the total capacity months ahead of the flight departure dates. Negotiations typically occur twice a year to concur with the summer schedule and the winter schedule. As part of the negotiation process, the block space on the requested routes and the offtariff fare are determined. Once negotiated, the block space on the various itineraries is assigned to the travel partners. On the host airline reservations system, dummy PNRs are created to decrement the block space from total scheduled inventory. Once the space has been decremented, the airline is only responsible for inventory control of the remaining residual inventory on future flights. As part of the agreement with the travel partners, any unsold inventory from the block space is returned to the airline at an agreed upon period (e.g., 7-days, 14-days) before departure. This results in exposure for the airline, especially if the unsold inventory that is returned before the departure date is greater than the forecast of remaining demand. This risk can be mitigated by implementing a business process to establish allotments to maximize revenues for the planning period by simultaneously allocating capacity for higher valued individual reservation requests and recommending allotment inventory allocations for the major travel agencies and tour operators. To determine the optimal allocation of block space to the travel partners, an allotment planning decision support system should consider pertinent factors such as expected attrition, value of the agency, agency productivity expressed as bookings delivered against past allotments and requested fare to maximize revenues for the planning period. Once the optimal allocation for the travel partners has been finalized, these allocations must be used to determine the residual inventory for revenue management controls for individual customer booking requests. Allotment planning decision support is a strategic tool to ensure that revenue dilution is minimized by allocating and protecting the required number of seats to higher valued individual passengers who will book closer to the flight departure date. Tour operators that manage negotiated allotments with airlines utilize a block space group PNR capability on a GDS where the allotment is established, and individual names are associated PNRs with the master Block Space Group (BSG) PNR.

4.17

Revenue Management for Groups

221

4.17.4 Group Demand Forecasting Group demand forecasts and group attrition rates play an important role in revenue management and support the planning process (Wood, 1992). Forecasting group demand and using an estimate of remaining group demand is rarely used directly in the revenue mix optimization process. This is because group demand is treated as a group block (BSG PNR) on the reservations system and managed independently from individual booking requests. For the group block that has been established in the reservations system, an estimate of group attrition is required to determine the effective capacity for transient (individual demand). Overbooking limits are calculated based on the effective capacity available for individual demand. For example, consider a cabin with 100 seats and a group block that has been set aside for 40 seats. If the estimate of group attrition is 25%, then the effective group size is 40 × (100% – 25%) ¼ 30 seats. The overbooking limit for transient (individual) demand is calculated based on an effective capacity of 100 – 30 ¼ 70 seats. Group demand is an intermittent process and can be modeled as a random sum of random variables. Y ¼ X1 þ X2 þ X3 þ . . . þ Xn where the Xi values are identically distributed by some function p(x) and n is a Poisson random variable distributed by p(n). Hence, the total group demand for a flight is a function of the number of groups (n) and the average group size (X). Using nested geometric transforms (Giffin, 1975); the first two moments are given by: Expected Value ðY Þ ¼ μn μx Variance σ 2Y ¼ μ2x σ 2n þ μn σ 2x Group demand can be modeled as a compound Poisson process when n follows the Poisson distribution and the xi are identically distributed by some distribution f (x). If n is Poisson distributed, then Mean ¼ Variance ¼ γ where γ is the mean occurrence in a finite time interval. Now, if we assume that the xi are geometrically distributed, then Mean ¼

1 ¼G p

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4

Variance ¼

Revenue Management of the Base Fare

ð 1 – pÞ p2

This is the Compound Poisson process. By the method of matching moments, the parameters of the distribution γ and G can be determined. as follows. Let μY and σ 2Y represent the sample mean and variance. Hence, μY ¼ μn μx ¼ σ 2Y ¼

γ ¼ γG p

γ ð 2 – pÞ ¼ γGð2G – 1Þ ¼ μY ð2G – 1Þ p2

This simplifies to G¼

( ) σ2 1 1þ Y μY 2 γ¼

μY G

Hence, two specific elements of group demand that are forecast are the number of groups and the average size per group. The forecast of group demand for future flight departure dates also requires historical data, creation dates for group PNRs, and changes to the group PNR over time. Additional information that should be factored into creating a group demand forecast are the type of group, seasonality and point of sale (point of booking). Group demand forecasts are typically not included in the revenue mix network optimization model since it is difficult to forecast the distribution of fares for the group passengers on a flight. Hence, a capacity adjustment is made to account for the net group demand. When group demand materializes in the host CRS after the negotiation process, the attrition rate is applied against it and capacity is reduced.

4.17.5 Group Attrition Estimation Estimating group attrition is required to determine the expected number of seats that can be made available for individual passengers. Unlike show up rates for individual passengers that are estimated by segment class or leg class, group attrition rate estimation requires group passenger name record (PNR) data. Figure 4.59 illustrates the profile of an individual group, with changes to the size of the group over time and the number of individual names that are registered against the group PNR. The estimation of individual group attrition rates at specific predeparture points in time requires access to group passenger name record (PNR) data. Causal factors that may contribute to group retention rates are the group/sub-group type, season,

Revenue Management for Groups

Group Block and Group Names Activity

4.17

223

Individual Group Profile

70

Changes to group block over time

60 50 40 30

Registered group names against the group Passenger Name Record

20 10 0 0

6

13

20

27

34 41

48 55

62

76

90

104

Days to Departure

Fig. 4.59 Group attrition

booking region, origin city/region, destination city/region, day of week, GDS source, single or multiple deposit amounts, payment status, travel agency productivity, number of named individuals against a group block, ticketing time limits for the group and days to flight departure. Group retention rates can be predicted with a reasonable degree of accuracy with a logistic regression model or Classification and Regression Trees (CART), wherein the dependent variable (the attrition rate) is based on the known values of the independent (causal) variables. Attrition rates will typically improve closer to departure as the group firms up. In situations when a group PNR is split, the parent child hierarchy needs to be preserved to determine group and agent productivity. Estimating the group attrition rate by group type by reading day is required to determine the remaining effective capacity for individual passengers. For example, consider a flight with a capacity of 100 seats. A group of size 50 was negotiated by sales. If the group attrition rate is estimated to be 30%, then the effective group size is 50 × (100% – 30%) ¼ 35. The remaining capacity for accepting individual passengers is 100 – 35 ¼ 65 seats. The overbooking limit for the flight is computed based on an effective capacity of 65.

4.17.6 Group Performance Measurement An audit trail of group acceptance is required to determine the effectiveness of sales agents that negotiate group deals. For each group request that is negotiated, it is important to capture the minimum acceptable fare for the specific group request, the size of the group and the negotiated fare. With this information, we can compute the

224

4

Revenue Management of the Base Fare

intrinsic value of the group reservation, based on the assumption that the minimum acceptable fare is the break-even or indifference fare. Value of Group ¼ ½Group Negotiated Fare þ Ancillary Profit – Minimum Acceptable Fare]Þ x Group Size Net Value of Group ¼ ½Group Negotiated Fare þ Ancillary Profit – Minimum Acceptable Fare] x Effective Group Size where Ancillary Profit ¼ Ancillary Revenue – Ancillary Cost Effective Group Size ¼ Forecast Group Size x ð1 – Attrition RateÞ This performance metric can be used not only to determine the effectiveness of an airline’s group management program, but also to modify the incentive programs for sales. The sales organization is normally measured on seats sold and not on how profitable the group sale was to the airline. An alternative is to create a graded commission structure based on sales points per month. Sales points are simply the sum of the group value measures; the net profit negotiated by the sales agent per month. Hence, for N groups negotiated in a pre-defined time period, Sales Points ¼

N X

Net Value of Groupi

i¼1

Such an approach, albeit radical from a salesperson’s point of view, will benefit the airline and sales agents who drive incremental revenues with each sale. It is also a mechanism to provide a budget for a salesperson to manage against.

4.18

Role of Revenue Integrity

Revenue integrity (RI) plays a key role after the booking has been made. It addresses the gap between booked revenue and actual revenue by detecting and cancelling problem bookings to resell the inventory. While flight firming is treated as a separate function within an airline, it complements airline pricing and revenue management. Paul Rose was the first to introduce the concept and value of revenue integrity when he was working at British Airways. Subsequently, he established the Airline Revenue Integrity Group (ARIG), a non-profit group focused on revenue leakage. Revenue integrity started as a manual process in the 1980s when analysts reviewed flights at a fixed number of days before departure to identify spurious

4.19

Impact of Revenue Management in Travel and Other Industries

225

bookings such as duplicate bookings, fictitious names, duplicate segment, etc. This was especially important on high load factor flights, to avoid re-opening booking classes that were closed for sale, after fraudulent bookings were identified and cancelled. To reduce costs, manual retrieval, and examination of PNRs by analysts was replaced by robotics in the 1990s with companies like Lanyon Ltd and Airline Automation Inc. The later generation revenue integrity systems like Calidris are less dependent on the host CRS, where booking and ticketed data are stored in an operational data store in real time and supports analysis of transaction data. While e-ticketing has helped to reduce no-shows, a revenue integrity platform can further improve on the no-show rates. Another area addressed by RI is identifying travel agents who circumvent ticketing time limits (TTL) which are enforced as a pricing rule. Travel agents can create churn by cancelling and rebooking the same passenger multiple times on the same flight to extend the ticketing time limits. The role of RI in the future is changing from reactive to proactive. For example, from the operational data store, airlines can identify high valued passengers and determine steps they could take to enhance customer satisfaction. Uniformity of ticketing time limits can be enforced across airlines in an alliance. Static ticketing time limits can be dynamic based on revenue management metrics such as booked load factor. On high booked load factor flights, tighter TTL can be enforced.

4.19

Impact of Revenue Management in Travel and Other Industries

The success of yield management at American was made possible with advances in inventory control on the host CRS (Vinod, 1990); where American’s schedules and fares were stored. The DINAMO system used leg class nested controls with segment limits. When this was determined to be inadequate to support flow traffic over American’s hub and spoke network, which had a mix of 30% local and 70% connecting traffic, the first O&D yield management system was deployed in 1987 with virtual nesting (Smith, 1986; Smith et al., 1992). Virtual nesting produced an additional two percent in incremental revenues over leg-based controls. The inventory module in Sabre’s PSS was enhanced to support virtual nesting. Optimal mapping of itineraries to buckets based on value produced an additional 0.4% in revenues (Vinod, 1989). American calculated that the systematic use of yield management enabled the company to generate $1.4 billion in incremental revenue between 1989 and 1991, whereas AMR’s (the parent company) profits were $892 million over the same period (Smith et al., 1992). Bob Crandall has stated on numerous occasions at public forums and employee conferences that “yield management is the single most important technological development in transportation management since airline deregulation”. In 1992, Robert Crandall, Chairman and CEO of AMR (formerly American’s parent company) estimated that “yield (revenue) management has generated $1.4 billion in incremental revenue in the last three years by creating a pricing structure that responds to demand on a flight-by-flight basis” (Smith et al., 1992). By 1998 Tom Cook, President of Sabre Decision Technologies, had increased the estimated

226

4

Revenue Management of the Base Fare

impact to “almost $1 billion in annual incremental revenue” (Cook, 1998). Revenue management is one of several applications that was classified as strategic operations research (Bell, Anderson, & Kaiser, 2003) since it creates a sustainable competitive advantage. For his many contributions that revolutionized the airline industry, shortly before his retirement in 1998 after 25 years at American Airlines, Scott McCartney of the Wall Street Journal stated that Robert Crandall was “the man who changed the way the world flies” (McCartney, 1998). Today revenue management is widely acknowledged to contribute between 3 and 8% in incremental revenues based on business process maturity and level of sophistication of the inventory controls. It is a mature and mission critical function (Donovan, 2005; Vinod, 2016a) with significant growth opportunities for management. Yield management has also found wide acceptance in travel verticals that have perishable inventory (Lieberman, 2010). They include hotels, rental car, cruise lines, passenger rail and ferry lines. Amtrak adopted yield management in 1988 with market limit sales and market inhibit sales inventory controls in the Arrow reservations system. SNCF (the French National Railroad) was also an early adopter of yield management in 1994 with virtual nesting controls (Daudel & Vialle, 1989; Mitev, 2004) on the Socrate (Système Offrant à la Clientèle des Réservations d’Affaires et de Tourisme en Europe) rail reservations system, which was based on the Sabre reservations system. Marriott (Hanks, Cross, & Noland, 1992) and Hilton were early adopters of ratebased controls with length of stay restrictions. Holiday Inn Worldwide’s (HIW) Michael A. Leven, President of the Americas Division, standardized two-way connectivity for all North American properties (Leven, 1994) for a single image of room inventory prior to deployment of hurdle rates on Holidex (HIW’s host CRS) to control availability by rate and length of stay in 1993 (deCardenas, Hobt, Vinod, 1992, Smith, 1994; Vinod, 2004). Bill Marriott Jr., a proponent of revenue management, was quoted (Cross, 1997), stating, Revenue management has contributed millions to the bottom line, and it has educated our people to manage their business more effectively. When you focus on the bottom line, your company grows. Bill Marriott, Jr

Attribute-based room pricing and inventory control is the latest paradigm for generating incremental revenues in the lodging industry (Vinod, 2019). This approach allows customers to select property and room attributes to purchase what they precisely need for their stay. For example, if a property had a larger king room, it could be priced higher than a standard king room. Attribute-based room pricing supports a restructuring of the legacy inflexible rate structures to a more dynamic environment with infinite price points based on the room attributes selected by the customer during shopping. The concept of making an offer from infinite SKU’s (stock keeping unit) generates incremental revenues for the hotel and simultaneously improves customer satisfaction since a customer pays for what matters most for the stay (Sorrels, 2018a).

4.19

Impact of Revenue Management in Travel and Other Industries

227

Social media has had an impact on influencing inventory controls for hotels. User generated content in the form of traveler reviews influence customer purchase behaviors. Higher user ratings command a higher average daily rate (ADR), and hence used as input to determine the best available rates (BAR) and hurdle rates. There is a strong correlation between user ratings and revenue per available room (REVPAR) (Anderson, 2002) with a one-point lift in aggregate review score equating to a 1.4% increase in REVPAR. Since the early 1990s the car rental industry has adopted revenue management to control vehicle and rate inventory by pick-up date and length of rental at a rental location based on the value of the reservation request. Early adopters include Hertz (Carroll & Grimes, 1995), Avis and Budget. National Car Rental attributes yield management to saving the company from bankruptcy in 1995 (Geraghty & Johnson, 1997). The rental car revenue management model also has a fleet distribution component that makes specific recommendations for pre-positioning of vehicles based on demand between individual rental locations in a geographic area. Among cruise lines, early adopters in the early 1990s were Admiral, Carnival, and Royal Caribbean (Fisher & Mongalo, 1993) to manage the sale of reservation requests and upsell to higher valued cabins based on value. Cruise lines also exercise gateway control and optimal air planning (Lieberman & Dieck, 2002). In addition, the application of revenue management for restaurants (Bertsimas & Shioda, 2003; Kimes, Barrash, & Alexander, 1999; Kimes, Wirtz, & Noone, 2002) and golf courses (Kimes & Schruben, 2002) have been proposed. In the retail industry, pricing decision support has gained acceptance to manage the life cycle of a product which goes through three phases: category pricing, promotions, and markdown pricing (Vinod, 2005a). Retail revenue management is part of the merchandise planning workflow to exercise demand and supply levers to meet the quarterly key performance indicators (KPI). Academic focus was introduced in the late 1980s (Belobaba, 1987, 1989), the 1990s (Chatwin, 1993; Kärcher, 1996; Lee, 1990; Weatherford, 1991; Weatherford & Bodily, 1992; Williamson, 1992) and 2000s (Li, 2008). With the growing interest in this discipline, several books have appeared that focus on both the practice and the theory (Cross, 1997; Gallego & Topaloglu, 2019; Garrow, 2016; Ingold, McMahonBeattie, & Yeoman, 2000; Ng, 2008; Phillips, 2005; Talluri & van Ryzin, 2004; Yeoman & McMahon-Beattie, 2011).

5

Low-Cost Carriers and Impacts on Revenue Management

5.1

Introduction

Low-Cost Carriers (LCC) came into existence in the mid-1990s with aggressive pricing and marketing tactics that resulted in renewed competition and threatened the very existence of major U.S. airlines, global pure play, and international flag carriers (McDonald, 2006). The largest LCCs are Ryanair, easyJet, Air Asia Group, Lion Air Group, and GOL Transportes Aéreos. Spirit Airlines is the largest LCC in North America. Today’s LCCs have viable and sustainable business models, accounting for more than 30% of industry capacity (Fig. 5.1). While traditional network carriers have reduced in size, LCCs have grown at the rate of 10–20% per year. With their rapid growth, they now compete directly with traditional U.S. carriers on more than 90% of their route networks. LCCs are known for having a simple, no frills product offering for the price conscious customer— high frequency, point-to-point operation to secondary airports, low operating costs with a homogeneous fleet, high fleet utilization and consumer direct distribution. With their distinct lower-cost-per-seat-mile advantage, airlines such as AirAsia, Spirit Airlines, GOL Transportes Aéreos, easyJet, Frontier Airlines, JetBlue, Ryanair, and Southwest Airlines, are proactively setting the fare structure in major markets and rapidly altering customers’ valuation of air travel. Most LCCs also have a cost per seat mile that is significantly lower than network carriers. But, before LCCs entered the scene, the largest airline in the world attempted to change the industry with simplified pricing in 1992.

# The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 B. Vinod, The Evolution of Yield Management in the Airline Industry, Management for Professionals, https://doi.org/10.1007/978-3-030-70424-7_5

229

230

5

Low-Cost Carriers and Impacts on Revenue Management LCC Market Share

35.0%

Market Share (%)

30.0% 25.0% 20.0% 15.0% 10.0% 5.0% 0.0% 2004

2006

2008

2010

2012

2014

2016

2018

2020

Year

Fig. 5.1 Growth in worldwide market share of LCCs. Source: www.statista.com

5.2

Value Pricing

American introduced value pricing on April 9, 1992 under CEO Robert Crandall. It was the first attempt at value pricing in the airline industry (McDowell, 1992). American’s initiative was to transition to a simplified fare structure that had grown in complexity since airline deregulation (Michael & Silk, 1994). Instead of selling seats at several prices, American offered only four types of fares at lower price points: first class, regular coach, and two discount coach fares which had 7-day and 21-day advance purchase restrictions. The simplified fare structure was a radical departure from the inherent complexity of airline fares with their discounts and restrictions. Crandall emphasized the “simplicity, equity and value” of the new fare structure that everyone could understand. With the new fare structure, each market would have only four distinct fares in the hierarchy; first class, regular coach, discounted coach with a 7-day advance purchase requirement and a discounted coach with a 21-day advance purchase requirement. The fares were mileage based and they were cheaper than prevailing fares in the market. First class fares dropped 20–50%. The regular coach fares were the AAnytime Fares with no restrictions and were cheaper than prevailing full coach fares by at least 38%. There were two PlanAAhead Fares, one with the 21-day advance purchase and Saturday night stay requirement and the second with a 7-day advance purchase requirement with also significantly cheaper than prevailing fares. There were some cases where the lowest discounted fare was more expensive. The lower discounted fares were nonrefundable but could be re-issued for a fee. With the new simplified fares, American also eliminated all special discounts for corporate fares, conventions, and the military which resulted in an 86% reduction of the prevailing fares. The lower fare structure made flying accessible to a segment of the population that had never flown before.

5.3 Low-Cost Carrier Dynamics

231

Acknowledged by industry analysts as ahead of its time, the value pricing initiative by American, however, collapsed when major competitors who had initially matched American’s tariff structure quickly retracted with a wave of restricted, discounted fares which prompted American’s CEO Robert Crandall to remark famously in an interview with Time magazine “this industry is always in the grip of its dumbest competitors” (Castro & Crandall, 1992). At the annual employee conference (“President’s Conference”) in Dallas, Crandall concluded his remarks on value pricing with the statement “you are only as smart as your dumbest competitor.” Further, Northwest Airlines and Continental Airlines filed a lawsuit against American Airlines with the claim that American aimed to monopolize the markets with their revamped value pricing fare structure. After a 4-week trial in July/August 1993, the jurors came to a swift conclusion that American Airlines did not try to drive weaker competitors out of the market with predatory pricing the previous year with value pricing (Crandall, 1993; Jones, 1993). After the verdict, Robert Crandall made a statement that his airline was merely trying to compete, and that Northwest Airlines and Continental Airlines were “hoping to accomplish in the courtroom what they couldn’t accomplish in the marketplace.”

5.3

Low-Cost Carrier Dynamics

With the arrival of the LCCs, network carriers must counter the competitive threat, and this frequently leads to irrational pricing. Network carriers usually price connecting flights cheaper than nonstops. A LCC in the same nonstop market forces network carriers to offer cheaper fares to compete, resulting in the connecting fares being more expensive. Some of the LCCs also offer cheap competitive first class service and network carriers are forced to match these fares, resulting in the full fare coach being more expensive than first class (McCartney, 2004). LCCs have grown in strength with a worldwide total seat capacity share of 15.7% in 2006 to 31% in 2019 (Mazareanu, 2020). LCC economics are quite different from full-service carriers. They offer passengers lower ticket prices and charge additional fees for services such as checked baggage, meals and typically operate from secondary airports that have lower landing fees. Tickets issues are typically one-way fares, they do not offer interlines and all bookings are consumer direct since most LCCs do not participate in a GDS, so travel agents do not have access to most LCC schedules, fares, and availability. Traditional revenue management has always focused on what the airline is willing to accept as opposed to what a customer was willing to pay. Low-cost carriers practice a unique brand of revenue management that is based on a customer’s willingness to pay (Boyd & Kallesen, 2004; Dunleavy & Westermann, 2005; Gorin & Belobaba, 2004; McGill & van Ryzin, 1999). Traits that make the LCC revenue management model different from the full-service network carriers are summarized below.

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Table 5.1 Traditional tariff structure for a market Booking class Y B M H V Z Q

Fare ($) 599 499 349 299 269 229 149

Advance purchase – – 3AP 7AP 14AP 21AP 21AP

Minimum stay – – 1 3 3 7 7

Cancellation penalty (%) – 25 50 50 50 50 100

Table 5.2 Pure restriction free tariff structure for a market Booking class Y B M H V Z Q

Fare ($) 279 249 209 179 159 139 109

Advance purchase – – – – – – –

Minimum stay – – – – – – –

Cancellation penalty (%) 100 100 100 100 100 100 100

1. One-way fares, fewer fare rules and restrictions. Typical restrictions imposed are refund penalties and ticketing constraints (instant purchase or 72-h time limit). The fare is the primary determinant of the customer segment. 2. In a restriction free or lightly restricted tariff world, the assumption that demand for each booking class is independent is no longer true. This has an impact on the demand forecasting and discount allocation models 3. Overbooking is very conservative. No-show rates are lower because of ticketing requirements and most fares are nonrefundable. This is augmented by the absence of interline agreements to book denied boarding passengers on alternate carriers. 4. There are fewer booking classes and smaller fare differences between booking classes. 5. Most LCCs do not participate in a GDS. All bookings are consumer direct, on the airline website, thereby avoiding GDS fees. In addition, there is also little or no barrier for entry of a new LCC, which can change the landscape of competitive fares overnight. Tables 5.1, 5.2, and 5.3 explain the differences between traditional tariffs and restriction free tariffs with a simplified example. Table 5.1 illustrates a traditional tariff structure for a market. Each fare product is independent and governed by a set of restrictions. The booking classes define unique customer segments by applying restrictions. In this scenario, traditional revenue management forecasts demand by booking class and determines the allocations against available capacity.

5.4 Inventory Control with Restriction Free Fares

233

Table 5.3 Hybrid tariff structure for a market Booking class Y M M H V Z Q R W

Fare ($) 279 249 209 189 169 149 129 109 99

Advance purchase – – – 7AP 7AP 7AP 21AP 21AP 21AP

Minimum stay – – – 3 3 3 7 7 7

Cancellation penalty (%) 25 25 25 50 50 50 100 100 100

Table 5.2 illustrates the original LCC model with a pure restriction-free tariff structure for a market. The fare products are not independent and are totally unrestricted. The booking classes have a lower fare differential. While multiple fares are filed, they have identical restrictions and hence promotes 100% sell down to the lowest available booking class. Hence, the fare is the only determinant of the market segment and the demand for a booking class is therefore dependent or contingent on the lower class being closed. In this scenario, traditional revenue management must forecast dependent demand based on the booking class that is open. Active monitoring and closure of selling fare at the right time is required to promote sell up to a higher fare Table 5.3 illustrates the hybrid LCC model for a market. The fare products (booking classes) with identical restrictions are not independent and form part of a “class group”. Multiple fares are filed with identical restrictions, which promotes nearly 100% sell down since multiple classes with different restrictions may be open for sale. Revenue management must forecast dependent demand and active monitoring and closure of selling fare at the right time is required to promote sell up to a higher fare. To describe dependent demands, Boyd and Kallesen (2004) use the term “priceable” and “yieldable” to differentiate customer types that downsell (priceable) versus those that book in higher classes despite the availability of lower valued booking classes with more restrictions (yieldable). Not all carriers classified as LCCs practice pure restriction free pricing. For example, Southwest Airlines is an LCC with a vastly simplified tariff structure with one-way fares compared to network carriers but does not practice restriction free pricing. Southwest Airlines defines a limited number of unique fare products for each market that they serve.

5.4

Inventory Control with Restriction Free Fares

Figure 5.2 illustrates how inventory is controlled with pure restriction free pricing. In this scenario an airline would file multiple fares with the same identical minimal fare restrictions. Hence, the probability of selling a fare higher in the hierarchy is

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Low-Cost Carriers and Impacts on Revenue Management

Class Fare Y

Reservations Holding

Authorization 100 Limit

Selling Fare

B M H

V Z Q

Booking Class Closing Condition

Q

$59 Flight is open

Z

$79 45 days before departure or after 12 bookings

V

$99 40 days before departure or after 18 bookings

H

$129 30 days before departure or after 40 bookings

M

$159 16 days before departure or after 58 bookings

B

$179 10 days before departure or after 70 bookings

Y

$209 2 days before departure

0 0

6 13 20

27

34 41 48 55

62

76

90

104

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Fig. 5.2 Restriction free pricing inventory controls

contingent on the immediate lower fare being closed for sale. When a flight is first detailed in the reservations system, there is a single one-way fare in the market. Over time, as bookings build up for the flight, the fares are progressively increased until flight departure. With the absence of restrictions, the real time reservations inventory control environment for both LCCs and network carriers that file restriction free tariffs must be very dynamic to ensure that target bookings are being achieved for each booking class before the selling class is closed to promote upsell to the next higher valued class. For carriers operating point-to-point without significant connecting traffic, the inventory control environment can be based on authorization levels and time limits based on days to departure as described above. The first generation of LCC revenue management systems relied on rules-based heuristics to control seat inventory. In the absence of advance purchase time limits, the offline revenue management planning process must provide frequent updates to the real time reservations inventory control environment based on the pace of bookings observed in the reservations system. A time-of-day specific limit may also be required for airlines that want to override the selling fare at a specific departure time (e.g., 9:00 am versus 2:00 pm) for competitive reasons. Hence, in a restriction free pricing environment, the fare restrictions are eliminated and rely on the revenue management system to control availability of the filed fares by setting inventory controls that influences the required sell up behavior (Vinod, 2005c, 2008).

5.5

Coexistence of Inventory Controls for Network Carriers

As the LCCs matured from start-ups to established carriers, the network carriers could not afford to ignore them. To target specific customer segments, network carriers need to compete with LCCs with restriction free pricing tariffs on domestic markets and compete with international carriers with the traditional restricted tariffs. Frequently, a hybrid inventory control structure may be more appropriate for network carriers that files both restriction free tariffs and regular tariffs based on

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Fig. 5.3 Sample network with restricted and unrestricted tariffs

competitive need. In this scenario, it is required to discern the restriction free tariffs from the traditional tariffs for forecasting demand and subsequent determination of discount allocation controls to control these customer segments independently. Consider the hypothetical network shown in Fig. 5.3. An international carrier like LATAM must compete on the Sao Paulo (GRU) to Rio de Janeiro (GIG) local market against LCCs and simultaneously compete with restricted tariffs against international carriers for the GRU-JFK and GIG-JFK international markets. With the coexistence model, the tariff structure targets both schedule sensitive and price sensitive customer segments depending on the market. Typically, restriction free tariffs are prevalent on domestic markets while restricted tariffs exist in international markets. To compete and protect market share, network carriers must file restriction free tariffs in local markets to compete against the LCCs and continue to file restricted fares in international nonstop and connecting markets. Therefore, a customer who books a local flight may be on a restriction free tariff while a connecting passenger to an international destination on the same flight will be on a restricted tariff. Two types of distinct inventory control structures coexist in the same cabin for local traffic control and connecting international traffic control. For inventory control purposes, an invisible curtain exists in the same cabin on the local domestic flight. Figure 5.4 illustrates the coexistence of inventory controls on the GRU-GIG flight leg. The business and leisure booking classes for the domestic market GRU-GIG are based on assumed values of the booking class hierarchy. For the international market GRU-JFK, the business and leisure booking classes based on value are shown. The nested inventory control structure for domestic and international are two separate parallel structures (“the electronic curtain”) that is nested into the highest valued booking class for international, Y.

5.6

Impact of LCC Pricing on Revenue Management

Restriction free pricing introduces some unique challenges for traditional revenue management, which assumes that the booking classes are independent. With tickets being sold at various price points for the same product, the assumption of independent demand for each booking class is no longer true. The demand is dependent or conditional on the selling fare being offered which is the only determinant of demand for the segment.

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Y

Leisure Business

Local Leg: GRU-GIG Co-existence of Restriction Free and Restricted fares

Domestic POS

International POS

M, V, Z

Y, B, H, K

L, N, P

M

B

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H

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K

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R, T, U

Fig. 5.4 Coexistence of inventory controls

Over the past two decades, much of the research and advancements in demand forecasting and optimization have focused on restriction free fares, lightly restricted fares, and restricted fares as they coexist in a network (Ratliff & Weatherford, 2009). With the proliferation of less restricted fare products, the rapid adoption of customer choice models and the validity of using historical data for forecasting is often debated (Zeni, 2007). While LCCs may practice only restriction free pricing, network carriers need both regular restricted fares in international markets and restriction free fares in markets where they compete with the LCCs. Revenue management systems had to adapt and forecast dependent demand based on which flights and booking classes are currently open and closed for sale. Active monitoring and closure of selling fare at the right time is required to promote sell up to the higher fare and maximize flight revenues.

5.6.1

Multi-class and Multi-class Multi-flight models

Restriction free pricing led to the realization that revenue management needs to look beyond the basic single booking class model where demand between booking classes is independent of each other. As a result, there are three classes of problems in increasing order of sophistication (and complexity) to estimate untruncated demand from bookings, forecast, and optimize inventory controls. They are the

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single class, multi-class, and multi-class multi-flight models (Ratliff, Rao, Narayan, & Yellepeddi, 2008). The single class models are typically used when demand between booking classes is independent. These models have the disadvantage of not considering demand interaction between booking classes to address upsell, downsell, and recapture. The multi-class models are required with restriction free tariffs that result in dependent demand for fare products. These models can address demand interaction between booking classes on the same flight. With restriction free pricing, booking class availability determines the booking class that will be sold. The multi-class model considers the joint demand across booking classes but does not model cross flight recapture. Multi-class multi-flight models are the most general since they explicitly consider upsell and downsell on the same flight and cross-flight recapture. However, these models require more complex methods to handle the demand interactions. Traditional O&D service class demand untruncation methods applied to multi-class models lead to double counting; this is because the true demand on a customer’s first choice flight during closed periods is counted as spill and subsequent recapture on alternate flights with the same airline is counted as observed traffic. O&D service class bookings always consist of a mix of first choice demand, recapture from the same carrier and capture from other carriers. Discrete choice models such as the multinomial logit (MNL) have been used (Gallego, Li, & Ratliff, 2009) to calibrate the underlying demand models. With this approach untruncated demand can be estimated from bookings. While the MNL is difficult to calibrate and requires extensive data, estimates of upsell, downsell and recapture are a byproduct of the MNL calibration.

5.6.2

Impact on Revenue Management Models: Demand Forecasting

With the multi-class demand unconstraining methods, a multivariate censored regression model with EM to estimate passenger demand was proposed by McGill (1995). The explanatory variables in the regression model were used to estimate passenger demand subject to nested booking limits. Figure 5.5 illustrates the price sensitivity of demand by time to departure. To forecast demand an approach is to estimate the price-demand relationship and fit a causal model for forecasting conditional demand based on historical price points, observed bookings and inventory controls. Buy-up and buy-down behavior can be estimated using the proximity of fares by days to departure (Mishra & Viswanathan, 2003). Q-Forecasting techniques (Boyd, Kambour, & Tama, 2001; Hopperstad, Zerbib, & Belobaba, 2006), are based on bookings, availability, fares by booking class and price elasticity to estimate the equivalent lowest nested class or Q class, and uses a negative exponential function to model the median upsell from Q to the higher classes.

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$89

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Fig. 5.5 Price sensitivity

A popular approach to estimate upsell because of its simplicity is the FRAT5 curve (Hopperstad, 2004). An alternative, more flexible and versatile approach to estimate same-flight upsell and cross-flight recapture is the logit demand model, which is widely used in scheduling applications to measure schedule profitability. The simplest of these models is the multinomial logit (MNL) model which is easy to calibrate and more accurate than the FRAT5 curves because of flight and class specific considerations. There are more advanced logit models such as the nested logit (Gallego et al., 2009; Mishra, Ratliff & Vinod, 2005; Parker, 2004) and Generalized Extreme Value (GEV) which produce more accurate estimates at a significant cost of compute cycles. With dependent demand, one approach is to split the booking class demand into the “priceable” and “yieldable” components based on historical bookings and availability data (Boyd & Kallesen, 2004). The “yieldable” segment is defined as bookings in all but the lowest open class. Untruncation is based on the single class Expected Maximization (EM) method. The priceable segment assumes pure restriction free pricing and works with upsell rates from FRAT5 curves. Booking classes that are lightly restricted use both techniques in tandem. Revenue improvements based on improvements in forecast accuracy have been reported in the Passenger Origin-Destination Simulator (PODS) studies (Belobaba & Hopperstad, 2004; Fiig, Isler, Hopperstad, & Cleaz-Savoyen, 2005). The multi-class multi-flight models consider recapture. A demand and recapture estimation model using regression was developed (Ja, Rao, & Chandler, 2001) to solve for unknown values of uncensored demand and recapture. A multivariate demand model (Stefanescu, deMiguel, Fridgeirsdottir, & Zenios, 2004) considers correlation across flights, fare products and time periods over the booking horizon and estimates are derived with the EM algorithm. The EM discrete choice model approach with estimates of the no-fly purchase probabilities (Talluri & van Ryzin, 2004; Vulcano, van Ryzin, & Chaar, 2010) exploits the EM algorithm to iteratively estimate the demand arrival rates and the MNL discrete choice model parameters. A

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computationally efficient method for estimating demand, spill and recapture (Ratliff et al., 2008; Vulcano, van Ryzin, & Ratliff, 2012) from historical demand and availability data for unconstraining demand across multiple flights and classes avoids double counting demand by jointly estimating spill on closed flights and recapture on to open flights.

5.6.3

Impact on Revenue Management Models: Optimization

Conditional demand versions of expected marginal seat revenue (EMSR) with pricedemand curves can be used with available seats to determine the optimal timing and price to be offered, subject to business constraints encapsulated as rules. This has a revenue advantage over a manual rules-based environment practiced by many LCCs. When the demand between booking classes is independent, neither the fare nor the demand is modified. With dependent demands, models based on unadjusted demand and/or fares are less effective in a restriction free pricing environment (Belobaba, 2008; Gallego et al., 2009; Weatherford & Ratliff, 2010). A common approach to address dependent demands is the fare adjustment approach (Belobaba & Weatherford, 1996) that incorporates sell up probabilities directly into the standard EMSRB formulation. A more versatile approach is the Displacement Adjusted Virtual Nesting—Marginal Revenue (DAVN-MR) (Fiig et al., 2005) model where dependent demands are transformed into approximate independent demands which can be solved using traditional methods. This method incorporates upsell into the optimizer with fare and demand adjustments and provides a reasonable approach to incorporating dependent demands into a two-stage decomposition method where the deterministic network results from the first stage are used as input to a second stage stochastic leg optimizer. Cross flight recapture effects are not considered in this model. Simulation studies have shown that the choice based EMSR heuristic (Gallego et al., 2009) produces results close to the optimal solution for leg/segment carriers. Choice-based deterministic network linear program (CDLP) optimization models (Chaneton & Vulcano, 2011; Gallego, Iyengar, Phillips, & Dubey, 2004; Liu & van Ryzin, 2006) propose an efficient formulation for same flight upsells, while recapture required explicit enumeration of open/close status across all services in a market. As a result, large scale problems are difficult to solve without using column generation. A related method is the choice-based network optimization (Talluri & van Ryzin, 2004) model that used dynamic programming at a flight leg level to determine the optimal policy. This method is computationally intensive and relies on knowing the time dependent arrival rates, which is not practical from a deployment perspective. Fiig, Isler, Hopperstad, and Belobaba (2010) proposed a fare adjustment transformation that changes fares and demand of a general, discrete choice model to an equivalent independent demand model. With this approach, once the dependent demand was restated as independent demand, the standard revenue mix optimization

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models can be used to determine inventory controls. Further, these fare adjustments were extended to branded fare families (Fiig, Isler, Hopperstad, & Olsen, 2012). A choice-based network model that considers same flight upsell and cross flight recapture (Gallego, Ratliff, & Shebalov, 2015) for dependent demand was developed as a General Attraction Model (GAM) that permits varying the re-attraction rate separately for each item in a choice set. The optimization model is a deterministic formulation that optimally handles dependent demands with upsell and recapture. Dependent demand also has an impact on the revenue opportunity model (ROM) discussed in Chap. 4 with independent demands. What is required is a solution that includes all three types of pricing environments: traditional, pure restriction free pricing and lightly restricted pricing. This led to the development of a choice-based ROM model (Liang, Ratliff, & Remenyi, 2017; Ratliff, Manjot, & Guntreddy, 2013) that models same flight upsell and cross flight recapture. The choice-based ROM also introduces two new metrics, the net spill rate (NSR) and the recapture fare ratio (RFR). Net Spill Rate =

Spill – Recapture Demand

and Recapture Fare Ratio =

Average Value of Recaptured Demand Average Value of Spilled Demand

The Recapture Fare Ratio (RFR) is used to determine the fare upsell amount. For dependent demands, the ROM model based on a sales-based linear programming formulation (Gallego et al., 2015) explicitly models upsell and cross-flight recapture.

6

Offer Management

6.1

Origins of Merchandising

Can airlines become retailers? Is transforming network and regional full-service airlines from service providers to retailers the answer to the volatile airline industry? It is an aspirational goal and requires a well thought out merchandising strategy. After years of cost reductions to stay competitive, intelligent retailing is a fundamental business model change. The trend started with the LCCs in the 1990s, when they started offering bargain basement fares and started charging additional fees for extra services. An independent survey conducted by Leflein Associates in January 2006 (Alexander, 2006) showed that travelers would pay for extra perks such as more frequent flyer miles, more overhead bin space, and the ability to sit in a childfree section of the aircraft. A comprehensive program to market and sell ancillary products can be a daunting task. Selling ancillary products requires a deep understanding of the customer base to price, market, sell and fulfill the product which in turn requires co-ordination from several organizations in an airline such as sales, marketing, pricing, revenue management, E-Commerce, in-flight services, airports, loyalty programs, call centers, ticketing, finance, mobile applications, and revenue accounting. Customer insights are critical to increase the probability that a customer or customer segment will purchase the ancillaries offered. Network carriers first started selling ancillaries as miscellaneous sales requests. Each ancillary product, such as pre-reserved seats, lounge access or onboard meals, was in its own category and charged to a separate account. Revenue accounting was tasked with aggregating the sales by category. It was not possible to associate the ancillary sale with markets or flights or customers. When it became clear that customers were willing to pay for air ancillary products, ATPCO, working with the airlines, developed the global Electronic Miscellaneous Document (EMD) standards to track ancillary sales to markets, flights and customers using industry standards. EMD is not merchandising, but a key enabler to support merchandising with a consistent paperless way to process ancillary sales across all distribution # The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 B. Vinod, The Evolution of Yield Management in the Airline Industry, Management for Professionals, https://doi.org/10.1007/978-3-030-70424-7_6

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channels, attribution, and to eliminate fraud with the electronic audit trail. Besides EMD, ATPCO and IATA have worked with the airlines since late 2000s and established a fee filing standard, called optional services, to facilitate the creation, issue, fulfillment, and accounting of ancillaries. ATPCO OC is an IATA-defined code for fare related optional services or fees applicable to the validating carrier and they are not interlineable. The OC filing does not standardize the product and service offerings of airlines but provides an efficient approach for carriers to fulfill and track their offerings to travel agents, corporations, and consumers. EMD and OC filings are fundamental building blocks for intelligent retailing. Revenue accounting is also a key enabler for merchandising. Data from GDSs, airline websites and agency sales reports (ASR) are captured by airline revenue accounting systems to support revenue recognition and gauge the performance of each distribution channel. Revenue recognition is an essential step in an airline’s end-to-end merchandising strategy. Revenue accounting and revenue recognition are back-office functions but are required to track and attribute ancillary revenues faster and accurately, so an effective merchandising strategy can be developed. Transparency into sales will allow airlines to fine tune their product and service offerings rapidly by customer segment. In the fast-paced world of airline retailing, the objective is near real time revenue accounting and revenue recognition, to deliver data efficiently to support quantitative decision-making leveraging business intelligence tools. As the industry transitions into retailing, airlines want to know the ancillaries customers want and what they are willing to pay. Travelers in turn are empowered to control their travel experience. There is an opportunity for retailing to be a positive experience for both airlines and customers. Initial efforts at merchandising were not well received by travelers since features included in the price of a ticket were taken away. When American Airlines introduced a fee for checked baggage in May 2008, there was backlash since bags were previously included in the price of a ticket. This was perceived by travelers as a takeaway, also known as the endowment effect in behavioral economics (Morello & Lopatko, 2012), which states that changes framed as losses are weighed more heavily than changes framed as gains. Product positioning is also important. When baggage fees were introduced, it was a way to offset rising fuel costs. Fuel costs have since come down, but baggage fees have increased over the same period. After the terrorist attacks of September 11, 2001, sale of à la carte ancillary products and services was the first foray by network carriers into retailing. After 9/11 many airlines eliminated meal service and started selling box lunches onboard the flights for a fee. US Airways took this to an extreme in 2008 when they started charging for water, soda, and coffee. This was short lived when competing airlines did not follow; they stopped this practice in 2009. The sale of individual products that are priced and purchased separately from an airline ticket are easy to implement and can be done by channel or device. While it is easy to implement, it can also have a negative impact on the brand when customers perceive it as nickel and diming. This was followed with a simple extension of bundled ancillaries that were not tied

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243

to the base fare. It provided an opportunity to sell more ancillary products at a discount, when part of a bundle, than when sold separately. Branded fares or fare families were introduced by carriers like Air New Zealand, Air Canada, and Qantas in 2006. Frontier Airlines was the first to introduce branded fares in the U.S. in 2008. Branded fares are the bundling of optional services with the fare within each fare family and sell up amounts between fares were fixed. Today most airlines have gravitated toward a hybrid model of branded fares and à la carte ancillary offerings that maximizes revenue by stimulating sell up from the base fare and increasing the probability of purchase of à la carte ancillaries. A question that needs to be addressed is the propensity of an ancillary product to drive sell up in the fare versus à la carte. Today the sale of air ancillary products bundled with the base fare is called offer management. Airlines generate significant revenues through the sale of ancillaries such as baggage fees and pre-reserved seats. Customers purchase these ancillaries as a prerequisite to complete their travel plans. For air, offer management is the process of selling the right bundle of base airfare and air ancillaries to the right customer at the right price at the right time (Vinod, 2017, 2021c; Vinod, Ratliff, & Jayaram, 2018).

6.2

Offer Management

Though many believe this to be true, sale of ancillary products and services is not a panacea for airline profitability. Offer management is an extension of revenue management of the base fare to the sale of the base fare and air ancillaries. It is the process of selling the right bundle, base fare, and air ancillaries. Airlines worldwide are selling air ancillary products such as checked bags, excess baggage, pre-reserved seats, meals on board, lounge access, priority check-in, premium seats, priority boarding, wireless Internet access, etc. to generate incremental revenues beyond the base fare. Branded fare families and the sale of à la carte ancillaries have generated billions of dollars for airlines in recent years. While the average base fare in the airline industry has declined by 0.9% per year over the past decade (IATA, 2018a), ancillary sales have grown 40%. The five largest U.S. carriers (American Airlines, Delta Air Lines, United Airlines, Southwest Airlines and Alaska Airlines) generated over $29 billion in ancillary sales in 2019. Global airline ancillary revenues in 2018 were $93 billion (Ideaworks and Cartrawler, 2018) and in 2019 revenues exceeded $109.5 billion (Ideaworks and Cartrawler, 2019), which is a five-fold increase in ancillary revenues reported in 2010 of $22.6 billion. However, the airline practice of charging more for services that historically were included in the price of a ticket has created much customer angst and dissatisfaction (Reed, 2019). But airline executives view this revenue stream as a fundamental requirement for profitability. Ancillary revenue streams are here to stay and will continue to increase in the next decade, but adjustments are frequently made based on market conditions. For example, during the COVID-19 pandemic of 2020, U.S. majors stopped charging change fees due to

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the downturn in travel and the economic climate. Change fees was a large component of ancillary sales. Offer management, the stated direction of the airline industry, is behind the times. There are several examples in brick-and-mortar retailing that airlines can learn from to master the art of offer management. Bundling is a marketing tactic to offer two or more products and services as a packaged deal for a discounted price to promote value and convenience. For example, fast food chain McDonald’s introduced the Happy Meal with a toy for children in 1979 and the Value Pack in 1985 which consisted of a Big Mac, supersize fries and a Coke for $2.59. These iconic product introductions, besides generating significant incremental upsell revenues for McDonald’s, are also synonymous with the brand. In addition to McDonald’s there are several successful bundling strategies that greatly increased the value of corporations. Perhaps the most well-known example is Microsoft’s bundling of various software applications like Access, Word, PowerPoint, and Excel into Microsoft Office. Later when they bundled their Internet browser with their operating system, Microsoft saw a steep increase in market share for their browser, leading to protracted anti-trust litigation. Other popular examples of bundled offers are automobile packages (e.g., heated seats, keyless entry, adaptive cruise control and sunroof), and computer OEM laptop packages (screen size, processor speed, memory). The rapid growth in ancillary revenues has forced airlines to rethink their strategy for generating custom offers based on customer preferences. This has resulted in an accelerated investment in data mining, business intelligence and advanced data analytics to understand customer traits, behaviors, preferences, and customer segments to attract customers to their websites, improve customer retention, acquire new customers, and maximize the revenue-generation potential from the customer base (Saudi Gazette, 2019). For corporate managed travel and leisure customers that book through the travel agency channel, IATA’s New Distribution Capability (NDC) is a future key enabler of custom offers from airlines to customers through an intermediary such as Amadeus, Sabre and Travelport. It is anticipated that much of the innovation in the future for offers will be in the NDC environment. The renewed focus on customer loyalty (Neff, 2017) and the customer experience are key areas of investment for travel suppliers to differentiate themselves from their competitors, protect and grow their brand and the direct channel. With the arrival of NDC, airlines are also focused on indirect distribution channels such as brick-andmortar and online travel agencies (OTA) to promote their products and ancillaries. Like airlines, hotel chains and independent hotels are focused on driving more direct bookings online to counter the 10% to 25% merchant model fees they pay Online Travel Agencies (OTAs) for each transaction. To regain control over product distribution and attract direct bookings, hotels have focused on optimizing their websites for tablet and mobile users, requiring direct bookings as a condition to accrue loyalty points for frequent stays, optimizing search engines (SEO) to get their websites on the top of the search results on Google and Bing, and optimizing keyword spend and paid search ads with search engine marketing (SEM). They

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245

have also instituted best rate guarantee (BRG) by informing customers that if they find a lower rate on an OTA within 24-hours of making a booking, the lower rate will be honored. Regardless of the supplier line of business (air, hotel, car, cruise line, rail), online interactions with customers require customer segmentation, a recommendation engine, and an offer engine to generate offers that maximize conversion rates. Customer segmentation determines the type of customer and associated preferences, the recommendation engine determines likely bundles that are pertinent for the customer segment or persona, and the offer engine determines the specific personalized offer, for a segment of one. An important consideration during online interactions with customers is that they could be anonymous or registered (declared) and recommendation engines and offer engines need to be relevant and targeted in both scenarios.

6.3

An Omni-Channel Strategy

An omni-channel strategy is often discussed at industry forums. Many airlines have promoted their products across all channels of distribution. However, one of the biggest challenges with ancillary products and services is an omni-channel strategy, the ability to create a seamless customer experience through all channels with identical offers. The challenge is for customers to experience the airline brand through all channels; direct and indirect. The fundamental issue is that there is no standardization of branded fares across airlines. The airline websites showcase the airline branded fares exactly how the airline wants to display it. Through the agency channel, the GDSs must display content from many airlines and herein lies the problem. Hence, regardless of how the airline showcases their brands on their websites, the GDSs determine the minimal set of attributes that agencies and their customers would like to see and uses this as a baseline to display non-homogenous content across airlines. This issue will continue to persist with NDC. Value scoring of itineraries across non-homogenous brands using choice models or hedonic regression (Bacon, Besharat, Parsa, & Smith, 2016) or other methods is therefore critical to normalize the content and determine the display order of the itineraries based on value. Normalizing non-homogenous content across airlines can be sophisticated, yet it is difficult for customers to experience a true omni-channel experience through the agency channel.

6.4

The Stages of Travel

While the stages shown in Fig. 6.1 are relevant for leisure, there are key differences for business travel. Besides the absence of the dream and plan phase for travel, business travelers have a preferred arrival time at their destination to conduct business. Ancillaries proposed to the traveler must fulfill travel policy requirements established by the corporation’s travel manager.

Anonymous / Declared Trip Purpose Segmentation

Anonymous or Declared Customer Segmentaion Air Shopping - Market, Schedule, Fare Attributes Offers by Segment: Airfare + Air Ancillary Bundle

Air Shopping (Pre-booking) Declared Customer Inflight Incremental Ancillary Offers

Inflight

Declared Cutomer Purchase History

Declared Customer Inflight Preferences

Experience at Destination Upgrade Offers for Return Trip Incremental Ancillary Offers

Declared Customer Destination Preferences

Progressively greater visibility into Customer’s Trip

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Customer Retention Strategy

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Post Travel

6

Fig. 6.1 The stages of travel

Segmentation With Limited Data

Anonymous Users Segmentation Inspiration Shopping - Market or Theme - Extended Calendar - Budget Offers / airfare + Air Bundle

Dreaming and Planning

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6.4 The Stages of Travel

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Recommender systems play a key role in offer management; which is the capability to recommend relevant offers to customers (Dadoun, Platel, Fiig, Landra, & Troncy, 2021; Jiang, Qi, & Sun, 2014). The dream and plan phase of travel is the first stage. Customers at the stage are anonymous users who want a broad view of airfares over a range of dates to one or more destinations. The shopping experience they seek is inspirational shopping. For leisure customers, the answers they seek are: I want to go to Cancun in the Spring for 5 days. What do airfares look like? I want to go to Napa in the Fall for wine tasting with my spouse departing on Thursday and returning on Monday on a $1,000 budget. What are my options?

For corporate customers, some activities are planned over a longer period such as attending a conference or visiting a corporate office at a different location. The answers they seek are estimates for the trip for internal pre-travel trip approval. I need to go to Amsterdam for the AGIFORS Symposium from June 3 to June 7. What are my options? I have to visit a customer in the month of June in Berlin for three days mid-week. What is the best time to travel in business class?

Implicit customer segmentation during the inspirational shopping experience based on limited information such as origin and destination, length of stay, number in party and departure/arrival day of week enables the creation of shopping responses that are specific to the persona, albeit limited to what is known about the customer at that stage. This customer segmentation is called trip-purpose segmentation (TPS) which is segmentation based on the context for travel. In the second stage, the customer could continue to be anonymous or declared when the search is conducted for specific departure and return dates. This provides an opportunity to apply the shopping parameters associated with the trip-purpose segment (personas) such as departure time window, elapsed time, return time window, etc. to return targeted itineraries. In the third stage, the customer is declared. By accessing a customer’s loyalty tier and history of past purchases on the profile, the offer can be personalized and repriced for the individual customer. The fourth stage is inflight services that is typically based on the frequent flyer tier and/or the type of ticket purchased by the customer. At the destination, customers expect a collaborative travel experience delivered to their mobile devices. The fifth stage is when the airline can offer upgrades and related packages for the return trip when the customer is at the destination.

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6

Context for Travelbased Segmentation Trip-Purpose Segmentation (TPS)

Ancillary Product Preferences by TPS Market Basket Analysis

Offer Management

TPS and Customer Travel History

Based on TPS, Branded Fares and Status Entitlements

Blended Preferences

Bundled Offer Generation

Feedback / Bandit Updates

Booking?

Complete Booking or Abandoned Shopping Cart

Dynamic Discounting of Offer Discount based on Total Ancillary Revenue Spend

Test and Learn Experimentation Multi-armed Bandit based Recommendation

Fig. 6.2 Offer creation

The last stage is post travel, after the customer has completed the trip. Customer retention is the most important criteria and based on consumer intent to travel that is stored on the profile, the airline can send targeted relevant offers to the customer. Figure 6.2 illustrates an approach for generating offers to customers. Note that the multi-armed bandit is a reinforcement learning technique. It is a test and learn experimentation model that is discussed in further detail in this chapter.

6.4.1

Customer Segmentation

Airlines have for many years wanted to segment customers beyond the traditional booking class that is used for inventory control and distribution of availability through the Global Distribution Systems (Vinod, 2008). Customer segmentation outside of travel may focus on demographics such as age and income and resident postal code to understand purchase habits and propensity to buy a specific product. Examples are millennials versus baby boomers, affluent versus budget conscious, single versus families, etc. Airline customer segmentation has less to do with demographic data and is based on behavior patterns and reason for taking a trip. For example, business travelers book closer to departure and require flexibility with flight exchanges and refundability while leisure passengers are budget conscious and can accept more stringent fare rules associated with cheaper fares. Before personalized travel became a priority, the traditional customer segmentation was based on the class of service: first class, business class and economy. Within each class of service, customer segmentation was based on RBD. Frequent flyer segmentation was based on tiers and qualification was based on the amount of travel spend. In the corporate space employees must conform to the corporate travel policy in varying degrees referred to as unmanaged, lightly managed, managed and highly

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managed. Unmanaged travel typically only applies to small companies with fewer than 500 employees. The traditional corporate segments are the road warrior customers who are frequently on the road (e.g., sales executives), the corporate masses who do a fair amount of travel and, the infrequent traveler (e.g., employees who attend conferences). Traditional corporate travel is highly managed from a travel policy perspective while the lightly managed traveler profile applies to executive travel.

6.4.1.1 Frequent Flyer Segmentation and Customer Lifetime Value A subject that is frequently debated is whether the most valuable frequent flyers, those in higher tiers, should receive preferential seat availability at time of shopping and booking. While this is an active topic of discussion, preferential availability results in revenue dilution and hence has never been pursued in the airline industry. This is, however, practiced in the hospitality industry. Corporate rates negotiated with hotel chains frequently have the last unit availability (LUA) or limited last unit availability (LLUA) clause in the contracts. With LUA or LLUA, a corporate customer can book the corporate preferred rate if a room is available at the hotel. In the hotel industry, qualified rates and corporate rates are absolute rates and do not float with the prevailing rack rate. Hence, LUA can lead to revenue dilution since a corporate customer paying an absolute rate that is well below the rack rate can displace higher valued customers closer to the check-in date. Unlike the hotel industry, which has been slow at adopting corporate rates as a discount off the prevailing rack rates, corporate fares (CAT 25) are discounts off the selling fares in the market. Hence, if only the Full Y fare is available for sale on a flight, a corporate customer can book Full Y with the associated corporate discount. However, if a booking class is closed, a frequent flyer or corporate customer cannot override the closed condition of the booking class. Customer lifetime value (CLV) is a measure of how valuable a customer is to an airline. It can be based on past purchases and future potential revenue or just based on future potential revenue. The simplest formula for customer lifetime value (CLV) is CLV ¼ Customer Revenue – Cost of Customer Acquisition and Service Estimation of CLV in the airline industry has never been about preferential availability for higher-tier frequent flyers. Its importance lies in optimizing customer acquisition costs and segmenting frequent flyers in unique ways to support promotional campaigns. A better formula for CLV is to break it down into past historical performance and expected future performance. This is the well-known standard CLV formula. Customer Lifetime Value ðCLV Þ ¼ –CCA þ VTD þ

T X ðRt – Ct ÞRRt ð1 þ i Þt t¼1

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Table 6.1 RFMVT segmentation measures Measure Recency (R) Frequency (F) Monetary (M) Variety (V) Tenure (T)

Description of measure The length of time in days since the customer made a purchase The number of times the customer has visited the storefront and made a purchase The total dollars spent by the customer on all visits combined The number of distinct purchases by the customer in all visits Number of days since the first purchase

Where CCA is the cost toward customer acquisition, VTD is the historical value to date from inception to the current date, Rt is the expected revenue (estimated from predictive analytics) in future period t, Ct is the direct variable cost in period t, RR is the customer retention rate, and i is the cost of capital. i is also referred to as the discount rate. The residual CLV or potential future value (PFV), which is simply the term in the summation, is more important that the CLV itself if past revenue performance does not correlate with expected future revenue performance. This formula is also deficient since the retention rate is not constant over time but improves (increases) over time when a customer gains familiarity and confidence in the airline brand. Estimates of CLV from existing frequent flyers can be used for segmentation (persona identification) and hence develop guidelines and a strategy toward customer acquisition. CLV is also useful for promotional campaigns that target frequent flyers. For example, the airline can target a promotion for frequent flyer customers who have not flown for a specified time-period (e.g.,12 months). In this scenario, the data are first segmented to only include frequent flyers who have not flown for a specific time-period. Next, these customers can be clustered by CLV and specific CLV tiers in this population can be selected for the promotional campaign to achieve the desired marketing objective of generating incremental bookings by providing incentives for these target customers to fly. Such campaigns are important, keeping in mind that the typical traveler only flies once every 18 months. A model for customer centric discounts based on customer willingness to pay (WTP), potential future value (PFV) of the customer, frequent flyer tier and trip-purpose segment was calibrated with Qantas Airways data (Keenan, Santos, & Curran, 2015). The model was calibrated for the lowest branded fare family and should be extended to all branded fare families and associated booking classes for broader applicability. An alternative to CLV is the traditional segmentation technique used in retailing by Customer Relationship Management (CRM) applications, called RFMVT. The analytics for RFMVT revolves around five key attributes—Recency (R), Frequency (F), Monetary value (M), Variety (V) and Tenure (T). All RFMVT measures are relative to a particular customer and measurement time period (week, month, quarter, etc.). Table 6.1 describes the RFMVT measures. Adapting RFMTV in the airline context for variety (V) can be the number of distinct trip purpose segments that the customer was in, based on context for travel, when a purchase was made. RFM is by far the more common level of segmentation.

6.4 The Stages of Travel

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This type of segmentation can be used for promotion offer targeting, market penetration analysis and profitability analysis. RFM segmentation as a basis for summarizing customer data can be improved by addressing “clumpiness” to account for intermittent purchases by customers which adds to the predictive power (Zhang, Bradlow, & Small, 2015). They argue that while statistical models based on RFM summaries can fit well in the aggregate, ranking of customers at a micro-level can result in significant prediction errors unless clumpiness Is captured. There is a significant body of research on CLV and RFM and a detailed discussion is beyond the scope of this book. A more advanced method to calculate the expected lifetime value, E(CLV), was developed by Fader and Hardie (2009) using probability modeling. The E(CLV) for a yet to be acquired customer is given by Z1 E ½vðt Þ]Sðt Þdðt Þdt

E ½CLV ] ¼ 0

where E[(v(t)] is the expected value of the active customer at time t, S(t) is the probability that the customer has remained active to at least time t, and d(t) is the discount factor that reflects the present value of money received at time t. The expected residual CLV at time T for an existing customer is given by Z1 E½vðt Þ]Sðtjt > T Þd ðt – T Þdt

E ½Residual CLV ] ¼ T

Schmittlein, Morrison, and Colombo (1987) proposed the Pareto/NBD (negative binomial distribution) model to predict future customer purchases based on booking history, frequency, and recency of purchases. A simpler model which is a variant of the Pareto/NBD called the beta-geometric/NBD model (Fader & Hardie, 2009; Fader, Hardie, & Lee, 2005a), is easier to estimate the model parameters and deploy. Using RFM inputs to make CLV projections bridges the gap between these two paradigms (Fader, Hardie, & Lee, 2005b) using a concept of “iso-value” curves which groups individual customers who have different historical behavior but similar future valuations.

6.4.2

Personas for Offer Creation

Customer segmentation is the first step in the offer creation process (Kothari, Madireddy, & Sundararajan, 2016). User attributes related to travel such as preferences, schedule and fare attributes, site activity, affinities and travel history are features that can be used to create customer segments. Customer segmentation is not unique across all applications, Instead, customer segmentation is based on the marketing objective of an initiative. Customer

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segmentation creates personas, combining traits of a collection of travelers who have similar preferences. In travel, the personas come with a twist since a typical customer’s preferences change based on the context for travel—a business trip, a weekend getaway, visiting friends and relatives, family vacation, etc. The context for travel is used to define trip purpose segments which are mutually exclusive and collectively exhaustive. It is a practical step toward grouping of customers with similar purchase behavior characteristics, for two reasons. First, not all customers are registered users; most shoppers are anonymous until they make a booking, unless shopping with an OTA that uses browser cookies. Second, the typical traveler has multiple profiles depending on the purpose of the trip. With knowledge of the context for the trip, determining the trip purpose segment has a strong influence on customer preferences and price sensitivity. Trip segmentation can be augmented with customer-specific data resident in a customer profile such as name, credit card, frequent flyer status and past trips when the customer is declared, to fine tune the recommendation arrived at with trip purpose segmentation. Traditional segmentation of travelers in air comes from airline pricing and is based on fare rules and restrictions which are encapsulated in the reservations booking designator (RBD). The RBDs or booking classes are also used to distribute availability to Global Distribution Systems (GDS). While price discrimination based on customer segmentation or individual customer behavior did not work for mass product items sold such as DVDs sold by online leader Amazon in 2000 (Turow, Feldman, & Meltzer, 2005), travel suppliers do not anticipate such a backlash from customers. Today’s travel customers accept price differentiation with different price points for products that are perishable. Every traveler has a single profile, but multiple personas based on the context for travel. Flight and bundle (air and non-air) vary based on the type of trip—business trip and its variants, leisure trip and its variants. Given the importance of the context for travel which can lead to different offers for the same customer, the figure below provides a sample set of attributes for customer segmentation. Unsupervised or supervised (if a marketing survey customer surveys past customers and labels their segment) learning techniques can be used to generate the various personas which have been identified with a label as shown in Fig. 6.3. Leading machine learning toolkit providers often advocate a black box approach to customer segmentation. Managing inventory-controlled ancillaries is within the domain of airline revenue management analysts and visibility and interpretation of the customer segments is important to act upon changes to custom offers and inventory controls. Hence, a black-box approach is not advocated due to existing revenue management business process workflows and methods for controlling flights. The same is true for dynamic segmentation, based on the same argument. Note that the trip purpose segments shown in Table 6.2 are mutually exclusive and collectively exhaustive. Hence, in a session, a customer can only belong to one persona which defines the traveler’s context for travel. Personas can be created for other lines of business such as hotels, rental car, cruise lines and ferry lines in a similar way. A hotel example is shown in Table 6.3.

6.4 The Stages of Travel

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Attributes for Customer Segmentation Advance Purchase Length of Stay Number in Party Domestic / International Destination Theme Length of Haul (Short / Medium / Long) Equipment Type (Narrow body/Widebody) Mid-week vs Weekend Departure / Return Saturday Night Stay Day Flight vs Overnight Flight Departure Day of Week Return Day of Week Outbound Departure Window (Morning, Afternoon, Evening) Inbound Departure Window (Morning, Afternoon, Evening) Temperature Difference between Departure City and Destination City

Personas Short Domestic Business Trip A

Long Domestic Business Trip A

Short International Business Trip A

Long International Business Trip A

Short Domestic Business Trip B

Long Domestic Business Trip B

Short International Business Trip B

Long International Business Trip B

Short Domestic Gateway for 2 Visiting Friends & Relatives (VFR) - Short Stay

Long Domestic Gateway for 2 Visiting Friends & Relatives (VFR) - Long Stay

Family Vacation with Children – Short Stay

Family Vacation with Children – Long Stay

Family Vacation with Adults – Short Stay

Family Vacation with Adults – Long Stay

Fig. 6.3 Sample attributes for airline customer segmentation to create personas Table 6.2 Air trip purpose segmentation example Trip purpose segment A1 A2 A3 A4 A5 A6 A7 A8

A11 A12 A13

Customer type Individual Individual Individual Individual Individual Individual Individual Couple (2) Couple (2) Family (>2) Individual Individual Individual

A14

Individual

A15

Couple (2) Family (>2)

A9 A10

A16

Business/Leisure Business Business/Leisure Business/Leisure Leisure Business/Leisure Business Leisure Leisure

Advance purchase 0–6 days 0–6 days 7–13 days 7–13 days 14–20 days 21+ days 21+ days 0–20 days

Length of stay 0–1 days 2 + days 0–3 days 4+ days Any 0–3 days 4+ days Any

Leisure

21+ days

Any

Leisure

Any

Any

One-way, business One-way, business or leisure One-way, business or leisure traveler that plans ahead One-way, leisure traveler that plans ahead One-way, couple on leisure trip

0–6 days 7–13 days 14–20 days

One-way One-way One-way

21+ days

One-way

Any

One-way

One-way, family vacation

Any

One-way

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Table 6.3 Hotel trip purpose segmentation example Trip purpose segment H1 H2 H3 H4

Customer type Individual Individual Individual Individual

H5 H6 H7

Individual Individual Individual

H8

Individual

H9

Couple (2) Couple (2) Family (>2)

H10 H11

6.4.3

Business/ Leisure Business Business Business Business/ Leisure Leisure Leisure Business/ Leisure Business/ Leisure Leisure

Midweek/ Weekend arrival Midweek Midweek Midweek Midweek

Advance purchase 0–6 days 7–21 days >21 days >21 days

Length of stay 0–5 days 0–5 days 0–5 days > 5 days

Weekend Weekend Weekend

0–13 days >13 days 0–13 days

1–2 days 1–2 days 2+ days

Weekend

>13 days

2+ days

Anytime

0–20 days

Any

Leisure

Anytime

>20 days

Any

Leisure

Anytime

Any

Any

Personalizing the Best Fare Based on Trade-off Analytics

During air shopping, low fare efficacy, which is finding the lowest fare in a market, is important for customers since it serves as a reference point to their desired fare. Diversity of itineraries by persona maximizes conversion rates. Based on the context for travel, a shopping algorithm can return itineraries by persona by using the dominant shopping parameters (departure time window, nonstop vs connection, return time window, etc.) derived from booking data. For example, for a business trip, nonstops would be preferred over a connecting fare even if the nonstop fare is more expensive. The collection of itineraries returned for display after this process, while consistent with the persona, does not necessarily produce the best fare for a specific customer. Itineraries returned from shopping in this fashion can be evaluated by multi-criteria decision-making models that work with interval data such as TOPSIS, a Technique for Ordering Preferences by Similarity to Ideal Solution, and VIKOR (VlseKriterijumska Optimiza-cija I Kompromisno Resenje) to rank itineraries based on a traveler’s relative trade-off between schedule and fare attributes (Opricovic & Tzeng, 2004). Typical trip characteristics used to define the relative importance of these attributes by persona specifies the degree of importance and value (if applicable) expressed as a percentage. Typical trip characteristics are outbound departure or arrival time (%), inbound departure/arrive time (%), travel time (also called elapsed time) (% and value), connecting airports, connection time (% and value), refundable/ partially refundable /nonrefundable fare (%), etc. will form the basis for a customer to adjust the relative importance of these parameters to create a 1:1 personalized

6.4 The Stages of Travel Fig. 6.4 Default and customer modified preferential weights

255

Default Weights by Trip Purpose Segment

Outbound departure time, 40%

Elapsed time, 35%

Fare, 25%

Customer Modified Weights Elapsed time, 15%

Outbound departure time, 30%

Connecting Airport Preference, 20%

Inbound arrival time, 15%

Fare, 20%

response with the best fare. If the default preferences by trip purpose segment are modified by a customer, the modified preferences should be stored on the customer profile against the trip purpose segmentation id. This is required because a customer who returns at a later point in time to make a purchase can pick-up where they left off on the previous visit and not start over. Figure 6.4 illustrates an example of default and modified preferential weights by a customer. A preference driven air shopping display algorithm (Vinod, 2016b; Vinod, Xie, & Bellubbi, 2015) based on trade-off analytics is superior to traditional travel website filters, since a filter would exclude an itinerary based on one attribute, even though it would have been outweighed by the goodness in the other attributes.

6.4.4

Types of Recommendation Engines

The most rudimentary recommendation engines are based on rules. With this approach, rules are defined to create offers. The primary issue with this approach is that the quality of the recommendations is based on expert opinion. Second, the

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rules are static. Maintenance and updates to the rules engine to reflect current market conditions is time consuming and may not reflect true market conditions. Data analytics powered recommendation engines for bundles comprising the base fare and air ancillaries are superior to the traditional rules-driven recommendations engines. Predictive models of this type that are calibrated with booking and ancillary data are popular. Developing predictor models to recommend bundles based on a range of input variables such as customer segment, schedule attributes, fare attributes and customer preferences is appealing, but not scalable. The primary issue with predictor models is the need for frequent recalibration based on changes to the composition of bundles offered and introduction of new ancillary products. An alternative, and more versatile, approach is to determine default bundle recommendations based on historical purchase behavior by trip purpose segment. This static bundle recommendation model can serve as a warm start for the deployment of a multi-armed bandit reinforcement learning model that recommends bundles based on continuous learning to maximize the cumulative reward based on trials.

6.4.5

Recommendation Engine for Bundles

Data analytics powered recommendation engines for bundles comprising the base fare and air ancillaries are periodically calibrated to detect and adapt to changes in customer behavior patterns using either traditional statistical techniques or machine learning models (Fox, 2019). Typical models based on data availability include techniques such as collaborative filtering, classification and regression trees and gradient boosting models. Using historical purchase data by persona, recommendation engines can deliver bundled offers for the customer segment after factoring in ancillaries included in a branded fare selected by the customer. If the customer is declared, loyalty program tier, entitlements (such as free bags for a specific tier, free first bag as part of a credit card program, etc.) and prior purchase history stored on a customer’s profile can be used to weigh in on the recommended bundle. Historical purchase history should be tagged by persona id to ensure that the context for travel is considered. The recommended bundles should ideally be by outbound itinerary and inbound itinerary since preferences vary if the flight is an overnight trip (e.g., U.S. to Europe) or a day trip (e.g., Europe to U.S.). Before the bundle is created, the ancillaries included in the airline branded fares must be considered, to ensure that the lowest cost option is offered to the customer, thereby promoting upsell of individual ancillaries. A recommendation engine should have access to a flexible, easy to use ancillary product catalog that is maintained internally by the airline. The catalog should maintain relationships between ancillaries included in a branded fare family as well as an ancillary catalog with à la carte prices subject to various qualification rules such as the branded fare selected to qualify for the ancillary purchase.

6.4 The Stages of Travel

6.4.6

257

Offer Engine

Subsequent refinements to the bundle from the recommendation engine take place after the customer is declared, and history and preferences can be used to personalize the offer. While preference driven air shopping determines the best fare for a customer based on preferences, a quantity discount model, calibrated with airline input, can be used to customize the air bundle (Vinod, 2021c; Vinod et al., 2018). The greater the total value of the bundle purchased by a customer, the greater the discount, subject to minimums and maximums. For example, the quantity discount model can be a simple hyperbolic discounting model to compute the discount amount. The model can be calibrated to define the maximum discount regardless of the total value of ancillaries purchased. Figure 6.5 shows the discount percentage applied as a function of total ancillary sales. With NDC, an airline can also apply the discount to the total price of the ticket inclusive of the base fare, if they so choose, since the settlement is direct with the airline. Figure 6.6 illustrates the offer creation process for airfare and air bundles.

6.4.7

Displaying Offers on the Consumer Direct Channel

Once the air bundles have been created, careful consideration needs to be given for the display of pertinent offers to a customer. As a rule, displaying many offers is never a good idea. Also, the order in which the offers are displayed matters. This is true for travel and nontravel applications. Based on a customer’s attention span and cognitive ability to compare offers, three offers are considered ideal. There are many types of offers that can be displayed. For example, the offers can be:

Discount %

Discount Model

$0

$100

$200

$300

$400

$500

Total Value of Ancillaries Purchased

Fig. 6.5 Example of a discounting model based on total ancillary spend

$600

$700

6

Fig. 6.6 The offer creation process for air bundles

258 Offer Management

6.4 The Stages of Travel

259

The Best Offer The best offer is the air bundle that is recommended based on knowledge of the customer segment, context for travel and past purchase history. It represents the most likely bundle that the customer will purchase. The Decoy Offer The decoy offer is to display an inferior bundle next to the best offer, that increases the propensity of the customer purchasing the best offer (Ariely, 2010). Popular Picks Offer The third option is not based on context for travel or any type of customer segmentation. Instead, the components of the ancillary bundle are based on the most popular ancillaries purchased by all customers. None of the offer management solutions in the literature address the dis-utility associated with consuming multiple products and services as part of a bundle that are caused by the effects of substitution. For example, the value of lounge access is diminished if the customer purchases a meal on board a flight. Bundled offers should address the effects of substitution and the dis-utility of a bundle when setting the price. Talebian, Li, and Lu (2020) address a limited case of bundling when there are two products in a bundle.

6.4.8

Test and Learn Experimentation

To evaluate alternate offer strategies for the various customer segments, A/B testing plays an important role in the validation process. A/B testing is also sometimes referred to as split testing or bucket testing. In an A/B testing framework, alternate versions—current (control) versus proposed (variation) split the incoming traffic and are compared against each other to determine if there is a positive, negative, or neutral impact on a metric such as conversion rates with statistical analysis. An alternate approach to A/B testing is deployment of the multi-armed bandit problem (Gittins, Glazebrook, & Weber, 2011; Robbins, 1952; White, 2013) which relies on trying out each alternative (arm) in an exploratory phase for a small percentage of the traffic (e.g., 10%) to find the best one and in the exploitation phase, sending the bulk of the traffic to the alternative (arm) that gives the best payoff. A one-armed bandit is a slot machine. When the “arm” is pulled, the “bandit” will take your money most of the time but there is a finite probability that you could also win big. A “multi-armed bandit” (MAB) is a slot machine with multiple arms, each with a different probability of winning. Since the probability of winning is unknown, it is a trial-and-error process to converge on the optimal lever. This is a reinforcement learning technique to manage online controlled experiments to learn customer behavior. The goal of the experiment is to find the best or most profitable action to take and the randomization distribution can be updated in real time as the experiment progresses. As stated earlier, the term “multi-armed bandit” describes a hypothetical

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experiment where you face several slot machines (“one-armed bandits”) with potentially different expected payouts. The objective is to find the slot machine with the best payout rate, with the goal to maximize winnings in the shortest amount of time. In a Bernoulli bandit problem, the payout is either 1 or 0. Pulling an arm gives a stochastic reward of +1 for success or 0 for failure with the objective of maximizing the total reward. This is a non-trivial problem because the true bandit probability distributions are not known, and the learning process is one of trial and error. The fundamental tradeoff is between “exploiting” arms that have performed well in the past for immediate rewards and “exploring” new or seemingly inferior arms in case they might perform even better in the future. This approach develops a sequential strategy for recommending bundles that balance the tradeoff between exploration and exploitation to maximize the total expected reward or minimize the total expected regret. Feedback is provided for every success or failure to convert, which supports continuous learning and self-correction to improve the recommendations over time. “Smart” experiments drive intelligent recommendations for a range of applications in travel such as air shopping, offer management, agency storefront design for GDSs, ancillary pricing and much more. Figure 6.6 illustrates the difference between the standard fixed allocation traditional A/B testing and the continuous reallocation multi-armed bandit model. When the number of ancillary products offered increases, the number of potential bundles increases by an order of magnitude. The number of possible bundles when there are n ancillary products is 2n – 1. For example, with 6 ancillary products, the number of possible bundles is 63 and with 10 ancillary products it is 1023. However, the demand for the ancillary products is not uniform, but a few such as pre-reserved seats, checked bags, and upgrades dominate and then there is the long tail that should not be ignored but sampled periodically. The objective is to find the right bundles that maximize customer conversion rates. The multi-armed bandit problem can be implemented with different sampling methods such as epsilon-greedy, upper confidence bound (UCB), Thompson Sampling, etc. to improve future actions to maximize the reward potential. Initially it is assumed that all levers (bundles) have the same probability of success and update the probabilities based on success or failure. In a pure greedy algorithm, for each successive lever we choose the lever that has the highest probability of success and does not consider recent information which could be different from the average probability calculated thus far. With epsilon greedy, we ignore the highest probability of success lever a small percentage of the time by selecting a random bundle. This brings new information into the mix, but convergence is slow, and the solution may converge on a local maximum. The UCB overcomes this problem by selecting a bundle based on the level of uncertainty of a given selection. It samples the unknown bandits quickly to reduce the uncertainty of the unknowns before converging on the right solution. Thompson sampling is quite different from epsilon greedy. It is not a greedy method; exploration is more sophisticated and is a Bayesian approach to the problem. Thompson sampling can be generalized to sample from any arbitrary distribution though the Beta Bernoulli is

6.5 Dynamic Pricing of Offers and the Role of the GDS A/B Testing

Multi-Armed Bandit

Ancillary Package 2 Ancillary Package 3 Ancillary Package 4

Allocation of Experiments

MANUAL SELECTION

Ancillary Package 1

261

Low Reward Very low Reward High Reward Medium Reward

Time Fixed Allocation

Time Continuous Reallocation

Source Created by Paula Lippe, October14, 2020 (www.paulajlippe.com) Adapted from FROSMO, Multi-Armed Bandit by Joni Turunen, 2017

Fig. 6.7 A comparison of A/B testing and the multi-armed Bandit

frequently used in practice, where the prior is from the beta family of distributions. The overriding objective of all these sampling methods is to find the most profitable bundle. “Smart” experiments drive intelligent recommendations for air shopping, offer management, intelligent storefront (the agency desktop) for GDSs, ancillary pricing and much more. Figure 6.7 illustrates the difference between the standard fixed allocation traditional A/B testing and the continuous reallocation multi-armed bandit model. Contextual bandits are an extension of multi-armed bandit test and learn experimentation (Byrd & Darrow, 2021). The contextual bandit extends the MAB model by making it contingent on the current state. With this model, a decision is made not just based on past history, but the decision can be personalized based on context— which is information about a user such as where they came from, previously visited pages on the site, device used, etc. Microsoft Research published a testing service called Vowpal Wabbit, which is an open-source library that implements online and offline training algorithms for contextual bandits (Dudik, Langford, & Li, 2011). These researchers previously worked at Yahoo! where the model was developed.

6.5

Dynamic Pricing of Offers and the Role of the GDS

With the growth in ancillary sales, an area of growing importance is the concept of total revenue management (Rickey, 2014) or offer management, which entails the creation of dynamic custom personalized offers based on customer traits, customer value score, flight and schedule attributes, ancillaries, and non-air products through the airline’s preferred channels of distribution. The initial focus has been to sell ancillaries and sell up of branded fares through the direct and agency channels for online and codeshare partner flights (Smith et al., 2007; Vinod & Moore, 2009). Airlines that fail to adopt à la carte pricing model will likely lose customers and potential revenue (Nason, 2009).

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With Electronic Miscellaneous Document (EMD) data, a consumer’s willingness to pay can be calibrated for dynamic pricing of ancillaries. Techniques such as adaptive conjoint analysis (Orme, 2014) and the van Westendorp price sensitivity meter (van Westendorp, 1976) can also be used for the product design of branded fare families (Ratliff & Gallego, 2013) and dynamic pricing of ancillaries. The challenge to pricing air ancillary bundles for an individual customer is much talked about in the industry with the arrival of NDC. However, with the General Data Protection Regulation (GDPR) in place today, calibration must be done at a higher level of aggregation such as market or trip purpose segmentation data. Shao and Kauermann (2020) recommend calibration price elasticity at the market level to segment markets and determine what offers to recommend to customers. Offer management is not specific to IATA’s New Distribution Capability (NDC) but is a capability that can be enabled across all channels of distribution. But the role of the GDS is changing. In the future they will no longer be able to generate the offer from filed fares and filed ancillaries. At the heart of NDC, airlines want to know the customer making the request and be able to respond with a contextual custom offer, which could include a dynamically priced fare and ancillary bundle. In a NDC world, the GDS will play a much smaller role, focused on how to normalize the non-homogenous bundles promoted by airlines with a value score to make comparison shopping and booking easier for a travel agent.

6.6

Corporate Travel and Offer Management

Corporate travel (B2B) is the dominant component of GDS bookings and is quite different from leisure travel (B2C). Leisure demand is B2C demand, and a supplier deals directly with a consumer and offers relevant product bundles for sale. Corporate bookings on the other hand are B2B demand where suppliers negotiate deals with a corporate buyer. The customers are employees of the organization who book travel based on the negotiated deals. Corporate travel managers are responsible for managing the corporate travel program. Corporate bookings must adhere to company policies and practices. Corporate bookings fall under the realm of managed travel with the active participation of a travel management company (TMC) through which bookings are made. Planned travel must conform to a corporate travel policy definition on fares, business versus coach based on distance traveled, preferred carriers, preferred hotels, and cars. Corporate travel governs elements such as leisure side-trips as an add-on to a business trip, traveling with spouse to industry conferences, type of hotel room and size of the rental car. Corporate travelers make bookings using the corporate booking tool and non-compliance with the policy will be flagged as an exception and generate notifications up the management chain in the organization. Travel policy is also dictated by the employee profile based on the miles flown in a year. Allowances vary based on infrequent travel (e.g., 1–2 trips per year), average travel (e.g., 6 trips per year) and road warrior (e.g., 12 or more per year).

6.7 Attribute-Based Room Pricing for Hotels

263

Companies negotiate corporate fares with airlines, which is a discount off the prevailing selling fare (ATPCO Category 25). Similar deals are negotiated with hotels and car rental companies. Business travel also requires pre-trip approval based on an estimate of the cost of the trip and the available travel budget. Further, all business travel expenses must be charged to a corporate card so that the company can review detailed reports on aggregate spend by category and take steps to reevaluate and fine tune travel policy. Duty of care is an integral component of the travel management program. Companies have a legal obligation to ensure that employees are safe. Duty of care applies to employees on both domestic and international travel. Aspects of duty of care can be automated using machine learning. Duty of care includes educating employees on recommended inoculations before traveling to certain regions of the world, ensure access to adequate medical treatment while working overseas, business continuity plan in the event of unusual events, evacuation plans in the event of an accident or uprising, pre-trip briefings of political unrest and other risks where employees are sent. Central to duty of care is the ability to find and communicate with employees impacted by an event and allow employees to “check-in” periodically for a status update. Over the past decade airline contracts with corporations have evolved beyond schedules and fares. Corporate traveler preferences and how suppliers prefer to sell travel will influence the evolution of corporate contracts, which will allow corporations and suppliers to mutually achieve their objectives (Bradberry, 2013). What an airline is promoting in the marketplace is not necessarily what a corporation wants. For example, an airline’s pricing and revenue management function will focus on published fare levels, published ancillaries, airline branded fares, airline bundled fares, and airline branded bundles. In contrast, the corporate objective is to negotiate the corporate discounted airfares, discounted ancillaries, company branded fares, company bundled fares and company branded bundles that are consistent with corporate policy. A related area is corporate air contracts, which are also on the verge of major changes with the incorporation of negotiated ancillary, merchandising and personalization content, which will allow buyers and sellers to mutually achieve their objectives. B2B personalization of offers has a greater number of constraints than B2C and should be balanced with corporate objectives.

6.7

Attribute-Based Room Pricing for Hotels

The intrinsic value of offers, which is a collection of products to create a bundle based on room attributes, is significant for hotels (Vinod, 2019) where customers are willing to pay for extras for a memorable stay or cruise. Attribute-based room pricing is a significant advancement for hotels to generate infinite bundles based on the value of room attributes. For example, a property may have only two king size rooms that are larger than the standard for which they want to charge extra. In this scenario, the larger king size room is consumed at the time of booking in the property

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management system (PMS) while the room type (king in this case) is consumed in the hotel CRS. Attribute-based pricing also has the advantage of eliminating legacy hotel rate structures with derived rates to compute the total value of the room and attribute bundle for the customer.

6.8

Extensions to Non-Air with Stopovers

While offer management extends the traditional revenue management process of optimizing allocations for the base fare to include air ancillaries offered by an airline, it is not limited to airline bundles, but can be extended to include hotels and local activities at the destination. This is commonly referred to as dynamic packaging. A special case of dynamic packaging is stopover packages. Several airlines, working closely with the country tourism boards, promote their hubs as stopover destinations. For example, Emirates (Dubai), Icelandair (Reykjavik), Singapore Airlines (Singapore) and Cathay Pacific (Hong Kong) promote their hub airports as stopover destinations by waiving the stopover fee (ATPCO CAT 8) and promote packages that include hotel, local transportation, and local activities at the stopover destination. The objective of the stopover recommendation engine is to offer a bundle consisting of flights, hotels, and local attractions in the stopover city. Customer segmentation is always the first step to generate stopover offers. Some of the trip attributes that need to be considered for effective segmentation are trip type (one-way vs roundtrip), advance purchase, length of stay, size of group, number of children, connection/layover time and travel day of week. In the absence of data, an initial set of recommended bundles can be created by customer segment followed with a test and learn experimental model that will dynamically allocate bundles across the requisite dimensions. Deploying a reinforcement learning technique of this nature provides a dynamic environment to reallocate web traffic proportional to the performance of the bundles, thereby maximizing the payoff. The approach ensures that the model will automatically react to market changes and nuances in customer behavior over time.

6.9

Offer Management and Value Scoring for GDS Displays

In the consumer direct channel airlines can showcase their branded fare products and support upsell across brands and à la carte pricing. The core issue with branded fares and ancillaries is that there are no standards for what constitutes a branded fare or an ancillary product or service. With the absence of standardization of branded fare products and ancillaries across airlines, how does the GDS support comparison shopping for a travel agency when they must display non-homogenous content across airlines in the agency desktop display? This problem exists today prior to NDC and will continue after the adoption of NDC.

6.9 Offer Management and Value Scoring for GDS Displays

265

Value scoring of itineraries with non-homogenous content from carriers is the first step toward normalizing content for display on an agency desktop. While the content displayed will never be identical to content on an airline website, the fundamental goal is to provide a comparison-shopping framework based on attributes associated with each “shelf” of the agency desktop. A shelf is screen real estate on the agency desktop that is designated with a selection of schedule, fare and comfort attributes and their minimum levels for an airline offer to be classified for display in that shelf. Estimating value of an itinerary is complicated by the fact that airlines promote branded fare products, and each branded fare product has a collection of attributes associated with it. To score the value of an itinerary, the marginal value of each ancillary product is required (Szymanski & Darrow, 2021). Implicitly determining the value of each attribute from sales data are the preferred approach but sometimes difficult to calibrate due to the absence of sufficient data. Survey based approaches are not perfect since there is no guarantee that the participant taking the survey is not giving a biased or faulty response. Survey participants should be preselected to represent the typical traveler based on criteria such as traveled for business at least n times in the last 12-months, take leisure trips, and ensure a good distribution of age, gender, and region. While crowd sourcing approaches such as Amazon Mechanical Turk (Mortensen & Hughes, 2018) and market research firms like Dynata and Qualtrics can be used in practice, a better approach is for travel entities to build the survey sampling feature directly into the airline website or agency desktop application so that surveys can be presented to every n-th customer to support continuous calibration. An approach is to use a choice-based conjoint (CBC) survey analysis to determine the value of each itinerary based on a customer’s willingness to pay. Survey participants are asked to book bundles virtually that include experimentally generated itineraries and sets of ancillaries. CBC is a specialized survey technique which closely simulates the consumer selection process for products in competitive contexts. Survey respondents are shown a set of itinerary options and asked to make a purchase selection. Participants view products which consist of various attributes (e.g., seat comfort) made up of multiple levels of the product (extra leg room, standard seat, 127-degree reclining seat, etc.). Each attribute included in the product will have multiple levels associated with it. A price point is also associated with each level in the display. From the survey, based on how respondents evaluated products in response to changes in attribute levels, the impact of each attribute (the ancillary) on product performance can be estimated. The utility of the ancillary products can be determined using the Hierarchical Bayes (HB) choice modeling technique as it models preferences heterogeneity at the individual survey participant level. By aggregating the HB model utilities, the significance of each ancillary can be determined on a normalized scale from 0 to 1. An easier way to interpret the ancillary utilities is to convert it into dollar equivalent terms which is the customer willingness to pay for the ancillary product or service.

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6.10

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Offer Management

Limitations of Supplier and GDS Influenced Offers

“Personalized Travel” as defined by suppliers and GDSs are limited in scope and do not manage the entire customer travel experience. An airline, for example, wants to maximize revenues with every customer interaction by selling the base fare and air ancillary bundle to a customer based on their preferences. While this is personalizing the offer from an airline perspective, it falls way short of managing a seamless, personalized travel experience for the entire journey. This is because individual suppliers and travel entities only represent a component of the customer’s journey. An approach to support a true seamless customer experience across various travel entities that are part of a customer’s journey requires the establishment of a universal profile and universal data exchange. The discussion in this chapter is limited to travel and will be extended to nontravel in Chap. 12.

6.11

The Universal Profile

Every supplier believes that they “own” the customer. In fact, every supplier may have some information about a customer, but not all the information about a customer that is required to provide advice on travel. This is because a supplier such as an airline or a hotel chain is merely a component in the travel value chain. There are five categories of information about a customer that can be stored on the profile. They are: 1. Demographic. Age, sex, income, race, employment, education, zip code, etc. 2. Psychographic. Personality, values, opinions, attitudes, interests, spending habits, hobbies, and lifestyles 3. Behavioral travel-related preferences Name Date of birth Passport Biometric data Allergies Vaccinations Email Mobile Phone Seating preferences (e.g., aisle vs window) Credit card on file Room preferences (e.g., king bed) Cruise preferences (e.g., with balcony) Rental car preferences (e.g., mid-size car) Affiliations (e.g., frequent flyer, frequent stay, frequent rental memberships and tiers, AAA, AARP, etc.) Context specific preferences History of travel patterns

6.11

The Universal Profile

267

4. Behavioral nontravel related attributes and preferences. Examples are: Theatre—musicals, comedy, drama, opera, etc. Wine tasting Activities—snorkeling, scuba, parasailing, ziplines Favorite beach destinations—Bora Bora, Tahiti; Kaanapali Beach, Maui, Hawaii; St. John, U.S. Virgin Islands Sports—San Antonio Spurs, Dallas Cowboys, Chicago Cubs, etc. Cruise Lines—High End, Standard, # in party 5. Intent. Examples of customer intent are: Attend AGIFORS conference in AMS from September 4–8, require a cheap fare < $800 R/T. Airline preference is Delta or KLM. Attend the balloon festival in Albuquerque for 3 days in October for the family of 4 contingent on spending less than $300/ticket. Preferred airline is United. Go to Napa in the fall for a 3-day weekend for a party of two. Depart Friday, return on Tuesday. No airline preferences. This discussion is limited to the behavioral content that should be associated with a customer profile as it relates to travel. Nontravel will be discussed in Chap. 12. The universal profile concept assumes that the customer always owns the data and determines who has access to the data. Customer data in the profile is at an atomic level and permissions are granted to travel and nontravel entities by the customer. The universal profile provides the capability for the customer to state their preferences, intent, and derived preferences can be augmented on the profile using machine learning and statistical models. The customer “owns” the data, determines which travel related and nontravel related entities can access it, and influences what specific travel content is sent to the traveler. In addition, the customer profile should be extended to store information about customer preferences based on the context for travel. Examples are: 1. History of past trips (36-month window) of a customer, inclusive of bundles purchased, by trip-purpose segment. 2. Shopping input parameters by trip-purpose segment if the parameters are modified. 3. Preference driven air shopping preference weights by trip purpose segment, if modified by user from the default. The benefits of a universal profile that stores customer modified content by trip purpose segment and the ability to capture future consumer intent to travel are far reaching. This also allows the recommendation engine to suggest similar destinations if the original destination is sold out or too expensive. The universal profile can be centralized or decentralized. A centralized profile is always problematic since a customer may not trust the entity that is storing and managing the profile on behalf of the customer. A decentralized profile, owned and managed by the customer, is the preferred alternative.

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6.12

6

Offer Management

The Universal Data Exchange

A door-to-door seamless travel experience is much talked about but does not exist today. The end-to-end travel paradigm begins with a decision on what time to leave a residence to travel to the airport, the ride to the airport, going through the shortest security lines, boarding a flight, travel to the destination, and engaging in local activities. The key to a seamless door-to-door travel experience will require the creation of a clearing house and universal data exchange between travel entities subject to fulfilling all the GDPR privacy laws. The data exchange must work with a decentralized universal profile. The data exchange is a secure communications message routing platform between travel partners (e.g., brands such as United Airlines and Marriott Hotels) based on the permissions granted by the customer. Every customer has an encrypted electronic key that is used to identify the customer across travel entities such as airlines, hotels, cars, rideshare, etc. The data exchange should not store any PII (personally identifiable information such as social security number, passport data, biometric data, date of birth, etc.) data. Figure 6.8 illustrates the concept of the universal profile and the universal data exchange to create a seamless customer experience. The customer maintains the data in the universal profile. When the customer makes a booking with an airline and the hotel independently for a future trip, the customer authorizes access to the data required to pre-populate content on the supplier websites to make a booking, avoiding duplication of data input. Further, the customer can authorize the airline to send the itinerary to the exchange and provides permission to the hotel to view the itinerary and status of the itinerary. The hotel can now plan for early arrivals and late arrivals and plan on additional product offers to recommend to the customer on arrival at the hotel. All travel entities need to register with the exchange to receive notifications authorized by the customer. To fulfill a customer’s travel experience, exchange of data authorized by the traveler between travel entities are required for frictionless travel. This concept can be easily extended to include other entities in the travel chain like rental car, rideshare, restaurants, and local activities for a true door-to-door seamless travel experience.

Fig. 6.8 Data sharing between travel entities

Hotel A

Universal Profile

Universal Data Exchange

Airline A

6.13

6.13

Altering the Customer Value Chain

269

Altering the Customer Value Chain

Offer creation has been discussed in the context of the customer value chain for travel as we know it today. This customer value chain is not necessarily fixed and can be altered to disrupt how consumers discover, buy, and consume products and services (Teixeria, 2019). To understand customer centricity, companies need to gain insights into how customers buy and consume products. In Teixeira’s insightful book (Unlocking the Customer Value Chain) he argues that an important form of disruption is decoupling. Examples are: 1. Car sharing companies decoupling the link between purchasing and driving a car 2. Amazon decoupling the link between physically trying out a new TV (at Best Buy) and purchasing it 3. Blue Apron decoupling the link between finding a recipe, shopping for ingredients, and cooking the meal Disrupting the customer value chain through decoupling should not be taken lightly. Suppliers that are successful will dominate the market.

7

Competitive Revenue Management

7.1

Introduction

In the Internet era with competitive price and schedule transparency, there is a growing recognition that the traditional airline revenue management process which relies solely on an airline’s historical booking and ticketed data, is myopic and does not provide inventory control recommendations that are reflective of true competitive market conditions. When the science of revenue management is not “competitor aware” (Ratliff & Vinod, 2005), an airline’s inventory controls will not reflect true market conditions and hence result in missed revenue opportunities. Monitoring of the competitive selling fare can be used to influence inventory controls. So, what is competitive revenue management? To overcome the fundamental shortcoming of traditional revenue management, which determines inventory controls based on historical data and without considering competitor selling fares (fares that are available) in a market, Competitive Revenue Management makes tactical adjustments to inventory controls based on real time shopping data. To achieve the strategic objectives by market, monitoring availability of selling fares in target markets is used to determine the probability that an itinerary will sell to determine the overrides to the existing inventory controls based on prevailing market conditions. The inventory control overrides raise or lower availability by booking class. Leg/segment based competitive revenue management solutions were deployed in the early 2000s (Ratliff & Vinod, 2005) and was followed by extensions to O&D controls for network carriers, frequently referred to as Dynamic Availability (Vinod, 2016b). The earlier models were business rules driven followed with session level optimization models that either maximized expected revenue or maximized net contribution. The attractiveness of an itinerary can be determined with a choice model that is calibrated from a shopping request and response dataset based on pertinent variables such as displacement time (difference between requested time and departure time), travel time (also known as elapsed time), fare, screen presence, etc. Since Dynamic Availability is an inventory control recommendation to promote upsell in non-competitive situations, it requires no changes to existing systems and business processes. # The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 B. Vinod, The Evolution of Yield Management in the Airline Industry, Management for Professionals, https://doi.org/10.1007/978-3-030-70424-7_7

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The two techniques used in the competitive revenue management landscape are dynamic availability and dynamic pricing. Both approaches require low fare search shopping, calibrated consumer choice models and an optimizer. For dynamic availability, the optimizer is an availability optimizer while for dynamic pricing it is a price optimizer. The key difference between the two approaches is that dynamic availability overrides inventory control recommendations in the airline’s inventory system while dynamic pricing determines a new modified optimal price for the session that is a perturbation off a filed fare.

7.2

Leveraging Competitive Shopping Data

Although it is common practice for airlines to gather competitor prices and availability from websites, GDS shopping data, and through airfare benchmarking data providers like Infare, existing tools for responding to uncompetitive (or overcompetitive) prices and availability are limited to user-defined rules such as a simple matching policy subject to conditions. However, forecasting and optimization methods can be used to recommend model-based price and availability changes that better maximize an airline’s expected revenue conditional on current and expected future competitive market conditions. With this approach, there are two components that use recently collected competitive information on current and expected future available travel options (itineraries) in the market. First, a customer choice model is used to estimate sales probabilities across the set of available travel options in the market, and second, an availability optimization model estimates the revenue-maximizing set of available products based on their prices and market conditions. Competitive revenue management does not replace an airline’s existing revenue management system. Instead, it augments its forecasting and optimization process with prevailing competitive market conditions to recommend alternate revenue generating controls. The customer choice model can consider a variety of factors in calculating the sales probability for an itinerary. These factors include price, elapsed time, number of flight connections, flight departure times, brand equity (based on bookings through the direct channel) and market share. The availability optimizer determines the set of possible price points to evaluate using fare structures, current sales versus history, competitive market data, brand equity and business rules. The expected revenue for each set of available price points is evaluated based on the estimated demand associated with each set of alternatives across a sample of recently collected competitive shopping sessions. The expected revenue calculations also consider additional network impacts to other travel dates and/or markets that could potentially be impacted by making the recommended availability changes; the impacts on surrounding travel dates are not yet captured in any known revenue optimization process (the “round-trip” problem).

7.3 Dynamic Availability

7.3

273

Dynamic Availability

Dynamic Availability makes inventory control recommendations by origin and destination (O&D) based on outbound and inbound itineraries. It consists of two primary components: shopping data collection and choice model calibration, followed with a recommendation engine that determines how to override host CRS inventory controls to maximize revenues. Figure 7.1 describes the data collection and calibration process. Figure 7.2 describes the approach to recommend inventory control overrides. The customer choice model that considers schedule and fare attributes for estimating sales probabilities across the set of available travel options in the market, and an availability optimization model that estimates the revenue-maximizing set of available products based on their prices and market conditions. The session-based fare optimizer determines the optimal price point which is then translated into inventory control recommendations (Gallego & Hu, 2014). The dynamic availability model can determine inventory control recommendations based on expected revenue or net contribution (fare minus total bid price). The net contribution model, since it considers upline and downline displacement costs from the O&D network optimization model, generates incremental revenues over the expected revenue model. The quality of the inventory control recommendations can be further improved by looking beyond competitive selling fares; by considering the booking pace of competitors as observed in daily-MIDT data.

Context for Travel based Segmentation

Choice Model Calibration by Market, Market Entity, Region, System

Choice Model Coefficients for Schedule and Fare Attributes

Fig. 7.1 Data collection and calibration

Identify Markets for Competitive Revenue Management Control

Air Shopping Data Collection, Processing and Cleansing

Choice Model Analytics. Quality of Service Calculation by Itinerary

Day-parting for High Frequency Markets

Adjustments to Host CRS Inventory Controls

Revenue Impact Assessment across Multiple Shopping Sessions

Price Recommendation for a Shopping Session (Net Contribution Model)

Fig. 7.2 Dynamic availability approach to override inventory controls

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7.3.1

7 Competitive Revenue Management

Pros and Cons of Dynamic Availability

There are two primary benefits. First, most airlines already have direct connect availability (DCA) level of participation with GDSs. Thus, availability changes by a carrier are reflected instantly in the agency channel and there is no need to change the existing distribution business process. Second, there are no real time interline impacts in the distribution process. There are two primary limitations of dynamic availability. First, it is limited to discrete rather than continuous price points associated with the booking classes, keeping in sync with how fares are filed. This also implies that the airline has less control over the actual price point since multiple fares are linked to a RBD and low fare search finds the lowest price point associated with an RBD. Second, it cannot provide price controls at the round-trip level since availability is always directional. These two limitations lead to lower revenues than dynamic pricing. The discrete price point issue can be overcome to a degree if an airline adopts two-character RBDs to provide more granular controls. However, the costs are exceedingly high to deploy two-character RBDs on a host CRS and the GDS.

7.4

Dynamic Pricing

Is dynamic pricing the next significant advance in revenue management (Choubert, Fiig, & Viale, 2015; Fiig, Goyons, Adelving, & Smith, 2016)? There are many airlines that find the concept of dynamic pricing appealing and see it as the mainstay in a NDC world for dynamically priced offers (Fiig, Guen, & Gauchet, 2018). It allows airlines to move beyond existing pre-filed fares with personalized fares that are created in real time based on competitive market conditions. Dynamic Pricing of the base fare is closely related to Dynamic Availability. Both techniques can leverage competitive selling fares to arrive at an inventory control or dynamic price recommendation. Instead of converting the optimal price point from Dynamic Availability to an inventory control recommendation, the dynamic price is used to approximate the ticketed price. A customer’s willingness to pay is the maximum price that the customer will pay for an itinerary given that there are no other alternatives in the choice set. However, when there are multiple alternatives in the choice set, the customer’s conditional willingness to pay is the highest price the customer will pay for a specific itinerary from a set of alternative itineraries in the choice set. The customer choice model is used to rank each itinerary in the choice set and then determine the markup or markdown of the itinerary to create a dynamic price that maximizes the net contribution. Table 7.1 illustrates this point. When there is only a single itinerary in the choice set (Choice Set A), a customer may be willing to pay more than the selling fare for the service. However, when there are multiple choices, the maximum willingness to pay goes down to $700. A calibrated choice model can estimate the probability an itinerary will be selected in a shopping session based on host and competitor itineraries. If an airline

7.4 Dynamic Pricing

275

Table 7.1 Choice sets and customer willingness to pay Choice sets Set A Set B

Available itineraries Departure time 8:00 am, Nonstop, Flight #100 Departure time 8:00 am, Nonstop, Flight #100 Departure time 8:00 am, Nonstop, Flight #200

Fare ($) 800 800

Willingness to pay Up to $1000 for the schedule/fare attributes Not more than $700 for the schedule/fare attributes

700

increases its price for an itinerary the probability will drop and vice versa. The price optimization model understands these trade-offs and can search to determine the set of itinerary prices that maximize the expected contribution which is the difference between the expected revenue for the host airline’s itinerary and the total bid price. An effective heuristic based on these factors was proposed by Gallego and Hu (2014). Their heuristic identifies price decreases in uncompetitive situations and price increases when the host airline is over competitive. The net contribution calculation is summarized below: Net Contributioni j Salei ¼ " # X Bid Price  Probability of Selectioni Farei  i2L

where i is an itinerary in a specific line in the shopping response and L is the set of legs in the airline network. The form of the equation displayed above is conditional on a sale occurring (i.e., someone makes a purchase in the session). It can be extended to estimate net contribution per session by multiplying by the sales conversion rate per session. Dynamic pricing models can use customer choice models to compare different itineraries across airlines. Choice models consider the attractiveness of available itineraries to customers considering schedule attributes (e.g., departure time, elapsed time, number of stops, airline brand, aircraft type, cabin type, interline itinerary, codeshare itinerary, etc.) and price. The choice models can estimate the probability that a customer will select a specific itinerary. Further, the choice models can be calibrated by trip purpose segment (discussed in Chap. 6) to reflect differences in the trade-offs made between business and leisure customer segments. The price optimization model for dynamic pricing optimizes host airline itinerary prices based on trade-offs between the probability of selection from the choice model and yield. The price optimization search across possible price points determines a set of itineraries that maximize the expected profit condition on the airline’s schedule and fare attributes. The selection probability of an itinerary varies up or down depending on the price in the net contribution calculation. Hence, the idea of dynamic pricing optimization is to modify the price directly to achieve the desired markup or markdown. The

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7 Competitive Revenue Management

session-based fare optimizer determines the optimal price point for the host airline based on the competitive set and current selling fares of competing airlines in the same market. Instead of converting the optimal price point to an inventory control recommendation, the dynamic price is used to approximate the ticketed price. There are three primary variations for deploying dynamic pricing: laddered pricing, dual RBDs, and continuous pricing. A pan-industry Dynamic Pricing Working Group was established in 2016 with ATPCO as the sponsor to develop specifications for laddered pricing. With this approach intermediate private fares are filed between public fares that can only be activated by an airline’s dynamic pricing engine (DPE). Airlines, GDSs, vendors and related entities participated in these working group sessions to finalize a specification and messaging standard for dynamically priced fares for distribution to airline websites and intermediaries. The ATPCO JSON/XML schemas for connectivity were intended to provide an interim step toward NDC (Dezelak & Ratliff, 2018). The ATPCO specification allowed airlines to connect their dynamic pricing engines to traditional distribution prior to NDC cutover. The second approach, dual RBDs, enables an airline to increase the number of price points. The second approach is laddered pricing, wherein the airline files a set of intermediate discrete price points, which are essentially private tariffs, between published tariffs that can only be activated by the airline’s dynamic pricing engine. The third approach is continuous pricing, where the dynamic price generated by the dynamic pricing engine is used as the selling fare. Of the three approaches, continuous pricing has the most traction and several airlines have implemented first generation versions on their websites for selected markets. This approach to dynamic pricing, since it is evaluated one market at a time, also does not explicitly model the higher order network effects discussed in Chap. 4 (Sect. 4.5.1).

7.4.1

Pros and Cons of Dynamic Pricing

There are several benefits associated with dynamic pricing. First, it provides the ability to specify the revenue maximizing exact, continuous price point that is closer to a customer’s true willingness to pay. Second, it maximizes the potential revenue benefit due to the round-trip control capability. This finer degree of control can drive additional revenue benefits. Third, by deploying dynamic pricing in a competitive revenue management framework with marketplace competitive shopping data, recommendations can be fine-tuned to reflect the selling fares offered by competitors. Dynamic pricing also has two primary benefits over the traditional continuous nesting approach with ATPCO fare filings. First, it bridges the chasm between booking class availability and airline pricing by ensuring that the ticketed fare is greater than the total bid price. Second, the ticketed fare paid by a passenger will be the same as the dynamic price for the itinerary.

7.4 Dynamic Pricing

277

The potential revenue benefit from simulation studies of dynamic pricing with markups and markdowns can be up to 4% (Belobaba, 2019). Airlines always worry about the spiral down effect with dynamic pricing and dynamic availability. Simulation studies have shown that since dynamic pricing considers demand forecasts, the bid price, customer willingness to pay by segment and relative attractiveness of each itinerary, rational pricing recommendations will prevail, and it is not a zero-sum game as is reported by Belobaba (2019) and Wittman and Belobaba (2018). When four airlines were deploying dynamic pricing in the PODS simulator, based on customer willingness to pay and not competitive shopping data, revenue gains ranged from 1.5 to 2.5%. However, the benefits can erode with irrational competitors. The potential benefits of leveraging competitive shopping data can be even higher. The quality of the dynamic pricing recommendations can be further improved by looking beyond competitive selling fares; by considering the booking pace of competitors as observed in daily MIDT data. An added benefit with continuous dynamic pricing is that it provides an infinite number of price points on the price demand curve that can be inventory controlled to generate incremental revenues. Dynamic Pricing will be disruptive to airline fare management processes, agency workflows and GDS processing for comparison search. Another barrier to the adoption of dynamic pricing in the GDS channel is the absence of an industry standard for roundtrip inventory control (Isler & D’Souza 2009).

7.4.2

Bridging the Chasm Between the Market Value and Ticketed Fare

Prior to dynamic pricing, an itinerary was priced based on booking class availability and the applicable fares for the itinerary. After a booking was made, during the itinerary pricing process, the total bid price for the itinerary was not considered as a hurdle price or minimum acceptable threshold. Therefore, the ticketed price for the itinerary can be below the total bid price for the itinerary. Dynamic pricing bridges the chasm between airline inventory and airline pricing by ensuring that the ticketed fare is greater than the total bid price for the itinerary.

8

Agency Revenue Management

8.1

Overview

Revenue management for travel agencies is all about optimizing their revenue performance across airlines who distribute their content through the GDSs. It enhances their operational efficiency and revenue-making potential. This is true for the globals, premier TMCs, and the smaller independent travel agencies. The term “globals” refers to travel management companies with a global presence. They are the largest customers of a GDS. Examples are American Express Global Business Travel, BCD Travel Group, CWT (Carlson Wagonlit Travel), Flight Centre Travel Group, Egencia, Travel Leaders Group, Fareportal, and many more. Travel agencies support both business and leisure travel, though a large amount of leisure travel is booked directly by customers through airline websites and Online Travel Agencies (OTAs). Examples of global account OTAs are Expedia, Despegar, StudentUniverse and Priceline (Booking Holdings). Unlike leisure travel, corporate travel is managed travel, and there are protocols to follow when booking corporate travel. TMCs manage corporate travel programs. They provide travel services, ensure bookings are compliant with corporate policy and optimize the travel spend budget based on guidelines established by the corporate customer they provide service for. TMCs also provide corporate travelers with mobile applications and online booking tools. They also use GDSs to book flights for their corporate customers. While traditional revenue management is seen as an enterprise centric solution, on the travel distribution side of the business, agency revenue management enables competitiveness and maximizes agency revenue potential. Revenue management for travel agencies is rarely discussed at industry conferences and public forums. This chapter provides a brief overview of the types of problems that are addressed to support the travel agency community.

# The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 B. Vinod, The Evolution of Yield Management in the Airline Industry, Management for Professionals, https://doi.org/10.1007/978-3-030-70424-7_8

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280

8.2

8

Agency Revenue Management

Aspects of Agency Revenue Management

Agency revenue management is a vast topic with several variations and targeted applications. Central to agency revenue management capabilities provided by the GDSs to agencies is to hold the line on agency incentives, augmenting efforts for agencies to generate incremental revenues in other ways. This section will outline some of the more significant value propositions to manage revenues in the agency space.

8.2.1

Front End Commissions

Travel agencies receive front end commissions in selected markets, usually governed by a contract between the airline and agency. Individual contracts are signed between each airline and agency. A ticket can have one or more fare components and ticketed commissions are applicable at a fare component level which can have one or more ticket coupons. Ticketed commissions should be claimed at the time the ticket is created. The payment of commissions to a travel agency is a transaction with the airline and has no bearing on the price the customer pays for a ticket. When a travel agent claims an incorrect commission amount at the time of ticketing it triggers the airline to submit an agency debit memo (ADM) to reconcile the error. Ticketed commissions processed by GDSs can easily exceed a billion dollars annually. To address ticketed commissions, what is required is an agency managed commissions tool that encodes ticketed commission contracts, calculate the commission amount (if any) for each itinerary returned in a shopping response and subsequently apply the commission to the itinerary selected for ticketing. An issue encountered by GDSs is the laborintensive task of encoding ticketed commission contracts, which can be quite complex. Every agency can potentially have several contracts and to read the contract and manually encode this into a relational database is a time-consuming task. Automation with natural language processing tools invariably fail since the context and caveats that are in a written contract are difficult if not impossible to automate. The agency labor associated with this problem is significant; analysts spend several minutes on each ticket performing post-ticketing analysis and qualitycontrol checks with no guarantee of accuracy. In 2018, travel agencies spent more than 2 million hours on manual commission capture—only to leave more than $200 million they had already earned, unclaimed (Sheppard, 2019). An alternative to the labor-intensive task of encoding contracts is to predict which of the tickets can be commissioned at time of ticketing and estimate the commission amount. Factors that must be considered to calibrate a machine learning model (such as gradient boosting regression tree, an ensemble of decision trees that iteratively fit trees on the residuals), are based on data elements on the ticket such as origin, destination, ticket issue date, PCC, SC Code, departure date, return date, currency code, base amount of ticket, marketing carrier, validating carrier, fare basis code, etc.

8.2 Aspects of Agency Revenue Management

8.2.2

281

Back End (Override) Commissions

TMCs are faced with increasing performance pressures from airlines and corporate customers. While carriers have, over time, significantly increased the share of agency compensation that is tied to performance targets, corporate customers demand savings guarantees. To be successful in this complex environment, agencies must maximize supplier and customer contract performance, by performing the complicated tasks of continuously tracking performance targets and resolving conflicting objectives across thousands of markets. Airlines negotiate override commissions with travel agents. These are performance-based contracts where the agency receives a lump sum payment from the airline when the target goal, such as bookings in a market, has been achieved. Override contracts are even more complex than front end commission contracts. A typical agency has several contracts with different airlines, providing complex rules for incentive payout. For example, if an agency sells $100,000 worth of tickets for United Airlines in a market over a predefined period, it receives a 1% incentive payout. An override commissions optimizer model for corporate and leisure agencies requires several components that needs to be assembled. First, contracts between airlines and TMCs are complex, with clauses and conditions that are unique to the airline, the TMC or both. A data model for contracts is required based on a common semantic definition across heterogenous contracts to drive decision making for an agency based on its contracts with airline suppliers. Common variables need to be identified across contracts such as coupon revenue, ticket count, segment count, and forecast. Second, forecasting bookings is required at the level of detail for an agency as identified in the contract with the airline. Third, an optimization model (Smith et al., 2007) is required to maximize override commissions by recommending optimal sales targets that can be achieved during the target measurement period, based on visibility across all contracts. The objective is to forecast the expected ticket sales by contract and optimize the share between airlines to maximize the revenues for an agency over the multiple contracts. The optimizer finds tradeoffs between contracts for an agency moving shares between contracts. The final step is to bias displays to ensure that override commissions provided by airlines are maximized based on contractual thresholds and bookings-to-date. Fourth, an alerts dashboard to manage exceptions to achieve targets is required.

8.2.3

Net Fare Markup

TMCs negotiate net fares with airlines. These negotiated net fares can be marked up by the travel agency and the markup is pure profit for the agency. The example in Table 8.1 illustrates this point in the context of a negotiate fare with airline A.

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Table 8.1 Illustrative example of net fare markup (Airline A) Booking class M

Fare type Public Corp fare for ABC Corporation Net fare with dynamic markup Net fare (wholesale price)

Amount ($) 1750 1650 1649 1600

Consider the example above where a TMC’s preferred carrier is Airline A. The TMC wants to determine the optimal net fare markup of the TMC’s net fare negotiated with Airline A. In practice today, most agencies use a static markup logic to apply to their net fares (e.g., +10%), but such fixed policies are often undermined by other, lower priced fare products. To determine the optimal markup, the TMC needs three fares—the public (ADT) fare in the market ($1750), the corporate negotiated discount fare ($1650) for their customer ABC Corporaton (this is a corporate discount negotiated by ABC Corporation with Airline A) and the net fare ($1600) negotiated by the TMC with Airline A that can be marked up for profit. In summary, to determine the optimal markup, the TMC needs to know the public ADT fare, the ABC Corporation corporate fare and the TMC negotiated net fare during shopping. Dynamic markups at time of shopping for TMCs requires a shopping API that can accept qualifiers to return multiple fares. In this example, it is the ADT fare for airline A ($1750), the corporate discount fare for Airline A ($1650) and the net fare of the TMC before the markup ($1600) along with the CAT35 indicator (L, T, C) that indicates the net fare can be marked up (indicator “C”) with any associated rules. Dynamic markup should also work in situations when an agent does not submit a shopping entry but does a simple book and price. In this scenario, the agent makes a booking on a specific itinerary and issues a pricing command to determine the lowest fare. The pricing entry must now evaluate three fare types to determine if the marked up net fare can be promoted.

8.2.4

Bulk Fares and Packages

TMCs and OTAs also negotiate bulk fares with airlines called JCBs (JCB is a passenger type code (PTC) for contract, bulk, adult) that can only be sold as part of a package. Inclusion of bulk fares in an air and hotel package can produce a competitive package price in situations when the bulk fare is lower than the selling public (ADT) fare. This is not always the case and situations arise when the bulk fare is more expensive than the selling public fare. When packages are created in markets where the TMC/OTA has negotiated bulk fares, the air shopping entry should return both the public fare and the bulk fare so that an automated decision can be made on inclusion of the bulk fare for the package to be promoted to the customer.

8.2 Aspects of Agency Revenue Management

8.2.5

283

Optimizing Screen Real Estate

Optimizing the screen real estate is applicable for air, hotel, and other lines of business (Guenther, Ratliff, & Sylla, 2012). Consider the following hotel example. When a B2C customer submits a hotel shopping request on an OTA, techniques can be applied to determine the order in which hotels should be displayed for a destination market. The objective of optimal hotel ranking is to improve site traffic conversion rates and generate incremental revenues. Consumer choice models can be calibrated from historical hotel shopping sessions to display hotel search results that maximize the probability of selecting a property on the first page. This approach also improves customer loyalty because properties displayed are relevant to the search request and a function of the hotel selling rate and popularity. To address hotel booking conversion rates, it is first important to understand consumer choice shopping behavior on an OTA site. Customers are clearly segmented by star ratings preference with a 90% correlation between 1st choice and 2nd choice. Over 97% of bookings are realized from pages one to three (systemwide) regardless of the number of properties returned in a shopping response, which usually runs into the 100s, and over 75% of shopping is for properties with a star rating of three or four. Finally, the neighborhood (location) is particularly important in the decision-making process to book a hotel. OTAs maintain hotel polygons that map properties into distinct neighborhoods. For example, in Boston, the polygons could be downtown, Logan airport, Back Bay, Cambridge, North Shore, South Shore, Waltham, Woburn, and Northwest Boston. For properties with the same star ratings, a calibrated consumer choice model will indicate the premium dollar amount a consumer will be willing to pay for a Back Bay property compared to a property in Waltham, a suburb. Properties can be ranked based on the property score calibrated from hotel shopping data. Property Score ¼ a * ln ðPriceÞ þ b * Star Rating þ c * Location Property ranking based on value improves the probability of selection on a shopping display page. Unlike air and rental car, customers perceive and appreciate a property’s unique attributes. To further distinguish property A from property B in the same location (polygon) with the same star rating, the location variable in the above equation can be replaced by unique property id which improves conversion rates for 3, 4, and 5-Star properties. On the air side of the equation, itinerary displays can be optimized to maximize conversion rates. Consumer choice models can be calibrated to score itineraries returned in a shopping request based on intrinsic value, based on schedule and fare attributes. Advanced techniques such as latent choice models with implied segmentation based on day of week and number in party, for example, can influence the display order and produce further improvements.

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Hotel Product Normalization

A new trend with GDSs is to source hotel content from multiple aggregators such as bedsonline (hotelbeds), Expedia Affiliate Network (EAN) and booking.com. The room rates and inclusions of certain hotel attributes (such as free breakfast) can vary from one aggregator to the next. Normalizing the content across multiple hotel aggregators for a specific property and rate is required for travel agents to comparison shop and make a booking on behalf of a customer. Performance is a key consideration; evaluation and normalization of a room rate across aggregators cannot exceed 0.25 ms. From information retrieved during hotel shopping, for each product (room type) description, the trie-based search algorithm, an efficient retrieval data structure, is ideal to parse the descriptions, convert keywords and words of interest into the common nomenclature and return a set of normalized attributes. This approach requires initialization with phrases to pre-build the tries to process the descriptions. An alternative approach is to use SHA256 (Secure Hash Algorithm 256) that builds hash maps by hotel chain for every property description.

8.2.7

Collaboration with Corporations to Optimize Travel Spend

TMCs add value to their corporate clients by optimizing the travel spend based on travel budgets established by corporations. One example is to use marked up net fares that are cheaper than the corporate fares. Another example is special deals negotiated between TMCs and hotels that further reduces the cost of a booking. A larger more complex task is to take historical sales data for a corporation serviced by a TMC and model alternate advance booking policies based on type of trip (domestic short haul, domestic long haul, international) to reduce the corporate spend by fine tuning corporate travel policy.

8.3

Summary

Real time marketplace intelligence across air, hotel, rental car, and leisure activities promotes operational efficiencies and revenue producing opportunities for travel agencies. Integrated with the agency desktop workflow, session-based revenue management capabilities such as identification of commissionable tickets and net fare markups serve as a new source of incremental revenue. Identifying a sales strategy to maximize override commissions maximizes revenue generating opportunities and adds to the bottom line. Optimizing screen displays results in improvements in productivity measured by bookings per day.

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The Last Frontier: Individual Seat Pricing

9.1

Individual Seat Inventory Control

Inventory controls have become more granular over time. For example, O&D inventory control was a more detailed method to accept and reject individual booking requests compared to leg/segment controls. Connectivity features such as journey controls and married segment controls support more granular controls. Revenue management of seats, by seat type for a section of the aircraft, or individual seat is generally viewed as the last frontier in revenue management since inventory is controlled at the most granular level of detail (Vinod, 2021a). Airlines are actively experimenting with seat pricing and mass adoption of individual seat pricing. This granular level of control with dynamic pricing of individual seats will replace the current rules-based static pricing practiced by the LCCs or the network carrier approach based on fare components of the itinerary that permit customers to select seats in a section of the cabin. Determining availability and price by individual seat, when the request is made, is considered as the holy grail of revenue management. To revenue manage an itinerary at the seat level requires a real time detailed seat inventory control component together with a revenue management capability to forecast demand by seat type and determine how individual seats or seat types should be priced. In the current environment, based on the section of the airplane, seat prices are static by product (e.g., premium economy, economy, main cabin). The opportunity for variable seat pricing depends on how customers value a specific seat on an airplane. The dynamic price for a seat is influenced by the region, market, passenger type, schedule attributes (aircraft type, departure time of day, nonstop versus connection), number in party (seats required together), channel, the seat type (aisle, window, exit row, leg rest available, premium economy (wider pitch), basic economy, bulkhead, minimum noise zone, extra leg room, uninterrupted view, etc.), length of haul (short, medium, long), restricted seat recline, seat availability, etc. These factors influence the passenger’s perception of the utility of a seat and the

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Fig. 9.1 Airline seat map. Source: Created by Joe Jennings, April 10, 2021 (www.joemjennings.com)

probability of purchase of a specific seat. Figure 9.1 illustrates the seat map on an aircraft.

9.1.1

Seat Map Cache for GDS Shopping

Displaying availability by seat type across multiple airlines when a shopping request is made has a significant impact on air shopping performance and response times. Online Travel Agencies (OTAs) display hundreds of flight options in a single shopping request. They also desire to display seat map counts by seat type based on customer preferences to enhance the display of itineraries returned as part of the shopping response. To avoid the transactional and computational burden of querying seat counts and maps on the host CRS in real time, a viable approach is to deploy a seat map cache. A seat map cache addresses the issue of an airline’s pre-reserved seats system resident on the host CRS from being overwhelmed with several hundred seat map requests following a shopping request from an OTA. The seat map cache is also required for individual seat pricing. For airlines hosted on a Passenger Service System (PSS) operated by a GDS, the seat map cache is a combined PULL and partial PUSH model. PULL implies that a seat map service is requested to populate the seat map cache. A PUSH implies that every time the seat map is accessed by a travel agent or end user, an update is sent to the seat map cache. In other words, PUSH is organic (driven by traveler actions) while PULL in inorganic (requested to fill the cache). For example, if a carrier is hosted on a specific PSS such as Amadeus, then the partial PUSH takes place only

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when an Amadeus travel agent requests a seat map for a non-hosted carrier. When GDS subscribers of Travelport and Sabre request a seat map, there is no mechanism to update the seat map cache in the Amadeus PSS without active collaboration between all parties including the airline. However, when the airline is hosted on Amadeus, this is not a problem, and the entire seat map may be cached and counts by seat type will have the same accuracy as RBD availability. There are two necessary and sufficient conditions that should always be satisfied to ensure consistency and accuracy of seat map counts. Consider a flight that goes from A to B to C. Condition 1: If a seat is consumed on a through flight (e.g., A to C), then the same seat should be consumed on the legs that make up the through flight (A to B and B to C). Condition 2: If a seat is consumed on a leg, it should not be available on a through flight that includes this leg. Hence, all seat maps are stored at a flight leg level and not at a flight segment level since all flight segments are made up of the underlying legs. When a seat map request is made for a given flight on an O&D, the cache examines the line of flight for this flight date to determine which leg maps to combine to create the requested segment map. The seat map cache should maintain counts of sold and available seats along the following dimensions in real time: total seats, aisle/window/center seats, exit row seats, premium seats, preferred seats, no charge seats, bulkhead seats, pay-for seats, minimum noise zone, extra leg room, uninterrupted view, seats together for a specific party size (two seats together, three seats together, etc.), and details for a specific seat (e.g., seat 12 A—window, premium, etc.). A seat map cache controller determines when an item in cache needs to be refreshed. The refresh frequency can be based on static predeparture reading day concept from revenue management or dynamic based on actual activity. The cache should also be able to display the physical seat map. Deployment of a seat map cache should support all the Passenger and Airport Data Interchange Standards (PADIS) seat characteristics an airline chooses to send. There are over 100 characteristics, which can be grouped into categories such as location of the seat (e.g., front of cabin, upper deck, adjacent closet, etc.), missing seats (e.g., no seats because of exit door, no seat because of upper stairs, etc.), seat characteristics (window, aisle, etc.), seat occupation details (e.g., occupied, advanced boarding pass issued, etc.) and seat blocking details (e.g., blocked for airport, blocked for through passenger, etc.).

9.1.2

Seat Map Cache for the Direct Channel

The seat map cache for a single airline to support seat-led shopping on the direct channel is like the GDS model, but easier to implement, since the vendor that

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provides the shopping service for the airline’s direct channel can request access to seat maps and counts as bookings are made. Every time a seat is consumed from any channel, agency or direct, the seat map cache can be updated in real time and shopping can access this cache in real time to support seat lead shopping. The seat map cache counts of inventory will have the same level of accuracy as RBD availability.

9.1.3

Seat-Led Shopping: Agency and Direct Channels

Another important consideration during flight search is seat led shopping. Here are a few scenarios for seat led shopping. Show me flights to San Francisco departing on June 4 before noon and returning June 8 in the evening for a family of 4 and we want to sit together. I want to go to Rome for 1 week, departing June 1. Only show me flights where an aisle seat in Business Class is available. I want to go to NYC on June 1, returning on June 5. Show me flights where exit row seats are available. In addition, I want to avoid regional jets. I want to go to London for 1 week, departing July 1 with my spouse. Only show me flights where seats 5A and 5B are available in Business Class on a Boeing 777.

The workflow for seat selection today in both the agency channel and the consumer direct channel occurs after the itinerary has been selected by the customer. This is a significant shortcoming since the seat map may display available seats that are not acceptable to a traveler. For agency (GDS) shopping, post processing of itineraries returned from shopping to select itineraries that fulfil the customer seat request is not an elegant solution since none of the itineraries returned may fulfill the seat request constraint. Seat-led shopping as a post process of the shopping process has its limitations, since there is no guarantee that the itineraries returned during shopping fulfills the seat request. Ideally, seat-led shopping should be in-path in the shopping algorithm and not a post-process. This implies that the seat selection constraint imposed by the customer is considered a priori when schedules are generated by the shopping algorithm. For example, if a customer wants four seats together, the only itineraries that should be displayed are those which have four seats together that can be selected. With seat-led shopping, the prices for the itineraries returned by shopping may be higher with the seat type constraint. Seat led shopping advances the user experience to select itineraries that guarantee the requested seat type request at time of shop. Querying the pre-reserved seats function on the host CRS in real time to support shopping is an expensive proposition, besides increasing latency in response times. Access to a seat

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map cache in real time during shopping addresses this gap to support seat-led shopping to fulfil the seat requirement.

9.1.4

Pricing of Seats

During the selection of an itinerary and subsequent booking, airlines allow customers to select seats on the airplane except for exit row seats and any blocked seats for premium passengers, bulkhead seats for families with small children or weight and balance restrictions. The pricing is static, and can be a paid seat for extra leg room, preferred seat, etc. Customers that do not pay for a seat can go through a standard seat selection process for seats that have not yet been consumed. The policy ranges from 24 h to 2 days before flight departure of the first segment. Return trips go through a similar process with the same policy for unpaid seats. During the seat selection process, every customer has a perception of the intrinsic value of a seat that is selected. This dictates the seat purchase rate which is the ratio of seat purchases to total seats by seat type. Given the value of a seat, especially for longer range trips, there is an opportunity to dynamically price the seats based on flight and customer attributes. Examples of flight attributes are market, aircraft type, travel distance (short haul, medium haul, long haul, and ultra-long haul), departure time, arrival time, season, and day of week. Examples of customer attributes are frequent flyer tier, gender, advance purchase, departure day of week, length of stay, number in party, sales channel, point of commencement, point of sale—country and city, currency of purchase and form of payment. Determining seat prices can be rules-based or based on advanced decision support that generates these price points for ancillaries by flight leg and date. Rules-based personalization is unwieldy when there are many pre-defined rules that need to be established and will require periodic updates to the rules. Rules need to be categorized and maintained in a catalog for ease of maintenance, updates, and deletions. In the absence of a rules catalog, a common problem encountered is the large number of inactive rules in the system that require periodic clean-up. Predictive personalization is a more sophisticated approach, and it does not require the creation of rules. Recommendations are based on a blend of purchases by customers in the same segment and actual purchases from past behavior. Sophisticated methods can also be used to determine seat pricing. Machine learning methods such as logistic regression, gradient boosting machine (GBM), random forest and deep learning can be calibrated to estimate the accuracy, sensitivity (recall or true positive rate), precision and false positive rates. The calibrated models can predict the probability of purchase at a specific price point. However, such approaches frequently require extensive calibration before the predictive model is deployed and impacts scalability of the solution. Besides, to address market changes and competitor responses, these models must be frequently re-calibrated, adding to the burden of scalability. Monetizing seats with a dynamic seat price is an active area of research by leading airlines today.

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Table 9.1 Price as a function of seats sold

Number of seats sold (aisle seats) 1–5 6–10 11–15 >15

Price ($) 15 18 22 25

To support continuous calibration, a reinforcement learning based test and learn experimental approach is preferred over traditional predictive model calibration which will have to be repeated periodically to address changes in customer preferences and willingness to pay behavior over time. This approach also has the advantage of continuously adapting to the environment based on market conditions. If seat pricing is by leg, there is an opportunity to incorporate a variable pricing concept like a bid price curve that makes the seat price a function of the seats sold for a specific type of seat. This will require the airline’s inventory control system to receive seat consumption updates in real time from pre-reserved seats (PRS) on the host CRS to maintain accurate seat counts by seat type by flight leg and date. Consider the example ancillary pricing structure for aisle seats, for booking class M for a specific flight leg by departure date as shown in Table 9.1. When availability is returned by an airline’s inventory system, booking class combinability that is validated during shopping is not yet known. Hence once the itineraries are priced by shopping based on RBD availability, a second pass is required where the itineraries are sent to the airline inventory system for a total seat price by itinerary. This information will be used to determine the top N itineraries in the shopping response. Assuming the pricing of seats is by flight leg (segments and market seat prices can be derived from the flight leg seat prices) this information can be stored on the flight inventory detail record in the host CRS inventory system. An issue that needs to be addressed is determining the total itinerary price with seats since it is possible that a more expensive RBD may have seats at no cost and shopping would miss this RBD in the first pass. A push-back mechanism is required to address this issue. With this approach, ancillary seat prices are dependent on total sales by seat type. In this scenario, an ATPCO OC (optional service fees) filing for an aisle seat will only have a reference fare by market and time of day (if the airline participates in ATPCO) for informational purposes, and then the ancillary price for a seat will be a function of the bookings by seat type and potentially type of customer—the higher the seats sold count by seat type in this scenario, the higher the price. In this scenario, all sales channels will have to go to the airline (host CRS) to price the ancillary services. This is also a requirement for NDC.

9.1.5

Impact of NDC on Revenue Management

In today’s environment, a travel agent subscribing to a GDS can shop, book, price, and ticket an itinerary for a customer. With IATA’s New Distribution Capability

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(NDC) that is currently being rolled out, pricing power shifts from the GDS to the airline. An agency must request itineraries and prices from an airline in an NDC world, and the task for the GDS is to normalize the non-homogenous content across travel suppliers for display to a travel agent. Hence, creation and delivery of content with this approach will be within the domain of an airline’s environment. The promise of NDC and the new XML-based messaging standard between airlines and GDSs are several. They are: 1. Enables an airline to differentiate and offer products and services to customers that differentiate it from a competitor. 2. Enable airlines to offer personalized offers with rich content that is not available in the GDS. Personalized offers can increase revenues at time of booking with the sale of ancillary products and services such as pre-reserved seats, baggage, Wi-Fi, lounge access, meals, etc. 3. Display fare families at multiple price points with increasing value to promote upsell. 4. Maximize revenues with dynamic pricing and dynamic ancillary bundles based on context for travel. There are two approaches to dynamic pricing for the base fare: laddered pricing and continuous pricing. 5. Reduce the total cost of distribution. Adoption of NDC is not without its challenges. First, when pricing power shifts from the GDS to the airline, a far greater investment is required by airlines in software and computing power to respond to every request from a travel agent. Second, scalability is a major concern, and it remains to be seen if NDC can scale to current GDS volumes for transaction processing without caching the customer offers (Vinod & Huff, 2019). Caching of offers has a drawback that the offers can be stored by trip-purpose segment and storing offers by individual customer may not be viable. Third, is the processing of interline booking requests. Another byproduct of NDC in the future is the so-called class-less revenue management. Revenue management without information on availability by booking class is a radical departure from the current environment (Isler, 2016; Isler & D’Souza, 2009). Booking classes are used today to distribute availability status using availability status (AVS) and direct connect between an airline and a GDS. In an NDC world, since an airline processes all the requests, booking classes are technically not required since the GDS does not book, and ticket a customer’s booking request based on RBDs. However, customer segmentation based on context for travel, will be required to generate dynamically priced content for each request. To support class-less revenue management, price elasticity curves should be constructed by customer segment to estimate demand along a continuum of fares to capture the inter-relationship of demand across fares. This ensures the migration from discrete fare ranges to continuous pricing. These curves should ideally be constructed across similar markets by trip-purpose segment to ensure a reliable and stable predictive model.

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9.2

9 The Last Frontier: Individual Seat Pricing

Milestones in Airline Revenue Management

Figure 9.2 summarizes the evolution of revenue management since deregulation of the airline industry. The most significant milestones are outlined in the figure below. 1978

1985

1986

Airline Leg/Segment O&D Deregulation Inventory Inventory Controls Controls Virtual Nesting

2014

2019

Competitive Dynamic Revenue Pricing Management (O&D Dynamic Control) Availability (O&D Control)

1992 O&D Inventory Controls Continuous Nesting (bid price controls)

2019 - 2022

Offer Management (Air + Air Ancillary Bundle)

2021 - 2024

Seat Type and Individual Seat Inventory Control

Fig. 9.2 Milestones in airline revenue management

2001 Low-Cost Carrier (LCC) Model Restriction Free Tariffs (Leg/Segment Controls)

2004 Hybrid Model Restriction Free Tariffs and Regular Tariffs (O&D Control)

Future

Class Less Leg/Segment and O&D Inventory Controls

2005 Competitive Revenue Management (Leg/Segment Carriers)

Influence of Revenue Management on the Airline Business Process

10.1

10

Impact of Revenue Management on the Airline Business

For several decades airline revenue management has proven to be critical for airline competitiveness and profitability. Since Littlewood’s Rule for discount seat allocation with two booking classes was introduced in 1972, revenue management has come of age. Previous studies on integrated decision making between scheduling, pricing and revenue management indicate that there are significant opportunities for overcoming missed revenue opportunities (Jacobs, Ratliff, & Smith, 2000) with alignment of resources across airline functions and synchronized decision making. Over the past two decades revenue management has played a central role as a key influencer in decision making across airline planning, marketing, and operations (Vinod, 2015). An airline’s revenue performance is influenced by business processes associated with airline planning, airline marketing and airline operations, decision making and execution. Revenue management data and key performance indicators (KPIs) can support timely and consistent decision making across airline planning, marketing, and operations. Operating an airline is a complex endeavor. Figure 10.1 illustrates the core airline functions that can benefit from revenue management data and associated KPIs.

10.1.1 Reservations and Inventory Control An airline’s reservations inventory control serves as the execution component for revenue management. The inventory system responds to availability and sell requests in real time. Nested inventory controls in a host CRS are required for airlines that deploy leg/segment revenue management controls. The seat availability calculation can be based on net nesting, threshold nesting or its variants based on the airline’s underlying strategy for seat inventory control.

# The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 B. Vinod, The Evolution of Yield Management in the Airline Industry, Management for Professionals, https://doi.org/10.1007/978-3-030-70424-7_10

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10 Influence of Revenue Management on the Airline Business Process Network Planning & Flight Scheduling

Close-in Re-fleeting

Air Shopping

Loyalty & Coalition Programs

Interactive Marketing

Airline Operations

Fare Management

Revenue Management

Screen Display Optimization

Offer Management

Pricing of Air Ancillaries

Inflight Catering

Fig. 10.1 Revenue management influences decision making in planning, marketing and operations

For airlines operating in an O&D inventory control environment, the host CRS should support continuous nesting or virtual nesting controls. Managing seat inventory by O&D also requires investment in direct connect availability, direct connect sell, married segment logic, journey data controls and marriage to journey controls to avoid revenue leakage. Capabilities specific to codeshare flights include cascading codeshare (also known as seamless codeshare) and bid price exchange (Vinod, 2005d; Weatherford & Ratliff, 2013). These capabilities on the host CRS are required for revenue management to be effective. PNR data, inventory detail record data, post departure data and ticketed data represent core requirements for any revenue management system to support demand forecasting, no-show forecasting, overbooking and revenue mix controls. With the growth in ancillary product sales such as baggage fees, pre-reserved seats and priority boarding, there is a growing emphasis to enhance reservations systems for selling inventory-controlled ancillaries at the time of booking. Special service record (SSR) (e.g., wheelchair assistance), special service record inventory (SSRI) (e.g., seat requests) and electronic miscellaneous document (EMD, an IATA standard for documenting ancillary sales) are used for the tracking, inventory control and sales of ancillaries.

10.1.2 Network Planning and Flight Scheduling The airline planning process begins with fleet planning, followed by route planning, schedule development and integrated airline planning. Fleet planning has the longest time horizon of several months to a few years, route planning has an intermediate time horizon and schedule development is typically less than a year from departure. During the schedule development process, the objective is to match demand to capacity by optimizing the schedule. The schedule development process begins with estimates for the total demand for air travel, referred to as market size. This requires data from public and commercial sources such as Marketing Information Data Tapes (MIDT), Department of Transportation (T100), Official Airline Guide, Airline Tariff Publishing Company (ATPCO), air shopping data from GDSs, and travel and

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Impact of Revenue Management on the Airline Business

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tourism bureau statistics. World Bank data as also a good source, since it includes GDP, per capita income, population, and literacy rate. Another useful data source is IHS Markit Ltd. that provides access to global financial market information. Methods to estimate market size of an O&D include gravity models (Grosche, Rothlauf, & Heinzl, 2007) and machine learning models (Agrawal & Dasgupta, 2019; Gautam, Nayak, & Shebalov, 2021). From the market sizes, the market share is estimated for the host airline and other airlines (OAL) by market. Advanced consumer choice models (CCM) or quality of service index (QSI) models are used to characterize consumer preferences such as departure time, travel time (also called elapsed time), carrier preference, origin point presence, etc. to estimate market share. This is followed by a forecast of passenger demand for the host airline and OAL (other airlines). Passenger traffic is next estimated by applying the demand forecast with its associated variability against expected aircraft capacity based on the airline spill model. The traffic can then be used to estimate passenger revenue. Schedule profitability of the airline is determined after allocating the fixed and variable operational costs against the estimate of passenger revenue. In addition to the airline spill model that is used in schedule profitability, the network optimization model with upsell and recapture (Gallego, Ratliff, & Shebalov, 2015) from revenue management can be used to generate an optimal schedule as part of the network planning process. The approach starts with a good baseline schedule and then intelligently overbuilds the schedule by adding frequencies in existing profitable markets, strategic markets, and new markets. Schedules should consider other airline (OAL) services. Market sizes are created for all potential flow markets and cost data for new markets and stations to measure schedule profitability. The network optimizer then selects the best schedule. Decisions that are typically made include what markets should be served, frequency of operation, optimal codeshare markets and partners, retiming flights into and out of a hub, balancing the time in the air and on the ground for each flight, and refactoring schedules to enter new markets or to terminate services to existing markets. Each of these network planning decisions identifies the most profitable network structure that can be operated by a fleet of a given mix and size that maximizes the demand flow in the network. The sensitivity of capacity planning to oversale costs, service level constraints and the marginal cost of capacity when overbooking limits are considered has been explored (He, 2019). The study indicates that capacity levels can be too low or too high if overbooking is not factored in the decision-making process and overbooking leads to a lower capacity level only when the marginal cost of capacity is below a threshold. A combined approach is shown to be beneficial to a sequential approach.

10.1.3 Close-in Re-fleeting Close-in re-fleeting, also referred to as Demand Driven Dispatch (D3), is the process of reassigning flights 30 to 60 days before departure to optimally match passenger demand to aircraft capacity to improve operating profitability. Peterson (1986) from

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the Boeing Commercial Group was the first to conceptualize this operating framework in an internal memo. This is accomplished by swapping aircraft of different sizes on flight legs to match capacity to demand based on tactical demand forecasts and expected marginal revenue estimates from revenue management (Shebalov, 2009). Various approaches to close-in re-fleeting are discussed in the literature (Berge & Hopperstad, 1993; Fry, 2015; Wang & Meng, 2008). Traditional fleet assignment (Jacobs, Smith, & Johnson, 2008) is based on aggregate revenue estimates, while revenue management is granular and forecasts demand by booking class, models demand uncertainty, considers the nesting structure and accounts for changes in revenue caused by demand interactions. The revenue management estimate of expected marginal revenue is more accurate. Estimates of expected marginal revenue and tactical demand forecasts by flight leg (or service) from revenue management can be used by the leg-based (or O&D based) fleet assignment model to improve network revenue performance. The success of close-in re-fleeting lies in understanding demand volatility. If aircraft swaps are made well in advance of the departure date and demand volatility is high, there can be revenue dilution with greater capacity allocated than needed or spill if demand is greater than capacity. The benefits of close-in re-fleeting are well known. Common cockpit compatible fleet types such as the A330-200/300, Dash 8 200/300/400, A320/A321 and B-737/738/739 have demonstrated the biggest financial impact. Consulting studies with airlines adopting close-in re-fleeting have shown that the approach results in higher revenues (1% to 1.4%), lower load factors (0.5% to 0.7%) and higher yields (2.1%). Lufthansa realizes 5 million euros in monthly benefits (Subramanian & Stupfel, 2005). Several airlines routinely use close-in re-fleeting that have been discussed in industry forums and conferences. They include Aero Mexico, Alaska Airlines, Avianca, Delta Air Lines, Etihad Airways, Iberia, and LAN. Scandinavian Airlines System (SAS) has deployed a version of D3 for dynamic rotations that utilizes bid prices in the decision-making process to determine gauge changes within 10 weeks of departure (Warburg, Hansen, Larsen, Norman, & Andersson, 2008). Fry and Belobaba (2016) estimate the operating profit gains to be 0.04%–2.3% and revenue gains of 0.02% to 0.88% from the PODS simulator.

10.1.4 Fare Management Fare management has a significant impact on and synergies with revenue management and reservation inventory controls. Yet the two decisions—pricing and revenue management—are distinct and separate. In the future, dynamic pricing has the potential to bring fare management and revenue management into a unified process. Pricing is a core input into the revenue management process that forecasts demand for the various fare products and matches demand to available capacity to determine the optimal reservations inventory controls. Air pricing decisions are influenced by market conditions and competitive fare activity that serves as an

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input into revenue management and influences how seat inventory controls are established. Fare management also determines the weighted average fare values to be used in the revenue mix optimization model by matching future fares to historical revenue accounting data by matching the first few letters of the fare basis code. Periodic realignment of booking classes is required to ensure that fares filed are mapped to the correct booking classes to minimize fare overlaps and avoid fare inversions. The realignment process which remaps fare basis codes to booking classes based on value, is a required periodic business process for an airline to get the most out of revenue management (Vinod, 2010; Weatherford, 2002). Realignment of booking classes can result in higher demand forecast errors. An option is to restate history after the realignment is complete to minimize the impact of high forecast errors that lead to conservative inventory controls and revenue dilution. Fare management decisions are influenced by revenue management decision support capabilities for tactical and strategic pricing (Vinod, Narayan, & Ratliff, 2009). Tactical pricing predominantly relies on fare matching based on business rules which may not be the ideal response to a competitor’s fare actions. By using the revenue management consumer choice models with schedule and fare attributes, a quality of service adjusted price (QSAP) response can be computed based on the schedule and relative fare competitiveness of the host carrier. Matching competitor fares after considering the value of the quality of service is a vast improvement over the traditional fare matching process based on rules. An evolving trend is strategic pricing to proactively set the optimal tariff structure for a market for a future season by leveraging and adapting demand models and competitor response models from revenue management. This approach answers the fundamental question: What is the right price to charge to achieve a desired mix of traffic? Strategic pricing allows an airline to exhibit price leadership in the marketplace and to achieve management objectives such as the desired mix of traffic by fare product, under a range of competitive fare match scenarios. Various studies (Ratliff & Vinod, 2016) have demonstrated a 1–6% in yield (revenue/ticket) improvement with strategic pricing. These approaches to tactical and strategic pricing decision making bring pricing closer to what a customer is willing to pay rather than what the supplier is willing to accept. A related area for O&D seat availability and sell is itinerary pricing. A fundamental dilemma with implementing O&D revenue management is that the ticketed fare for an O&D could be less than the total bid price that was used to decide availability. O&D availability is directional and is based on the total one-way bid price. After a booking is made, the itinerary is priced to determine the lowest available fare, which is based on evaluating not just the through fare (the single fare component), but also sum of locals subject to fare combinability rules. The input into an itinerary pricing system is the itinerary and associated booking classes that needs to be priced. By providing the total bid price as an input along with the itinerary for itinerary pricing, revenue management can ensure that the itinerary prices returned are always greater than the total bid price of the roundtrip. This may entail a change in booking class. None of the itinerary pricing systems today take the

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total bid price as an input for pricing an itinerary. This is because factoring the total bid price as a lower bound threshold during itinerary pricing could make the airline’s itinerary uncompetitive. It can be argued that dynamic pricing, an active area of academic research (Christ, 2011; Gallego & van Ryzin, 1994; Nasiry & Popescu, 2011), can address this problem and ensure that the ticketed fare is always greater than or equal to the total bid price. While dynamic pricing can be deployed on a consumer direct airline website controlled by the airline, the travel agency channel that comprises a large percentage of bookings relies on pricing itineraries in their own environment based on filed fares and rules by the airlines. This will change when the IATA New Distribution Capability (NDC) gains adoption. Dynamic pricing is also applicable in the pricing of opaque airline tickets (Zouaoui & Rao, 2009).

10.1.5 Air Shopping Seat availability, returned from an airline’s reservations inventory system on request, is the end product of the revenue management process and is a key input into air shopping algorithms. The four components of air shopping are schedule generation, itinerary selection, booking class availability and itinerary pricing. Between schedules, airfares and availability used by air shopping algorithms, booking class availability is the most volatile. Air shopping volumes have been growing at a rapid rate over the past decade and have outpaced growth in bookings. Figure 10.2 illustrates the growth in shopping volumes for the Sabre GDS for the agency channel (Vinod, 2020a). The rate of increase from 2018 to 2019 was about 47%. The growth is expected to average approximately 50%.1 Unlike customer shopping through the direct channel, OTAs that shop through the GDS require advanced capabilities such as shopping across PCCs,2 and multiple fares per itinerary (based on customer defined attributes and rely heavily on private fares (CAT 25 and CAT 35)), which adds to the compute cost. Exchanges and refunds (CAT 31 and CAT 33) also contribute to the shopping volumes. Exchanges and refunds require access to historical fares to display alternate itineraries with the fare differentials to collect from passengers. The look-to-book metric is meaningful for airlines in the context of city pair availability. City pair availability entries always rely on a live Direct Connect Availability (DCA) call to query the airline’s host CRS. In this scenario it is the ratio of live DCA calls to the number of bookings. For shopping transactions, it is

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Q4 and FY 2019 Earning Call Script, February 26, 2020; https://investors.sabre.com/static-files/ 95fb05f9-5d8d-4549-b47c-37ba0bcec695 2 A pseudo city code (PCC) is a GDS code to identify the location of a travel agency. Negotiated fares can be secured to an agency using the PCC.

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less meaningful, but useful to understand growth in shopping volumes and planning for adequate server capacity. For shopping, the ratio is the number of shops divided by the number of bookings. Each shop, depending on the shopping entry parameter used, can return itineraries that range from a few itineraries to the hundreds. Look-tobook ratios are sometimes viewed in the context of conversion rates, but it is not very meaningful since look-to-book ratios vary widely by channel. Brick-and-mortar travel agencies typically experience a 10:1 to 20:1 look to book ratio while OTAs can range from 200:1 to 2000:1 in certain markets. Robotic shopping requests should be excluded from the calculation of the lookto-book metric. Robotic transactions can be isolated with a machine learning pattern recognition model that determines if the shopping request is robotic. For example, the pattern recognition model can be trained to detect shopping request sequences such as consecutive length of stay requests for a given advance purchase, consecutive advance purchase requests for a fixed length of stay or combinations of a range of advance purchase and a range of length of stay. However, it may not detect all the robotic shopping requests and hence serves as a lower bound on the total robotic transactions. This detection is done at the pseudo city code (PCC) level. An alternative metric is the modified look-to-book ratio, that is based on the itineraries returned in a shop request that can vary based on channel and parameters specified by agencies at the time of the shopping request. The modified look-to-book ratio is simply the total number of itineraries returned during shopping divided by the number of bookings. Airline inventory systems should be designed for scale to support these high transaction volumes that continue to grow unabated. Distributed availability, where multiple read-only proxies are deployed in the cloud for third parties to access seat availability, alleviates the problem.

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10.1.6 Loyalty and Coalition Programs Airline loyalty programs have been evolving over the past decade along two dimensions; the accrual of miles and redemption of miles. Airlines have been quick to adopt more stringent rules for the accrual of miles based on the actual value of the tickets (Raiford, 2015). However, they have been slow at accelerating redemption opportunities for frequent travelers to maximize the burn rates. The changes to loyalty programs are influenced by the new International Finance Reporting Standards (IFRS) rules for liability accounting. The core principle of IFRS 15 (IATA, 2020a) is that an entity should recognize revenue to depict the transfer of promised goods or services to customers in an amount that reflects the consideration (payment) to which the entity expects to be entitled in exchange for those goods or services. IFRS 15 states that the value of the loyalty currency needs to be accounted for as a “relative standalone selling price”. This suggests that fair value calculations need to be used for determining points/miles liability, as opposed to incremental cost. For loyalty programs, fair value represents how much the average seat would have sold for. While we expect airlines to determine the value of an average seat differently, this value will still be significantly larger than what is used under incremental cost. The power of an airline loyalty program to attract and retain customers is based on a customer’s perception of the value they receive from program participation. There are three dimensions to accelerate redemption of accrued frequent flyer miles: multitier extension of the fixed two-tier fixed rewards structure, flexible payments based on cash + miles and redemption opportunities for ancillary products, upgrades, partner channels and coalition programs. Fixed awards represent the traditional award travel that is based on availability of non-revenue booking classes that are capacity controlled by revenue management. The miles required is based on zones or distance flown. To maximize burn rates for accrued miles, a revenue-based redemption model is advantageous over a standard two-tier redemption structure by geographic region based on miles. For example, in North America the two tiers could be 50,000 miles for unrestricted redemptions (redemption from the Full Y coach reservations booking designator) and 25,000 miles for restricted redemptions (attached to a restricted booking class lower in the hierarchy, say X class). To maximize redemption opportunities for customers, the two-tier structure should be replaced by a continuous scale wherein the required miles could be across the entire range of booking classes based on value. With this approach, redemptions can take place across the entire booking class hierarchy, maximizing the opportunity for redemptions. Hence a traveler with 20,000 accrued miles, which was below the 25,000 miles for the two-tier model, should be able to do a redemption. Figure 10.3 illustrates this point with full Y as the unrestricted redemption class and X as the restricted redemption class for frequent flyers. An extension of the static fixed awards model is to extend it across the booking class hierarchy. It suffers from the same limitation as the two-tier model that displacement of revenue passengers is not considered.

Impact of Revenue Management on the Airline Business

50,000 miles RBD Value

Fig. 10.3 Fixed award redemptions

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Dynamic awards allow redemption on any available inventory, using selling fares in the market which are converted to miles using a conversion factor. With this model the actual displacement cost of revenue passengers must be considered to arrive at the miles or points required for a redemption. The higher the fare, the more miles that are required for a redemption and vice versa. The challenge is to determine the conversion rates at time of shopping based on the itinerary, price of the itinerary and expected future demand for the itinerary before departure. The conversion factors can be derived based on the displacement cost using the revenue management network revenue mix model that maximizes revenues subject to capacity constraints. The input for the model is the unconstrained historical demand by service class, and the capacity constraints by flight leg and date. Since the data used are historical, the model is deterministic, unlike the stochastic revenue mix model used to set inventory controls for future flight departures, to model demand uncertainty. To address dependent demands, the revenue mix network optimization model can be replaced by the sales-based linear program that models dependent demand, upsell and cross-flight recapture (Gallego, Ratliff, Shebalov, 2013). The objective is to determine the marginal cost of a frequent flyer redemption. Post departure flown passenger data can be used to run the revenue mix model to estimate unconstrained demand and the resultant inventory controls. The example in Fig. 10.4 assumes that frequent flyers do not incur any direct variable costs. Frequent flyers do incur some direct variable costs to airlines, and this includes fuel, food, insurance, and fees incurred when travel rewards are redeemed on the host and partner airlines. Including direct variable costs to compute the $/mile conversion factor is straightforward. The direct variable cost estimate can be converted to a $/mile estimate and added to

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Post departure sales including frequent flyers (iff)

Post departure sales excluding frequent flyers (eff)

O&D Revenue Mix Network Optimization Model

Optimal Sales Optimal Revenue Riff

O&D Revenue Mix Network Optimization Model

Optimal Sales Optimal Revenue Reff

Fig. 10.4 Estimating the revenue displacement cost of frequent fliers

the $/mile estimate based on displacement of revenue passengers to determine the conversion factor. The revenue displacement in the airline network is the difference in the value of the objective function of the revenue mix model when frequent fliers are included (Riff) and when they are excluded (Reff). Revenue Displacement = Reff – Riff With the total passenger miles flown (TPMF) by frequent flyers(TPMFff) over the same period as the calibration, the conversion factor expressed in $/mile is given by: Conversion Factor =

Reff – Riff TPMF ff

The dynamic redemption model can be readily extended to combined dollars and miles in any ratio. Coalition programs offers yet another venue for redemption of accrued miles and they improve customer engagement and generate incremental revenues (Vinod, 2011b). A coalition program is a multicompany shopper reward program and can be redeemed faster due to the breadth of partners that make up the coalition and greater selection of awards not limited to travel. Coalition programs can be very profitable for airlines since the unit of currency used for redemption is points and not miles which are opaque Coalition programs are also in a unique situation to collect customer data across partners in the coalition such as gas stations, supermarkets, retail stores, airlines, and hotels. This in turn provides partners in the coalition the opportunity to create targeted highly relevant offers to their customer base.

10.1.7 Screen Display Optimization Screen real estate should be used wisely. Regardless of the online channel, direct or indirect, information presented to a customer or a travel agent should be relevant based on stated preferences from surveys or derived preferences from data to maximize customer engagement, customer retention and customer conversion from a casual shopper to a booker.

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Optimizing screen displays from shopping requests on airline websites and online travel agencies (OTAs) can be influenced by revenue management bid price controls and consumer choice modeling techniques. Screen position for an itinerary that is displayed plays a key role in increasing conversion rates and maximizing revenues. For example, on an airline website, an airline can redirect demand to low booked load factor flights by changing the sort order of the itineraries during a shopping request from the lowest to the highest total bid price. By redirecting demand, an airline can retain market share within its network than have a displaced passenger that is recaptured by a competitor. Measuring screen quality enables an OTA or supplier site to determine how competitive they are in converting shoppers into bookers. During the shopping process, online customers do not automatically select the lowest priced itinerary. The generation of itineraries that provide diversity expressed by quality of service (for example, nonstops, single connect, double connect and interlines), fares and carriers on both the outbound and inbound schedules play a critical role in the selection process and influence conversion rates. Screen quality can be measured with a choice model that was calibrated for revenue management modeling purposes with schedule and fare attributes. Armed with the choice model coefficients, the probability that a displayed itinerary will be selected can be determined. Measuring screen quality fine tunes the display algorithms that rank itineraries based on the utility score from choice models to maximize conversion rates. Similarly, OTAs like Expedia and Booking.com routinely optimize screen displays for air and hotel displays. The optimal ranking of hotels to display on a page for each shopping request to achieve their desire objective: maximize customer conversion rates or maximize net revenues. Contrary to common belief, OTAs prefer to maximize net revenues due to the merchant rates they have negotiated with hotels. Consumer choice models used in revenue management can be calibrated from historical hotel shopping sessions to display hotel search results that maximize the probability of selecting a property on the first page. This approach also improves customer loyalty since properties displayed are relevant to the search request and a function of the hotel selling rate, popularity of the hotel brand, and individual property. From studies on screen quality and its relationship to market share (Jiang, 2009), the following insights have emerged: customers look beyond the cheapest fare during itinerary selection, context for travel-based customer segmentation and market influences itinerary selection, and shopping diversity (diversity of itineraries displayed which is the mix of nonstop, direct, single connect, double connect, interline and carrier) results in higher market share.

10.1.8 Offer Management Offer management is frequently described as an extension of traditional revenue management of the base fare to include airline ancillary products that are bundled with the base fare to create a bundled offer. Creating a bundle (base fare plus air

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ancillaries), requires customer segmentation based on the context for travel. Offer management recommendation engines predict bundles to be offered to customers based on the customer segment when the customer is anonymous followed by refinements when the customer is declared to create a personalized offer for a segment of one. Offer management is a key enabler for IATAs new distribution capability (NDC), and it extends across all channels of distribution.

10.1.9 Pricing of Air Ancillaries When airlines started selling ancillaries bundled as branded fare products or à la carte, the retailing focus was on execution, the ability to sell ancillaries and sell up of branded fares through the direct channel and agency channels for online and codeshare partner flights (Smith et al., 2007; Vinod & Moore, 2009). There are several components to execution to make this a seamless process. The deployment varies with some airlines filing ancillaries through ATPCOs fee types of OB (ticketing fees), OC (optional service fees), OA (booking fees) and the branded fares record that links the ancillaries to branded fare families (Record S8). Airlines that fail to adopt an à la carte pricing model will likely lose customers and potential revenue (Nason, 2009). A key component of ancillary sales has been the transmission of the industry standard Electronic Miscellaneous Document (EMD) to airline partners to enable revenue recognition and proration of fees. It also involves communicating ancillary information to Operating Carrier when not the Validating Carrier, receiving information from marketing carrier when they are the operating carrier, supporting associate and disassociate messages when the EMD and eTicket validating carriers differ and supporting ancillary sales and fulfillment when carrier both validates and operates. The data collected from EMD transmissions are needed for dynamic pricing of ancillaries based on estimates of a consumer’s willingness to pay, which is a core revenue management capability that is used to address unrestricted fares or lightly restricted fares in a low-cost world. A fundamental problem with calibrating a model to estimate price elasticities is the absence of sales data at various price points for an ancillary. This can be overcome by running test and learn experiments with the multi-armed bandit framework to determine ancillary prices that have higher conversion rates with customers and improves sales. Variable ancillary pricing by market for services such as pre-reserved seats, checked bags and upgrades based on competitive market conditions and distance is frequently referred to as dynamic pricing of ancillaries (Ødegaard & Wilson, 2016). Ancillary services typically have the same price across the airline network and do not vary by market or length of haul. Future pricing of ancillaries will vary by market for some of the ancillary services such as pre-reserved seats based on prevailing competitive market conditions and length of haul. When ancillary services are promoted by market based on consumer preferences and competition, an active area of research is to quantify a customer’s willingness to pay for ancillary

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services to determine the value of ancillary services. While there are many approaches to addressing this problem, established methods include multinomial choice analysis (Balcombe, Fraser, & Harris, 2009; Ben-Akiva & Lerman, 1985; Train, 2003), the van Westendorp pricing model (Hague, 2008; van Westendorp, 1976) and conjoint analysis (Green, Krieger, & Wind, 2001; Hair, Anderson, & Tatham, 1984) which considers tradeoffs between various combinations of price and product features. An experiment based on stated preferences (Martin, Roman, & Espino, 2008) was used to provide empirical evidence of estimated valuations air passengers have on various quality of service attributes such as comfort, food, ticket change fees, frequency, and reliability. Techniques such as adaptive conjoint analysis and the van Westendorp (1976) price sensitivity meter can be leveraged for the product design of branded fare families (Ratliff & Gallego, 2013) and dynamic pricing of ancillaries. A related area is corporate air contracts, which are also on the verge of major changes with the incorporation of negotiated ancillary, merchandising and personalization content, which will allow buyers and sellers to achieve their objectives mutually (Bradberry, 2013).

10.1.10 Inflight Catering The global in-flight catering services market is expected to grow with increasing business and leisure passenger traffic. Provisioning flights with lightweight packaging and contemporary menus enhance the brand image of the airline and future growth. The forecast of onboard passengers for future flight departures is required to forecast meals to be provisioned on a flight. Onboard passenger forecasts from revenue management supports meals forecasting to streamline the management of onboard catering and provisioning (Johan & Jones, 2007; Jones, 2004). The day of departure expected passenger boardings by cabin from revenue management is a core input into optimizing meal orders that minimize meal overage and underage levels.

10.1.11 Interactive Marketing Pay per click (PPC) is an online advertising model to generate traffic to websites and capture the demand. In this model the advertiser pays the publisher, a search engine like Google or Bing, when the ad is clicked by a prospective customer. While revenue management stimulates and restricts demand with inventory controls at the point of conversion, paid search is further upstream in the marketing funnel to capture qualified customers who are searching with specific keywords. With the rapid growth in online bookings over the past decade, collaboration between revenue management and interactive marketing has become a necessity to address two issues. First, improve the relevance of content displayed by optimizing the screen real estate of an airline’s website to maximize conversion rates

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continuously. Second, maximize return on investment of marketing dollars spent on paid search. When an airline has many services in a market, it is costly and inefficient to display either the wrong options (itineraries) or options on the website that do not improve conversion rates. If a limited number of options is displayed, single dimensional metrics such as lowest fare, number of alliance partner carriers, total number of itineraries, and website response time, for example, do not capture the required interactions. Given a set of alternatives, discrete choice analysis techniques can be used to improve screen quality. Besides optimally ranking itineraries based on schedule and fare attributes, unobserved factors such as market presence, promotions, or ease of use of the website can be aggregated into a website-specific constant (Jiang, 2009). Sponsored search advertising, where advertisers pay a fee to Internet search engines to be displayed alongside organic (non-sponsored) web search results is the most significant source of revenues for search engines (Ghose & Yang, 2009). For example, Google and Bing make money by auctioning advertisements and keywords in a pay-per-click (PPC) model. To realize the benefits of paid search, travel suppliers need to develop a keyword strategy (Blankenbaker & Mishra, 2009; Edelman, Ostrovsky, & Schwarz, 2007; Fjell, 2009; Mangani, 2004). A common strategy is to make the ads self-funding, which corresponds to earnings per booking (EPB) equal to zero which implicitly maximize site traffic. However, this may not be the right strategy since it depends on the popularity of the market. In markets where the airline has distressed inventory in a low season, managing to an EPB of zero by spending the profits that accrue from the keyword for more traffic is the right strategy. However, for a popular leisure destination such as Las Vegas or Orlando in the U.S., the objective would be to maximize profit for every dollar spent on keywords, since there is sufficient demand to sell out the seat inventory with less advertising. To ensure optimal advertising spend requires the interactive marketing group to do the necessary tradeoffs between maximizing site traffic and profit, which is possible based on the expected traffic flow estimate in the market from revenue management.

10.1.12 Airline Operations An airline’s revenue performance is influenced by a combination of factors such as a profitable schedule, a fare structure that attracts all pertinent customer segments, revenue management process for the optimal inventory control of flights, customer retention and efficient airline operations. With the growing importance of customer satisfaction and customer retention to retain profitable customers, airline operations can leverage variants of the passenger value estimation model during normal operations and during disruptions. For example, to minimize passenger inconvenience, assignment of aircraft to gates can be influenced by the passenger valuation model that minimizes the total walking distance to connecting gates for the most valued customers. This problem,

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however, is complex with a quadratic objective function since the walking distance for connecting passengers depends on both up-line and down-line gates assigned to the corresponding flights. During a schedule disruption, a passenger value estimation model can be leveraged to minimize passenger inconvenience during schedule disruptions caused by weather, aircraft out of order, crew legality or congestion in the national air space. In the U.S. domestic market, flights are subject to a range of disruptions that result in longer than expected travel times for the passengers. For example, over the past decade (2011 to 2019) on time performance has ranged between (74.39% to 82.50%), flight delays between (16.16% to 22.46%) and cancellations between (1.12% to 2.44%).3 Recovery from disruptions has three dimensions—aircraft, crew, and passenger. Passenger re-accommodation is usually the last step after generation of a new schedule, reassignment of aircraft, crew, gates, and ground staff. The passenger valuation model from revenue management can support the optimal re-accommodation of passengers based on value (Clarke, 2003). The term “value” can be based on the customer revenue spend over the past 12-months, lifetime value or frequent flyer tier status or a combination. The passenger re-accommodation model receives schedule change and disrupted flight information and evaluates each passenger’s itinerary based on the airline-defined passenger priority list. This priority is typically based on passenger attributes such as unaccompanied minors, frequent flyer status, fare paid, class of travel and customer lifetime value. Passengers are rebooked and notified by an automated alerting process that strives to accommodate the valued customers first, retain brand loyalty and yet minimize passenger inconvenience.

10.2

Coping with the COVID-19 Pandemic

The outbreak of the novel coronavirus, known as COVID-19, in Wuhan, China in December 2019 is a global humanitarian public health crisis that has affected millions of people and business entities worldwide. The impact on the travel industry is far greater than the terrorist attacks of September 11, 2001, the financial crisis of 2008, or any other event since World War II. In the U.S., the death toll surpassed half a million on February 22, 2021. More Americans have died from COVID-19 than on the battlefields of World War I, World War II, and the Vietnam War combined. We live in unprecedented times and the virus continues to be in our midst. IATA has forecast that air travel will not recover until 2024 (IATA, 2020b). According to IATA, carriers in North America are estimated to lose $24 billion and $84 billion in collective losses worldwide in 2020 (IATA, 2020b; Shapiro, 2020). The pessimistic outlook for recovery is based on three trends. First, containment of the virus has been slow in the U.S. and developing countries with periodic new outbreaks and spikes in

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Bureau of Transportation Statistics; https://www.transtats.bts.gov/HomeDrillChart.asp

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new cases. Second, corporations are under financial stress and corporate travel will continue to be vastly reduced until recovery and third, consumer confidence is weak with rising unemployment and job security concerns. In the travel sector, COVID-19 has had a greater negative impact on cruise lines and hotels than airlines, who can at least fly cargo if not passengers due to weak passenger demand. With the COVID-19 outbreak, airlines have been forced to cut capacity on a scale that we have never seen before. Capacity reductions even dwarf the fallout from the terrorist attacks from September 11, 2001. Under these circumstances, what can airlines do to cope with the pandemic? Historical demand patterns have little or no bearing on future demand for flights and markets. The Wall Street Journal (McCartney, 2020) featured an article on the pandemic and the impact on revenue management in the absence of demand forecasts. With the sporadic outbreaks in different locations that are not predictable, flexibility with schedule creation, pricing, and revenue management to generate cashflow is key to survival.

10.2.1 Flight Scheduling Flight scheduling is responsible for determining the markets that the airline will serve, the frequency of service in the market and the departure times for the individual flights. The COVID-19 pandemic is forcing airlines to rethink their hub and spoke network structure, markets they serve, and composition of the fleet. When historical demand patterns do not reflect the future, airlines need complete flexibility during the schedule creation process. What this implies is that pre-COVID-19 schedules may have little or no bearing on future schedules from the point of view of expected passenger volumes, competitive schedules, and fares. This is an opportunity for clean sheet scheduling. Clean sheet scheduling further diminishes the value of historical demand data. Even airlines that claim to use a clean sheet scheduling approach with market size data, always start with a skeleton schedule. The pandemic is forcing airlines to rethink the schedule creation process with clean sheet scheduling, that does not take into consideration prior constraints such as timing, routing, market frequency, and capacity. Clean sheet scheduling is also an opportunity to abandon sequential decision making for flight departures and arrivals, fleet assignment and aircraft rotations into a single model for smaller airlines where the computational burden associated with problem size can be managed.

10.2.2 Airline Pricing and Cash Flow With deteriorating demand for flights and flight cancellations, airlines are strapped for cash, and cashflow is a pre-requisite for survival. So, the fundamental question is whether a marketplace for airline seat inventory exists that provided mutual value to buyers and sellers during bad economic times. Seat inventory is a great asset and airlines can monetize seat inventory in innovative ways for mutual benefit to

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corporate customers and airlines. An approach is for corporations to pre-pay for travel and receive a deeper discount than the standard corporate discount that they are accustomed to negotiate. To make this model work, it should provide value to both the buyer (corporations) and sellers (airlines) (Vinod, 2020c). Corporate fares need to change for this to work. In the current environment, corporations negotiate a percentage discount off the selling ADT (Passenger Type Code (PTC) for adult, public fares) with airlines. Hence, the discount floats with the available ADT fares. This is implemented as a fare by rule (Category 25) where the discount is applied. The discount only applies to the top few booking classes in the hierarchy based on the value of the fare. When a flight is wide open, a leisure ADT fare for which the Category 25 discount does not apply could be cheaper than the lowest booking class (also open) for which the Category 25 corporate discount applies. This model can be tweaked to generate cashflow for airlines. Airlines should be willing to give a steeper discount that is applicable across all or most booking classes in the hierarchy based on ticketed sales volumes committed by a corporation. This can be specified by market, valid routing, seasonality, and departure month. The negotiated volume represents the maximum seat inventory that can be purchased as part of a “bulk buy” that provides a cash infusion with prepay to airlines. Incremental sales beyond the agreed upon threshold for the deeper discount will be ticketed at the standard corporate discount levels. This will require real time tracking of booking activity to know when corporate discounts should revert from the ultradeep discount to the standard discounts for corporate fares. Most corporate travel is typically booked within 30 days of the departure date; the prepayment to airlines should be made on a monthly or quarterly basis. There are several variations to this approach. Regardless, it should provide an infusion of revenue to cash strapped airlines to continue daily operations. In return for a lump sum prepayment, corporations benefit with deeper discounts on corporate fares. Such a model is required based on recent predictions from McKinsey (Curley, Garber, Krishnan, & Tellez, 2020) that corporate travel will rebound at a slower pace than leisure travel once the pandemic is under control.

10.2.3 Robust Revenue Management If traditional revenue management with demand forecasting and optimization models does not work in a highly volatile environment, how should high demand variability be addressed in a COVID-19 and post COVID-19 world (Yeoman, 2021)? Fiig (2020) proposes an Active Forecast Adjustment (AFA) by creating a feedback loop where the demand forecast error on live sales data are continuously monitored and adjustments are applied to minimize forecast errors. Adapting to market changes quickly is possible but effectiveness of active monitoring followed by subsequent corrective action is debatable. An adaptive algorithm to adjust seat protection levels that does not rely on traditional demand forecast and optimization models was proposed (Van Ryzin & McGill, 2000), but relies on historical data. Another approach proposed is the zerodisplacement cost model (Qiu, 2021) that relies on historical booking curves based

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Fig. 10.5 Continuous demand management framework

Monitor

Velocity

Act

Decide

on local demand to accept or reject connecting passengers. This is similar to the LCC approach of creating a reference booking curve with the objective of staying close to the curve. However, in a COVID-19 world, the booking curve is unknown. With the absence of forecasting and valuation of connecting services, such heuristics can result in significant revenue dilution. This concept of continuous monitoring, based on the assumption that it is difficult to forecast demand with any degree of confidence, can be applied to implement a continuous demand management framework to master variability in demand (Vinod, 2021b). Brick-and-mortar retailers use a similar framework as part of their business workflow called merchandise planning (Vinod, 2005a). This workflow has similarities with sales and operations planning (S&OP) workflows (Palmatier and Crum, 2003) of synchronizing demand, available capacity (supply) and resources for peak performance. This is practiced by computer original equipment manufacturers (OEMs) like Dell Computers and Acer that monitor sales of various products (verticals) against the sales plan (horizontal across products by region) to ensure that they achieve the financial targets every quarter. In the airline context, for continuous demand management to work efficiently, business process integration across the functional silos, shown in Fig. 10.1, is required. With this approach, key revenue management performance indicators are monitored continuously and when there is a deviation in the plan, demand and supply business levers are triggered to take corrective action and get the plan back on track. Figure 10.5 illustrates the three basic steps; Monitor, Decide, and Act of continuous demand management. It is an adaptive robust revenue management framework unlike the traditional robust revenue management found in the literature (An, Mikhaylov, & Jung, 2021; Birbil, Frenk, Gromicho, & Zhang, 2009; Gönsch, 2017; Liang, Ratliff, & Remenyi, 2017; Perakis & Roels, 2010; Queyranne & Ball, 2006; Rusmevichientong & Topaloglu, 2012) where the focus is on the validity of the assumptions made by revenue management. The core objective of this approach is not to maximize revenues or profit, but to minimize maximum regret. What are the actions that can be taken to minimize missteps, even if some revenue is left on the table?

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Coping with the COVID-19 Pandemic

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PLANNING Events, Update KPIs, Thresholds, Frequency of Monitoring

EXECUTION Continuous Monitoring, Generation of Alerts

Categorize Alerts

Root Cause Decision Tree Analysis

Recommend Business Lever(s) for Corrective Action

Optional

Continuous feedback

What-if Analysis

Execute Business Lever(s)

Fig. 10.6 Events and alerts resolution with continuous demand management

To address variability, a critical element is to overcome latency with proactive management of the key performance indicators. Latency results because of slow propagation of alerts and data across the organizational silos in an airline, resulting in excessive cycle times for decision making (Vinod, 2021b). Continuous demand management requires an event management framework, which serves as a central nervous system, to manage alerts for timely resolution as shown in Fig. 10.6. The revenue management KPIs are monitored at distinct levels of aggregation, frequency of monitoring (by the minute, hourly, daily, weekly, etc.) and possible recourse for a corrective action by invoking a demand lever or a supply lever. Examples of demand levers are pricing actions, promotions, sales incentives, inventory open/close overrides, etc. Examples of supply levers are close-in refleeting, frequency change in existing markets served, entry into new markets, or abandon existing markets, etc. Invoking a demand lever will be felt immediately while supply levers are less agile, and the impacts will only be realized after a few weeks. Figure 10.7 illustrates an event and the root cause analysis framework for the primary KPI (system revenue) to understand what caused the deviation from the plan and the resulting business lever (supply or demand) that should be invoked to take corrective action and achieve the plan. The activation of the business lever may lie in the same or different organizational silo such as pricing, revenue management, sales, capacity planning, etc. A similar decision tree framework will be required for all KPIs that need to be monitored to ensure consistency in decision making. This approach is vastly different from the traditional revenue management analyst workflow of reviewing a problem window with critical flights and markets to take corrective action. Instead of the traditional bottom-up approach of reviewing individual critical flights and markets in the problem window, this is a top-down approach to take corrective action since it is unlikely that historical demand for individual flights and markets will have any bearing on the future state. Planning processes for scheduling, pricing and revenue management will rely more on incremental planning and short term adjustments to adapt to prevailing market conditions.

10 Influence of Revenue Management on the Airline Business Process

Fig. 10.7 Alerts monitoring, root cause analysis and resolution

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11.1

11

Introduction

The underpinnings for advancements in airline planning and airline operations since airline deregulation has been based on Operations Research techniques with a foundation in statistics. It is anticipated that problem solving methods will evolve over the next decade with the growing adoption of Artificial Intelligence (AI) across industry verticals including travel. Operations Research (OR) came into existence to solve logistical problems during the Second World War (1939–1945). A few years after mathematician Alan Turing cracked the “Enigma” code (an enciphering machine used by the Germans to send messages securely) at Bletchley Park during the war, he wrote his landmark paper “Computing Machinery and Intelligence” (Turing, 1950). This paper attempted to answer the question “Can machines think?” This led to the Turing Test, a test of a machine’s ability to exhibit intelligent behavior that was comparable to a human. Some researchers now propose the Winograd Schema Test as an alternative (Levesque, 2011). Francois Chollet has proposed a new framework called the Abstraction and Reasoning Corpus (ARC) based on Algorithmic Information Theory (Chollet, 2019) where he argues that solely measuring skill at any given task falls short of measuring intelligence and provides guidelines for developing a benchmark of general intelligence. The term “Artificial Intelligence” was coined a few years later by John McCarthy, a math professor at Dartmouth College in 1955. He developed LISP, the second oldest high-level programming language, after Fortran, and influenced the development of ALGOL. Economist Herbert Simon was one of the inventors of the first AI language at the RAND Corporation, called Information Processing Language (IPL). In 1957, while Simon worked at the Carnegie Institute, he made a bold prediction that computers would beat humans at chess within 10 years. While it did not happen in 10 years, it took almost 40 years for IBM’s Deep Blue to beat world chess champion Garry Kasparov on February 10, 1996 (Seirawan, Simon, & Munakata, 1997). In 2016, Google’s Deep Mind AlphaGo received a lot of press when it # The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 B. Vinod, The Evolution of Yield Management in the Airline Industry, Management for Professionals, https://doi.org/10.1007/978-3-030-70424-7_11

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defeated Lee Sedol, the world’s best Go player (Gershgorn, 2016). Other AI pioneers include Ross Quillian, Edward Feigenbaum and Marvin Minsky. As a discipline, AI is extremely broad and embraces the concept of machines being able to carry out tasks in a way that we would consider “smart”. There are three primary domains in AI: machine learning, natural language processing and deep learning. Machine learning and deep learning are directly applicable to revenue management decision support and all three domains can be leveraged across the travel value chain. Machine learning is an application of AI for predictive modeling. Machines are given access to data to make data-driven predictions. Machine learning supports continuous learning and self-recalibration so that the recommendations improve over time. Since it learns from examples, it is quite different from the traditional approach to create software where code is developed for a specific outcome. Machine learning techniques can be grouped based on the types of problems the techniques are designed to solve. 1. Unsupervised Learning: Clustering (e.g., k Means, k Medians, Fuzzy cMeans, Hierarchical), Gaussian Mixture, Hidden Markov Model. 2. Supervised Learning: Classification (e.g., Support Vector Machines, Discriminant Analysis, Naïve Bayes, Nearest Neighbor) and Regression (e.g., Linear, Generalized Linear Model (GLM), Decision Trees, Ensemble Methods, Neural Networks). 3. Reinforcement learning: Genetic Algorithms, Multi-armed Bandit test and learn, Approximate Dynamic Programming, Markov Decision Processes. 4. Deep Learning: Multi-layer Neural Networks, Convolution Neural Networks (CNN), Recurrent Neural Networks (RNN).

11.2

Travel Complexity and AI

The promise of AI in travel is to master complexity and vastly simplify the travel experience. There are several dimensions to travel complexity. They are growth in air shopping volumes, growth in traffic, content fragmentation, IATA NDC, dynamic pricing and payment systems to name a few.

11.2.1 Growth in Air Shopping Volumes Air shopping volumes have grown in leaps and bounds, mostly fueled by the OTAs. Prior to the COVID-19 pandemic, the total volume of travel agency shops in 2019 was about ~249 billion on the Sabre GDS. In 2016 this volume was 60 billion, in 2017 it was 106 billion and in 2018 it was 169 billion. Further, peak transactions per second in 2019 was approximately ~25,487 (Vinod, 2020a). Displaying a large number of itineraries for a shopping request is not the answer. What is important is to display the best fare for a customer based on their schedule and fare preferences.

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11.2.2 Growth in Air Traffic Volumes Over the past decade, travel complexity has grown at a rapid pace. Prior to the COVID-19 pandemic of 2020, the annual growth in global air traffic from 2006 to 2018 averaged 6.0% with only 1 year of negative growth, in 2009 at –1.2%, with the worldwide financial crisis of 2008. Growth in air traffic requires greater focus on revenue integrity to identify speculative and spurious bookings.

11.2.3 Content Fragmentation Fragmentation of content leads to complexity. Hotel content has historically always been fragmented. With the growth in LCCs in the global market, there is fragmentation of content and lack of transparency into schedules and selling fares since most LCCs do not participate in the GDSs. Customers require access to content, regardless of source, to make a purchase decision that they are comfortable with.

11.2.4 IATA New Distribution Capability NDC also leads to fragmentation of content since airlines no longer have to publish their schedules and fares to aggregators. In addition, it is anticipated that with NDC there will be an increase in the volume of airfares with the proliferation of time-ofday specific fares, date specific fares and routing specific fares. When airlines return offers on request to a travel agent, the science of normalizing non-homogenous content across airlines on an agency desktop is far from perfect to support comparison shopping and booking.

11.2.5 Dynamic Pricing Dynamic pricing is the latest evolution of revenue management where the price for an itinerary is dynamic and may be different from the filed fare. There are two primary variations of dynamic pricing—laddered pricing and continuous pricing. Dynamically priced content is not filed, but calculated on demand and available directly from the airline in real time via the NDC XML messaging standard. A challenge to dynamically priced content is consistency to avoid customer confusion and shopping cart abandonment.

11.2.6 Payment Systems This is an example of complexity that originated in other industries that impacts travel. There are several new forms of payment to make customer purchases easier and more secure than the use of traditional credit cards. The number of choices for

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payment is increasing every year. A few years ago, there were the well-known credit card companies and PayPal. Today it is increasingly fragmented with more players and more solutions such as Apple Pay, Samsung Pay, Android Pay, etc. Payment alternatives present new challenges to airlines that need to be addressed such as dealing with multiple currencies, virtual currency, appealing to global markets and local payment preferences, fraud detection, and controlling costs.

11.3

Approach for Adoption of AI in Travel

What is the approach for adoption of AI in travel? Successful organizations that have adopted AI have taken an incremental approach (Davenport & Ronanki, 2018) with small well-defined initiatives rather than taking on large, complex problems at once. AI applications can be broadly categorized into three categories. They are:

11.3.1 Robotic Process Automation Robotic process automation (RPA) is quick and easy, and very often has a high return on investment. RPA does not necessarily classify as a “smart” application, but it is useful and can be adapted for multiple backend systems for productivity improvements. Routine tasks are plentiful such as accounts payable, IT spending, financial portfolio management and human resource tasks.

11.3.2 Cognitive Insight The objective is to detect patterns and hidden signatures in vast volumes of data and interpret the meaning of the results. Examples are detection of credit card fraud, insurance claims, warranty data to detect safety and quality issues, personalized ad targeting, and actuarial models.

11.3.3 Cognitive Engagement These are the most data intensive applications, extensively trained on data and quality of results improves over time. Examples include engaging employees and customers using chatbots and intelligent agents. Cognitive engagement begins with customer data that is processed with predictive analytics to produce insights into future customer behavior to personalize customer engagement. Cognitive engagement is at the heart of airline intelligent retailing to automate and generate relevant personalized offers.

11.4

11.4

Operations Research at the Crossroads

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Operations Research at the Crossroads

OR professionals solve complex business problems with their advanced problemsolving skills, using established techniques such as mathematical modeling, stochastic processes, deterministic and stochastic large-scale network optimization models, data analytics, algorithms, statistical analysis, and decision sciences. All the major advances in decision support for airline planning and operations including flight scheduling, pricing, revenue management, crew planning, airline operations, flight planning, staff planning, and cargo have been solved with techniques in OR (Horner, 2000). So, what does the future hold for OR with the advances in AI? OR will not be replaced by AI, but instead be complemented with AI to automate and improve the value propositions (Vinod, 2020d, 2020e). Many of the OR models can be enhanced with Artificial Intelligence. AI is at the intersection of technologies that reason, interact, and learn. In an AI-enabled landscape there are opportunities to introduce the concept of continuous learning without human intervention. An example of robotic process automation is the revenue opportunity model (Ratliff, Manjot, & Guntreddy, 2013), which can be augmented with pattern recognition augmented with expert opinion to resolve complex inputs and prompt users on a corrective course of action (Toyoglu, 2019) for inventory control decisions. Machine learning is used to predict outcomes, while optimization models deal with optimal decision making. An area of debate and active research is the value of machine learning based predictions on input data to optimization models (Bertsimas & Kallus, 2020; Kraus, Feuerriegel, & Oztekin, 2020). Another area of active research is the value of leveraging machine learning to solve combinatorial optimization problems, especially NP-hard (nondeterministic polynomial time) integer constrained problems. Early studies indicated that machine learning models can complement existing optimization approaches that exploit structure detection, branching and heuristics (Khalil, Dilkina, Nemhauser, Ahmed, & Shao, 2017; Khalil, Le Bodic, Song, Nemhauser, & Dilkina, 2016; Lodi & Zarpellon, 2017). Traditional optimization techniques such as the branch-and-bound algorithm can potentially benefit from machine learning to explore the branch-and-bound tree (Khalil et al., 2016). They address the Generalized Independent Set Problem (GISP) class of problems where finding good solutions is challenging and report improvements in primal integral and time to best incumbent, and the benefit lies in the method learning to run heuristics more aggressively early in the search. Machine learning can be applied to systematically apply heuristics to solve combinatorial optimization problems (Bengio, Lodi, & Prouvost, 2020). Demand forecasts are a fundamental input to determine optimal inventory controls. Research into the applicability of reinforcement learning with exploration to predict revenue management inventory controls without a demand forecast model for single resource problems have shown some promise (Bondoux, Nguyen, Fiig, & Acuna-Agost, 2020) by integrating deep domain knowledge with a deep Q-learning neural network. Though promising, these approaches are several years away from mainstream adoption to network problems.

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Adaptive critic heuristic-based algorithms, in the reinforcement learning and adaptive/approximate dynamic programming with an underlying semi-Markov decision processes, have been used to solve the traditional EMSRB heuristic (Kulkarni, Gosavi, Murray, & Grantham, 2011). Markov decision process problems invented by Bellman (1957) are sequential decision-making models governed by Markov chains. In each state, the controller must select from two or more actions where the overriding goal is to maximize the reward earned over a finite or infinite time horizon. They have published empirical results that claim to outperform the wellknown heuristic method. However, optimization models to solve the network revenue mix problem using machine learning techniques have not achieved mainstream adoption but may in the future. Besides core optimization, AI can augment and complement a range of use cases that use OR techniques. These applications include controlling the cost of air shopping with caching strategies, segmentation based on consumer preferences, demand forecasting, recommending bundled offers, personalization, hotel rates anomaly detection, fraud detection, hotel product and rate normalization across aggregators, design of user interfaces based on click-through behavior, experiential learning, reservations workflow automation and anomaly detection of internal systems to prevent failures.

11.5

Role of AI in Travel

From its early days, AI has gone through various stages of evolution and over the past decade, AI has seen rapid progress and continues to evolve and improve. Today we live in an AI-enabled landscape (Musser, 2019). Over the last decade various industry verticals including travel have been adopting AI. According to McKinsey & Co., compared to other industries such as electronics, semiconductors, aerospace, defense, automotive and healthcare, the travel vertical has been slow (Chui et al., 2018). The report also states that AI can improve performance beyond that provided by other analytic techniques and the travel vertical has the highest potential. So, the question remains, how should this vast untapped potential be prioritized and realized (Vinod, 2020d, 2020e)? Well known applications of AI in airlines are speech recognition, facial recognition for passenger identification at airports, chatbots to respond to basic customer queries, use of biometric data to speed passengers through airports, augmented reality, virtual reality, and AI powered autonomous multilingual robots (BreakingTravelNews, 2019) to guide passengers through the airport terminal. However, the potential for AI lies beyond these well-known use cases. While the scope is large, here are a few examples in travel (Vinod, 2021e).

11.5.1 Passenger Name Recognition There are some countries that issue passports to its citizens that are not ICAO (International Civil Aviation Organization) compliant. In this situation, it is not

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possible to identify the first and last names on passports from these countries. When the passport is not compliant with the ICAO requirement, it causes two problems: Inability to use the MRZ (Machine Readable Zone) to delimit first and last names which prevents airline kiosks from processing the name information needed for PNR and departure control system (DCS) customer lookup. Second, is the inability to pass accurate APIS (Advanced Passenger Information System) data when the flight is closed. The name recognition problem can be solved as a two-step process. First, supervised learning techniques, where the model is trained with labeled data (sample data that are tagged with one or more labels), can be used to predict customer origin and demographics. Second, given customer demographics, a rules engine can determine the first and last names of passengers. Evaluation of machine learning models led to the conclusion that support vector machines (SVM) and recurrent neural networks (RNN) with Long Short-Term Memory (LSTM) performed the best during out-of-sample validation, with an accuracy of about ~80%.

11.5.2 Customer Segmentation Customer segmentation, the process of clustering customers into discrete groups based on similar characteristics, is a fundamental building block for intelligent retailing to generate personalized offers. Airlines have wanted to segment customers in ways that go beyond traditional booking classes for many years. There are three approaches to segmentation. The first is based on business rules where customers are grouped based on business knowledge from experts. The second is based on unsupervised learning with customer attributes and a distance metric to measure similarity among customers. The third is based on supervised learning which uses labeled data, perhaps from a marketing research company that has surveyed previous travelers and asked them about the reasons for their travels, to perform model-based clustering. Unsupervised learning techniques such as hierarchical clustering, k-means, sequential k-means, agglomerative hierarchical clustering, density-based clustering, clustering through decision trees, model-based clustering, etc. can be used to create personas across a range of dimensions such as advance purchase, length of stay, departure day of week, mid-week vs weekend, number in party, length of haul, etc.

11.5.3 Test and Learn Experimentation In travel, where consumer preferences and attitudes are dynamic and are influenced by their interactions outside of the travel domain, test and learn experimentation is an essential component of any recommendation engine. Experimentation provides a direct approach to running controlled test experiments to learn customer behavior. Unlike model-based decision support tools, business experimentation with the multiarmed bandit, a reinforcement leaning application, in many respects is simpler to use

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and implement. The idea is to test new prices directly. Product designs or systems/ processes are compared to current ones to see if there is a performance improvement. Simple binary tests (e.g., old versus new) are commonly referred to as A/B testing. A multi-armed bandit implementation is more general, is adaptive, learns over time and can support running multiple experiments simultaneously. For example, experimentation can help answer common business questions such as: 1. What is the best price point for maximizing revenue? 2. Which algorithm or business approach works best? 3. How are my revenues and conversion rates impacted by changes to my screen display rules? 4. Which bundled offer maximizes revenue?

11.5.4 Fare Prediction Predicting when airfares may go up or down is inherently a difficult problem to solve since there are several factors that impact the outcome, such as revenue management strategy, inventory control updates from revenue management, fare changes distributed by ATPCO, and promotional fares. Fulfilled data such as ticketed data are not a good source for fare forecasting since what a consumer purchased may not necessarily be the lowest price. Air shopping data are an ideal source for developing machine learning algorithms, to make “wait” and “buy” recommendations. A “buy” recommendation is made when fares are expected to increase and a “wait” recommendation is made when fares are expected to go down. Supervised learning techniques can be used to predict when fares will go up or down in a market. This is reverse engineering revenue management (which airlines dislike), but OTAs and brick-and-mortar travel agencies need this information to advise customers with a “buy” or “wait” recommendation. The fare prediction model can recommend when fares will go up or down. Q-Learning, a specific case of reinforcement learning, can be used to find the optimal action-selection policy to maximize the expected value of the total reward. With reinforcement learning, an agent classifies the recommendation as right or wrong. If the recommendation was right there is a “reward”, and if the recommendation was wrong there is a “penalty”. Hence the model is “learning” and prediction quality improves over time. The stated objective is to improve the accuracy of prediction over time. Sample recommendations, besides the “buy” or “wait” decision, are listed below. 1. “There is an 85% probability that fares will go up by $25 or more” 2. “There is a 60% probability that the fares will go down by $40 or more” 3. “There is a 35% probability that the fare will stay the same”

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Challenge of Interpretability

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11.5.5 User Interfaces and Experiential Learning Perhaps the most pervasive use of machine learning is to make the existing user interfaces of enterprise applications used at airlines “smart”. The idea is simple. Base typical user interactions with B2B applications such as crew planning, airline operations, revenue management and scheduling applications on a scenario and send an alert when the business scenario needs correction or action. Experiential learning is a process to understand what works with customers, what workflows are redundant and what changes can be made to enhance productivity. The idea is to capture business scenarios and resolutions and train a model to make consistent predictable outcomes (using machine learning techniques such as random forests, reinforcement learning, etc.). With this approach the user interfaces become “smart” and “make recommendations to the user based on an observed business scenario”. This will require an extensive capture of clickstream data, predictive modeling and a new user interface that offers solutions to the user based on prior interactions and outcomes. Experiential learning can bridge the gap between theory and practice, increase consumer engagement levels, and accelerate the adoption of changing consumer behaviors as the landscape, both competitive and technology, changes over time. Experiential AI advances the human-centered approach to achieve greater productivity and consumer satisfaction.

11.6

Challenge of Interpretability

In revenue management, probably the single largest factor that stands in the way of acceptance of machine learning techniques is interpretability. Revenue management analysts for example want to know how a forecast was produced by the system? Unlike statistical models, a machine learning model is typically a black box. It is important to understand “why” a model produces meaningful results and when it does not. With machine learning, most of the time is spent on setting up the environment and the data to feed the black box models. When a machine learning model produces results, it is important to understand the expected behavior of a trained model. Frequently, a machine learning model can produce results that are counter intuitive. Such scenarios should be debugged by data scientists who developed the model. Interpreting deep learning models are an even bigger challenge given the large number of parameters and how features are combined. An active area of research in academia and corporations is to develop methods to identify issues such as model bias, linking model inputs to the predictions and interpretation of the response produced by the model to gain insights. The acronym for this area of research is XAI (Explainable AI) (Arrieta et al., 2020).

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COVID-19 and AI

How can AI help in a COVID-19 world? Can AI identify infections and support monitoring the infected patients? Can autonomous mobile robots (AMR) help first responders and healthcare workers? Can AI support contactless customer identification during travel? Unrelated to travel, there are various robotic solutions that are being rapidly adapted to combat the COVID-19 pandemic (Cardona, Cortez, Palacios, & Cerros, 2020). AI used in AMRs are centered around machine learning and vision systems. AMRs are being deployed to combat the COVID-19 pandemic by helping first responders and healthcare workers. Robots can effectively manage low value tasks, freeing the workforce for more meaningful activities, thereby reducing the workload of healthcare workers. Efforts are underway to autonomously clean facilities using hydrogen peroxide vapor or ultraviolet-c light using robots. AMRs are also being used to develop advanced contact tracing techniques. In the new normal, living with the pandemic, robots are being used to empower and protect the workforce.

11.8

Quantum Computing and AI

Over the past three decades, physicists have debated the promise of Quantum Computing from technology heavyweights IBM, Microsoft, Google, and Intel. China has invested billions in research to develop Quantum Computers. Traditional computer chips require that the data be encoded into binary digits represented by zero or one (binary notation). A quantum computer uses quantum-mechanical phenomena called quantum bits or “qubits” which are a coherent superposition of both states at the same time. Attempts have been made to solve a range of large-scale optimization problems such as the customer choice deterministic linear program (CDLP), gate assignment problem, crew recovery and crew rostering problem (Hur, 2018). To provide a form suitable for use on quantum computers, the traditional optimization problem must be converted into an equivalent QUBO (Quadratic Unconstrainted Binary Optimization) formulation. Formulating a QUBO is a pattern matching technique, common in machine learning applications. It is the problem of minimizing a quadratic polynomial over binary variables. The conversion to QUBO for the crew rostering problem, for example, with more than 11,000 variables and over 1300 constraints, resulted in one million terms that could not be directly imbedded on a quantum computer due to the high connectivity of the constraints. This experiment showed that we are a few years away from using quantum computers for large scale optimization problems. Despite the negative results on quantum computing, Youngbum Hur’s findings (Hur, 2018) won the Best Innovation Award at the AGIFORS Symposium in Tokyo in October 2018. Training deep learning neural networks is compute intensive, requiring Graphical Processing Units (GPUs) and perhaps requiring quantum chips in the future. This is

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Building an Organization

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probably at least a decade away, so conventional chips optimized for AI/ML algorithms will be the mainstay for the next decade.

11.9

Building an Organization

The adoption of AI in a company poses a fundamental challenge on how to educate employees, raise employee awareness and take the necessary steps to increase active participation across the organization. To build awareness and develop an internal communication forum, a team should be tasked with establishing a special interest group on AI and ML with membership to employees who will be active in exchanging ideas, evaluating available data sources, and participating in putting forward new ideas and value propositions for the corporate AI/ML roadmap. This team should hold frequent town hall meetings with employees, communicate active corporate use cases being developed, solicit feedback and, if time permits, also publish a monthly or quarterly newsletter that highlights and showcases use cases in action. There are two key challenges with leveraging AI/ML in any corporation. First is the skill to identify areas where AI can be leveraged to create a new solution or enhance an existing solution. Second, is the ability to scale the adoption of AI/ML across the organization (Leff & Lim, 2021).

11.9.1 Identifying Opportunities for AI Identifying opportunities for AI in an organization begins with basic training with a toolkit before the analyst can advance into problem definition, model development, data access, calibration, and deployment. Raising awareness can be accomplished with small focus groups that meet periodically to discuss use cases, success stories and pitfalls. Many vendors provide “black-box” toolkits for AI/ML. Every organization should evaluate toolkits, including cloud-based solutions, proprietary solutions, and open-source solutions (in-house and cloud) to enable data science and development teams with AI tools and capabilities. This approach can also provide internal consulting support to teams that are developing AI/ML applications and have a preferred toolkit. The evaluation of AI toolkits should be based on criteria such as data ingestion and connectors, data wrangling and visualization, flexible modeling capabilities and deployment capabilities that support automation. Further, given the mix of employees, a factor to be considered is a tool set for citizen data scientists, who prefer drag and drop capabilities, versus technical data scientists who prefer to write code and build models using Python, R, Scala or other programming languages.

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11.9.2 How to Scale Foremost on the minds of corporations as they leverage AI for competitive advance is how to scale AI across the organization (Leff & Lim, 2021). Deborah Leff and Kenneth Lim from IBM draw upon their experience working with many Fortune 500 companies to provide insights into the organizational barriers to scale AI. These companies do not have the right data architecture, underestimate the data science lifecycle, have an unclear strategy to operationalize models, possess insufficient business leader involvement and have insufficient executive sponsorship.

11.10 The Future of AI Over the past decade there has been remarkable progress with AI and machine learning. Proof points include speech recognition, image recognition, autonomous driving cars, robotic automation, and recommendation engines. Deep learning models have advanced pattern recognition and can identify patterns that traditional regression models cannot. These applications require a large amount of data for calibration. There are also a range of problems related to uncertainty, inference, decision-making, robustness, and scale that remain to be solved (Moritz et al., 2018). Will this trend continue, or will AI be transformative, be distributed, use less data from multiple sources, explore solutions, and learn on the fly (Darrow, 2021)? According to Michael I. Jordan, Professor at University of California, Berkeley, the future of AI is the marketplace (Jordan, 2018, 2019). His key message is to empower users to broadcast what they want (publish) and allow vendors (subscribers) to respond with offers. The current AI model is quite different: it is a top-down push model. Jordan is of the opinion that the “emerging generation of AI is not just one agent making a decision or sequence of decisions, but a huge, interconnected web of data, agents, and decisions.” From his point of view, autonomous self-driving cars can be vastly improved with a networked system. If a selfdriving car identifies a cyclist on the right shoulder ahead, it should notify all other self-driving cars and make the whole system collectively aware. The next generation of AI applications will continuously interact with the environment and learn from these interactions. This will require a distributed system with new requirements for performance and flexibility. This is vastly different from what current AI is all about. Recommendation engines for travel will have to adapt to this new reality in the future. What is proposed by Professor Jordan is remarkably similar to where NDC is headed. With NDC, travel agents will broadcast (publish) a message to airlines with details of the market to book travel for a customer. Airlines (subscribers) will then respond with offers. In his opinion, Facebook and other social media platforms do not get this concept and rely on a top-down push model.

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11.11 Role of Big Data One of the biggest trends that businesses are grappling with is Big Data, data so large that it requires new tools and processes to understand it. Generated in everincreasing amounts by social media and online transactions, and fueled by new developments in technology, the key is how to harness the value of the data. In the travel industry, travel suppliers, Online Travel Agencies (OTAs) and GDSs have access to an extensive amount of data captured across the travel value chain from marketing and lead generation, interactive selling, fulfillment, and customer care. Yet, these entities capture, store, and leverage this data for competitive advantage only to a limited extent. This vast amount of data can be used to provide unique insights into consumer preferences and behavior patterns to improve conversion rates and improve revenues (Vinod, 2013a, 2016b). In today’s digital world, entities in travel are awash with galloping growth in the volume of raw data that needs to be captured and which can easily run into terabytes (1012 bytes) and petabytes (1015 bytes). Big Data is the term used to describe the data that can be typically hundreds of terabytes or petabytes in size. The data that is being collected grows very quickly. For example, the shopping responses generated across travel agents and OTAs when they access a GDS can run into hundreds of terabytes a day and this data are growing at an increasing rate since shopping volumes have outpaced bookings over the past decade. Volume, Velocity and Variety are the three dimensions of Big Data as defined by the MetaGroup’s Doug Laney (now part of Gartner) in a MetaGroup Research publication (Laney, 2001). Volume refers to the amount of data, which has been growing at an increasing rate. Travel shopping volumes have grown at an increasing rate over the past decade. From consumers across the globe, Sabre processed around 249 billion shopping queries in 2019 and over 25,000 shopping queries per second at peak times (Vinod, 2020a). Velocity refers to the speed with which data are collected and processed. Since its inception in the 1980s, revenue management systems were batch-oriented and processed data captured nightly from the reservations system. To unlock added value from the revenue management process, these systems have transitioned to process streaming data such as bookings and inventory alert messages to adapt to real time changes in the marketplace and ensure that the inventory controls are based on up-to-date information. Another example is dynamic intervention, where streaming data are leveraged by OTAs to promote offers based on the number of times a customer has visited the site. Variety refers to the various types of data such as text, audio, video, sensor data, documents, geo-spatial data from satellites and structured data that may be required to be processed using specialized techniques. Traveler targeted technologies, ranging from smartphone apps to wearable computing, can flood the space with a large volume of rich data sets containing personalized information. Google has worked with the transportation departments at local, state, and federal levels, which have installed solar-powered traffic sensors on major roads to

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determine traffic conditions. In some cases, transmitting rich data from an aircraft to the ground can be prohibitively expensive. In the context of airline safety, for decades, NOTAMs (Notice to Airmen) have been enabling pilots to receive alerts of potential hazards. Commercial airlines transmit short message to operators on the ground. Radio transponders identify them when scanned by radar and are fitted with ACARS (Aircraft Communications Addressing and Reporting System) that periodically relay text messages about the status of the aircraft in flight to ground stations by radio or satellite. The case of the missing Malaysia Airlines flight MH370 that disappeared over the Pacific Ocean on March 8, 2014 begs the question—why was rich flight performance and pilot voice communications data not transmitted to the ground? First, transmitting data continuously through satellites is expensive and second, pilot union contracts may not allow it. Kavi (2010) suggests some combination of encryption and privacy policies like medical records may be sufficient to overcome their objections. If this data from aircraft sensors had been made available, it could have been mined with machine learning tools to identify abnormal behaviors and recommend engine removals and a disaster such as MH370 might have been averted. Big Data consists of structured data and unstructured data. Examples of structured data are booking and ticketing transactions, post departure data, etc. Examples of unstructured data include user generated content from hotel reviews, posts on social media sites, sensor data, audio, video, clickstreams, and log files. Insights into consumer behavior, process efficiencies and website design can be found when these different types of data are analyzed together. Varian (2014) provides an overview of analytic tools required for data scientists. Big Data is more than just handling the exponential growth in volume of the data; it encapsulates the tools that can be used to process this data efficiently, gain insights into the business and make a corporation more agile. In addition, the data like clickstreams, travel reviews and social media are highly unstructured and infeasible to be stored and processed in a Relational Data Base Management System (RDBMS) such as Oracle, DB/2, or Teradata. For example, the Twitter API could be used to capture terabytes of travel-related tweets each day for sentiment analysis, trend detection, lead generation and serve as an impulse signal for demand forecasting. When a decision has been made to capture data from operational systems to store in a data warehouse for analysis purposes, a first key question to ask is what approach should be taken to ensure that the framework allows the storage of any data type in a low-cost, scalable environment that reduces the cost of processing massive volumes of data. Google developed a proprietary, distributed file system called Google File System (GFS) (Ghemawat, Gobioff, & Leung, 2003) and a parallel programming technique and framework called MapReduce for its web search purposes (Dean & Ghemawat, 2004). Hadoop was derived from these papers. Its core components are MapReduce and the Hadoop Distributed File System (HDFS). The Google papers inspired Doug Cutting (McKenna, 2017) to create the Java-based Hadoop, which he named after his son’s toy elephant. Hadoop and a few open-source tools that complement it make huge, diverse datasets readily accessible for quick analysis

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using clusters of relatively inexpensive commodity hardware. Big data open-source software (OSS) like Apache Hadoop and Apache Spark became industry standard in enterprise data lakes. A data lake is a relatively new term for a huge repository for structured and unstructured data. Considered mission critical, it is widely used by the U.S. Government, the National Security Agency and Web giants such as Facebook, Twitter, and Yahoo. Over the recent years, Hadoop has evolved into an ecosystem with many sub-projects such as Pig, Hive, Hbase, etc. Shopping data are one of the newer data streams that is being collected. It is the consumer’s interaction with a request to fly to a destination, the shopping responses produced for the request and linking these shopping sessions to the actual booking made by consumers. Historically this information was stored in a data warehouse using the Teradata DBMS. These systems excel in storing structured tables and handling a mixed workload from hundreds of simultaneous users. The downside is that we must spend a lot of time modeling the data before storing it, as well as the cost of the machine for storing very large datasets. Ideally the calibration is most effective if the data are stored in a raw file-based format, instead of normalizing the data into Teradata tables, so that inclusions and exclusions of attributes can be made directly during the calibration process from a Big Data environment. With HDFS, shopping logs can be stored in their native format very economically and Map Reduce enables the calibration and development of advanced analytical models. The output of these models, which is orders of magnitude smaller than the raw data, can then be loaded into our operational and warehousing systems for use by a broader audience. Most contributors in the Big Data community are computer scientists. The analytical software packages available today are designed from a data mining, machine learning and artificial intelligence point of view. Analytical tools in Hadoop must be developed with Java and MapReduce, which many statisticians are unfamiliar with. As a result, there are limited statistical tools for linear regression, hypothesis testing, design of experiments, time series modeling, multivariate and non-parametric analyses. What is required is a statistical toolkit for Hadoop written as user defined functions for Apache Pig so that statisticians can easily conduct various analyses without having to learn Java and MapReduce. Ideally, corporations developing the toolkit should publish it under the open-source Apache license so that the entire Big Data community can use it and improve on the toolkit. The evolution of big data OSS (open source software) started with on-premises deployment of Apache Hadoop in the late 2000s to early 2010s followed by Apache Spark. These enterprise data lakes have limitations since the machines could not scale independently and required significant tuning and testing. The exposure to spikes in data or the next OSS release were addressed with the cloud-based OSS, where on-premises constraints such as virtualization, I/O bandwidth, and storage performance were overcome. With the cloud OSS, compute was decoupled from storage to take advantage of scaling on-demand. Serverless OSS is seen as the next stage in the evolution to manage costs and support autoscaling. In travel, the question remains: What types of business problems can a big data platform solve? Potential applications of Big Data are summarized below:

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11.11.1 Demand Forecasting Based on Consumer Preferences Understanding consumer preferences and modeling demand requires access to shopping data. Shopping data describes a consumer’s interaction—the requests made, the information displayed, and what was booked based on what the consumer saw. Calibration of a consumer choice model for forecasting demand requires access to shopping data. It is a sophisticated method to forecast demand by modeling the consumer’s selection process based on schedule and fare attributes.

11.11.2 Hotel Shopping and Dynamic Ranking For hotel shopping requests, techniques can be applied to dynamically determine the order in which hotels should be displayed for a destination market. The objective of optimal ranking is to improve site traffic conversion rates and generate incremental revenues. Consumer choice models can be calibrated from historical hotel shopping sessions to display hotel search results that maximize the probability of selecting a property on the first page. Typical factors that need to be considered are property attributes, selling rates, length of stay, advance purchase, etc. to create a response that simultaneously maximizes revenues and conversion rates. Creating a targeted response instead of a generic response also improves consumer loyalty since properties displayed are relevant to the search request and a function of the hotel selling rate and popularity.

11.11.3 Optimizing Air Screen Display Measuring screen quality enables an OTA to determine how competitive they are in converting shoppers into bookers. The air itinerary selection process is daunting because typically for a shopping request on an OTA, shopping algorithms generate at least 1000 outbound flight schedules and 1000 inbound flight schedules. From the 1000 × 1000 = 1 million itinerary options, the optimal set of itineraries needs to be selected that provides the best alternatives for a consumer. Keeping in mind that online consumers do not automatically select the lowest priced itinerary, the itineraries that are displayed for each shopping request should provide diversity expressed by quality of service (e.g., nonstops, single connect, double connect, interlines), fares and carriers on both the outbound and inbound schedules play a critical role in the selection process. Screen quality can be measured with a calibrated choice model that determines the probability that a displayed itinerary will be selected. Inputs into this choice model may include the selling fare and schedule attributes. Measuring screen quality offers continuous improvement to algorithms developed to not display either wrong options or options on that do not improve conversion rates. Itineraries can be ranked based on the utility score from the choice model to maximize conversion rates.

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11.11.4 Dynamic Intervention Big Data can support dynamic personalization by dynamically configuring pages based on recent past behavior as revealed from clickstream data. Examples of intervention are: 1. Selection of an expensive nonstop flight by a consumer leads to business hotels being featured during subsequent hotel shopping. 2. When a consumer requests only 4-Star and 5-Star hotels, adjusts the hotel display ranking algorithm to be less price sensitive. 3. Repeated shops by a consumer for the same resort destination can lead to a dynamic discount that is typically valid from a session to a day to make the sale. 4. Past purchase behaviors of consumers can launch dynamic on-the-spot promotions to improve conversion rates.

11.11.5 Hotels Dynamic Pricing Hotel shopping data can be leveraged to assess current competitive position and optimize a property’s pricing decisions and room rates to maximize expected revenue. OTAs such as Booking.com and Expedia have access to competitive hotel rate search results which are collected in real time. Hotel chains typically purchase competitive rate information from companies like Rubicon and QL2, but that information can be outdated since the robots typically execute as a batch process. By calibrating a consumer’s preferences from the rate shopping data, newly “optimized” rates can be sent to the hotel in real time as a web service so that the hotel websites can be updated. This approach provides valuable, real time rate corrections while avoiding the expense of complex PMS/CRS integration.

11.11.6 Hotel Competitive Sets Hotel and package clickstream data from OTAs can be used to provide unique insights into who a hotel operator’s competitors really are. From multi-supplier clickstream data collected from OTA hotel and package shopping sessions, we know that consumers who selected a specific property also reviewed the hotel detail pages of other properties. Click-stream data tells us the asymmetric relationship between properties that a consumer reviews during a shopping session. Armed with this knowledge of the true competitors in a market, a hotel operator can focus on understanding the competitor’s products, promotions, and services to determine the appropriate course of action to retain customers and transform them into loyal customers. For example, at Travelocity, we conducted an analysis of Cancun hotels to find their true competitive set as perceived by consumers as they were shopping and

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comparing properties on Travelocity. The competitors were not always intuitive or obvious to a hotel operator. The definition of competitive sets is never static. It is dynamic, since the best available rate of the hotel that is trying to determine their competitive set has an influence. Take for example, hotels in Cancun, Mexico. The perceived competitive set for the Hyatt Regency Cancun, a 4-Star property located in the heart of the hotel zone of Cancun is different than you might think. Surprisingly, it includes a range of properties from 2-Stars on up, while other near-by 4-Star properties were not included and are perceived to be dissimilar to the Hyatt. This is because the Hyatt was running a promotion and customers who would have stayed at a 2-Star property will now consider an upgrade to the Hyatt for a competitive rate. The relationship between properties is never symmetric. A customer used to staying at a Hyatt will not stay at a 2-Star property but someone who stays at 2-Star properties will upgrade to a Hyatt if the rates are competitive. Determining competitive hotel sets is of critical importance for hotel revenue managers to control room inventory.

11.11.7 The Chatter Index It is difficult for hotel chains and airlines to know about all the significant events, annual and one-time events, in destinations within a 50-mile radius. Understanding the popularity of these events is critical in effectively controlling room and seat inventory. In the absence of this information, suppliers may inadvertently run promotions and risk revenue dilution in the process. By picking up chatter from Twitter, blogs and boutique websites that target segments of the population (e.g., marathons, wine tasting events, food festival, etc.), they can be categorized to create a chatter index based on keywords such as “travel” which in turn can be used to alert travel suppliers, when the chatter index exceeds a predefined threshold, early in the booking cycle to protect rooms and seats for late booking higher valued passengers. Signal intelligence agencies such as the No Such Agency,1 a multi-faceted boutique agency that specializes in fashion, music, art, media, and lifestyle, have exploited the chatter index to good effect.

11.12 Shopping Query Data Academic journals and practitioners frequently discuss the value of shopping response data and how it can be leveraged to solve airline problems. However, shopping query data are rarely mentioned. Airline websites, OTAs and GDSs receive millions of raw search requests daily (Vinod, 2011a). About 10% of all queries receive no shopping response for a range of reasons. For example, a customer on an

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https://www.nsa-international.com/

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airline website may request a destination that the airline does not serve. These queries can be captures and processed to develop metrics and analytics to gain additional insights into customer behaviors. Typical shopping requests can be generic (origin, destination, departure date, and return date) or specific (origin, destination, departure date, return date, carrier, cabin, service, etc.). It can be argued that by developing the right performance metrics, this data can be used by an airline to influence key airline marketing planning functions such as flight scheduling, fare management, revenue management, marketing programs, and frequent flyer redemptions. Shopping queries can be categorized as generic or specific. The categorized shopping queries can be aggregated and summarized by key input attributes of the shopping request as well as derived attributes. Examples of key input attributes are destination city, origination city, number in party and distribution of number in party (adults, children), carrier preference and service preference. Examples of derived attributes are shopping queries by days to departure (e.g., 3-day advance purchase, 7-day advance purchase, etc.) and minimum stay requirements (e.g., 3 days, 6 days, greater than 7 days, etc.) Sample measures that can be derived from shopping query data are: 1. Destinations ranked by popularity (queries from multiple origin points to the same destination). 2. Destinations ranked by popularity from the most popular to the least popular (queries from multiple origin points to the same destination) by carrier. This data can be used by airlines to influence pricing actions and exploit pricing power in popular destinations. 3. Distribution of number in party by destination. This can determine the destinations where two-for-one promotions are popular, child-travel-free promotions, etc. 4. Origin cities with the most to least requests by carrier. Origin cities with the least requests can be targeted by airlines for marketing spend in offline channels, e.g., radio.

11.13 Blockchain in Travel In a recent IATA study “Future of the Airline Industry 2035” (IATA, 2018b), blockchain has been identified as one of the technologies that may have a significant impact on the future of aviation. Blockchain is a disruptive emerging technology that is gaining momentum with a range of applications in travel including loyalty, contract management, security and personal identity, revenue management, secure payment, and related areas. The value of blockchain in travel is in the early stages of discovery and could lead to new business models that could increase resilience, reliability, transparency, and trust. A blockchain has three components—a ledger, cryptocurrency, and a mechanism to execute smart contracts. The decentralized,

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distributed ledger allows entities to securely work together. Smart contracts are used to automatically settle negotiated agreements among all parties without an intermediary. A blockchain is ideal for storing smart contracts because security and immutability are built in. All payments and settlements on the blockchain are made with a cryptocurrency. The most well-known cryptocurrency is Bitcoin, though there are over 1000 other cryptocurrencies. In addition to Bitcoin, the most widely traded are Ethereum, XRP, Litecoin, and EOS. Bitcoin is a virtual currency with no governing authority, such as a central bank. In June 2019, Facebook announced that they were launching a new cryptocurrency, Libra (now called Diem), in 2021. To avoid the volatility that most cryptocurrencies are famous for, some (such as True USD (TUSD), Gemini Dollar) are pegged to the U.S. dollar. There have been active debates on how blockchain could be leveraged in travel for competitive advantage. Proponents of blockchain with its distributed ledger advocate the end of entrenched intermediaries such as Global Distribution Systems (GDS), Online Travel Agencies (OTA) and Travel Management Companies (TMC). The value proposition is reducing costs without intermediaries while simultaneously lowering the barrier for new entrants. Recently, the Airline Tariff Publishing Company (ATPCO), SITA and a blockchain vendor, Blockseye (Sorrells, 2018b) were working on a proof of concept of offer and order management by a neutral authority with the IATA New Distribution Capability (NDC). Regardless of the promise of blockchain, there are many critics that question its viability. For example, a core concern is that blockchain technology can only support low transaction volumes. IBM’s Hyperledger boasts a maximum transaction rate of 3800 per second, which is well below what is required for the likes of GDSs and OTAs. Here are a few examples where blockchain has a unique value proposition (Vinod, 2020b).

11.13.1 Loyalty Programs In 2018, Singapore Airlines announced the launch of its blockchain based airline loyalty digital wallet app for its KrisFlyer loyalty program (Shayon, 2018). The benefit of blockchain technology has been attributed to flexibility in redemption options for customers who can redeem the accrued digital tokens at retail outlets. Thus, there is no depreciation of accrued miles over time since the digital tokens can be exchanged at will for other cryptocurrencies. This reduces the displacement of higher valued passengers closer to flight departure since customers may redeem the cryptocurrency for nontravel related purchases. For airlines, liability on the books for accrued miles is reduced, which is important in 10 K filings. Critical to the success of blockchain based loyalty programs is the ability of the airline to sign up retailers and credit card companies quickly who will accept the cryptocurrency as a form of payment when a customer wants to make a purchase. Added benefits are the instant accrual of mileage equivalent cryptocurrency when the travel segment is completed and the inherent security benefits that reduce fraud.

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The use of blockchain with digital tokens also intrinsically increases the value of loyalty coalition programs by an order of magnitude.

11.13.2 Interline Ticketing Airlines that use origin and destination (O&D) revenue management apply varying degrees of sophistication to determine availability for interline itineraries (Vinod, 1999, 2005d), which accounts for approximately 10% of the total traffic for network carriers. Dynamic interline proration is typically applied to determine the airline’s revenue share of the total itinerary prior to determining booking class availability. The simpler approach is to prorate the itinerary based on the IATA standard cost weighted mileage factors and the more sophisticated proration method is based on the special prorate (bilateral) agreements, known as SPAs. After the segments have been flown, revenue accounting prorate engines are used to determine the revenue share for each airline, and the settlement is handled by intermediaries such as ARC (Airline Reporting Company) in North America and IATA BSP (Billing and Settlement Plan) worldwide. When airlines migrate to the new IATA New Distribution Capability (NDC) the settlement will be handled directly by the airlines involved in the interline itinerary without an intermediary like ARC or BSP. The workflow for interline ticketing using blockchain without an intermediary is shown in Fig. 11.1.

Fig. 11.1 Interline ticketing using blockchain without an intermediary

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11.13.3 Airline/Agency Contracts In the world of airline commissions, there are two types of contracts, front-end commission contracts and back-end commission contracts. Front-end commissions are paid to travel agents by airlines for sale of tickets in specified booking classes (reservations booking designators or RBDs) in certain markets or specific routings. The front-end commissions may be a flat (dollar) amount or a percentage of the value of the ticket with a maximum payout per ticket. Back-end commissions are sometimes called override commissions and they represent a performance-based compensation to travel agents by airlines for fulfilling specific targets that are specified in the contract. For example, for a market or market entity (group of markets) the airline may specify the performance criteria, such as improvement in market share by a defined percentage, bookings exceeding a threshold, etc. Override commissions are only paid out by the airline as a lump sum payment if the performance criteria outlined in the contract is fulfilled. Commission contracts are complex and involve multiple factors including schedules, fares, commission structures, and ticketing instructions. However, by using a structured data model, a digital representation of a contract can be created in blockchain. The data model can then be used to build and execute a rules engine that calculates commissions using a smart contract. Contracts can then be distributed using blockchain’s distributed model. So, what are the benefits of using blockchain for airline/agency commission contracts? Legacy paper contracts cause inefficiencies with contract approvals, implementation and execution of the contract with a travel agency. Once approved and implemented, the contract is typically effective for a quarter to a year. With a private blockchain, a secure, digital version of the contract can be made accessible to all parties in the chain thereby streamlining the process of automating contract approvals and payments. In addition, since all changes are tracked, blockchain becomes a trusted source. With a digital version of the contract that can be viewed by all parties, contract addendums can be effective near-real time, thereby eliminating the legacy periodic cycle of contract renewals, e.g., quarterly, bi-annual, or yearly. In addition, blockchain facilitates and automates the task of secure payment. Figure 11.2 outlines the workflow for front end commissions with blockchain.

Fig. 11.2 Front end commissions with blockchain

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11.13.4 Revenue Management Blockchain is an open decentralized database. Every transaction is recorded, linked, and made secure with cryptography. So, can blockchain be applied to revenue management to manage inventory in real time? Webjet, the Australian OTA, uses blockchain to address disputes with a reconciliation service. The solution involves a smart contract that creates an indisputable and permanent record of a hotel booking between the parties involved with the transaction (Page, 2019). Once recorded, copies are retained across multiple decentralized, distributed nodes in the network. This minimizes errors between travel partners and hotel suppliers. TUI AG, the travel and tourism company, that specializes in packages, has developed a private blockchain-based inventory system for hotel bookings (Marr, 2018). TUI owns over 300 hotels, and it distributes the room inventory to its various points of sale. The process is inefficient if demand does not materialize in one channel but peaks in another, resulting in lost bookings. TUI’s “Bed-Swap” private blockchain initiative allows the company to move hotel inventory between the various points of sale in real time to match demand. Hotel revenue management infrastructure can also be connected to the property management system, which has guest data, through blockchain to enable personalization. The TUI AG use case is very specialized and applicable to similar tour operators that owns hotels and needs to distribute hotel inventory to the various outlets. The question that needs to be answered is whether airline seat inventory, today centralized by an airline’s reservations inventory system, can be decentralized? Over the past decade the look to book volumes have grown disproportionately to bookings. The greater the look to book, mostly driven by OTAs, the greater the burden on an airline’s reservations inventory system to return accurate availability when requested. In 2019, the transaction processing volumes for some of the largest airlines in the world have an average daily peak of 27,000 transactions per second or 1.1 million segments per second; and the sell volume peak is 900 transactions per second.2 Blockchain is not a high-volume transaction processing system and is not applicable with the current technology.

11.13.5 Known Traveler Digital Identity With the growth in cross-border travel, an application of blockchain for safe and secure travel is the known traveler digital identity (KTDI). This is promoted by the World Economic Forum (World Economic Forum, 2020), working with Accenture, and public and private partners. This is a decentralized identity model

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Sabre Inference from Transaction Processing Data.

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where travelers can self-manage, control, and share their credentials selectively. The public blockchain ledger allows trusted organizations to issue credentials to individuals who in turn can present the credentials to verifying organizations on demand.

11.14 The Role of Machine Learning with Blockchain Leveraging machine learning with blockchain technology is intriguing. The largely unanswered question is whether there is a role for machine learning with blockchain? Can machine learning algorithms address some of the well-known weaknesses such as security, scalability, and efficiency with blockchain? Blockchain is an execution system. The question is: is there is a role for advances in machine learning with blockchain? Machine learning can play a role in the creation of smart contracts by processing a large amount of historical contract data and market forecasts. Airlines typically budget for two types of commissions: front end commissions and back-end (override) commissions. With backend commissions, airlines negotiate contracts with travel agencies to achieve specific goals related to bookings and market share. The various initiatives are prioritized. From a strategic perspective, even though the initiatives are prioritized, multiple initiatives or a minimal set of priorities should be completed over a pre-defined period, usually quarterly or half yearly. This is a multi-criteria problem subject to a budget constraint. Machine learning models can be leveraged to create intelligent contracts that fulfill airline corporate objectives, and smart contracts serve as the self-execution component of the intelligent contracts in a blockchain. The creation of contracts between airlines and travel agencies for front-end and back-end commissions can be taken to the next level. Based on past agency performance, historical demand and market size data can be used by machine learning models to recommend terms and conditions for how agencies should be rewarded for achieving specific milestones in the broader context of the airline’s corporate objectives. The parallel example is revenue management where reservations inventory control serves as the execution component of revenue management. Revenue management is an advanced planning application which has of late embraced machine learning techniques for demand forecasting and offer creation.

11.14.1 Maturity of Blockchain in Travel The adoption of blockchain in the travel industry is in its early stages. It is anticipated that blockchain will over time gain acceptance in travel with wellknown and new use cases that will become reality. Customer experience is critical for the success of blockchain. If travel experiences and security attain a new level

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with blockchain, it will gain faster adoption. Blockchain technology is disruptive, and it has the potential to revolutionize travel. Over the next decade, several hurdles in the areas of security, high volume transaction processing, airline planning and airline operations business processes must adapt to work with this technology; business interoperability between multiple blockchains and regulatory constraints will have to be overcome to ensure the mass adoption of blockchain.

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The question that is foremost on the minds of travel suppliers and customers is: what is the future of travel after the COVID-19 pandemic ends? A related question is: What is the future of airline pricing, revenue management and inventory control? A larger question is: what is the future customer experience? With the rapid changes in the business and technology landscape, it is difficult to predict the precise future state. What is clear is that there are several core elements that need to come together over the next decade to fulfill the future customer experience. As Nawal Taneja would say it, “I will connect the dots” (Taneja, 2017) from the previous chapters in this book to highlight a future state that begins with travel and extends to nontravel.

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What is foremost on the minds of industry analysts and airline executives is: will air traffic volumes reach 2019 levels by the end of 2024 as predicted by IATA (IATA, 2020b). While it is likely that travel volumes will recover, the mix of business versus leisure traffic will be different. The growing consensus among experts is that corporate travel will never return to pre-COVID 19 volumes. Corporate travel will shrink in size and leisure travel will grow. This is not good news for the airline industry since corporate yields are higher than leisure. In 2020, corporate travel has become accustomed to transacting business with the latest video conferencing technologies. This will continue unabated in the years ahead as we come out of the COVID-19 pandemic.

# The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 B. Vinod, The Evolution of Yield Management in the Airline Industry, Management for Professionals, https://doi.org/10.1007/978-3-030-70424-7_12

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Future State

Core Airline Revenue Management

Revenue management will continue to play a key role in a post-COVID-19 world. The core components that will continue to evolve as an integrated workflow are customer segmentation, air shopping, pricing, revenue management, and inventory control. With NDC adoption and the demise of full content agreements between airlines and GDSs as we know it today, airlines can provide identical content across all channels of distribution, but they can also offer different bundles and prices for different channels if they choose. A horizontal enabler for these core functions is the universal profile (UP) where customer preferences are stored. Innovation will be strongly influenced by IATA NDC to generate offers to customers on demand across all channels of distribution. The promise of advanced data analytics and artificial intelligence to help airlines understand their customers better should over time positively transform the travel experience with highly targeted personalized offers. This will require customers to give information to suppliers and travel management companies from their universal profile that they manage, in real time, to ensure timely notification messages for a frictionless journey. Customer segmentation will always be at the heart of revenue management. How customers are segmented and what attributes need to be considered will change as personas evolve over time. The intent of segmentation is to understand the customer’s context for travel and then determine how to price the product being offered and to display relevant content in an online setting. The so-called classless revenue management will be reality when the airline controls the dissemination of all offers to customers through their websites and through intermediaries on demand, but customer segmentation is a prerequisite to maximize revenues. Customer segmentation for air will extend to other lines of business to create dynamic packages with hotel, rental car, and local activities at the stop-over city or destination. Customer travel begins with the dream and plan phase with air shopping to search for flights. While low fare efficacy will continue to be important as a reference fare, customer segmentation identifies the dominant shopping parameters (e.g., departure time window, return time window, elapsed time, etc.) that should be used by the shopping algorithm to return relevant itineraries with the best fares for customers based on their preferences. Customer segmentation used for the generation of the air ancillary bundle will also be leveraged to display itineraries during shopping consistent with a customer’s preferences. Customer segmentation will also influence dynamic pricing of the base fare and air ancillaries and the composition of the targeted offers that are sent to customers. Boutique vendors and GDSs that provide enterprise IT solutions to suppliers are working on the plumbing and the algorithms to sell or license this technology to airlines to remain relevant in the future. In the absence of booking classes in the future, trip purpose segmentation will drive demand forecasting and optimization of inventory controls to create dynamically priced offers to individual customers. Airlines will manage the recommendation engines that generate offers on demand based on customer personas. Air

12.3

Future of the GDS

341

ancillary products such as seat counts by seat type and section of the aircraft that are inventory controlled should be managed by the host CRS inventory system. Most of the innovation in airline offers will be directed toward leisure travel. Airline offers for corporate customers will be limited in scope, constrained by corporate travel policy. Besides the leisure agencies, corporate booking tools and large TMCs that service the corporate channel such as Amex GBT, BCD Travel and CWT will have to adapt to a world with bundled offers. Beyond customer segmentation, personalization of the offer with the best fare and air ancillaries for a segment of one based on individual preferences will be central to attract and retain profitable customers. Determining the best fare based on customer preferences will continue to be an active area of research. When NDC matures, all offers, base fares or base fares with ancillary bundles, will always be generated by the airline on demand. Travel agencies that subscribe to a GDS and airline consumer direct websites can request schedules, fares, and air bundles which airlines will respond to in real time. This is like how hotel and car shopping, booking, and pricing works today through the agency channel. For example, GDSs do not price and calculate room rates and taxes for hotel content but rely on the hotel to provide this formation on demand.

12.3

Future of the GDS

Adoption of NDC by the GDSs is a pre-requisite to receive air content in the future. Beyond NDC adoption, the future of the GDS will be influenced by three key factors. First, segment booking fees for full-service carriers should be at a price point that is acceptable to the airlines. Second, is the ability to provide price points for segment booking fees that is economically viable for LCC participation in the GDS. The LCCs control one third of the market share and this content should be made available to travel agents for comparison shopping, booking and fulfillment. Third, is the ability for the GDS to gain access to infinite content and transform itself into an open marketplace whose revenue streams extend beyond travel. Infinite content is not just air, hotel, car, and cruise line content, but the ability to sell and fulfill nontravel content such as destination activities and nontravel products customers wish to purchase at the destination. Transforming the current GDS model to sell and fulfill nontravel content in an open marketplace is a significant investment. It will require a publish and subscribe environment. The GDS must publish the travel information such as shopping results and airline bookings that enables travel and nontravel suppliers to subscribe to various queues such as city pairs, markets, and destinations to proactively promote their content. To promote content proactively, suppliers must register to subscribe to the various queues. The GDS can post an event calendar to make it easier for suppliers to promote offers. For example, the All-Star Game, NFL Super Bowl, Consumer Electronics Show (CES), Balloon Festival in Albuquerque, New Mexico,

342

12

Future State

Retail Entities Airlines Hotels Rental Car

Publish Retail Entities Subscribe to Viewership of Shopping Cart

Offers Travel + non-travel

Theatre

Subscribers

GDS Shopping Cart

Retailer #1 Matching Model

API Templates Retail Entities Place Offers

Logic for determining content to promote

. . . Local Attractions

Fig. 12.1 GDS Transformation to an open marketplace

etc. In the case of bookings, only the specific airline on which the booking was made will be notified so that they can determine what ancillaries they want to promote for the specific request with a published price or a dynamic price through the NDC gateway. Typical nontravel related marketplace content providers such as theatres, professional or college sporting events and local activity providers (e.g., snorkeling, parasailing, etc.) can also subscribe and promote their content. It will also require the GDS to share parts of a travel agency session (AAA) with a supplier in real time to enable them to promote their content. Generic application program Interface (API) templates are required for non-travel suppliers to promote their content to the GDS marketplace. The matching algorithm determines specific content that should be promoted to the travel agent for display. It can be based on travel agency preferences, GDS preferences, and customer preferences for non-air content based on the economics of the revenue share model. The sale of nontravel products through a GDS should be based on a revenue share model, which will require enhancements to existing billing systems. The revenue share is between the retail entity, the GDS and the travel agency. Figure 12.1 illustrates the proposed transformation of a GDS to an open marketplace.

12.4

E-Commerce Giants and Travel

A key element for the success of bundling nontravel products into a travel package is fulfillment of the nontravel products. Fulfillment should be handled by the entities from whom the product or service was purchased. Fulfillment of nontravel products by intermediaries like the GDS or a travel supplier will always be less than optimal, leading to customer dissatisfaction.

12.5

Seamless Customer Experience for Travel

343

Fulfillment is a tough nut to crack, yet E-commerce giants like Amazon and Alibaba have mastered it. They are well positioned to enter this space with a significant investment in travel. But will they? Or will they create partnerships with a focus on interoperability? For example, Amazon can continue to sell nontravel products to customers from the time of booking that are tailored to the destination and delivered at the destination. This is an untapped market opportunity for Amazon to generate incremental sales of nontravel products. Correlating air travel bookings to purchase behavior patterns on Alibaba sites, for example, can create a new definition of segmentation to support campaign management, and the sale of combined air and non-air products. Redefining offers to include non-air travel components is intriguing since it gives them a definite competitive edge over competitors in travel. This is especially true in China where large OTAs like CTRIP give discounts to customers and sell air products below the airline published selling fares to retain and grow market share. In this scenario, entities like Alibaba can make up the revenue shortfall for the transaction by actively promoting nontravel products. Alibaba can link non-air buying behavior patterns with their travel site and address competitor discounts by compensating with non-air travel components, with a competitive discount combined with the air and non-air bundle. There are multiple approaches such as providing a discount voucher for non-air travel products for future purchase when an air booking is made, offering a discount for non-air products based on what is in the customer’s shopping cart, and, based on implicit preferences for non-air products derived from the click-stream data across various Alibaba sites.

12.5

Seamless Customer Experience for Travel

What is the future of frictionless travel? A vision for a frictionless travel experience in the future where the savvy traveler traverses channels and brands has been articulated (Locke, 2009). Such an experience is indeed attainable in the future. Travel is experiential, and the question to ask is: What has changed around the experience itself? Has technology made the experience of travel better? (Klein, 2016). To understand what will change and why, we should examine the following traveler scenarios of the future: I plan to go to Napa wine country for a romantic weekend getaway with my spouse in October, departing on a Friday and returning the following Monday. I prefer to rent a mid-size car from Hertz, stay at the Westin in Napa, want a reservation for dinner at Auberge du Soleil and my budget is $2,000. I plan to go to Kaanapali beach, Maui from Los Angeles in July for 4 weeks with my wife and 2 children. I prefer a nonstop flight. I am traveling light and want a pair of beach towels, suntan lotion and snorkel gear delivered to the Westin where I will be staying. I am on a 7:00am flight on June 7 to Atlanta on business, to train my clients. My meeting is at 300 Dearborn St. I need an airport pick up at 9:00am and be dropped back at the airport

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Future State

by 8:00pm. I need 3 flip charts and colored markers delivered at the office. A working lunch should be catered from Chez Paul for 12.

None of the scenarios described above is purely travel. All of them include nontravel components. Over the next decade the lines between travel and nontravel activities will blur because customers are strongly influenced with their purchase behaviors outside of travel and prefer a one-stop shopping experience. Revenue management will create dynamic bundles that extend to other lines of business (e.g., hotel, car, etc.) and local activities at the destination. The universal profile and universal data exchange clearing house concept is not limited to travel. Instead, it holds the promise to promote a seamless customer experience across travel and nontravel entities. To promote a seamless customer experience across multiple travel entities, there are three components that must be inter-operable: decentralized digital identity verification from a trusted source in real time, a decentralized universal profile that is owned by the customer and stored on their mobile device or elsewhere, and a data exchange to transfer customer data to entities that the customer has authorized. It can be argued that the universal profile can be centralized or decentralized. If it is centralized, the identity verification can take place with the provider of the universal profile. However, for flexibility and security, the profile must be decentralized. The World Wide Web Consortium (W3C) is finalizing standards for the decentralized identifier. Digital identity solution for travel cannot be provided by a for-profit entity, travel or otherwise. It will require a vendor agnostic non-profit organization that operates internationally. The Sovrin™ Network (Sovrin, 2018), a non-profit company, is working with various travel entities including SITA to determine how to standardize the verification of digital signatures of credential issuers using blockchain for international travel. A public blockchain serves as a decentralized self-service registry for public keys. Every identity owner is their own self-sovereign identity provider, can issue a digitally signed credential and any entity can verify it. All transactions have a digital signature that require a private key. Individuals (also known as identity owners) can register with the Sovrin™ blockchain ledger and are issued Sovrin™ verifiable credentials that is stored in the digital wallet. Entities that receive the credentials can authenticate the information provided. The customer owns the data in the universal profile and determines who has access to data elements in the profile. Data permissions will be at the atomic level, by individual data element, and access is provided to an entity at the discretion of the customer. Triggered by a customer, the universal profile will work with a data exchange clearing house to provide proactive notifications to travel and nontravel entities that subscribe for up-to-date information and are given access. Consider the following examples: 1. The universal data exchange (UDX) can be used to send advanced notification on status of checked luggage lost in transit. The hotel where the customer is staying can subscribe to the service to determine when the bags will be delivered to the

12.7

Administration of Key Horizontal Enablers by a Neutral Entity

345

hotel. In the scenario the customer authorizes the airline to provide the hotel (subscriber) access to the status of the lost bag. 2. The UDX can be used to send a customer’s airport limo service a message from the airline on the status of the arrival time of the flight for customer pick-up. In the scenario the customer authorizes the airline to provide the limo service (subscriber) information on the precise arrival time at the airport.

12.6

Beyond Travel for a Seamless E-commerce Experience

Looking beyond travel, customers want a frictionless E-commerce experience. Even E-commerce giants like Amazon and Alibaba do not have access to all the content available for sale. The digital experience of the future will allow a customer to access infinite content when a message is broadcast (publish) with the request and subscribers send push notifications to an offer store for the customer to view and accept or reject. The same core components described for travel will be required for a seamless E-commerce experience: decentralized digital identity, universal profile, and universal data exchange. If the GDSs do not transform their current business model to an open marketplace to sell any type of content, someone else will, to own the consolidated order. The question is, who will own the order management system with an offer store that provides a single view of travel and nontravel purchases, like a bill of material, to a customer, which they can access on demand?

12.7

Administration of Key Horizontal Enablers by a Neutral Entity

The management and administration of the digital identity verification, universal profile and universal data exchange must fall into the domain of a neutral entity, preferably a nonprofit organization. For example, customers who download the app for the universal profile should have the confidence to store their preferences for travel and nontravel. It should include customer’s affiliations to retail and non-retail entities. Retail examples are banking and financial services, Macy’s, Amazon, Alibaba, etc. Non-retail entities can include professional organizations, global conferences, etc. It cannot be an existing entrenched operating-for-profit entity like a GDS, a travel supplier or an E-commerce giant. Customer, travel entity and nontravel entity participation in this collaborative model is critical for success and a neutral entity will be well positioned to win the trust and support that is required to make this work.

Appendix A: Traffic Freedoms

The aviation term “freedom” refers to the rights granted to an airline to carry revenue traffic from an origination city to a destination city. Understanding traffic freedoms is essential for the effective management of seat inventory. Freedoms of the Air are the fundamental building blocks for international commercial aviation. The first “five freedoms” were defined in the Convention on International Civil Aviation in 1944, known as the Chicago Convention, that was attended by 54 nations. The traffic freedoms acknowledge an airline’s negotiated traffic rights. ICAO only recognizes the first five freedoms as recognized by international treaties. In total, there are nine traffic freedoms, several of which are governed by bilateral treaties. In the figures below, country A is considered the home country with the home carrier.

First Freedom The First Freedom gives an airline the right to fly over a foreign country. This is also known as overflight rights.

Country A

Country B Overfly

Second Freedom The Second Freedom gives an airline the right to make a technical/re-fueling stop in a foreign country without embarking or disembarking passengers or cargo. # The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 B. Vinod, The Evolution of Yield Management in the Airline Industry, Management for Professionals, https://doi.org/10.1007/978-3-030-70424-7

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348

Appendix A: Traffic Freedoms

Country A

Country B Technical Stop

Third Freedom The Third Freedom gives an airline the right to carry traffic from the home country to a foreign country.

Country A

Country B

This provides a U.S. flag carrier the right to carry passengers from a specific gateway in the U.S. (e.g., Chicago O’Hare) to a specific foreign destination (e.g., London Heathrow).

Fourth Freedom The Fourth Freedom gives an airline the right to carry traffic from a foreign country to the home country.

Country B

Country A

This provides a U.S. flag carrier the right to carry passengers from a specific foreign destination (e.g., Paris CDG) to a specific destination in the U.S. (Dallas/Fort Worth).

Appendix A: Traffic Freedoms

349

Fifth Freedom The Fifth Freedom gives an airline the right to pick up traffic in a foreign country and carry the traffic to another foreign country. This is also known as beyond rights.

Country A

Country B

Country C

Country C

Country B

Country A

This traffic right has the requirement that the flight must originate or end in the home country. Hence, the Fifth Freedom route will operate with a single flight number. For example, a Cathay Pacific flight may originate in Hong Kong, stop in London, Heathrow and continue on to Amsterdam, Netherlands. Passengers and cargo may travel between Hong Kong and London without proceeding to Amsterdam. Due to local competition, airlines with Fifth Freedom traffic rights typically offer heavily discounted fares. In the example above, instead of paying hundreds of dollars for a round trip ticket, a traveler can purchase a ticket on Cathay Pacific for a fraction of the fare offered by the local airline. However, the primary disadvantage faced by Fifth Freedom operators is that they are constrained to operate in the two foreign destinations only as long as the flight originates in the home country. In this case, they will typically offer one flight a day while British Airways and KLM may offer several flights a day between London and Amsterdam, since they are governed by Third and Fourth Freedoms. In Asia, after World War II, both Pan Am and Northwest Orient had Fifth Freedom rights to pick up passengers and freight in Japan and take them to other countries in the Pacific Rim.

Sixth Freedom The unofficial Sixth Freedom combines the Third and Fourth freedoms and gives an airline the right to carry traffic from one foreign country to another foreign country via the home country.

Country B

Country A

Country C

Country C

Country A

Country B

350

Appendix A: Traffic Freedoms

For example, the Trans-Tasman treaty, whereby Air New Zealand can carry revenue passengers from Australia to a foreign destination (e.g., Fiji Islands) via Auckland, governs Australia and New Zealand. The right to carry Sixth Freedom traffic implies that the airline can submit valid connections to the major GDSs so that the origin and destination can be displayed as a valid connection on the city pair availability display. Combining existing Third and Fourth freedom traffic rights does not automatically provide an airline with Sixth Freedom traffic rights. Having Sixth Freedom traffic rights improves the traffic flow for the origin and destination since the connection will be displayed as a competing service on GDSs. There is also an unofficial modified Sixth Freedom which implies indirect cabotage. This is the right to carry passengers or cargo between two points in a single foreign country while making a stop in the home country. For example, this can be a flight between two cities in the U.S. flown by a carrier based in a foreign country (e.g., Canada) with a stop in Canada. This is also called indirect cabotage.

Seventh Freedom The Seventh Freedom is a variation of the Fifth Freedom. It gives an airline the right to originate a flight in a foreign country and carry passengers and cargo on to another country.

Country A

Country B

Country C

This applies to an airline operating turn around service carrying passengers and cargo between two foreign countries without serving its home country. For example, if a U.S. carrier had aircraft stationed in Tokyo to carry passengers and cargo to countries in Asia and the Pacific Rim. This is also referred to as standalone cabotage.

Appendix A: Traffic Freedoms

351

Eighth Freedom The Eighth Freedom is a variation of the Fifth Freedom. It gives an airline the right to originate a flight in the home country, stop in a foreign country to pick up passengers and cargo and proceed to another city in the same country. This is also referred to as consecutive cabotage.

Country A

Country B

Country B

These rights are rare outside the EU where these rights exist between member nations.

Ninth Freedom The Ninth Freedom is a variation of the Fifth Freedom. The Ninth Freedom gives an airline the right to originate a flight in a foreign country and carry revenue passengers and cargo to another city in the same country. This is also referred to as standalone or pure cabotage, which is the transport of passengers or cargo between two points in the same country by an aircraft registered in a different country. Pure cabotage traffic rights are generally not given since it creates competition on a flag carrier’s home turf that can be avoided.

Country A

Country B

Country B

When Germany was divided into east and west after the Second World War, communist East Germany did not permit the West German carrier Lufthansa to operate flights from West German cities into West Berlin (deep inside East Germany), which was governed by the U.S., France, and Great Britain. Prior to German reunification in 1990, U.S. carriers Pan American World Airways and Trans World Airlines, Great Britain’s British Airways and French national airline Air France had traffic rights to operate flights between Frankfurt and West Berlin.

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Appendix A: Traffic Freedoms

Today, Ninth Freedom is allowed in the European Union (EU) and the European Economic Area (EEA). For example, Ryanair based in Ireland has domestic flights in the United Kingdom.

Appendix B: Airline Industry Acronyms

Category General

Acronym AMR ATI CFR CRS CRM DOT GSA GDS FAR 121 FAR 135 KPI LCC NOTAM OAL

OTA OSS SAGE SKU SNCF Socrate W3C

Description Autonomous Mobile Robots Anti-trust Immunity Code of Federal Regulations Computerized reservations system used as a primary computer sales system by an airline. Also called a host CRS Customer Relationship Management Department of Transportation (U.S.) General Sales Agent Global Distribution System used by travel agencies to buy/sell travel products, e.g., Sabre, Travelport, Amadeus Federal Aviation Regulation rules for scheduled air carriers Federal Aviation Regulation rules for commuter and on-demand (charter) operations Key Performance Indicator Low-Cost Carrier Notice to Airmen Other Air Line (also called OA, but used infrequently to avoid confusion with Olympic Airlines (formerly Olympic Airways) that uses the OA airline code Online Travel Agency Open-Source Software Semi-Automatic Ground Environment Stock Keeping Unit Société Nationale des Chemins de fer Français (French National Railroad) Système Offrant à la Clientèle des Réservations d'Affaires et de Tourisme en Europe World Wide Web Consortium

# The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 B. Vinod, The Evolution of Yield Management in the Airline Industry, Management for Professionals, https://doi.org/10.1007/978-3-030-70424-7

353

354

Appendix B: Airline Industry Acronyms

Category GDS global Id

Category Mainframe

Acronym ACP ALCS ATARS CRT IPARS PARS TPF TPFDF z/os

Acronym 1A 1B 1C 1D 1E 1F 1G 1J 1K 1L 1M 1N 1P 1Q 1S 1U 1V 1W 1X 1Y 1Z F1

Description Amadeus Abacus International EDS Information Business Radixx TravelSky Infini Galileo Axess Sutra Open Skies Sirin Navitaire Worldspan Sirena Sabre ITA Software Apollo Sabre (old) GETS—Gabriel Extended Travel System EDS Shares Fantasia Farelogix

Description Airline Control Program Airline Control System (running TPF in a MVS environment, now z/ os) Automated Travel Agency Reservations System (based on PARS) Cathode Ray Tube International PARS Programmed Airline Reservations System Transaction Processing Facility TPF Database Facility Widely used mainframe operating system—system is a shareeverything runtime environment that provides for resource sharing through its heritage of virtualization technology. z/OS gets work done by dividing it into pieces and giving portions of the job to various system components and subsystems that function interdependently.

Appendix B: Airline Industry Acronyms

Category Messaging Protocols

Acronym AIRIMP API EDI EDIFACT ICOT

JSON NDC OTA

PADIS

REST API SIPP SOAP Type A Type B TTY XML Zulu Time

Category Schedules

Acronym ASM Cirium DEI INNOVATA MCT OAG QSI SSIM SSM

355

Description A4A/IATA reservations Interline Messaging Procedures Application Program Interface Electronic Data Interchange—a format in which business data are represented using national or international standards Electronic Data Interchange for Administration, Commerce and Transport, messages that are approved as standards for EDI The traditional piece of computer hardware for using network services. Terminals usually have minimal computing function, being completely dependent upon their host, and are often referred to as “dumb terminals.” Java Script Object Notation New Distribution Capability messaging standard for airlines to communicate to intermediaries Open Travel Alliance, a non-profit standards body that is creating messaging standards across all lines of business (air, hotel, car, etc.) The Passenger and Airport Data Interchange Standards (PADIS) Board develops and maintains Electronic Data Interchange and XML message standards for passenger travel and airport-related passenger service activities Representational State Transfer API Standard Interline Passenger Procedures Simple Object Access Protocol Processor to Processor (P to P) communication (EDIFACT) Teletype is used to communicate reservations and messages between carriers using the AIRIMP standards The airline industry uses teletype messages over ARINC or SITA networks to communicate between reservations systems eXtended Markup Language Greenwich Mean Time (GMT), due to “Z” used in CRSs to indicate GMT

Description Ad hoc schedule message Schedule aggregator (like OAG and Innovata) Data Element Identifier in SSIM. Schedule aggregator (like OAG and Cirium) Minimum Connect Time Office Airline Guide (schedule aggregator, like Innovata and Cirium) Quality of Service Index, simpler linear form of CCM used in flight scheduling Standard Schedule Information Manual Standard schedule message

356

Category Industry Data/ Data Science

Appendix B: Airline Industry Acronyms

Acronym AI AIT AR ARC ARMA ARIMA CART CBC CCM CDLP CLV CNN DDS EM EPB FSI GAM GBM GEV GFS GLM Hadoop

HB HDFS HIVE LSTM MA MAD MAE MIDT ML MNL MSE OR PFV PPC RFMTV RNN

Description Artificial Intelligence Algorithmic Information Theory Auto Regressive Abstraction and Reasoning Corpus Auto Regressive Moving Average Auto Regressive Integrated Moving Average Classification And Regression Trees Choice-based Conjoint Consumer Choice Model Choice-based Deterministic Linear Program Customer Lifetime Value Convolution Neural Network Direct Data Solutions (from IATA in collaboration with ARC and Cirium) Expected Maximization Earning Per Booking Free Standing Insert General Attraction Model Gradient Boosting Machine Generalized Extreme Value Google File System Generalized Linear Model Java based open-source software that supports data intensive distributed applications on large clusters of commodity hardware Hierarchical Bayes Hadoop Distributed File System based on GFS and MapReduce A data warehousing system that runs on top of Hadoop to allow SQL-like queries Long Short-term Memory Moving Average Mean Absolute Deviation Mean Absolute Error Marketing Information Data Tapes Machine Learning Multinomial Logit Mean Squared Error Operations Research Potential Future Value, synonymous with Remaining or Residual CLV Pay per Click Recency, Frequency, Monetary Value, Tenure, Variety Recurrent Neural Network (continued)

Appendix B: Airline Industry Acronyms

Category

Acronym RPA SVM T100 TOPSIS TPS WTP XAI

Category Reservations/ CRS and GDS

Acronym AAA ACH ACARS ADM ADS AHA ALTEA APIS

A-PNR ARNK

ASR ATARS ATO ATSE AWB BABS BIDT BSG

CPA CRC CRS CTO DCS DOARS

357

Description Robotic Process Automation Support Vector Machines Department of Transportation arrivals data Technique for Ordering Preference by Similarity to Ideal Solution (multi-criteria decision making) Trip Purpose Segmentation Willingness to Pay Explainable AI

Description Agent Assembly Area U.S.-based Airline Clearing House Aircraft Communications Addressing and Reporting Agency Debit Memo Agency Data Systems Airport Handling Agents Amadeus Customer Management Suite (ALTEA RES, ALTEA INVENTORY and ALTEA DCS) Advanced Passenger Information System with pre-arrival and departure manifest data on passengers for border security agents Associated PNRs are associated with a master BSG PNR A surface sector segment in the PNR which means method of travel or arrival is unknown”. These segments represent a break in journey. Pronounced ARUNK Agent Sales Report Automated Travel Agency Reservations System Airport Ticket Office Air Travel Shopping Engine Air Waybill British Airways Booking System Billing Information Data Tapes Block Space Group PNRs used for repetitive/serial bookings from tour operators. Passenger names are placed into Associated PNR’s City Pair Availability Central Reservations Control Computer Reservation System (used as the primary computer sales system by an airline); also called host CRS City Ticket Office Departure Control System Donnelly Official Airline Reservations System (continued)

358

Category

Appendix B: Airline Industry Acronyms

Acronym ET EMD

ESV FPC GDPR GSA JICRS KTDI LNIATA MAARS MRZ OLTP PCA PII PNL PNR PRS PSS SABER SABRE SabreSonic SSR SSR-I STARS TMC TOC TTL TVL UDX UC UP VTCR VCR

Category Organizations

Acronym A4A AAA AARP AEA

Description End Transaction Electronic Miscellaneous Document, industry solution for collection and settlement of air ancillary fees (e.g., Air Extras) via ARC and BSP. Estimated Seat Value Fare Pricing Complex General Data Protection Regulation General Sales Agents Joint Industry Computerized Reservations System (JICRS) Known Traveler Digital Identity LiNe Interchange Address Terminal Address Multi-Access Agent Reservations System Machine readable zone Online Transaction Processing Participating Carrier Agreement Personally Identifiable Information Passenger Name List Passenger Name Record Pre-Reserved Seats Passenger Service System Semi-Automatic Business Environment Research Semi-Automated Business Research Environment SabreSonic CSS (Customer Sales and Service) reservations system Special Service Request Special Service Request—Inventory Sabre Traveler Automation Records Travel Management Company Total Cost of Ownership Ticketing Time Limit Travel Segment Universal Data Exchange Unable to Confirm at Sell Universal Profile Vendor, Tariff, Carrier, Rules Virtual Coupon Record

Description Airlines for America, a trade organization representing U.S. airlines American Automobile Association American Association of Retired Professionals Association of European Airlines (continued)

Appendix B: Airline Industry Acronyms

Category

Acronym AMR Corp. ARC

ARIG ARINC

ASTA ATA ATPCO BEA BOAC BSP CAAC CAB CASMA CDC Cirium DOT Dynata EU EEA FAA FCC IATA IBM ICAO ICH IFRS INNOVATA ITT OAG Qualtrics SABRE SODA SITA

359

Description Parent company of American Airlines, American Eagle, AmericanConnection and Executive Airlines until 2013 Airlines Reporting Corporation (ARC) is a technology solutions company providing transaction settlement and data information services Airline Revenue Integrity Group Aeronautical Radio, Inc. was established in 1929, is a major provider of transport communications and systems engineering solutions for aviation and airports American Society of Travel Agents (Advisors) Air Transport Association Airline Tariff Publishing Company (airline industry fare aggregator) Bureau of Economic Analysis (U.S.) British Overseas Airways Corporation Billing and Settlement Plans (like ARC in the U.S.) for billing statements reflecting ticket sales made by each travel agent Civil Aviation Administration of China The Civil Aviation Authority was formed in 1938 and later renamed as the Civil Aviation Authority (CAB) in 1940 Computerized Airline Sales and Marketing Association Control Data Corporation Schedule aggregator, like OAG and INNOVATA Department of Transportation (U.S.) Online market research firm European Union European Economic Area Federal Aviation Administration Federal Communications Commission International Air Transport Association International Business Machines Corporation International Civil Aviation Organization IATA Clearing House International Finance Reporting Standards Schedule aggregator, like OAG and Cirium International Telephone and Telegraph Official Airline Guide—schedule aggregator, like INNOVATA and Cirium Online market research firm Semi-Automated Business Research Environment System One Direct Access Société Internationale de Télécommunications Aéronautiques

360

Category Connectivity

Appendix B: Airline Industry Acronyms

Acronym AVN

AVS BBR DAI DCS DCA IDR IND OAC PCC POC POS P2P

Category Pricing, Revenue Management and Inventory Control

Description Numeric Availability Status sent from a host CRS to a GDS. AVN can use POS information to determine number of seats to be displayed at a specific location Availability Status message sent from a host CRS to a GDS Based Booking Request Direct Access Interactive Direct Connect Sell (seamless sell) Direct Connect Availability (seamless availability) Inventory Detail Record, alternate term for IND Inventory Detail Record, alternate term for IDR Office Accounting Code Pseudo City Code Point of Commencement Point of Sale Processor to processor (P to P) communication (EDIFACT)

Acronym ADR ADT AFA AP BRG CASK CASM CER CLV CN CWMF DAVN-MR DACS DCP EMD EMSR ePMP eTicket FBC FBR FC FCU

Description Average Daily Rate Adult Fare Active Forecast Adjustment Advance Purchase Best Rate Guarantee Cost per Available Seat Kilometer Cost per Available Seat Mile Cumulative Effective Revenue Customer Lifetime Value Continuous Nesting (also known as Bid Price Controls) Cost Weighted Mileage Factors, published by IATA quarterly Displacement Adjusted Virtual Nesting – Marginal Revenue Dynamic Availability Calculation System Data Collection Point Electronic Miscellaneous Document Expected Marginal Seat Revenue Electronic Prorate Manual – Passenger Electronic Ticket Fare Basis Code Fare by Rule Fare Component Fare Construction Unit (continued)

Appendix B: Airline Industry Acronyms

Category

Acronym FET FQ IFRS ITAREQ ITARES LFCF LOS LUA LLUA MAF MAT MCFA MPA MR-Flights MVT MFEM NLF NSR NUC OLF PAOREQ PAORES PFC PFV PODS PNI Reconciliation

PRM PTC PYM QSAP RASK RASM RBD

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Description Federal Excise Tax Fare Quote International Finance Reporting Standards IATA DCS message request for sell transactions IATA DCS message response for sell transaction request Load Factor on Closed Flights Length of Stay Last Unit Availability Limited Last Unit Availability Minimum Aacceptable Fare Market Adjustment Table, synonymous with MCFA and MVT Market Class Fare Adjustment table, synonymous with MAT and MVT Multilateral Prorate Agreement Market Restricted Flights Market Value Table, also called a MAT or MVT Multi-flight Expectation Maximization Nominal Load Factor Net Spill Rate Neutral Unit of Construction (Currency), superseded FCU Observed Load Factor IATA DCA message request for availability transactions IATA DCA message response for availability transaction request Passenger Facility Charge Potential Future Value Passenger Origin-Destination Simulator Passenger Name Index inventory count reconciliation request between host CRS and inventory to synchronize inventory counts on demand or during file maintenance Pricing and Revenue Management Passenger Type Code Pricing and Yield Management Quality of Service Adjusted Price Revenue per available seat kilometer Revenue per available seat mile The prime Reservation Booking Designator is usually (though not required) the first character of the fare class code (fare class code is synonymous to fare basis code). The RBD is equivalent to the booking class code. (continued)

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Category

Appendix B: Airline Industry Acronyms

Acronym RD REVPAR RFDB

RFP RFR RI ROM RS-13

SEM SEO SIFL SITI SOLO SOTO SPA SCLP SPLP SRP TC TPM TPMF VFR VN Yield YQ/YR

Description Reading Day Revenue Per Available Room Resident Fares Database, same as MVT/MAT but on TPF / Sabre PSS to support O&D controls for American Airlines Restriction Free Pricing Recapture Fare Ratio Revenue Integrity Revenue Opportunity Model Inventory control in BABS, called RS-13 when they had 13 booking classes, later expanded to 26 Search Engine Marketing Search Engine Optimization Standard Industry Fare Levels Sold Inside Ticketed Inside Sum Of Locals Sold Outside Ticketed Outside Special Prorate Agreements Set Covering Linear Program Set Partition Linear Program Straight Rate Prorate Traffic Conference Ticketed Point Mileage Total Passenger Miles Flown Visiting Friends and Relatives, a restricted leisure fare Virtual Nesting Revenue per revenue passenger mile (or kilometer) Surcharges used in International Markets

Appendix C: Glossary

Aircraft Communications Addressing and Reporting System (ACARS). It a digital datalink system for transmission of short, relatively simple messages between aircraft and ground stations via radio or satellite). The protocol was designed by Aeronautical Radio, Incorporated (ARINC) to replace their very high frequency (VHF) voice service and deployed in 1978, uses telex formats. Airline Code Designator. A two-character code designated by IATA to identify an airline (e.g., Alaska Airlines (AS), Air France (AF), United Airlines (UA)). Airport Code Designator. A three-character code used to identify an airport (e.g., Dallas/Fort Worth (DFW), London Heathrow (LHR)). Air Waybill. A document that accompanies international cargo with details about the shipment. Artificial Intelligence. The development of computer-based methods able to mimic human-like processes such as learning, reasoning, and self-correction. Broad categories include machine learning, natural language processing and deep learning. Availability. Availability of seats by booking class observed at a point of sale Availability Cache. Storage of availability data in cache, collected organically, frequently for a large number of airlines, to support air shopping. All GDSs have some form of availability cache; leg/segment based or O&D based. Availability Proxy. A read-only version of the airline’s reservations inventory system that replicates the availability processing logic resident in the inventory system. More accurate than the Availability Cache since it reflects the business rules resident in the airline host CRS for availability determination. B2B. Business to business. B2C. Business to consumer. Big Data. Structured and unstructured datasets so large and complex that it requires new tools and processes. Volume, Velocity and Variety are the three dimensions of Big Data. Bid Price. A bid price is the opportunity cost of not having an incremental seat on a leg in the network. It can also be interpreted as the minimum acceptable fare for a reservation to be accepted on a flight leg.

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Bid Price Vector. Also called a bid price curve, it is constructed from the network optimization model that assumes primal feasibility is unchanged. It is a set of prices as a function of seats sold or seats available. Booking Class. An identifier used to represent fares of a particular type and/or value for selling, inventory control and ticketing purposes. Booking classes are mapped into base compartments and is also referred to as reservations booking designators (RBD). Revenue management analysts frequently refer to booking classes and fare classes synonymously, which is incorrect. The RBDs are defined by the “owning carrier” of a fare and are unique to the airline. Booking Limit. The booking limit is the maximum number of seats that can be sold in a booking class or cabin. In a nested inventory control environment, the booking limit is the maximum bookings that can be sold to a given booking class and all booking classes that are nested into it. Bucket. Buckets are virtual and were introduced with virtual nesting inventory controls, where multiple service classes were mapped to a virtual bucket to control availability. Buyer. Refers to the role of the entity that purchases goods from an online merchant. Could be a single individual or could represent an organization. Cabotage. Cabotage is the transport of goods or passengers between two points in the same country by an aircraft registered in another country. CRM. Customer Relationship Management (CRM). Cabin or Base Compartment. A cabin is a section of the flight leg that has a different seating configuration or is associated with a different level of inflight service. Examples are first class (F), business class (J) and coach class (Y) cabins. Capture. The capture rate is the likelihood of a customer booking an alternate flight on any carrier if the first choice is unavailable. Central Reservations System (CRS). Airlines, hotels, rental car, and cruise lines own a central reservations system where inventory is hosted (airline seats, hotel rooms, rental cars, cruise line cabins). An entity’s own CRS is commonly referred to as the host CRS. Change of Gauge. Airline marketing term used to designate that a specific flight number changes aircraft, part way through the direct flight. City Code. The three character designation for a city or airport. These codes are assigned by IATA. City Pair Availability. Six characters, a combination of 2 city or airport codes, the departure airport, and the arrival airport, served by direct or connecting flights. The display includes flights in the city pair with numeric booking class availability Codeshare. Airlines enter partnerships to create marketing flights that are operated by a partner carrier to extend their reach. Coefficient of Variation. The coefficient of variation is the ratio of the standard deviation to the mean. Communication Channel. A type of medium for communicating marketing incentives. Examples include Web, Email, Call Center, Catalog, TV, etc.

Appendix C: Glossary

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Competitive Revenue Management. A revenue management capability to control what is available for sale based on prevailing market conditions such as competitor selling fares. Conjoint Analysis. A survey-based statistical technique used in market research to determine how consumers value features of a product or service. Connecting Market. A connecting market represents a service with pre-defined reasonable estimates of minimum and maximum connect time. Stop-overs do not constitute a connecting service. Connectivity. Connectivity defines the level of participation between the airline and the GDS. Consolidator Fares. Consolidators negotiate contracted rates with multiple airlines to sell tickets to qualified travel agents. These fares can be marked up by the travel agent. These consolidator fares are called net fares and bulk fares. Net fares (also called nett fares) can be marked up by the travel agent. Bulk fares normally can be sold as is and can only be sold as a package with a hotel, car, etc. Consumer. A consumer refers to the ultimate end-user of a product or service in a value chain. Continuous Nesting. Continuous nesting is also called bid-price controls. It is an inventory control technique for origin and destination control. Corporate Fares. These are private fares negotiated between an airline and a corporation. Cost Weighted Mileage Factors. IATA defined mileage factors weighted by traffic, used in some cases for interline proration. Published quarterly. Cumulative Effective Revenue. The cumulative effective revenue (CER) on a flight leg is the reservation value of an O&D over the leg, net of upline and downline displacement costs. Customer. A customer could refer to a single individual or could represent an organization in the value chain. For example, a travel agency is a customer of the GDS. Customer Lifetime Value. The customer lifetime value is an estimate of the future value of a customer combined with historical value provided to date. Deep Learning. A branch of AI that mimics the workings of a human brain in processing both unstructured and unlabeled data to make decisions by detecting objects, recognizing speech, and translating languages. Demand Factor. This defines the ratio of demand over capacity for a flight. Dilution. Revenue dilution occurs when adequate seats are not protected for higher valued passengers. Distributed Availability. Distributed availability is deployment of an Availability Proxy of an airline’s inventory system in a public or private cloud to support the growing demands of air shopping worldwide. Deploying the solution in the cloud at multiple locations worldwide reduces network latency. Downsell. Downsell is a special case of recapture to a lower fare on the same flight. Cross-flight recapture denotes recapture to other flight(s) on the host airline.

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Appendix C: Glossary

Dynamic Availability. A competitive revenue management capability to respond to competitive market conditions by modifying inventory controls in real time. Dynamic Pricing. A competitive revenue management capability to respond to competitive market conditions by generating a dynamic fare in response to a customer request. There are two versions of dynamic pricing: laddered pricing and continuous pricing. EDI. Electronic Data Interchange is a format for e-commerce in which business data are represented using national or international standards. EDIFACT. Electronic Data Interchange for Administration, Commerce and Transport. These are messages that are approved as standards for EDI and can be exchanged through a communication network. Expected Marginal Seat Revenue. The expected marginal seat revenue is the expected revenue of an incremental seat based on a distribution of demand. Extensible Markup Language. Extensible Markup Language (XML), the standard communications format in the Internet era. Fare Basis Code. The fare basis code appears on the ticket. It can include letters, numbers and up to two slashes (/). A fare basis is a compilation of the fare class or ticketing code and one or two ticketing designators. Multiple fare basis codes map to a booking class code. The fare basis code is distinct from the fare class, which is associated to each fare and used in pricing. Fare Breakpoint. These are the terminal points of a fare component for fare construction. It is the destination point where a fare begins or ends. Fare Component. The fare component is the most basic unit of fare construction and represents a specific fare between two city pairs. Fare Class Code: A fare class specifies the rules of an airline’s fare. Every fare has a fare class code, and this code appears on the ticket. It is synonymous with fare basis code. Revenue management analysts incorrectly refer to booking classes (RBDs) and fare classes inter-changeably, which should be avoided. Fare Management. Business process for managing all active fares by monitoring and responding to competitive fare activity. Fare by Rule. The creation of new fares using rules data to specify the market fares and the amounts. The fares can either be calculated from existing fares and rules in the market or specified to create a new fare using the rule provisions in Category 25. Federal Excise Tax. A federal tax that is charged on airfare. The segment fee of US$4.00 per segment, the September 11 security fee of US$5.60 per one-way flight, and the Passenger Facility Charge (US$4.50 per segment and US$18.00 per round trip) are in additional to the federal excise tax. Financial Availability. The process of determining whether selling a seat to a customer over the requested origin and destination is financially viable using the net contribution calculation. Flight Leg. A flight leg is a nonstop flight departure from a board point and an off point with a specific flight number and departure time.

Appendix C: Glossary

367

Flight Number. A flight number is associated with a specific aircraft routing and can consist of one or more flight legs. Funnel Flights. A funnel flight is an artificial direct flight that maps to operating flights. Future Customer Value. The future customer value is the net present value of a customer based on projections of future revenue and costs over the duration of the relationship. Global Distribution System (GDS). A Global Distribution System maintains airline schedules and accepts bookings for the requested itinerary. The major GDSs are Amadeus, Sabre, and Travelport. Go Show. Passengers who show up for a flight, without a confirmed reservation for a flight or those that show up with a confirmed reservation number for which no reference is found in the airline’s host reservations system. Gradient. The gradient is the incremental bid price and is the adjustment that is made to the bid price for every sell and cancel over a specific leg. It can be interpreted as the rate of change in bid price for a unit change in capacity. Gradient Boosting Machine. A machine learning predictive modeling technique for regression and classification problems. Also known as multiple additive regression trees. Group Bookings. Bookings for a group of passengers that are negotiated with an airline sales representative. Hidden City. A hidden city is a O&D connection with a long layover. Leisure travelers search for cheap fares to their destination that is a hidden city of an O&D. Applicable when a local one-way fare to the destination is more expensive than the connecting O&D. Hurdle Rate. The hurdle rate is synonymous with the bid price and is a term that is used in the lodging industry. IATA. The International Air Transport Association founded by member airlines in 1919. Today, IATA has over 270 airline members. IATA Carrier. An airline that is a member of the International Air Transport Association. Indexing. This is the process of assigning a service class to a bucket on a flight/ leg/date. Indexing can be static or dynamic. Interline. An itinerary where two or more airlines operate flights to complete the customer itinerary. Inventory Control. Ability to control seats sold by booking class in an airline’s reservations inventory system. Inventory control can be by leg/segment or O&D. Itinerary. An itinerary is a complete trip for a passenger as determined from the passenger name record (PNR). Hence an itinerary may be one-way or round trip. Leg. A leg is a nonstop board point and off point. Leisure Fares. Leisure fares are discounted fares with restrictions and sold to the public. They can be accessed from airline websites and through travel agencies.

368

Appendix C: Glossary

Lifetime Customer Value. The lifetime customer value is the net present value of a customer based on historical performance and projections of future revenue and costs over the duration of the relationship. Local Availability. Local availability is the availability for each RBD when an O&D is requested, and the O&D has only one segment. In an O&D inventory control environment, local RBD availability will be quite different from RBD availability for the same segment when it is part of a connecting O&D. Load Factor. The ratio of seats sold to the capacity expressed as a percentage. Load factor is post-departure. Predeparture load factor is called booked load factor. For reporting purposes, load factor is expressed as a ratio of RPK/ASK or RPM/ASM. Logistic Regression. Logistic regression is a predictive model that is used when the dependent variables are categorical variables. Machine Learning. Machine learning uses statistical techniques to enable computer systems to "learn" (i.e., progressively improve on a specific task) with data, without being explicitly programmed. Broad categories include unsupervised learning, supervised learning, reinforcement learning, neural networks, and deep learning. MapReduce. Developed by Google, it is a data processing technique for distributed computing. The MapReduce algorithm contains two tasks, Map and Reduce Map, which converts a set of data where individual elements are broken down into tuples (key-value pairs). Market. A market describes a passenger’s one-way origin and destination pair; regardless of connect points and time of day. An on-line market consists of one or more flight legs that are on a specific carrier. An interline market consists of one or more flight legs that are on a combination of two or more carriers. Market Adjustment Table. Required for O&D control. The conditioned fares are averaged by fare qualification rules and stored on the host CRS inventory system. This is called the market adjustment table which can be modified to increase or decrease availability by service class and POS. This is also called Market Class Adjustment Table or Market Value Table. Marketplace. The electronic medium that brings together buyers and sellers to trade products and services online through fair and competitive means. A Global Distribution System (GDS) is a marketplace that brings suppliers (sellers) and travel agents (buyers) together to transact business. Marketing Information Data Tapes (MIDT). Electronic records of a travel agency's sales history. The GDS records, owns and market MIDT data based on booking transactions generated by travel agents. Market Restricted Flight. A market restricted (MR) flight is a specific flight posted on a GDS for which availability must be directly queried on the host CRS via a seamless request for true last seat availability. Minimum Connect Time (MCT). The minimum connect time is specified in the airline schedules published by schedule aggregators.

Appendix C: Glossary

369

Multinomial Logit Model. A multinomial logit (MNL) model is a classification method that generalizes logistic regression to multiclass problems with more than two discrete outcomes. Multiple Virtual Storage. More commonly called MVS, was the most used operating system on the System/370 and System/390 IBM mainframe computers for commercial applications. Nesting. The hierarchy of booking classes determines the order in which the booking classes should be nested. The objective of nesting is to ensure that lower valued classes are not open for sale when higher valued classes are closed for sale. Net (standard) nesting and threshold (theft) nesting are the two methods for calculating availability. Applicable for both booking class controls and virtual nesting controls. Net Contribution. The net contribution is the difference between the market fare value for an O&D and the total bid price for the one-way (directional) itinerary. The net contribution calculation can also be extended to round trips on airline websites that exercise round trip control. Neural Networks. Algorithms that attempt to recognize underlying relationships in data through a process that mimics how a human brain operates. NP-hard. A problem is NP-hard if it can be translated into one for solving any NP-problem (non-deterministic polynomial time) problem. NP-hard problems are at least as hard as any NP problem. It is likely that there are no polynomial-time algorithms for NP-hard problems. Network (airline). A network represents the schedule for an airline for a day or a sub-set of the schedule for the day. No Shows. Passengers who have a booking and do not show up for the flight at departure time. Offer Management. Offer management is an extension of traditional revenue management to offer a base fare and ancillary bundle to a customer. Opaque Fares. Opaque fares are frequently restricted and cannot be sold as is, and only as part of a package (with a hotel or car package). Open Travel Alliance. The Open Travel Alliance is a non-profit standards body that is creating messaging standards across all lines of business (air, hotel, car, etc.) Operation Research. Techniques based on mathematical methods—discrete optimization, stochastic modeling, large scale optimization modeling Optimization Group. An optimization group represents a selection of flights from the schedule that can be grouped together based on the arrival departure pattern. For example, the optimization group can represent the entire schedule for the day. Optimization groups are required for O&D revenue management to capture all interactions in network flow traffic. Origin and Destination. An origin and destination is a nonstop or connecting market with the departure time of day qualifier. It is synonymous with service. O&D Inventory Control. The control of seat inventory by origin and destination of the request.

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Appendix C: Glossary

Overbooking. Authorizing more reservations than capacity to be accepted to compensate for the effects of cancellations and no shows. Overlapping Flights. Overlapping flights are also referred to as back-to-back ticketing. Passengers book overlapping flights to circumvent minimum stay restrictions. PADIS Board. The Passenger and Airport Data Interchange Standards (PADIS) Board develops and maintains Electronic Data Interchange and XML message standards for passenger travel and airport-related passenger service activities. Passenger Facility Charge. Commercial airports controlled by public agencies began imposing passenger facility charges on June 1, 1992 at $3.00 per passenger enplanement ($12.00 per round trip). The cap was raised to $4.50 ($18.00 per round trip) effective April 1, 2001. PFCs are federally authorized but levied by local airport operators, which set the amounts. Passenger Type Code. The passenger type codes are defined by ATPCO. It is a fare related classification for each passenger. For example, ADT is adult passenger, GVT is government travel, VFR is visit friends/relatives, AST is airline staff standby, etc. Personalization. Used in the context of offer management, to personalize the offer consisting of the base fare and ancillaries. Personalization is always 1:1 (for a segment of ONE). Physical Availability. This process determines whether a seat can be sold in a cabin by comparing the authorized capacity (including overbooking) against the seats sold count. If the authorized capacity is greater than seats sold, seats are physically available. Point of Commencement. The point at which a customer’s journey originates. Point of Sale. Identifies the location of the travel agency where the booking was made. Potential Future Value.The potential future value is an estimate of the residual future value of a customer. Priceable Unit. Also called a Pricing Unit (PU). One or more fare components make up a PU. One or more PU combinations produce a pricing solution for a trip. Private Fares. Private fares are only available through consolidators and travel agencies. Examples are corporate fares, net fares and bulk fares. Protected Seats. Seats that are protected in a booking class from lower valued booking classes. Protection Levels. This represents the seats protected for a booking class from lower valued booking classes. Public Fares. Also known as published fares, these fares are available for immediate purchase through any travel agency or airline website. Quality of Service Adjusted Price. An airline’s competitive fare response that is based on schedule and fare attributes. This is an improvement over traditional rulesbased matching. Quality of Service Index. The quality of service index (QSI) has been used by airlines to predict their “fair share” based on relative attractiveness of their schedule

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versus competitors. QSI accounts for a range of schedule attributes such as aircraft type, frequency of service, type of service (nonstop, single connect, double connect, interline, etc.). Reading Day. A reading day is a pre-departure snapshot. It is also referred to as data collection point (DCP). Recapture. Recapture is a special case of capture on to the same (host) airline. Regression. A predictive modeling technique that determines the relationship between a dependent variable and a set of independent variables. Reinforcement Learning. A machine learning technique to train machine learning models to make a sequence of decisions. With a goal to maximize the total reward, the agent learns to achieve the goal and receives either rewards or penalties for the actions it performs. Request for Quotation. Prior to negotiating the contract, a customer may submit a request for quotation (RFQ) to multiple entities. Restriction Free Pricing. Fare filings introduced by the LCC’s where the fares have no restrictions (absence of fences such as advance purchase and minimum stay restrictions) and the fare amount is the only determinant of the market segment. Revenue Opportunity Model. A method to determine the total revenue opportunity after departure, for a flight or network, and determine the effectiveness of the revenue management system by estimating the percentage of the revenue opportunity that was captured. Seamless Availability. A seamless availability request is a direct connect availability request wherein a reservation request originating from a GDS will be queried for availability directly on a host CRS. Seamless Sell. A direct connect sell request is also called a seamless sell request. Interactive sells are made against the airline inventory system. Seamless sell allows full usage of host CRS O&D controls to compute availability before the booking is made. Segment. A segment is a sequence of one or more flight legs with the same flight number. Segment Close Indicator. An indicator on the airline’s inventory control system that closes a booking class for future sales. Segment Limit Sales. A numeric limit on the airline’s inventory control system that limits sales in a booking class. Usually used in conjunction with leg class controls. Seller. Refers to the role of the entity that is selling products online. Could be anywhere along the supply chain (e.g., supplier, manufacturer, distributor, e-tailer). Semi-Supervised Learning. Situations arise when there is a large amount of input data and only some of the data are labeled. Service. A service describes the market plus the sequence of connections and time of day. Sovrin. The Sovrin Foundation is a nonprofit organization established to promote Internet identity for all and to administer the Governance Framework for the Sovrin

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Appendix C: Glossary

Network, a decentralized global public network enabling self-sovereign identity on the Internet. Spill. Estimate of passengers who were turned away because their first choice was unavailable. Spoilage. Seats that are empty on a flight that was closed for sale before departure. This is called overbooking spoilage, which is different from discount allocation spoilage, discussed with the revenue opportunity model. Station. A station is equivalent to an airport. Supervised Learning. These techniques are designed to learn by example. The input data for this method is a labeled training dataset. The labeled data has the correct answers, and the algorithm learns from this dataset to make predictions. Supplier. The term “Supplier” in travel refers to airlines, hotels, rental car, rail, cruise lines and ferry lines. Support Vector Machines. A set of supervised learning modes used for classification, regression and outlier detection. Thru Availability. Thru (through) availability is the availability for each RBD when an O&D is requested, and the O&D has more than one segment. Pricing analysts frequently refer to connecting availability as “thru” availability since fare construction is based on “thru fares” Thru Fare: A thru (through) fare is a fare for the market with a fare class (fare basis code) regardless of the number of connections in the schedule. Ticketed Point Mileage. This is the shortest distance between any two points on an operating route, used in airline fare calculations regardless of the airports used. Time Series. A series of data points indexed in time order. Time series models are useful when the data are serially correlated. Transaction Processing Facility. Transaction Processing Facility (TPF) is a realtime operating system for IBM mainframe computers. Travel Management Company. Travel management companies are large agencies that manage corporate business travel programs. Trip Purpose Segmentation. A method to implicitly segment customers based on the context for travel. Unsupervised Learning. A collection of techniques used to draw inferences from data that does not have any labeled responses. Upgrade. An upgrade is an offer to a customer to sit in a higher class of service at no additional cost. Upsell. Upsell is a special case of recapture to a higher fare on the same flight. Cross-flight recapture denotes recapture to other flight(s) on the host airline. Use Case. A use case is a collection of workflows that together complete a particular business objective. Value Pricing. An approach to pricing products based on what the market would be willing to pay for the service level of the product. Virtual Nesting. An O&D inventory control technique where service classes are mapped into virtual buckets. Web Link. Text that contains the address to other content.

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Workflow. A workflow is a collection of activities and events executed by systems and external agents participating in a business process. Yield. Yield is defined as the passenger revenue per revenue passenger mile (kilometer). Yield is monitored at different levels such as route, market, market entity and system. YQ/YR. Surcharges used in International Markets. Sometimes YQ/YR can be greater than the base fare and airlines do not pay commissions on YQ/YR (only pay commissions on the base fare). Fuel surcharges filed as YQF or YRF, Insurance charges filed as YQI or YRI. YQ/YR fares are not a tax, but a validating carrier specific fee, not interline able, not commissionable. The Carrier-Imposed (YQ/YR) Fees solution provides marketing carriers (carriers that appear on the flight coupon) the ability to control and collect fees at the sector (coupon), at the portion of travel (multiple sectors), or on the journey.

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Index

A Agency revenue management, 279 Agent assembly area, 191 Air shopping, xv, 22, 112–114, 204, 206, 254, 255, 257, 260, 261, 267, 282, 286, 294, 298, 314, 318, 340 Alliance revenue management, 210, 212 Alliances, xiii, 34, 206–210 Artificial Intelligence, 313–337 Availability proxy, 181, 204–206 Availability status messages (AVS), 180, 185–188, 193, 202, 205, 210, 291 B Bid price, 114, 167–170, 175–178, 181, 189, 194, 199, 204, 205, 210, 273, 276, 277, 290, 294, 297, 298, 303 Bid price exchange, 194, 294 Big Data, 113, 325–330 Blockchain, xiii, 331–336 Boeing spill model, 76 Branded fare families, 182, 183, 240, 262, 304, 305 C Coach spill model, 84 Codeshare, 18, 36, 206, 294 Commissions, 16, 28, 46, 47, 50, 91, 115, 139, 280, 281, 284, 334, 336 Competitive revenue management, xv, 35, 271, 272, 276 Connectivity, xv, 26, 27, 178, 184–187, 199, 201, 226, 276, 285, 322 Constructed fares, 54

Consumer choice model, 15, 69, 112, 120, 283, 328 Continuous nesting, 167–170, 176, 178, 181, 276, 294 Covid-19, 314, 315 Customer lifetime value, 249, 307 D Demand forecasting, xiii, 92, 93, 95, 96, 100, 102, 107, 109, 111, 112, 116, 118–120, 152, 153, 164, 198, 232, 294, 309, 318, 326, 336, 340 Dependent demand, 92, 95, 112, 233, 236–240 Deregulation, xi, 6–7, 22, 41, 56 Discount allocation, 8, 9, 11, 76, 95, 133, 137, 141, 142, 154, 232, 235 Distributed availability, xv Dynamic availability, 114, 181, 271–274, 277 Dynamic pricing, xiii, 30, 41, 114, 262, 272, 274–277, 285, 291, 296, 298, 304, 305, 314, 315, 340 E Economic overbooking model, 11, 128, 129, 132, 134 F Fare management, 41, 42, 55, 62, 66, 277, 296, 331 Fare prediction, 115, 320 Flight closing rate, 71, 80

# The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 B. Vinod, The Evolution of Yield Management in the Airline Industry, Management for Professionals, https://doi.org/10.1007/978-3-030-70424-7

395

396 Frequent flyer, 12–14, 37, 44, 50, 54, 56, 67, 72, 84, 182–184, 207, 217, 247, 252, 266, 289, 300, 301, 307, 331 Funnel flights, 151 G Group attrition, 219, 221–223 Groups, 43, 44, 56, 62, 117, 215, 217–219, 221, 222, 224, 319, 323 H Host CRS, 2, 11, 12, 16, 26, 27, 30, 37, 50, 56, 93, 142, 164, 168, 170, 177, 180, 181, 185–187, 190, 192–194, 199–206, 216, 222, 225, 226, 273, 274, 286, 288, 290, 293, 294, 298, 341 I IATA Tariff Conference Areas, 53–54 In demand forecasting, 93, 95, 113, 236 Independent demands, 92, 239, 240 Industry data, 35 Interactive availability, 193 Interactive sell, 193 Interline ticketing, 333 J Journey control, 12, 193 K Kalman Filter, 106, 109, 112 M Marketing planning process, 34 Market restricted flights, 214 Married segment, 189, 190 Minimum acceptable fare, 167, 169, 177, 219, 220, 223 Mixed classes, 53, 196–199 Multi-armed bandit (MAB), 248, 256, 259–261, 304, 319 Multilateral prorate agreement, 64 N Net fare markup, 115, 341–342 Net nesting, 9, 145–147, 162, 293

Index New Distribution Capability (NDC), xiii, 18, 29–33, 48, 244, 262, 290, 298, 315, 332, 333 O Offer management, xiii, xv, 243, 244, 247, 260, 261, 264 Off-tariff, 47, 149, 158, 192, 203, 215, 217, 220 Overbooking, xi, 8, 9, 11, 69, 72–75, 82, 119, 127–136, 141, 142, 152, 156, 161, 175, 176, 221, 223, 294, 295 Oversale rate constraint, 130 P Packaging, 264, 305 Passenger closing rate, 71, 79, 80 Passenger name record (PNR), 2–5, 35, 110, 149, 222 Point of commencement (POC), 35, 51, 54, 168, 190, 289 Point of sale (POS), 149, 190, 192, 203, 215 Potential future value (PFV), 250 Pricing of ancillaries, 262, 304, 305 Private fares, 46, 47 Profiles, 96–98, 101, 108, 252 Public fares, 46 R Recapture, 70, 84, 95, 112, 125, 139, 175, 237–240, 295 Recommendation engine, 245, 256, 257, 264, 267, 273, 319 Reinforcement learning, 115, 248, 256, 259, 264, 290, 317, 318, 320, 321 Restriction free pricing (RFP), 43, 61, 233, 234, 236–240 Revenue opportunity model (ROM), 152–154, 240, 317 RFMTV, 250 Robust revenue management, 310 S Seamless availability, 177, 185–187, 189, 199, 201, 215, 216 Seamless sell, 185, 186, 216 Special prorate agreement, 64, 65 Spill, 69–90, 101, 153 Spill rate, 71, 80, 86, 89, 141, 240

Index T Threshold nesting, 145, 176 Ticme series, 95, 102–105, 107, 109–112, 118, 129, 327 Trip purpose segmentation, 252, 255 U Universal data exchange, 266, 268, 344 Universal profile (UP), 266–268, 340, 344, 345 Upsell, 38, 43, 70, 95, 112, 116, 139, 175, 184, 227, 234, 237–240, 244, 256, 264, 271, 291, 295

397 V Value pricing, 230, 231 Virtual nesting, 12, 112, 160–164, 166–169, 177, 178, 181, 225, 226, 294 W Willingness to pay, 37, 67, 117, 184, 231, 262, 265, 274, 276, 277, 290, 304