Market Research with Panels: Types, Surveys, Analysis, and Applications 9783658376499, 9783658376505

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Springer Texts in Business and Economics

Martin Günther Ulrich Vossebein Raimund Wildner

Market Research with Panels Types, Surveys, Analysis, and Applications

Springer Texts in Business and Economics

Springer Texts in Business and Economics (STBE) delivers high-quality instructional content for undergraduates and graduates in all areas of Business/Management Science and Economics. The series is comprised of self-contained books with a broad and comprehensive coverage that are suitable for class as well as for individual self-study. All texts are authored by established experts in their fields and offer a solid methodological background, often accompanied by problems and exercises.

Martin Günther • Ulrich Vossebein • Raimund Wildner

Market Research with Panels Types, Surveys, Analysis, and Applications

Martin Günther Grossenseebach, Germany

Ulrich Vossebein Leiter Labor für Innovationsmanagement Technische Hochschule Mittelhessen Friedberg, Germany

Raimund Wildner Fürth, Germany

ISSN 2192-4341 (electronic) ISSN 2192-4333 Springer Texts in Business and Economics ISBN 978-3-658-37649-9 ISBN 978-3-658-37650-5 (eBook) https://doi.org/10.1007/978-3-658-37650-5 Translation from the German language edition: “Marktforschung mit Panels” by Martin Günther et al., # Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature 2019. Published by Springer Gabler, Wiesbaden. All Rights Reserved. # Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2022 This work is subject to copyright. All rights are reserved 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 Fachmedien Wiesbaden GmbH part of Springer Nature. The registered company address is: Abraham-Lincoln-Str. 46, 65189 Wiesbaden, Germany

Preface

The new edition of the book was used to publish the book in English to open it up to a wider circle of interested parties. To the best of our knowledge, no other book in the English-speaking world provides a comprehensive insight into panel research. The importance of panel research remains high, even if the panel concept is no longer as straightforward as it used to be. First and foremost, there are many online panels on offer, which have their place in market research, but in most cases, do not have the characteristics of “real” panels. They are therefore more suitable for crosssectional rather than time-series analyses. The lack of consistency in the sample and the type of data collection does not allow accurate change analyses over time. The book essentially follows the structure of the three German-language editions, which have proven themselves well for application. First, the basics of a panel structure are again clarified, with different panel types being discussed. Then, the most important panel types are discussed in detail, and application areas are pointed out. The next section focuses on the dimensions of a panel number. This section discusses in detail the breadth and depth of each panel number. Each panel number has four dimensions: Item—Fact—Segment—Period. Combining these four dimensions results in a wide range of analysis options that provide a fundamental basis for deriving targeted marketing and sales decisions. Due to the high importance of the retail and consumer panels, various special analyses and application examples are presented for these two-panel types. This is more a matter of showing what is possible, for example, and not an overview of what is possible in terms of analysis overall. This would go beyond the scope of any form of presentation. Due to the country-specific characteristics, this book lists panel types offered in different countries with corresponding country-specific adaptations due to the country-specific characteristics. In addition, there are undoubtedly many other panels that are in use around the globe for special markets. However, the basic

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Preface

structure and possible applications of panels are pretty homogeneous, so the book’s focus was placed on the structural aspects. The authors would be pleased to receive any suggestions, additions, or concrete proposals for improvement. Grossenseebach, Germany Friedberg, Germany Fürth, Germany

Martin Günther Ulrich Vossebein Raimund Wildner

Contents

1

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1 What Are Panels? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 What Characterizes Panels? . . . . . . . . . . . . . . . . . . . . . . . . . . Reference . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1 1 2 6

2

The Elements of a Panel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 The Universe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.1 Overview of the Universe . . . . . . . . . . . . . . . . . . . . 2.2.2 The Universe of a Retail Panel . . . . . . . . . . . . . . . . 2.2.3 The Universe of a Consumer Panel . . . . . . . . . . . . . 2.2.4 The Universe of a Television Audience Panel . . . . . . 2.3 The Sample . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3.1 Requirements for the Panel Sample . . . . . . . . . . . . . 2.3.2 The Sample of a Retail Panel . . . . . . . . . . . . . . . . . 2.3.3 The Sample of a Consumer Panel . . . . . . . . . . . . . . 2.3.4 The Sample of the Television Audience Panel . . . . . 2.4 The Data Collection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4.1 The Data Collection in the Retail Panel . . . . . . . . . . 2.4.2 The Data Collection in the Consumer Panel . . . . . . . 2.4.3 Data Collection in the Television Audience Panel . . . 2.4.4 Data Collection in the Internet Usage Panel . . . . . . . 2.5 Coverage of Retail and Consumer Panel . . . . . . . . . . . . . . . . 2.5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5.2 Coverage of the Retail Panel . . . . . . . . . . . . . . . . . . 2.5.3 Coverage of the Consumer Panel . . . . . . . . . . . . . . . 2.6 Extrapolation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.6.1 Extrapolation in the Retail Panel . . . . . . . . . . . . . . . 2.6.2 Extrapolation in the Consumer Panel and the TV Audience Panel . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . . . .

7 7 7 7 8 12 14 15 15 18 22 26 27 27 27 31 31 32 32 33 34 35 36

. .

38 40

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3

The Production Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 The Production Process in the Retail Panel . . . . . . . . . . . . . . . 3.2.1 Data Input and Verification at Shop Level . . . . . . . . . 3.2.2 Validation on Article Level . . . . . . . . . . . . . . . . . . . . 3.2.3 Extrapolation and Reporting . . . . . . . . . . . . . . . . . . . 3.3 The Production Process in the Consumer Panel . . . . . . . . . . . . 3.4 The Production Process in the TV Audience Panel . . . . . . . . . 3.5 Aspects of International Panel Research . . . . . . . . . . . . . . . . .

41 41 41 41 42 43 44 45 45

4

The Market for Panel Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

47 48

5

Institutional Panels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1 Classifications of Panels . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 The Retail Panel: the Origin of Institutional Panels . . . . . . . . . 5.2.1 Current Developments in the Retail Panel . . . . . . . . . 5.2.2 Types of Data in the Retail Panel . . . . . . . . . . . . . . . . 5.2.3 Data Sources and Data Availability . . . . . . . . . . . . . . 5.2.4 Specific Complements to the Retail Panel Nonfood . . . 5.3 Consumer Panels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3.1 Consumer Panels: Household Versus Individual Panel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3.2 Possibilities of Data Collection in the Consumer Panel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3.3 Reporting Cycles in the Consumer Panel . . . . . . . . . .

49 49 53 55 56 56 57 58

6

Panels for Media Markets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1 The GfK Crossmedia Link Panel . . . . . . . . . . . . . . . . . . . . . . 6.2 Television Audience Panel . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2.1 Special Features of the TV Audience Panel . . . . . . . . 6.2.2 Survey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2.3 Important Facts . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2.4 Important Segments . . . . . . . . . . . . . . . . . . . . . . . . .

67 67 70 70 71 72 74

7

Special Panels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.1 Panels for Pharmaceutical Products . . . . . . . . . . . . . . . . . . . . 7.2 Agriculture Panel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.3 Innovation Panel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.4 Mobility Panel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.5 Socio-Economic Panel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.6 EBDC Business Panel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.7 Test-Panels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.8 Conclusion and Outlook . . . . . . . . . . . . . . . . . . . . . . . . . . . .

75 75 77 79 82 84 85 86 87

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8

Product and Period Description . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.1 Product: Article Description . . . . . . . . . . . . . . . . . . . . . . . . . . 8.1.1 Definition of a Product Group: Category . . . . . . . . . . 8.1.2 The GTIN Code . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.1.3 Instore Codes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.1.4 The ISBN and ISSN Code . . . . . . . . . . . . . . . . . . . . 8.2 Periods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.2.1 Base Period Week . . . . . . . . . . . . . . . . . . . . . . . . . . 8.2.2 Aggregated Periods . . . . . . . . . . . . . . . . . . . . . . . . .

89 91 91 92 94 94 96 96 97

9

Shops and Household Characteristics . . . . . . . . . . . . . . . . . . . . . . . 9.1 General Segmentations in the Retail and Consumer Panel . . . . 9.1.1 Distribution Channel . . . . . . . . . . . . . . . . . . . . . . . . . 9.1.2 Shop Types . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.1.3 Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.1.4 Regions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.2 Special Shops of the FMCG Product Groups . . . . . . . . . . . . . . 9.2.1 Drugstore/Perfumery . . . . . . . . . . . . . . . . . . . . . . . . 9.2.2 Beverage Specialty Stores . . . . . . . . . . . . . . . . . . . . . 9.2.3 Department Stores . . . . . . . . . . . . . . . . . . . . . . . . . . 9.2.4 Convenience Channels . . . . . . . . . . . . . . . . . . . . . . . 9.3 Special Outlets of the Consumer Panel . . . . . . . . . . . . . . . . . . 9.4 Special Outlets of the SMCG Product Groups . . . . . . . . . . . . . 9.5 Household Characteristics in Consumer Panels . . . . . . . . . . . .

101 101 102 103 105 105 105 105 106 106 106 106 107 111

10

Facts of the Retail Panel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.2 The Basic Facts of the Retail Panel . . . . . . . . . . . . . . . . . . . . . 10.3 The Calculated Facts of the Retail Panel . . . . . . . . . . . . . . . . .

117 117 118 119

11

Facts of the Consumer Panel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.1.1 The Quantitative Facts of the Consumer Panel . . . . . . 11.1.2 The “Qualitative” Facts of the Consumer Panel . . . . .

127 127 128 139

12

Special Analyses Retail Panel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.1 Price-Related Analyses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.1.1 Kinked Demand Curve . . . . . . . . . . . . . . . . . . . . . . . 12.1.2 Monopolistic Sector . . . . . . . . . . . . . . . . . . . . . . . . . 12.1.3 Price Elasticity of Demand . . . . . . . . . . . . . . . . . . . . 12.1.4 Dependence on the Prices of Competitors . . . . . . . . . 12.2 Evaluation of Promotions . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.2.1 The Success of a Promotion . . . . . . . . . . . . . . . . . . . 12.3 Shopping Cart Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.4 Distribution-Related Analyses . . . . . . . . . . . . . . . . . . . . . . . . 12.4.1 Effect of an Expansion of the Product Range . . . . . . .

143 143 143 144 146 147 148 148 149 150 150

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12.4.2 Result of a Distribution Expansion . . . . . . . . . . . . . . 12.4.3 Out-of-Stock Analysis . . . . . . . . . . . . . . . . . . . . . . . Multidimensional Analyses . . . . . . . . . . . . . . . . . . . . . . . . . . 12.5.1 The Launch Report . . . . . . . . . . . . . . . . . . . . . . . . . . 12.5.2 Portfolio Analysis Categories . . . . . . . . . . . . . . . . . . 12.5.3 Portfolio Analysis Key-Account . . . . . . . . . . . . . . . . Outlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

151 154 155 156 157 158 159

Market Analyses Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.1 Superordinate Perspective . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.2 The Beer Market in General . . . . . . . . . . . . . . . . . . . . . . . . . . 13.2.1 Development of Key Facts in the Overall Beer Market . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.2.2 The Importance of Individual Consumer Groups . . . . . 13.2.3 Origin of the Purchase Value (Sales Value) . . . . . . . . 13.2.4 Interim Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.3 Price-Related Issues in the Context of Market Structure Analyses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.3.1 Which Price Position Does a Product Reach in the Market? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.3.2 Price Comparison with the Competition . . . . . . . . . . . 13.3.3 Effects of a Price Change . . . . . . . . . . . . . . . . . . . . . 13.3.4 What Is the Best Price Difference to the Competitor? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.4 Analysis of Promotional Activities . . . . . . . . . . . . . . . . . . . . . 13.4.1 What Actions Are Observed in the Market and how Often? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.4.2 The Importance of Promotions for Overall Sales . . . . . 13.4.3 Promotion Frequency Analysis . . . . . . . . . . . . . . . . . 13.5 Distribution-Related Aspects . . . . . . . . . . . . . . . . . . . . . . . . . 13.5.1 Which Distribution Channels Are Particularly Important? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.5.2 The Importance of the Distribution of Competitor Products . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.5.3 Stability of an Achieved Distribution . . . . . . . . . . . . . 13.6 Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

161 161 163

182 183 184

14

Application Examples Communication Analysis . . . . . . . . . . . . . . . 14.1 Question and First Attempts at a Solution . . . . . . . . . . . . . . . . 14.2 The Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14.3 The Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

187 187 188 190 191

15

Special Analyses Consumer Panel . . . . . . . . . . . . . . . . . . . . . . . . . . 193 15.1 The Measure-Tree . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 194 15.2 Brand Health Check . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 196

12.5

12.6 13

163 169 170 171 171 171 172 175 176 177 178 179 180 181 181

Contents

15.3 15.4 15.5 15.6 15.7 15.8 15.9

15.10 15.11 16

17

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Cumulative Buyers: Repeat Buyers . . . . . . . . . . . . . . . . . . . . Combination Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Duplication Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15.5.1 General Buyer Definitions . . . . . . . . . . . . . . . . . . . . . Assortment Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . Gain & Loss . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Brand Switching . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Buyer Migration: New-Lost-Retained . . . . . . . . . . . . . . . . . . . 15.9.1 Comparison of Gain & Loss, Brand Switching, and Buyer Migration (N-L-R) . . . . . . . . . . . . . . . . . . Launch Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Market Share Simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Category Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16.1 The Role of the Consumer Panel . . . . . . . . . . . . . . . . . . . . . 16.2 Potential Indicators in Category Management . . . . . . . . . . . . 16.2.1 The Determination of Buyer Potential: Buyer Potential Exploitation . . . . . . . . . . . . . . . . . . . . . . . 16.2.2 The Determination of Value Potential: Value Potential Exploitation . . . . . . . . . . . . . . . . . . . . . . . 16.2.3 The Ancillary Expenses: Missing Opportunities . . . .

197 199 201 202 203 204 209 209 212 212 214

. 217 . 217 . 220 . 220 . 222 . 224

Outlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 227

Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 231 Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 239

1

Introduction

1.1

What Are Panels?

Manufacturers of fast-moving consumer goods and durables are often quite well informed about their ex-factory sales through their field service. However, this information is not sufficient for the control of marketing and sales. The sales of competitors are also crucial for assessing own sales. Do products perform better or worse than the market? Which product group segments show above-average growth and promise success in market development? Such questions are at the beginning of analyzing a market and one’s position in it. A retail panel or a consumer panel can deliver the answers. For sales management, further questions arise: In how many and which store(s) is (are) my product(s) listed? Which ones do they additionally support through promotions? Where are above-average sales achieved? The same questions arise for online sales. All this, of course, must continuously be assessed in comparison to competing products. A retail panel provides information to answer such questions. This information is also essential because it usually forms a basis for discussing the negotiated terms and conditions between retailers and manufacturers. Sales volumes and the situation at the retailer must be supplemented by information about product buyers to identify the profitable target groups for implementing advertising or other measures. It is also essential for market development to know whether a product is purchased by many but only repurchased by a few or whether a small but loyal group of buyers repeatedly buy it. A consumer panel provides such information. Data from media panels, particularly television audience panels and Internet usage panels, are required to target defined groups by advertising. They provide continuous information on TV and online behavior and answer, for example, the question at which times and with which broadcasting genres or via which websites target groups are particularly well reached. Special panels such as in Germany the Drotax company report on advertising with trade ads, usually connected with promotions (cf. www.drotax.com). The classic advertising of manufacturers is # Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2022 M. Günther et al., Market Research with Panels, Springer Texts in Business and Economics, https://doi.org/10.1007/978-3-658-37650-5_1

1

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Fig. 1.1 Different aspects of market activity

represented by advertising statistics, such as those delivered by the Nielsen company. The panels mentioned above describe different facts (such as displays, sales, purchases, TV ratings, website views) at different objects of study (such as shops, individuals, households, websites, TV stations). What they all have in common, however, is that they aim to describe aspects of market activity as comprehensively and continuously as possible (cf. Fig. 1.1). In addition to the market’s current situation, it is usually the changes that trigger measures or provide assessment criteria for actions carried out in the past that are of particular interest to marketing management. Marketing management is therefore particularly interested in the changes in market activity. Consequently, it is understandable that each panel is optimized in several respects to measure market changes as precisely as possible. This can be seen if we look at those aspects that characterize a panel.

1.2

What Characterizes Panels?

First, panels generally observe a constant set of facts over a long period. This is the essential precondition for measuring change. Admittedly, this only applies to a limited extent: Product groups are added to the survey program or dropped if the market research institute has gained or lost customers. Changes are even more frequent in test panels, where the survey program only remains constant for the test duration. However, the final result of the panel, the report, is conditional upon that the survey subject remains constant during the reporting period. Second, panels also try to work with a sample as constant as possible over a long period. Given the large amount of data collected, a completely identical replacement of one sample element by another is impossible. Therefore, any change in the sample means a change in the result, which might not correspond to any real change in the universe and thus distorts market changes. Of course, the goal of a constant sample

1.2 What Characterizes Panels?

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+RXVHKROGVVWDUWHG UHSRUWLQJLQSHULRG QHZKRXVHKROGV

Fig. 1.2 Panel mortality and continuous mass using the example of a household panel

can only be achieved to a certain extent. The loss of sample units in a panel, which then have to be replaced by new units, is referred to as “panel mortality.” In contrast, the panel members who report consistently over two or more periods are called “continuous mass“or “continuous sample” (cf. Fig. 1.2). There are a variety of reasons for panel mortality. Inevitably, panel members leave the universe for various reasons. In the case of the retail panel, going out of business is an important reason for panel mortality. In the case of the consumer panel, it is due to death, relocation abroad, or relocation to a retirement home. This is not problematic for the panel quality as these changes represent changes in the population. More troubling, however, and usually far more extensive, is the change in the sample due to the panel member ceasing to participate because they have lost interest. Low panel mortality is an important quality measure for any panel for these reasons. It is, therefore, the goal of every panel institute to minimize panel mortality. Panel institutes, therefore, have entire departments dedicated to permanently motivating panel members to continue participating. If the sample is to be kept as constant as possible, then only those measures are suitable for a panel survey where the repeated survey has no or only a minor influence on the result. An example of this is a store’s sales in a product group. The replicated data collection by the market research institute will hardly influence the amount and structure of the store’s sales. Things are different with the continuous survey of advertising awareness. Here it is to be expected that the detailed questioning about advertising will change the future advertising perception of the interviewees. Therefore, a panel survey would have the problem that the reported changes would be caused more by the method than by the advertising. Therefore, advertising awareness is surveyed using a survey in which the sample is wholly exchanged from survey to survey. Still, otherwise, as many survey elements as possible, such as the questionnaire or the survey dates, are kept constant. Such a survey is called a “wave survey.” The effect of a constant sample (panel) compared to a survey of changing samples (wave survey) can also be calculated. For this purpose, let X1/n be a mean value measured in a panel of size n at time 1 (e.g., the purchased quantity of a particular

4

1 Introduction

item measured in a household panel in units per household), X2/n be the corresponding number at time 2. Then, the dispersion of the change in the mean value applies to measure its measurement accuracy (see Heinhold & Gaede, 1979, p. 108). σ 2 ðX 2 =n
X 1 =nÞ ¼ σ 2 ðX 1 Þ=n þ σ 2 ðX 2 Þ=n
2  CovðX 1 ; X 2 Þ=n

ð1:1Þ

It is therefore equal to the sum of the variances minus twice the covariance of their means. Since the correlation coefficient r between X1 and X2 is equal to the covariance divided by the standard deviations of the variables, the equation can be modified as follows. σ 2 ðX 2 =n
X 1 =nÞ ¼ σ 2 ðX 1 Þ=n þ σ 2 ðX 2 Þ=n
2  r  σðX 1 Þ  σðX 2 Þ=n

ð1:2Þ

If for simplicity, it is assumed that the dispersion of the variables is the same at both points in time, the above equation simplifies to the following: σ 2 ðX 2 =n
X 1 =nÞ ¼ 2σ 2 =n
2r  σ 2 =n ¼ 2σ 2  ½1
r=n

ð1:3Þ

Now let the fraction of the continuous mass be f. Only for this fraction can it be assumed that X1and X2 are correlated. This part applies the following:   X2 X ð1:4Þ σ2
1 ¼ 2σ 2  ½1
r=ðf  nÞ f n f n Further, for part 1
f, which was replaced due to panel mortality, r is 0. For this part, the above equation, therefore, simplifies to the following: σ 2 ðX 2 =½n  ð1
f Þ
X 1 =½n  ð1
f ÞÞ ¼ 2σ 2 =½n  ð1
f Þ

ð1:5Þ

The total dispersion is then obtained by the weighted addition of the formulae (Eqs. 1.4 and 1.5), where the “continuous mass” gives the weights:   X X σ 2 2
1 ¼ f  2σ 2 =ðf  nÞ þ ð1
f Þ  2σ 2 =½n  ð1
f Þ
f  2r n n  σ 2 =ð f  nÞ

ð1:6Þ

If f and 1
f are reduced as far as possible, the following short formula results1: σ 2 ðX 2 =n
X 1 =nÞ ¼ 2  σ 2  ½1
f  r=n:

ð1:7Þ

In consequence, this means the accuracy of the measurement of the change increases with

1

To our knowledge, Dr. V. Bosch (internal GfK paper) first derived this formula.

1.2 What Characterizes Panels?

5

• The amount of positive correlation between the periods. This will be higher; the more frequent an item is purchased, the more habitual the shopping behavior is, and the longer the observed period is. • The proportion of the continuous sample, given that the correlation is positive. The formula thus also provides a theoretical justification for the value of a high “continuous sample.” If the continuous sample equals zero, it is not a panel but a wave survey. For frequently purchased items, a correlation coefficient of approx. 0.6 is common for 1 year. In contrast, the correlation coefficient can even become negative for items that are rarely purchased: Anyone who buys a TV in a year will usually not do so again the following year. This also makes it possible to calculate how large the sample size n2 of a wave survey must be for changes to be measured with the same accuracy as in a panel survey of size n1: 2σ 2  ½1
f  r=n1 ¼ 2σ 2 =n2 :

ð1:8Þ

Resolved to n2, the result is: n2 ¼ n1 =½1
f  r:

ð1:9Þ

Example: A household panel has 10,000 households. The continuous sample is 75%, the correlation r ¼ 0.6. Then, according to the formula (Eq. 1.9), a wave survey must have a sample of 18,182 households to determine the changes in the same way as the panel. In addition to the survey object and the sample, in a panel, the survey method is also kept constant for an extended period as far as possible. Changes in the survey method can also lead to a change in the result that is only method-related. For example, the traditional inventory method (determination of sales by recording purchases and changes in stocks) tends to result in higher reported sales figures in the retail panel than the recording of sales via the scanner checkout. This is because shrinkage due to spoilage or theft is recorded as sales in the inventory method but not via the scanning checkout. In the consumer panel, the influences of the collection method are even more significant. This is discussed in more detail in Sect. 2.4.1. However, even changes in the method are not always avoidable in practice. For example, in the retail panel, the inventory method was replaced on a large scale in the 1980s by electronic recording via scanner cash registers and by data exchange with the retail companies. In the consumer panel, traditional written recording became less important in favor of electronic recording. Such transitions must be made very carefully and controlled if they are substantial. Thus, a distinction must be made between changes in outcome due to method change and market change. This can be done, for example, by carrying out the change in a first step only for a part of the panel members. Thus, the developments in the panel participants with the same survey method can be compared with those with the changed survey method delivering information about the change in market data due to the method change.

6

1 Introduction

It is also in line to measure changes if the surveys are repeated on the exact recurring dates. This is the only way to separate seasonal fluctuations from marketrelated changes. To summarize, a panel is characterized by the fact that—as far as possible—data are collected • • • •

About a constant content. At always the same, recurring points in time. Using a constant sample. Always the same data collection method.

It follows from this definition that the so-called survey panels or online panels, despite their term, do not belong to the panels in the sense of this definition. These are fixed master samples from which changing samples are drawn at irregular intervals for individual surveys on changing topics. The advantage of the constant master sample is that prior knowledge about the participants’ such as sociodemographics, tenure status, is known. This makes it possible to filter even small target groups without scattering losses. Thus, for example, to send owners of canaries a new special food in a product test to test its acceptance by the animal. Therefore, the aim of the survey panel is not a continuous survey to measure changes but to avoid false contacts.

Reference Heinhold, J., & Gaede, K. (1979). Ingenieurstatistik. R. Oldenbourg Verlag.

2

The Elements of a Panel

2.1

Overview

The following four elements fully define a panel: 1. The universe of a panel is a set of elements information is needed. Its size and its structure define it. 2. The sample is a set of elements from the universe from which data are collected. It is defined by its size, how the sample elements are selected from the universe, and how the total sample size is divided among the different parts of the universe. 3. The data collection is about getting the data of interest from the sampled elements. Different methods (questioning, electronic procedures, observation) are used. 4. Extrapolation represents the conclusion from a sample result to the corresponding value of the universe. These four elements are described below in more detail. Special attention is paid to the various forms of retail and consumer panels because these panel types are of particular importance for market research and marketing. However, TV audience and Internet panels are considered as well. In the case of Internet panels, the approach is analogous to the TV audience panel. Therefore, only the significantly different data collection is treated separately here.

2.2

The Universe

2.2.1

Overview of the Universe

The type of panel is determined by the universe, whereby an essential distinction is made between the following panels:

# Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2022 M. Günther et al., Market Research with Panels, Springer Texts in Business and Economics, https://doi.org/10.1007/978-3-658-37650-5_2

7

8

2

The Elements of a Panel

• Retail panels are used to make statements about specific retail businesses (mostly brick-and-mortar or online consumer retail businesses; panels of wholesale companies only play a minor role). Depending on the type of the shops and the product groups surveyed there, a distinction is made between retail food panel, drugstore panel, electrical panel, stationery panel, etc. • Consumer panels with the further distinction – Large-scale consumer panels such as canteen or hospital panels. These types play a minor role and are therefore excluded in the following. – Individual panels, where the individuals form the universe. – Household panels where the universe consists of private households. These are by far the most important form of a consumer panel. • Media panels such as the following: – TV Audience panels, in which TV audience behavior is continuously surveyed. Internet usage panels with the continuous recording of online behavior. – Radio panels with the continuous recording of radio listening behavior. These play a minor role and are therefore excluded in the following. • Other panels, such as the following: – Doctors panels, in which the prescribing behavior of doctors and the visits by representatives of the pharmaceutical industry are observed. – Advertising panels, in which the supply of advertising is surveyed. A combination of panels is possible. This will be described later. For a panel to work, the definition of the universe must be unambiguous. For a retail panel, for example, this means that it must be possible to determine whether or not it belongs to the universe for each existing store and each online retailer. This definition must be simple. It is the only way to ensure consistent recruitment of panel members by the field staff of the market research institute. In addition, a simple definition also makes it easier to communicate the results of a panel to the client. Due to the high importance of the Retail panel, the Household and Individual panel, and the Television Audience panel, their universes are discussed in more detail below.

2.2.2

The Universe of a Retail Panel

2.2.2.1 Definition of the Universe The definition of the universe of a Retail panel is usually done by defining several “store types” and combining them in one panel. Store types should be defined to represent homogeneous sub-samples of shops that the manufacturer manages similarly. For example, a grocery panel can be meaningfully subdivided into the business types “hypermarkets,” “discounters,” and “traditional food retailers.” Various criteria can be used individually or in combination to define the shop types. Common are:

2.2 The Universe

9

• Sales area: The specification of a minimum sales area is occasionally carried out when the effort of surveying many small shops is disproportionate to the market importance of these shops. Stores below this minimum sales area are then not surveyed at all. However, the sales area is used to distinguish between two shop types. For example, traditional food retailers are also defined to have less than 800 sqm of sales area. Stores with more than 800 sqm sales area are hypermarkets. • Assortment: It can be defined that certain goods are sold, and a specific sales focus exists or that certain sales shares are achieved. For example, it can be determined that shops that mainly sell fresh food, such as bakeries or butcher shops, do not belong to the food retail universe. • Form of organization or affiliation with a commercial enterprise: This characteristic is mainly used as an exclusion criterion if a trading company refuses to take part in a panel, and the importance of this company is so significant, and its business is so atypical that the transactions of other companies cannot represent it. In Germany this is the case for the retail company “Aldi.” This company is therefore excluded from the definition of the universe. • Turnover: Occasionally, the total turnover of a shop is also used as a definition, although this criterion has not proven effective because sales are not stable and cannot be easily collected. This makes it difficult to determine a stable universe and a stable sample. • Special exclusions: Particularly for technical reasons, certain shops are excluded. For example, in GfK’s Consumer Electronics Panels, duty-free shops at airports are not included in the universe, as the field service has no free access to these shops. For the same reason, the photo panel excludes photo retail outlets located in amusement parks, zoos, or similar. The definition of the universe is usually worked out together with the future clients of the panel reports when setting up a retail panel. In addition to a clear distinction from other shops, it must correspond to the market cultivation by the manufacturers. Examples of such definitions are provided in the description of the different types of retail panels.

2.2.2.2 Determination of the Universe of a Retail Panel The procedure to estimate the universe of a retail panel is shown in Fig. 2.1. The starting point are databases of addresses of stores with a high probability of belonging to the universe. Such databases can be purchased from address publishers or the panel customers provide them. Cooperating retail companies can also deliver such information in business addresses or as store directories on the Internet. As a first step, these files must be cleaned of any data records that exist more than once. Then, stores that are recognizably not part of the universe (e.g., stores in amusement parks or at airports) are removed. The result is a merged dataset of addresses. Let the number of addresses contained in this stock be M. There are three main types of errors in this address database:

10

2

The Elements of a Panel

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M

n

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b

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Fig. 2.1 Procedure of a universe evaluation study to determine the universe

1. Some of the addresses belong to shops that no longer exist. 2. Other addresses belong to existing shops, which, however, do not meet the definition of the universe. 3. The address dataset does not contain all the businesses in the universe, e.g., recently established businesses may not be included. Often small shops are insufficiently represented, as they are often not supplied directly or only by companies that do not provide their delivery addresses.

2.2 The Universe

11

In the next step, a sample of size n (depending on the size and heterogeneity of the universe as well as the accuracy to be achieved from about 300 to several thousand stores) is randomly drawn from this database and checked by the field staff of the market research institute through a visit or telephone contact. Corresponding to the types of error mentioned above, the possible results of such a review are: Case 1: The business exists and meets the definition of the universe. The corresponding number is denoted by b. Case 2: The shop does not exist anymore, or it exists but does not meet the criteria of the universe definition. The number of these addresses is denoted by a. For each existing store belonging to the universe, the address of the geographically closest competitor is collected in addition to the most important structural characteristics of the store. The number of competing stores mentioned is denoted by m, which usually is smaller than b because not all sampled shops name a competitor. Further, whether these shops are included in the previous address file is checked. The following cases can be distinguished as follows: Case 1: The shop is included in the previous merged address file. The corresponding number is denoted by d. Case 2: The shop is not included in the previous address file, e.g., because it is new. Their number is denoted by c. With this information, the size of the universe N can be estimated as follows: First, the number N1 of “good” addresses in the merged address set is evaluated. This results from: N1 ¼ M ∙

b : n

ð2:1Þ

where: M ¼ number of addresses in the merged address list. n ¼ number of addresses checked by the field service (sample). b ¼ number of addresses verified as belonging to the universe. The next step is to estimate the number N2 of addresses not included in the address set but part of the universe. It is calculated as follows.
 m ð2:2Þ N2 ¼  1 ∙ N1: mc where: m ¼ number of locally closest competitors c ¼ number of locally closest competitors not in the address file.

12

2

The Elements of a Panel

Adding N1 and N2 delivers N as an estimate for the total number of shops of the universe. Using Eqs. (2.1) and (2.2) we get after some computation: N ¼ N1 þ N2 ¼ M ∙

b m ∙ n mc

ð2:3Þ

An example (cf. Schlittgen, 1987) should clarify the procedure: In a universe evaluation study to determine the population of the retail photo sector in Sweden, a merged address database of M ¼ 1009 stores formed the starting material. Of these, n ¼ 310 addresses were verified by the field service. Of these, b ¼ 266 stores existed and met the definition of the universe. Of these, m ¼ 187 gave the address of the nearest store, 128 of these stores were included in the address stock, and the other c ¼ 59 were missing. The estimate of the size of the universe is thus obtained as follows: N ¼ 1009 ð266=310Þ ð187=ð18759ÞÞ ¼ 1265: Further refinements of this procedure are possible if the merged address list is not considered a whole, but the estimation is performed separately for each initial list. This process is, of course, laborious and costly and therefore cannot be carried out as often as desired. Consequently, it is challenging to run a high-quality retail panel in countries with a very dynamic universe development, for example, in Central and Eastern European countries after opening the borders in 1989. There are also problems when many sales are made in outlets that exist only temporarily, as is the case in some commodity sectors in emerging markets where sales are often made from the trunks of vehicles. This part of the sales is not captured in the panel and, therefore, reduces the panel’s value.

2.2.3

The Universe of a Consumer Panel

The universe of a Household panel is generally formed by private households with permanent residence in the respective country. The restriction to private households initially excludes people who live in institutions or barracks such as military or police barracks, hospitals, retirement homes, or prisons. The people living there do not provide for themselves, or only to a limited extent, and are therefore only a target group of little value for the manufacturers’ end-user marketing. Purchases by companies and public authorities are also excluded. The decision-making processes in these sectors cannot or only to a limited extent be influenced by marketing designed for private consumers. Therefore, purchases for offices, canteens, restaurants, and pubs are not included. One question is whether foreign households should be considered. There are different arguments pro and contra the inclusion of the foreign household. Including them will increase the reported market and, therefore, the panel’s value. On the other side, additional effort is needed for multilingual data collection. A pragmatic solution was found in Germany: The universe also includes foreigners with unrestricted

2.2 The Universe

13

residence and work permits for Germany. Foreigners of the corresponding nationality with good German language skills are recruited for the sample. Their purchasing behavior then represents the respective ethnic group as a whole. A further restriction relates primarily to developing and emerging countries. Since the customers of a household panel are mainly companies that produce packaged goods for daily consumption, only the purchasing behavior of households that can afford to buy such products is of interest. Therefore, in South Africa or China, households of the lower social strata, which often are self-sufficient, are excluded. Finally, a household panel can cover only certain regions of a country. This is mainly when a panel is set up in a large country. For example, GfK’s household panel in Russia was initially limited to a few metropolitan areas and was only gradually extended to cover the country. The household panel primarily records the purchases made by the person who predominantly purchases the goods for daily use (the so-called “household head”). The panel institute requests that the purchases of the other household members in the relevant product groups are also recorded. In practice, however, this happens only partially. This restriction has no effect when product groups are observed that are purchased by the principal shipper for the household as a whole. Examples of this are detergents and cleaning products such as household cleaners, all-purpose detergents, or fabric softeners, but also food products that are needed for cooking, baking, or frying or as side dishes or for preparation such as flour, oil, sugar, baking powder, rice or potatoes. However, the restriction will affect the product groups that each person buys for themselves. Examples are cosmetics, shampoo, cigarettes, many sweets such as chewing gum or individual packages of chocolate bars. Such product groups are therefore less suitable for the household panel. They are better captured in an Individual Panel, where individuals record purchases for themselves. The universe comprises people aged 18+ and living in private households. The age restriction is for practical reasons. Attempts to install a separate children’s and young people’s panel have failed in the past due to a lack of stable cooperation. Between these extremes are categories purchased partly for household use and partly for personal consumption. Examples of this are some drinks or chocolate bars. Here, the advantages of the different panels must be weighed against each other. A product group should be recorded entirely in one panel. Otherwise, no holistic evaluations are possible. If, for example, multiple packs of chocolate bars are mainly purchased for household use, but single packages are primarily purchased for individual use, then the entire product group needs to be reported in one panel. In practice, in Germany, confectionery and personal care categories are reported in the individual panel and all other product groups in the household panel. Special panels are necessary if the product groups to be observed require special survey techniques or the target groups included in the general panels are too small. For example, the survey of fresh food that is not barcoded requires special survey techniques. In contrast, observing baby food or paper diapers requires a baby panel

14

2

The Elements of a Panel

in which parents of small children report their respective purchases because the number of households with babies in the general household panel is too small.

2.2.4

The Universe of a Television Audience Panel

The universe of a television audience panel only includes people in private households with at least one stationary television set. The limitation to private households means that television viewing behavior in hotels, restaurants, retirement homes, barracks, or caravans, for example, is not included. The precondition that at least one TV set has to be stationary is due to the technique to measure television audience behavior (see below). There is another critical difference between consumer and TV audience panel research. Unlike in the consumer panel, the results of a television audience panel directly influence the income of the organizations that are clients of the service, in the case of the TV audience panel, the broadcasters. For example, the broadcasters can offer their advertising time at higher prices with increased contacts. The broadcasters, therefore, have a particular interest in influencing the methodology of the panel and what data are shown. Consequently, they have set up organizations to define the method and buy the data jointly in many countries. One example of such “Joint Industry Committee” (JIC) is in Germany the “Arbeitsgemeinschaft Fernsehforschung” (AGF), which has been operating since 2017 as AGF Videoforschung (cf. www.agf.de/agf/geschichte). The JIC in the UK is the “Broadcasters Audience Research Board” (BARB, cf. www.barb.co.uk). Often the state plays a vital role in such a JIC (e.g., in France or the USA, cf. Bourdon & Méadel, 2015). The data in a television panel are often purchased by the relevant JIC and then resold to the interested parties. The JIC also defines the panel’s methodology and determines what data are reported. For example, only audience ratings for the total commercial breaks are reported in Germany. Second-by-second reporting of the audiences during the commercial breaks is technically possible but is politically not desired. The situation is different from retail or consumer panels, where the collecting market research companies define the methodology, own the data, and then sell them to the firms interested. Another question is whether foreign households are to be included or not. One argument against inclusion is that in many countries, television services are primarily aimed at people who speak the country’s language. Since people who speak other languages often use the corresponding foreign stations, this results in a reduction of market shares of the domestic television broadcasters, which they do not like. In addition, the necessity of multilingual data collection instructions with corresponding contact persons of the institute and documents for the panel households would make the panel more expensive. On the other hand, the argument favoring inclusion increases the universe and the number of contacts. Additionally, it would create a more comprehensive picture of what is happening. Moreover, the representation of minorities can also be politically desired.

2.3 The Sample

15

In practice, this may well lead to changes over time. In Germany, only the German population was included until 1995. Since 1995, households with a primary income earner from the EU are included, and since 2016 households from other countries with a German-speaking primary income earner are entailed (cf. www.agf. de/bewegtbildforschung/methode/tv).

2.3

The Sample

2.3.1

Requirements for the Panel Sample

2.3.1.1 Representativeness A panel sample is, first of all, expected to be representative. The characteristic of representativeness often mentioned in the literature is that the sample reflects a reduced image of the universe because its proportions correspond to those in the universe (e.g., Homburg & Krohmer, 2006, p. 203). According to this very narrow definition, a stratified sample (strata being a division into subpopulations) would only be representative if the allocation of the sample is made proportionally to the distribution of the universe. However, it is well known from sampling theory that this rule is usually not optimal (c.f. Thompson, 2012, p. 146f). For this reason, retail panel samples are designed to be disproportionate. Therefore, representativeness in this narrow sense is not a quality characteristic of the sample, and it does not make sense to demand it. A sample is representative if a calculation rule exists so that the means of the calculated values of all possible samples are equal to the corresponding means of the universe (unbiased estimate). In other words: If not only one but all possible samples are mentally drawn from a universe according to the rule applied to the sample, and if the mean value of the sample results is always calculated according to the same calculation rule, representativeness in this sense means that the mean value of all possible samples is equal to the mean value of the universe. Therefore, a sample is described here as a representative sample if it allows unbiased estimation of the universe’s parameters. Not representative in this sense are, for example, arbitrarily drawn samples since there is no calculation rule with the mentioned property for such samples. 2.3.1.2 Low Standard Deviation of the Characteristic Which Is to Be Estimated (Reliability) Another requirement of the panel sample is that it provides a sufficiently accurate estimate of the values of the universe. In this context, “sufficiently accurate” means that no wrong decisions are made due to the inaccuracy of the estimation of the data. The content of this requirement thus depends on the further use of the data. Suppose prices for advertising time are determined based on panel results in the media sector. In that case, a higher degree of accuracy will be demanded than if a marginal product of the competition is observed in a retail panel.

16

2

The Elements of a Panel

Systematic errors and sampling errors can restrict the accuracy of a panel. Systematic errors can be caused by poor data collection and errors during the further processing of the data. Sampling error is caused because the estimation is based on just a part of the universe. The term “accuracy” of a panel refers to the sampling error. Many factors influence the accuracy of a sample result. Among them are: – – – – –

The size of the universe The size of the sample The standard deviation of the characteristic to be estimated The division of the universe into strata The allocation of the sample to the strata

The accuracy of a sample is assessed based on the so-called sample standard deviation. Here, all possible samples according to the drawing rule are mentally drawn, and the standard deviation of the results of the samples is calculated. If one wants to estimate the influence of the individual components on the precision, one encounters the first difficulty that all panel samples are drawn according to quota selection. Consequently, the sample standard deviation cannot be computed because the formulas for the sample standard deviation presuppose random selection. In practice, the formulas are used for a one-dimensional stratified random sample. This is done because a good panel sample stratified multidimensionally will, according to all experience, at least meet the accuracy determined in this way. Let us first consider the precision of the mean for the entire universe (the generalization to many values and values of individual strata, as collected in the panel, will be made later). Sampling theory provides the following formula for the standard deviation of the mean of a stratified sample (c.f. Thompson, 2012, p. 144): vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi u L uX S2 W 2h ∙ h ∙ ð1  f h Þ ð2:4Þ s ð yÞ ¼ t nh h¼1 with L ¼ number of strata, Wh ¼ Nh/N ¼ proportion of stratum h in the universe, where Nh ¼ size of the universe in stratum h and N ¼ total size of the universe, S2h ¼ variance of the variable in the stratum h, nh ¼ size of the sample in the stratum h, fh ¼ nh/Nh ¼ sampling fraction in the stratum h, i.e., the proportion of elements in the universe in stratum h that is sampled. Let us look at the individual influencing variables in the following. The influence of the size of the universe is negligible in practice, as an example shows: Let us assume a panel sample consists of 700 stores, and the universe is reduced from

2.3 The Sample

17

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Fig. 2.2 Influence of the size of the universe on the sampling error

60,000 to 30,000 stores. Then the accuracy of the mean only increases by about 0.6%. The difference is, therefore, negligible. Figure 2.2 shows by way of example that the influence of the size of the universe on the accuracy only becomes significant when it approaches the size of the sample. In contrast, the influence of sample size is much more significant. The precision of two samples with sample sizes n1 and n2 differs, all other things being equal, essentially by the factor: rffiffiffiffiffi n1 F¼ n2

ð2:5Þ

Consequently, a quadrupling of the sample only halves the sample standard deviation. The precision thus increases disproportionately, while the survey costs rise proportionately. The standard deviation of the parameter to be estimated also has a significant influence. The sample standard deviation of the total value increases approximately proportionally with the average standard deviation of the parameter estimated in the strata. However, this also means for the figures of a panel report that the results for homogeneous segments (e.g., grocery up to 400 sqm sales area) are much more accurate for the same sample size than for heterogeneous segments (e.g., a region containing all store types and organizational forms). Therefore, the division of the population into strata also plays an important role: the more homogeneous the strata are, i.e., the lower the standard deviation within the strata, the more accurately the corresponding total value is estimated. As a rule, this intention coincides with the objective of a panel to report subpopulations that are as homogeneous as possible and thus easy to manage.

18

2

The Elements of a Panel

Finally, the sample allocation across strata also offers significant potential for improving accuracy. This is particularly true for the retail panel, where individual stores may have very different importance in the segments. For example, a grocery store may have a sales space of 50 sqm or of 20,000 sqm. In contrast, in the consumer panel, the range of importance of households or individuals is much smaller. For this reason, the consumer panel often uses essentially proportional samples, while the retail panel uses disproportionate samples.

2.3.2

The Sample of a Retail Panel

The first question is how to determine the sample in principle. Here, quota sampling and random sampling are possible. Random sampling has essential advantages, such as calculating confidence intervals and thus making statements about sampling error is possible. At least, in theory, samples determined with quota sampling do not have this possibility. Random sampling, however, requires that each element of the universe has a calculable probability greater than zero of being included in the sample. This, in turn, is only the case if the two following conditions are met: 1. There is a “list” of all elements of the universe. The “list” can be in the form of a card index, a file, or even (as in the case of a random route sampling plan) in the form of front doors in the street. Thus, in the case of the retail panel, a list of all relevant retail outlets would be required. Such a list usually does not exist but, in principle, could be generated. 2. The randomly determined elements of the universe can also be surveyed. This is not possible in retail panels, not even approximately, since the cooperation of the store management is needed, in most cases additionally the cooperation of the management of the central office of the retail company. As a result, no random selection, and thus only a quota selection is possible. For the shop to be recruited, the following quota criteria are applied: – – – –

Shop type Shop size Region Affiliation to trading company/distribution channel

The recruitment takes place directly on-site or via the head office, depending on the organization of the retail company. The heterogeneity of the retail panel universe usually argues in favor of giving greater weight to large stores in the sample than in the universe. There are important reasons in favor of such a disproportionate sample: • The absolute standard deviations of the characteristic variables in the large stores are significantly larger than in the small stores. If the overall standard deviation should be minimized, then the large stores must be taken into account to a greater extent.

2.3 The Sample

19

Table 2.1 Universe and sample of the IRI-grocery panel 2009 in Germany

Grocery 199 sqm 200–399 sqm 400–799 sqm Discounters without Aldi Hypermarkets Grocery Total

Universe Turn over € billion 2.75

Sample Turnover % 2.5%

Number of shops 11,490

Quantity % 29.8%

Number 190

Quantity % 21.1%

3.3 9.35 38.75

3.0% 8.5% 35.1%

3450 3640 11,510

8.9% 9.4% 29.8%

75 100 130

8.3% 11.1% 14.4%

56.25 110.4

51.0% 100.0%

8510 38,600

22.0% 100.0%

405 900

45.0% 100.0%

Source: Information Resources Universe 2009

• Large stores are much more critical to manufacturers’ marketing than small stores. In the large stores, new products are usually introduced first. This is where their failure or success first becomes apparent. In addition, the large stores are often handled directly by the manufacturer’s sales teams. In contrast, small shops are usually indirectly controllable via the head office. An example of such a disproportionate sample is shown in Table 2.1. It can be seen that the smaller shops are significantly less represented in the sample than their share in the universe. In contrast, the proportion of hypermarkets in the sample is more than twice as large as in the universe. The sampling theory provides the well-known result of the Neyman–Tschuprow formula to optimize the allocation of the sample so that the standard deviation of the mean of the total is minimized (c.f. Thompson, 2012 p. 147; Cochran 1977, p. 96ff): nh ¼ n ∙

W h ∙ Sh

L P

h¼1

ð2:6Þ

W h ∙ Sh

The parameters are defined as in Eq. (2.4). This means that the sample size of a stratum should be increased if its share in the universe or the stratum’s standard deviation increases. It thus justifies the fact that inhomogeneous segments (e.g., hypermarkets) are considered more strongly in the sample than homogeneous segments (e.g., discounters). Otherwise, Eq. (2.6) is of little practical use: It only minimizes the sample standard deviation of the total value of one variable. However, for many product groups, which may consist of hundreds of items, and for numerous variables (e.g., distribution, sales volumes, prices), the retail panel also reports values for many segments in addition to the value for the total market, which must also be sufficiently accurate. If Eq. (2.6) is used, then the values of the segments with small shops, in

20

2

The Elements of a Panel

particular, will be reported with such a high sampling error in comparison to the mean that the result for the segments becomes useless. In this context, the stratifications applied in panel research are often complex because they are multidimensionally stratified. Stratification features are: • Areas, i.e., sub-regions of a country. • Store types, which are further divided into size classes according to retail space size, e.g., hypermarkets from 800 to 1499 sqm, 1500–4999 sqm, and 5000+ sqm. • Retail organization group, i.e., named accounts or groupings of companies, e.g., Walmart, Tesco, or Rewe. This means that the universe is internally broken down much more deeply than is shown in the report. A distinction is therefore made in the following between extrapolation cells and segments. • Extrapolation cells: Internally used parts of the universe. Extrapolation cells must be complete and must not overlap. An extrapolation cell for the retail panel in the US could be, for example, Walmart hypermarkets in Texas that have between 1500 and 4999 sqm retail space. • Segments: Parts of the universe are shown in the report. Segments do not have to be complete and may overlap. For example, one segment can be the area “Texas” or the shop type “hypermarkets.” These segments overlap concerning the hypermarkets in Texas. In the past, the sample allocation to the extrapolation cells was mainly solved by rules of thumb. The rule was that designated segments should have a sample share of about the mean of the percentage of shops and the percentage of turnover in the universe and should comprise at least 70 stores. This rule has proven to be a good rule of thumb. A more elaborated allocation can be found as follows (cf. Bosch & Wildner, 2003; Wildner & Bosch, 2004): Since several variables are estimated “as accurately as possible” in several segments, multiple objectives must be pursued simultaneously. As in a scoring model, the relevant variances must be added up in a weighted manner. This leads to the following objective function.   J X I X b j ! Min! 2 ð2:7Þ ωji  Var Y Q¼ i j¼1 i¼1

j

b is the estimated mean value of variable Y for segment j, e.g., the Here, Y i i estimated mean price per unit of an item (variable) in the convenience stores   b j is the associated sample variance. These parameters are (segment) and Var Y i

calculated as follows.

2.3 The Sample

21

bj ¼ Y i

L X

b W jh ∙ Y ih

ð2:8Þ

h¼1

  X L W 2 ∙ S2 ih jh bj ¼ Var Y i n h h¼1

ð2:9Þ

where Wjh is the numerical share of the h-th extrapolation cell in the j-th segment in the universe, suppose an extrapolation cell is not included in a segment, Wjh ¼ 0. If a segment consists of only one extrapolation cell, then Wjh ¼ 1. The sum of these proportions per segment is 100%. S2ih is the variance of variable i in the extrapolation cell h. Furthermore, weights ωji have to be defined, which express the importance of the estimated value of the variable i in segment j. The higher a weight relative to the other weights, the more the associated variable in the associated segment is considered in the optimization, thus the smaller the associated sample variance becomes. Accordingly, the weights are to be specified according to the importance of the segments and variables for the report. They can but do not have to add up to 1. If Eqs. (2.8) and (2.9) are substituted into Eq. (2.7), the value of the objective function also depends on the sample sizes in the extrapolation cells nh. Thus, the nh can be determined to minimize the value Q of the objective function (Eq. 2.7). The solution is described in the following: ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi rP P 2 ωji ∙ W 2jh ∙ S2ih j

i

ffi nh ¼ n ∙ Prffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi PP 2 ωji ∙ W 2jh ∙ S2ih h

j

ð2:10Þ

i

The application of the formula can be demonstrated using an example universe and assuming standard deviations for the turnover and the distribution of a category (cf. Table 2.2). It is further assumed that only the segments: – – – –

Traditional grocery Discounter Hypermarkets Total are to be reported.

The column “Part of 2.10” shows the numerator of the fraction of formula (Eq. 2.10) belonging to the respective line. For example, the number in the first row of this column is as calculated in the following:

22

2

The Elements of a Panel

Table 2.2 Exemplary application of the optimum allocation according to Eq. (2.10)

Grocery 199 sqm 200–399 sqm 400–799 sqm Discounters without Aldi Hypermarkets Total

S (Mean T.)

S (Mean D.)

41 60 234

0.0376

0.0444

0.0785

0.0327

435 900

0.1391 0.0426

0.0144 0.0237

Universe 11,490

S (T) 0.4

S (D) 0.7

ω (T)

ω (D)

Part of 2.10 1.2994

Opt. nh 130

3450 3640 11,510

0.6 1 1.2

0.6 0.6 0.5

1

1

1

1

0.4106 0.5954 2.3338

8510 38,600

2.9

0.3

1 5

1 5

4.3392 8.9785

Explanation: T: Turnover, D: Distribution Source: Number of shops: as Table 2.1, otherwise own calculations


1:2294 ¼ √ 12  ð11, 490=ð11, 490 þ 3, 450 þ 3, 640ÞÞ2  0:42 þ 12  ð11, 490=ð11, 490 þ 3, 450 þ 3, 640ÞÞ2  0:72 þ 52  ð11, 490=38, 600Þ2  0:42 þ 52  ð11, 490=38, 600Þ2  0:72 Þ The first summand under the root reflects the grocery sales, the second the grocery distribution, and the third and fourth summands are used to account for the sales and distribution for the Total. The sample allocation is proportional to these parts, as shown in column Opt. nh. Columns S (Mean T.) and S (Mean D.) show the resulting sample standard deviations for the turnover and distribution. Table 2.3 shows the consequences of three different weighting schemes based on the values in Table 2.2. In Table 2.3a, only the value for the distribution total was given a positive weight. This is identical to Neyman’s solution of the formula (Eq. 2.6) for the distribution. Table 2.3b shows the corresponding weighting for turnover. In Table 2.3c, both variables have got the same weight. Here, an appropriate trade-off is made. Although the variances of the total values of distribution and turnover have increased in Table 2.3c relative to Table 2.3a, b, respectively, the loss of precision in one variable is more than offset by a significantly larger gain of accuracy in the other variable.

2.3.3

The Sample of a Consumer Panel

For the consumer panel sample (and here as an example for the household panel sample), in addition to the size of the sample, the main thing to determine is whether

Source: Own calculations

(a) Whole weight on distribution total Universe S (T) Grocery–199 sqm 11,490 0.4 200–399 sqm 3450 0.6 400–799 sqm 3640 1 Discounters without Aldi 11,510 1.2 Hypermarkets 8510 2.9 Total 38,600 (b) Whole weight on total sales Universe S (T) Grocery–199 sqm 11,490 0.4 200–399 sqm 3450 0.6 400–799 sqm 3640 1 Discounters without Aldi 11,510 1.2 Hypermarkets 8510 2.9 Total 38,600 (c) Weights on total distribution and total sales Universe S (T) Grocery–199 sqm 11,490 0.4 200–399 sqm 3450 0.6 400–799 sqm 3640 1 Discounters without Aldi 11,510 1.2 Hypermarkets 8510 2.9 Total 38,600

Table 2.3 Influence of weighting on dispersions

S (D) 0.7 0.6 0.6 0.5 0.3

S (D) 0.7 0.6 0.6 0.5 0.3

S (D) 0.7 0.6 0.6 0.5 0.3

ω (D) 0 0 0 1 ω (D) 0 0 0 0 ω (D) 0 0 0 1

ω (T) 0 0 0 0 ω (T) 0 0 0 1 ω (T) 0 0 0 1

Part of 2.10 0.24 0.0758 0.11 0.3876 0.6428 1.4562

Part of 2.10 0.1191 0.0536 0.0943 0.3578 0.6394 1.2642

Part of 2.10 0.2084 0.0536 0.0566 0.1491 0.0661 0.5338

Opt. nh 148 47 68 240 397 900

Opt. nh 85 38 67 255 455 900

Opt. nh 351 90 95 251 112 900

0.0775 0.1455 0.043

0.0352

S (Mean T.)

0.0752 0.1359 0.0421

0.0402

S (Mean T.)

0.0757 0.2746 0.0659

0.0267

S (Mean T.)

0.0323 0.0151 0.0225

0.0416

S (Mean D.)

0.0313 0.0141 0.0271

0.0524

S (Mean D.)

0.0315 0.0284 0.0178

0.0286

S (Mean D.)

2.3 The Sample 23

24

2

The Elements of a Panel

the sample should be drawn randomly or whether a quota sample is preferable. The same prerequisites apply to the random sample as already mentioned in the previous section for the retail panel sample, although they cannot be fulfilled here either. In the case of the household panel, in many countries (e.g., the UK, USA, Germany, or Italy), there is no list of the private households. However, a recognized random sampling procedure such as the random route procedure could be used, and thus, at least an approximation could be achieved. In other countries (e.g., Sweden), such a list exists. So, this prerequisite typically could be met, at least in good approximation. The second prerequisite is that it must be possible to survey the randomly determined units. As already mentioned in the previous section, in market research, a sample is usually still accepted as a random sample even if the proportion of elements that cannot be surveyed (the so-called “refusal rate”) is up to 30%. These values are not achievable today, even in surveys with limited and only short-term exposure of the interviewees. However, this is entirely impossible in the case of panel samples, where long-term cooperation, if possible, lasting several years, is essential and requires considerable commitment on the part of the panel household. The refusal rate typically exceeds 95% when recruiting panel households, so no random sample is possible. Nevertheless, as many random elements as possible are retained in the sampling process, which is achieved through a multi-stage procedure. For this purpose, small, regional units, the so-called “sample points,” are first stratified according to federal state and town size. Then a proportional, stratified sample of sample points is drawn. The field service is then instructed to recruit defined households according to quota specifications in the selected sample points. The specifications are made in the so-called “quota characteristics.” The quota characteristics can be, for example, the following: • Household size as a vital determinant of the level of consumption • An occupational group of the primary income earner as an expression of the economic performance (the desirable net income is not suitable as a quota characteristic because of the low willingness to provide income information) • Age of the head of household (defined as the primary shopper of fast-moving consumer goods) as a “life-cycle variable” • The number of children under the age of 15 as an essential determinant of the household structure. In a household panel generally, the allocation of the sample is proportional to the allocation of the population. Exceptions are possible if certain groups are difficult to reach or contribute little to the total purchasing volume. For example, in the GfK Household Panel in Germany, the proportion of one-person households in the sample is only half as large as in the universe. Both reasons apply here. First, single-person households buy significantly less in the relevant product groups. Second, young single-person households’ recruitment and ongoing motivation are challenging and expensive.

2.3 The Sample

25

Another possibility is to combine or supplement various existing samples. For example, the GfK Individual Panel in Germany uses the purchases of the 30,000 household heads in the household panel. In addition, a sample of 5000 people who are not household heads was recruited. The individual panel, therefore, comprises 35,000 persons and is disproportionately designed. The disproportionality is firstly due to the survey costs: the data from the household panel accrue anyway. However, this is also expedient because household heads also purchase significantly more than non-household heads in the product groups relevant for the individual panel (confectionery, personal care, and cosmetics). Despite meticulous sampling, a household panel gives a weaker representation of society’s lower and upper strata than the middle class. To put it plainly: It is unlikely that people without a fixed abode will report their consumption to a market research institute, and it is likewise doubtful that the CEO of a public company will do so. Consequently, this means that luxury categories such as champagne or expensive fragrances can only be inadequately represented in a consumer panel. In addition, panel households tend to be somewhat more interested in product purchases, prices, advertising, etc., than the average population. This bias is called panel bias. This panel bias was examined in detail by Petzold (2004) using the example of the GfK Household panel. It results in the following picture: Once stable cooperation has been established, there is no change in reporting behavior. Therefore, the fear that panel members will change their purchasing behavior over time due to their participation is unfounded. On the other hand, the fact that a panel is socio-demographically well-sorted, but panel members tend to be more conscious of their purchasing behavior overall leads to deviations. To answer this question, panel household members and an ad-hoc sample (omnibus survey with n ¼ 2243) were presented with certain statements and asked about their degree of agreement. The result is: 1. Panel households inform themselves more intensively about the product range. 79% of the panel households, but only 65% of the ad-hoc sample regularly inform themselves about promotions. 16% of the ad-hoc sample, but only 11% of the panel households have a sticker on the letterbox that prohibits the posting of advertising. 2. Panel households are less brand-oriented. With 26% of the ad hoc sample, only 12% of the panel households affirm the statement that (well known) branded products are better than products with an unknown name. 3. Panel households are slightly more price-conscious than the average population (70.5% vs. 68.6%). 4. Panel households are more innovative. 57% of panel households agree with the statement “I like to try out new products,” but only 48% of the population. 5. Panel households are less likely to shop spontaneously. 84% of them write a shopping list before shopping, but only 68% of all households. 6. On the other hand, some statements are judged the same or almost the same. These mainly concern the range of products and the service. 79% (panel) and

26

2

The Elements of a Panel

78% (ad hoc) prefer shops with a comprehensive range of goods. 36% and 38%, respectively, do not need any service in the store. 42% vs. 40% attach importance to competent advice. The relevance of these differences was examined using a simulation calculation. It was assumed that people in the panel and the ad hoc sample make the same purchases if they agree with the corresponding statements. This results in relatively minor differences. For example, in the panel, the share of the private label in one product group is 68.1%, while the simulated share is 70.1%. Overall, these biases should be considered when assessing the panel figures. However, they are generally relatively insignificant for practical work, especially as changes are primarily interpreted and these distortions are constant. Achieving the highest possible consistency in the panel is the task of panel maintenance, which uses a whole bundle of measures to achieve this. Material incentives are only part of the measures. Small gifts or points, which the household receives for proper reporting and for which the household can choose goods from a catalog, are common. Finally, raffles for material prizes such as trips or motor vehicles are also common. Overall, however, the material incentives are more like a recognition award for volunteer work than a payment. Apart from the associated costs, payment is also undesirable. It would represent a stronger incentive for low-income groups than for people with higher incomes and would thus ultimately impair the representativeness of the sample. Intangible motivational measures are at least as necessary as material incentives. These include, in particular, regular contacts via brochures, a free hotline, and fixed contact persons at the institute. Despite these measures, panel mortality is 20–30% in the case of a panel that has been in operation for some time and is well maintained. Depending on the panel structure, this average value is significantly exceeded or undercut in some cases. Panel mortality is exceptionally high among young singleperson households. Panel mortality is also generally considerably higher in the case of a panel that is still being set up or is new. The households that remain in the panel from the beginning to the end of a period are called the “continuous sample” or “continuous mass.” A sizeable continuous mass is an important quality feature of a panel, not only for the continuity of the data already mentioned but also for the accuracy of the estimation of changes. Only the “continuous mass” households are the basis for many special analyses.

2.3.4

The Sample of the Television Audience Panel

It is important to note how the TV set(s) receive their signal, whether conventionally via antenna, satellite dish, cable, or Internet. The sample of the TV audience panel largely follows the same rules as the sample of the consumer panel, but due to the definition of the universe, only those households are taken into account that own at least one stationary TV set. The sample must be adjusted to depict the viewer’s behavior correctly.

2.4 The Data Collection

27

Random selection is—as in other panels—not possible due to the high refusal rates. Nevertheless, certain random elements can be used. So, in Germany, it was decided to conduct many short interviews in which the structural data are collected. Then the system of television research is presented, and the willingness to participate in the panel is asked. If a household is recruited, it is randomly selected from the households willing to participate and with the necessary structure. The television audience panel is not strictly proportional. Besides unintentional distortions due to the loss of households, recruiting more households from broadcasting areas of small stations is necessary to represent them adequately. The resulting skewness is compensated in the same way as in the consumer panel by projection.

2.4

The Data Collection

2.4.1

The Data Collection in the Retail Panel

Until the late 1980s, the traditional survey in the retail panel by the field service of the market research institute was carried out using the so-called inventory method. For this purpose, the inventories and purchases since the last shop visit were recorded. Sales for the current period were then calculated: Sales ¼ Inventory previous period þ Purchases  Inventory current period: The inventory method was associated with high personnel costs. As a consequence, there were monthly or bi-monthly surveys. Prices were reported as they were found by the field staff of the market research company on the day of the data collection (fixed date prices). Since then, electronic recording via merchandise management systems has almost completely replaced the inventory method. The resulting weekly reporting of sales and the replacement of the fixed date prices by exact average prices enable the detailed analysis of trading actions. As a negative consequence, the stocks previously shown in the retail panel reports are no longer available.

2.4.2

The Data Collection in the Consumer Panel

2.4.2.1 Overview Until the 1990s, purchases were collected in writing, using a so-called “household calendar.” Like in a calendar, reporting sheets were torn off each week. These had to be filled in and sent back to the institute. This method was burdensome for the panel members and the institute likewise to operate. It was also slow and error-prone. It is no longer used today in daily consumer goods but still plays a role in particular areas, significantly when older target groups (panel members) are affected, such as in the medical sector.

28

2

The Elements of a Panel

Since then, the survey methodology has changed to Inhome-scanning. An electronic device reads the product’s barcode and asks the panel member for additional information like the number of items bought and the price paid. The Inhomescanning method is used in different variants. These will be presented in more detail. In addition, POS scanning, where the data are captured at the retailer’s cashier, was developed and used in a niche. This has some specific advantages and disadvantages but is not currently used.

2.4.2.2 POS Scanning POS scanning (the abbreviation “POS” stands for Point of Sales) can be briefly described as follows: Each panel household is equipped with one or more identification cards in credit card format. The household number is printed as a barcode. The household shows the card every time they shop in the cooperating stores. The card is swiped over the scanner, and the GTIN codes and the quantities and prices of the purchased items are stored together with the household number in a separate purchase record. These purchase records are transferred to the market research institute, where they can be evaluated on a household-by-household basis. The method was only used in test-market panels, first in ERIM panels in France and Germany in the 1970s and BehaviorScan panels in the US, France, and Germany in the 1980s. The BehaviorScan test-market in Germany was discontinued in 2021. The method has several distinct advantages and disadvantages: First of all, a significant benefit is that the effort for the household is kept to a minimum. The panel effect is reduced. Participation in the panel also does not lead to households becoming more aware of their purchases, thus avoiding the panel bias (see Sect. 2.3.3). However, the method also has clear disadvantages. For example, only products with a barcode can be recorded. The technique is unusable for some categories, especially for foodstuffs such as fresh meat, unpackaged bread, etc. Another disadvantage is that the scanner cash register of the store where the data collection takes place must be equipped with special software to meet the requirements of POS scanning, which involves considerable effort. Finally, the retail company must cooperate with the market research institute. This makes it impossible to record purchases in non-cooperating retailers like Aldi in Germany. Consequently, the method was not suitable for the widespread use and only used in test-markets. 2.4.2.3 Inhome-Scanning Inhome-scanning, initially developed by the market research companies AGB (1986 in Australia, 1990 in Great Britain, 1991 in the Netherlands) and NPD (1988 in the USA), uses a mobile electronic device for this purpose which is made available to the panel participants and which is equipped with a reading device for GTIN codes, a keyboard, and a display. The device also includes a base station in which a modem for data transmission via telephone line to the institute and the power supply for the handheld device are integrated. Figure 2.3 shows the successor to the device introduced by GfK in Nuremberg in 1997 and the input steps required for recording the purchases.

2.4 The Data Collection

29

Hello Choose one of the following opons:

Start survey

Start survey We are on vacaon No shopping trips Logoff

Input: Date and shop of purchase – purchasing person - whether a loyalty card was used or not - total amount paid

Scan item - input price and quanty and whether bought in promoon

Yes

More items purchased? No Logoff - put the handheld device in the cradle

STOP

Fig. 2.3 Electronic Diary device of the GfK Group and application (Source: GfK)

The process was a significant advance over the written entry for several reasons. First of all, the entry of the articles is much easier since only the barcode printed on the article has to be scanned. Further, since there are no postal lead times, the process is faster. Third, it is also less error-prone, and finally, the purchases are already in a format suitable for computers when they arrive at the institution. Nevertheless, the procedure also has weaknesses. Especially for purchases with many items, searching out the prices is time-consuming. The keyboard is also not ideal. GfK uses the method primarily for households with no Internet connection. Today this plays a minor role as almost all households have Internet access. For households with Internet access, the Scan-IT method is mainly used to record purchases of (daily) consumer packaged goods. This Scan-It method is a special form of Inhome-scanning. The household receives a small scanner that can capture GTIN codes. The data is then transmitted to the institute via the USB interface of the PC and via the Internet. From there, the item texts are reported back. After that, the panel member enters further information (place of purchase, date of purchase, and prices of the products). Figure 2.4 shows the Scan-IT procedure. All of these methods require panel members to enter the prices of the products. This very time-consuming process can be replaced by households taking a photo of the receipt with a smartphone and sending the image to the institute. The difficulty with this till-roll scanning method—receipt scanning method (cf. Fig. 2.5) is that the

30

2

The Elements of a Panel

1st Entry of shop: Who purchased? Was loyalty card shown? 2nd Selection of items for entry

3rd Entry of one item

Fig. 2.4 Scan-IT data entry procedure (Source: GfK)

item texts on the till roll often do not fully describe the article, which requires an attribution algorithm.

2.4.2.4 Internet Recording Internet recording is used for articles that do not have a printed barcode, such as textiles or fresh foods. It has many advantages compared to written purchase reports: Data entry is faster, as postal lead times are eliminated, and the data is already available in machine-readable form. It eliminates the expensive and time-consuming manual transfer from the calendar to the computer. Additional costs can be saved by removing the need to print, ship calendars, and return completed documents. In addition, checks can be carried out as soon as the data is entered, and inquiries can be made immediately in the event of incomplete or implausible entries. Furthermore, the medium is more modern and is, therefore, more likely to be accepted by younger target groups (panel members) than the calendar. Finally, it is also possible to quickly send additional questionnaires to the panel participants without long mailing times. The answers can then be correlated with the panel results. The disadvantage is that representative results are only possible if the country in question has a high Internet penetration.

2.4 The Data Collection

31

Fig. 2.5 Till-Roll scanning—Receipt scanning (Source: GfK)

For the design of the websites, it is essential that they can be used from a laptop and a smartphone. The entire support, such as raffles, collecting points for rewards, and contact, must also be carried out online.

2.4.3

Data Collection in the Television Audience Panel

TV viewer panels were the first panels that relied on a technical solution. Each company that is active in this field has developed its solution. Usually, a recording device called a “TV meter” is connected to the TV set, which automatically records the time and the channel to which the TV set is switched. In addition, households are given a remote control on which they can log in as viewers. The data is stored in the TV meter and transmitted to the institute at night for evaluation.

2.4.4

Data Collection in the Internet Usage Panel

Internet usage panels usually work with software whose functionality is explained to the panel member and installed on their computer. If more than one person uses the computer, the panel asks who uses it when accessing the Internet. The device records the websites visited and the duration of their use and transmits this information to the institute. Ideally, the Internet use on the private PC and the smartphone/tablet is recorded. Afterward, the data is combined for the person. One problem, however, remains. As the IT departments of the companies usually do not allow the installation of such recording software, only private Internet use can be recorded.

32

2

The Elements of a Panel

It is also possible to record Internet use and shopping behavior in one panel. For example, this is the approach taken by GfK in Germany, where Internet behavior is also recorded in a subsample of the household panel. Here it is possible to examine the effectiveness of advertising in the Internet.

2.5

Coverage of Retail and Consumer Panel

2.5.1

Introduction

The part of the total market mapped in a panel is called “coverage.” In this respect, the level of coverage is a crucial quality criterion for a panel. The ideal would be a complete representation of the market. However, the existing deviations from this ideal state must be distinguished very precisely according to their cause so that the quality of a panel can be adequately assessed. Part of the loss of coverage is because some market volumes cannot be covered in principle in a panel, such as a volume purchased by tourists in a household panel. It is also not useful to include quantities purchased by institutional households such as hospitals, prisons, nursing homes, etc., in such a panel since these quantities are affected differently from sales to private consumers. Amounts sold abroad are also excluded. Such quantities play an increasing role in the retail panel because of the growing size and international integration of retailer companies. Although this loss of coverage may be deplorable, it cannot be regarded as a deficiency in the quality of a panel. The reverse is also possible: retail companies exploit price differences and purchase goods from international groups abroad and sell them in their country. Manufacturer’s factory sales quantities in this country can be exceeded. Another part of the coverage loss stems from a narrow definition of the universe for various reasons, such as complex data collection as in the case of airport shops or impossible surveys as in the case of the discounter Aldi in Germany. As a rule, the parts mapped by the retail and consumer panels overlap but are not identical. Therefore, each panel represents a different part of the total market. Taking the example of grocery, this results in four parts of the total market (cf. Fig. 2.6). The relative size of the four fields depends on the product group, the distribution structure of the product, the package size, and other factors, which are discussed in more detail in the following sections. The first part is covered in both panels (area x1 Fig. 2.6) and can be paraphrased as the purchases of a private household from retailer A surveyed in the retail panel. A second part is collected in the retail panel but not the consumer panel. An example is purchasing for office consumption at retailer A (area X2). The third part is captured in the household panel but not in the retail panel, such as a private household’s purchase from a retailer B not covered by the retail panel (area x3). Finally, a part of the total market is not captured in any panel, e.g., a purchase for office consumption at retailer B (area x4).

2.5 Coverage of Retail and Consumer Panel

33

Household panel

Retail panel

Purchases of private households

Other purchases

Shops of the retail panel e.g.: Walmart, Carrefour, Edeka

Non cooperating retailers

area x1

area x3

area x2

area x4

Coverage household panel

Coverage retail panel Fig. 2.6 Coverage retail panel and household panel

Finally, there is a further deviation of the market quantities measured by the panel from the actual amounts, which arise due to errors during data collection, the sample’s determination, or the extrapolation. This can be described as genuine quality deficiencies of the panel. However, a distinction must be made: changes in coverage, in particular, are regarded as critical by the recipients of the panel data. Panels are primarily used to map market developments. Therefore, an error in the coverage is often accepted as long as the error remains constant over time and the market development is correctly depicted.

2.5.2

Coverage of the Retail Panel

As outlined in the previous section, the coverage of a retail panel can be affected in particular by the following factors: • Exclusion of certain types of shops or retail organizations from the universe (e.g., in Germany Aldi, etc.). • No data collection within shops that nevertheless belong to the population. This may happen if a smaller organization does not participate in the panel but is not so atypical that it can be duplicated from other organizations. • Wrong determination of the universe. In Germany at the end of the 1980s, for example, sales of “co op AG” was overestimated by all panel institutes because the company’s published figures portrayed the situation too positively. • Errors during data collection and in the subsequent production process. An example: an article is not processed. The coverage concerning individual articles can be checked very well in the retail panel. For this purpose, panel figures and the manufacturer’s sales statistics must be reconciled and then compared. The manufacturer’s sales statistics must be adjusted

34

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The Elements of a Panel

since panel figures cannot be shown in greater detail than in the report due to contractual ties between the market research institute and the retail companies surveyed. For this purpose, the quantities allocated to other organizational forms or other business types are deducted for each organizational form in the manufacturer’s sales statistics. If, for example, the coverage of chewing gum is checked, then quantities supplied to DIY stores or petrol stations must be deducted if these stores are not shown in the panel. Conversely, it may be that retail companies are provided from their headquarters abroad. Such quantities may then have to be added. The quantities thus adjusted are compared with the purchasing data according to the retail organization in the panel. This is not always clearly possible. For example, it may be that a hypermarket with an associated DIY store has only one delivery and billing address. In this case, estimates have to be made. Nevertheless, this is usually sufficiently accurate for practical purposes. If stores are not supplied directly, but only via wholesalers, and if an article shows strong (seasonal) fluctuations in sales (e.g., sparkling wine), then panel figures and factory sales may have to be examined with a time lag. If there are excessive deviations for specific retail organizations, the causes must be identified: • The data collection and subsequent production process are analyzed by the manufacturer supplying the quantities of individual shops and the market research institute holding the raw data of the shops against it. If manufacturer quantities do not appear here, this may indicate an error on the part of the market research institute (e.g., missing “translation” of article codes). If the quantities are too high, this may be due to other retailer sources, such as buying abroad products. • The universe, sample, and extrapolation are checked by breaking down the organizational forms into shop types and comparing the panel results of the shops’ type with the corresponding sales of the manufacturer. Overall, such a review can be a lengthy process that requires close and trusting cooperation on both sides. However, such a review can also provide essential impulses for improving the quality of a panel. Therefore, this process should be carried out in the event of deviations in developments.

2.5.3

Coverage of the Consumer Panel

The coverage of the consumer panel is mainly affected by the following factors: • Purchases from non-households; for example, a considerable amount of roasted coffee is consumed in institutions, offices, and restaurants. • Lack of coverage by panelists. This is particularly relevant for products that do not reach the household because they are consumed on the go. Examples of this

2.6 Extrapolation

35

are cans of cola or single packs of chocolate bars. However, this also applies to products that children buy on their own. • Errors in the data collection and processing process. Commonly, articles have not yet been created in the article master, and therefore, the GTIN—reported by the panel member—cannot be processed. Therefore, the actual coverage achieved for a product depends on many factors. Among other things, it depends on the following: • The product group (category): the extent to which non-private households or children purchase a product group. For example, shampoo has higher coverage than soap since soap is used much more frequently outside private households (e.g., at the workplace or the restaurant). • The package type: Beverage cans, for example, are more suitable for on-the-go consumption and are therefore less well recorded than reusable bottles. • The package size: For household cleaners, coverage is best for medium sizes. Petite sizes are dominated by the purchases of institutional households (e.g., older people’s homes or military barracks) and for large packages by the purchases of Doctors’ surgeries, public houses, cleaning crews, etc. • The type of shop: For example, roasted coffee is very well covered in hypermarkets. Bakeries, on the other hand, are poorly covered. This is because coffee for office consumption is often bought quickly at a bakery on the way to work. • The type of data collection: In the case of Inhome-scanning, for example, it plays a significant role in whether the product has a bar code. Finally, it is also important where the product is stored in the household. Products often held in the cellar (e.g., beverage crates, large detergent packages, frozen foods) may be carried directly to the basement and not recorded. In this case, the institute has to point out the necessity of recording all purchases. • With the calendar and Internet method, the design of the documents is essential. Here it is essential whether the categories are supported by appropriate drawings, the position of the category (categories at the front are preferred), and the total space allocated to the category.

2.6

Extrapolation

Extrapolation (sometimes called projection) is the process by which it is possible to conclude from the sample results to the corresponding values of the universe. For this purpose, the values collected in the sample are multiplied by the so-called extrapolation factors.

36

2.6.1

2

The Elements of a Panel

Extrapolation in the Retail Panel

The sample of a retail panel is disproportionately designed, i.e., large stores receive a higher selection rate (share of the sample in the universe) than small stores (cf. Sect. 2.3.2). This means that the retail panel sample has an intentional skewness. This “skewness” must be balanced by extrapolation. For the extrapolation, the universe and the sample are very finely subdivided according to the criteria: • Organization form • Region (federal state or even more detailed government district) and • Store type and store size (sales area) It has already been pointed out in Sect. 2.3 that considerably more extrapolation cells are used than the number of segments shown in the report. Some of these extrapolation cells are empty, i.e., there are no shops in the universe. Such cells are, of course, irrelevant for the extrapolation. However, if there are shops in the universe but not in the sample, a practical solution is to duplicate shops from a neighboring cell into the unoccupied one. For example, suppose the cell “Trad. grocery -400 sqm of Edeka in Bavaria” was unoccupied. In that case, a sample store from the neighboring cell of “Trad grocery 400–799 sqm of Edeka in Bavaria” with otherwise identical characteristics could be duplicated into this cell. As a result, it must be achieved that each extrapolation cell with a non-zero universe is equipped with at least one sample store. The number and turnover of shops in the universe must be known or estimated for each projection cell. This results in a first extrapolation factor as EF1 ¼ NðiÞ=nðiÞ, with: N(i) ¼ number of shops of the universe in the projection cell I and n(i) ¼ number of shops in the sample in the projection cell i. The aim is to populate an extrapolation cell such that the mean size of the stores in the universe is equal to the mean size of the stores in the sample. However, this is not always possible, for example, because the retailer determines the stores that can be surveyed. In this case, using only the EF1 would overestimate or underestimate the turnover. If this case occurs, then a second extrapolation factor can also be formed as follows: EF2 ¼ TðiÞ=tðiÞ, with: T(i) ¼ relevant turnover of the universe in the projection cell I and t(i) ¼ relevant sample turnover in extrapolation cell i. Relevant is the turnover that is to be represented by the panel. The retail panel food is the turnover with food, beverages, and drugstore products.

2.6 Extrapolation

37

For all results that can be coded yes/no or present/not present per store, the EF1 is applied. A store has sold or purchased an item, or it has not done so. Therefore, these results are needed to calculate the numerical distribution sales or numerical distribution purchases and numerical distribution total. EF1 is therefore also referred to as the “extrapolation factor distribution.” The reasoning behind this is that the fundamental decision to carry an item depends less on the size and more on the store’s type, region, and organizational form. For all quantity-dependent results (purchase quantity and value, sale quantity and value), on the other hand, EF2 is applied, which is therefore also referred to as the “extrapolation factor quantity.” The size of the shop determines these factors. In addition, there are calculated facts for which both extrapolation factors are required. One example is the “weighted distribution sales,” which indicates the share of sales of a category accounted for by those stores that carry this category. The fact as to whether the product is distributed is extrapolated using EF 1. In contrast, the category sales with which this is then to be weighted are extrapolated using EF 2. A (fictitious) example will illustrate the procedure. From a projection, cell X is known: • Universe: 120 shops with a combined relevant turnover of € 250 (million). • Sample: 2 shops with a combined relevant turnover of € 8 (million). Then it follows: • EF1 (extrapolation factor distribution): 120/2 ¼ 60.00 • EF2 (extrapolation factor turnover): 250/8 ¼ 31.25. Thus, the sample stores have a median turnover almost twice as high as the average in the universe. As a result of the survey following is known for the two-sample stores: • Shop 1: sales of product A is 20 pieces at € 2.99 each, turnover in the product group € 2750,  • Shop 2: product A is not listed, turnover in the product group € 4900,  Then, the following estimates are made for the universe cell in question. • Number of stores with distribution of A: 60 * 1 + 60 * 0 ¼ 60. Since the universe contains 120 stores, the numerical distribution sale is 50%. • Sales quantity (in pieces) of A: 20 * 31.25 ¼ 625 • Sales value of A: 20 * 2.99 € * 31.25 ¼ 1868.75 € • Turnover in the product group of A stores with distribution for A: € 2750 * 31.25 ¼ € 85,937.5 • Turnover in the category of all stores: (2750 € + 4900 €) * 31.25 ¼ 239,062.5 € • This results in a weighted distribution sales for A for the corresponding cell of € 85,937.5/€ 239,062.5 ¼ 35.9%.

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2.6.2

2

The Elements of a Panel

Extrapolation in the Consumer Panel and the TV Audience Panel

In contrast to the retail panel, the consumer panel aims at an essentially proportional sample. As a result, each element of the sample receives the same extrapolation factor, which in the household panel is calculated as follows: EF ¼ ðNumber of households in the populationÞ=ðNumber of households in the sampleÞ:

Example: With a population of 40.22 million households and a sample of 30,000 households, this results in an extrapolation factor of 1341 for each panel household. Each purchase made by a household represents 1341 purchases made by corresponding households in the population. In practice, however, extrapolation in the consumer panel is more complex because strict proportionality of the sample must first be established before extrapolation with the constant factor. Proportionality can be violated for various reasons: • A household has not reported or has not reported adequately in a reporting period and therefore is not part of the sample. • A household has terminated its cooperation with the panel institute and could not yet be replaced by a household with the same structure. • The one-person households are intentionally represented in the sample to a lesser extent than their share in the population. This is because they contribute relatively little to the total volume of the markets and are challenging to attract and retain. The extrapolation factors are determined by a procedure known as “iterative weighting.” In this procedure, the internal distribution of a table is changed in several iterative steps so that its marginal distribution corresponds to a specified target distribution. The table is first multiplied row by row by the factors target value/ actual value of the respective marginal distribution. The result is a table whose row sum corresponds precisely to the target value. The same procedure is then carried out for the columns. This destroys the identity of the row sum with the target value again, but the result is closer to the target value than the initial matrix. This is repeated until the deviation of the marginal sums from the target distributions falls below a predetermined small value. There is no known mathematical proof that this procedure converges. In practice, however, it always converges except for sporadic cases (e.g., when the initial matrix contains too many zeros). In Table 2.4, the first two steps of such an iterative weighting are shown using a fictitious example. The starting point is 345 households, broken down by household size (HH size) and head of household age. It is easy to see how the actual marginal distributions successively approach the target distributions. This iterative weighting is performed for all quotation feature characteristics. The result is a new multidimensional table whose internal distribution differs slightly from the initial distribution since the iterative weighting is also intended to

2.6 Extrapolation

39

Table 2.4 Fictitious example for iterative weighting Initial situation Age of the head of the household HH-size 60+ Total Target 29 39 59 1 40 30 20 30 120 150 2 30 30 30 30 120 125 3 10 20 20 10 60 50 4+ 1 10 5 10 26 20 Total 81 90 75 80 Target 90 100 70 85 345 1. Line correction: line by line multiplication with Target/Total Age of the head of the household HH-size 60+ Total Target 29 39 59 1 50 37.5 25 37.5 150 150 2 31.25 31.25 31.25 31.25 125 125 3 8.33 16.67 16.67 8.33 50 50 4+ 0.77 7.69 3.85 7.69 20 20 Total 90.35 93.11 76.76 84.78 Target 90 100 70 85 345 1. Row correction: row by row multiplication with Target/Total Age of the head of the household HH-size 60+ Total Target 29 39 59 1 49.8 40.28 22.8 37.6 150.48 150 2 31.13 33.56 28.5 31.33 124.52 125 3 8.3 17.9 15.2 8.36 49.75 50 4+ 0.77 8.26 3.51 7.71 20.25 20 Total 90 100 70 85 Target 90 100 70 85 345 2. Line correction: line by line multiplication with Target/Total Age of the head of the household HH-size 60+ Total Target 29 39 59 1 49.65 40.15 22.73 37.48 150 150 2 31.25 33.69 28.61 31.45 125 125 3 8.34 17.99 15.27 8.4 50 50 4+ 0.76 8.16 3.46 7.62 20 20 Total 89.99 99.99 70.07 84.95 Target 90 100 70 85 345 1. Row correction: row by row multiplication with Target/Total Age of the head of the household HH-size 60+ Total Target 29 39 59 1 49.65 40.15 22.7 37.5 150.01 150 2 31.25 33.7 28.58 31.47 125 125 3 8.34 17.99 15.26 8.4 49.99 50 4+ 0.76 8.16 3.46 7.62 20 20 Total 90 100 70 85 Target 90 100 70 85 345 Source: Own calculations

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The Elements of a Panel

compensate losses of panel members. The actual figures are usually lower than the target figures after the procedure has been carried out. The deviations between the actual sample and the corrected sample sizes can now be compensated for either of the following: • By duplicating households. Households in the cell in question are randomly selected and duplicated until the target number is reached. • By weighting: All household-related figures in the cell concerned are multiplied by the target value/actual value. The advantage of duplication is that whole-number values are achieved, and thus all buyer-related special analyses can be calculated. The disadvantage is that atypical households can also be duplicated and receive an even higher weight. On the other hand, weighting leads to non-integer buyer numbers, which are more challenging to convey. Overall, this procedure provides results with less sample dispersion and is therefore preferred in practice. The extrapolation is similar in the TV audience panel. Here, again, the sample is mainly proportional to the population. Here too, however, there are also deliberate disproportionalities. For example, it may be necessary to increase the sample in the distribution area of a tiny station to represent it with the required accuracy. Compensation for these intentional or unintentional disproportionalities is made the same way as for the consumer panel.

References Bosch, V., & Wildner, R. (2003). Optimum allocation of stratified random samples designed for multiple mean estimates and multiple observed variables. Communications in Statistics: Theory and Methods, 32(10), 1897–1909. Bourdon, J., & Méadel, C. (2015). Ratings as politics. Television audience measurement and the state: An international comparison. International Journal of Communication, 9(2015), 2243– 2262. Cochran, W. G. (1977). Sampling techniques (p. 96ff). Wiley. Homburg, C., & Krohmer, H. (2006). Marketingmanagement Strategie – Instrumente – Umsetzung – Unternehmensführung. Springer. Petzold, S. (2004). Unpublished research presentation dated Dec 3. Schlittgen, R. (1987). Zur Bestimmung von Grundgesamtheiten in der Marktforschung (the determination of universe in market research), discussion paper from the Department of Economics – at the University of Essen, Essen. Thompson, S. (2012). Sampling. Wiley. Wildner, R., & Bosch, V. (2004). Optimierung komplexer Stichproben. planung & analyse, 1, 84–89.

3

The Production Process

3.1

Overview

All work processes after data collection up to creating the tabular reports or the databases for the evaluation systems are referred to as production. The analytical, fast, and nevertheless careful work is of decisive importance for the quality of the results. The production represents a cost factor that should not be underestimated: About 10–20% of the total costs of a panel are allotted to production. Roughly speaking, all production processes in the panel area are divided into two phases. Several data checks are carried out in each stage to identify and correct errors as early as possible. The result of the first phase is a structured file of the checked raw data. In the second phase, the data is then extrapolated, and the table reports or the databases for the evaluation systems are created.

3.2

The Production Process in the Retail Panel

3.2.1

Data Input and Verification at Shop Level

The different data collection methods currently in use in the retail panel mean that the input data are available on various media and formats, depending on the retail company. In some cases, retailers do not use GTIN codes but their article numbers, so-called instore codes. In many cases, hybrid systems also occur: Instore codes are used for articles that are difficult to pull over the scanner due to their weight (e.g., 10 kg detergent packs), while GTIN codes are used for all other articles. Careful maintenance of the instore codes of the cooperating retailers by the panel institute is crucial for data quality. After the data media have been checked for completeness and essential readability, an initial check is carried out at the shop level. The following points, among others, are reviewed:

# Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2022 M. Günther et al., Market Research with Panels, Springer Texts in Business and Economics, https://doi.org/10.1007/978-3-658-37650-5_3

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3 The Production Process

• The total number of existing data records compared to the previous periods or—if a shop is included in the evaluation for the first time—compared to other outlets of the same size. If a lower limit is not reached, this is an indication that only part of the data has been delivered. For example, individual departments of the retail company may have been excluded from the delivery. The corresponding data can then still be requested. • Total sales compared to the previous periods: If the number of available data records are correct and nevertheless fall below a lower limit, then this indicates either a problem with the proper transmission of prices (for example, actual prices may have been replaced by 1-cent prices) or a partial delivery (e.g., the data of certain weeks were not delivered). If an upper limit is exceeded, it is possible that data of several shops or cumulated data of several weeks were delivered. • Share of sold article records that should be moved into the previous period. Are there large deviations within the shop or time dimension? Probably data for the wrong outlet was delivered. If abnormalities are discovered during this check, research is first conducted to determine whether an error exists. If this is the case, an attempt is made to correct the error in the panel institute. If, for example, the data is supplied for two periods instead of one and the shop has been in the panel for some time, the error can be eliminated by creating differences. If such a correction is not possible, a corrected data set is requested. Suppose such a set is not available or not available in time. In that case, the corresponding outlet is treated as a data failure in the case of serious errors, i.e., it is not included in the evaluation. In the case of minor errors (e.g., prices of individual articles are missing), estimated values (e.g., the prices of the previous period) may be possible.

3.2.2

Validation on Article Level

If a shop is accepted for further processing, the data records belonging to an article (purchase, and sales data) are combined. Then, the transaction data is compared with the article master in the next step. This involves a data check at the article level, including the following points: 1. Is there a corresponding article master record for each transaction record? The setup and maintenance of the article master files require considerable effort from the panel institutes. Attempts are made to include new items in the master file by evaluating catalogs before the articles are sold in the stores. The article master file (also called “dictionary” in the technical jargon) contains not only GTIN and/or an article number assigned by the institute but also a description of the product characteristics (also called “features”) to be reported. For example, in the case of category “Roasted Coffee,” not only the vendor and brand but also the pack size (e.g., 250 g, 500 g), the form (beans, capsules, ground, pads), and more dimensions are described. In the case of technical consumer goods, the number of relevant features is much higher (e.g., for

3.2 The Production Process in the Retail Panel

43

washing machines: speed, spinning capacity, water consumption, energy class, the existence of special programs). This information is indispensable for reporting. They control the classification of the article in the correct report rows and regulate the conversion of the pieces sold into the corresponding units (e.g., conversion of grams into packs). If an article master record does not exist, this information must be procured. References are price, the manufacturer number contained in the GTIN, and the store. If the data cannot beprocured in time, the article must be excluded from further processing for this period. 2. Are price limits respected? The check against fixed price limits is possible, then integrated into the article master, and can only be changed manually. Better is to check against variable price limits, which depend on the shop type and promotion information and are adjusted automatically to changed circumstances. A typical pricing error results from the so-called multipack problem. It may be that a scanner market, which only stocks crates of beverages, has stored the price for a crate using the GTIN of the single bottle. However, the production system can quickly correct this error by comparing the quantity with the number of bottles per crate. 3. Quantity check: The quantity check is only useful against variable limits that depend on the size of the shop, whereby a comparison with the data of the previous period is often carried out. Errors can occur, for example, when the sales of several periods are added up for individual articles or that only a partial delivery was made. The result of the check is checked raw data records, i.e., one data record per store, period, and article containing the basic data collected. In the traditional Retail panel, this is the following information: • Price per unit • Purchase units • Sales units

3.2.3

Extrapolation and Reporting

These raw data are then extrapolated using the extrapolation factors from the store master file. At the beginning of a year, there is usually a fundamental revision of the extrapolation, mainly due to changes in the universe. However, as panel shops have to be continuously removed from the reporting for various reasons (e.g., business closures) and replaced by other shops, ongoing adjustments are also necessary during the year. Particular care must be taken during the year to ensure that the integration of the new outlets does not lead to data breaks. The data is then “loaded” into the various reporting systems. It is done by aggregating and offsetting the raw data to generate the figures for standard reporting.

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3 The Production Process

In concrete terms, the following happens. The reported facts are calculated and stored: • Per article shown in the reporting system or per brand • Per reported segment (e.g., retail organization, region, shop type) and • Per reporting period. This often results in a pervasive data file. For a category with 1000 articles, 30 reported segments, and 50 facts, 1.5 million figures are delivered for each reporting period and 78 million figures for 52 weekly reporting periods per year. This underlines the need for efficient data access.

3.3

The Production Process in the Consumer Panel

In the case of the consumer panel, the first stage of the production process up to the verified raw data, in particular, is very much influenced by the data collection method. (a) Calendar method: In the calendar method, the report sheets sent in are separated according to category groups and assigned to the respective recorders. They have the task of checking the data and entering it into the system. This requires appropriate knowledge of the merchandise categories. Errors that cannot be corrected immediately are forwarded to the household support. From there, a clarification is tried by a call. (b) Inhome-scanning or Scan-It: In the case of Inhome-scanning, the data is transmitted from the device located in the household to the institute via modem and telephone line. With Scan-It, the data is transferred via the Internet. GTINs for which a corresponding article record exists in the article master file are immediately translated accordingly. Subsequently, the quantity and the price are checked for plausibility. GTINs that do not exist are collected in a separate file. The unclarified GTINs are clarified in order of frequency of occurrence. Complete clarification is not always necessary. The country and manufacturer codes in the GTIN provide initial clues about the manufacturer. If the manufacturer is only active in product fields that are not surveyed, an assignment to a pool of articles of non-reported product groups is sufficient. Otherwise, an attempt is made to clarify the GTIN by calling the manufacturer, the institute’s field service, or contacting the household directly. (c) Web capture: In the case of web data entry, an attempt is made to avoid the gross errors already during the data collection process. During this process, checks are processed. Any error or implausibility that occurs afterward must be clarified in the same way as for calendar entry.

3.5 Aspects of International Panel Research

45

Has each household reported? If raw data are available, an audit is carried out at the household level. Only households that have reported a minimum number of weeks are included in the reporting. It is also sufficient if a household has reported that nothing was purchased due to vacation or illness. Over longer periods, checks are also made to ensure that the total reported purchase quantities are plausible. This is done by checking against households of the same socio-demographic structure and by checking the development of each household’s purchases individually. Thus, it has been shown that when cooperation decreases, purchases from small stores are affected first. Households that cooperate insufficiently are kindly asked to improve their cooperation again. The institute will terminate the collaboration if this does not have the desired effect. The subsequent steps (extrapolation and loading of the data into evaluation systems) are similar to those of the retail panel.

3.4

The Production Process in the TV Audience Panel

The TV audience panel production process must be very fast and therefore largely automated, as the audience ratings must be available early in the morning of the following day. It must be checked whether the information from the TV meters could be retrieved. In the case of individual failures, the extrapolation may need to be adjusted. The program scheme replaces the dimension articles of the retail or consumer panel. This is prepared, and the devices are switched on to the corresponding channels, and the people watching are then assigned to the corresponding programs or commercial breaks. The key figures (e.g., audience share per advertising block) are calculated.

3.5

Aspects of International Panel Research

If international market reports are to be prepared, a whole range of special features must be considered. International reports have become increasingly important in recent decades, as large multinational companies require comparable data to allocate their marketing expenditures to different markets. The most crucial point here is an internationally uniform article master. An item sold in different countries is only recorded and described once in the item master file. Suppose each country has its own article master. In that case, the same articles must first be assigned to create international reports, which can only be done with great effort for article masters of several hundred thousand articles. In addition, such work is very error-prone. Furthermore, the panel members (e.g., shops or households) must be described in parts of their common characteristics. Only for these characteristics can meaningful information be provided across multiple countries.

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3 The Production Process

Finally, the product group definitions must be consistent. If, for example, tablets are assigned to smartphones in one country and computers in another, the market sizes for smartphones and computers will not be comparable between different countries. The fact is that different companies see markets and categories differently. Therefore, there is pressure on the account managers at the panel institute to be flexible in this respect. This pressure for standardization makes it necessary for the panel institute to have managers who can do the required persuasion work.

4

The Market for Panel Research

The overview of the market research industry published by the market research organization ESOMAR is published once a year. The following summary of panel market research is based on the report published in 2021 for the year 2020 (ESOMAR, 2021). According to this, the global market for market research amounted to US$ 89.75 billion in 2020 (p. 10). The share of panel research is shown for a total of 64 countries (p. 196f). The estimated weighted average is 40.7% (p. 197), resulting in a panel turnover of US$ 36.5 billion. According to ESOMAR, the highest shares are in the highly developed markets of North America and Europe with 44.6% and 36.5%, respectively, and the lowest in Africa with 4.6%. Panel research, therefore, accounts for a very significant share of market research as a whole. At the same time, this share is growing fast. In 2015, this share was only 28% (ESOMAR, 2016, p. 147). The development of panel research requires considerable financial resources. Therefore, it is not surprising that the main sponsors of panel research are prominent institutes. A few examples should illustrate this. The world’s largest market research company with a turnover of US$ 6.3 billion in 2020—the US company Nielsen—specializes in retail and media panels (cf. www.nielsen.com, ESOMAR, 2021, p. 126f). In the case of retail panels, the clear focus is on packaged goods for everyday consumption. The company is active worldwide in this field and is the global market leader. Consumer panels are also conducted in various countries, including Germany. The second-largest institute globally with a turnover of US$ 4.4 billion in 2020— IQVIA, formerly IMS, also based in the USA—also specializes in panels, namely in the pharmaceutical sector, where it is the undisputed market leader (ESOMAR, 2021, p. 126f). Another institute that is very active in panel research is Kantar. The company had US$ 2.8 billion in sales in 2020 (ESOMAR, 2021, p. 126f). It offers consumer panels in Western Europe, Asia, and South America. Kantar is the market leader in this field. Further, it maintains media panels in various countries (e.g., the UK and Brazil) and is active in the ad-hoc sector. Kantar cooperates with the GfK Group in # Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2022 M. Günther et al., Market Research with Panels, Springer Texts in Business and Economics, https://doi.org/10.1007/978-3-658-37650-5_4

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the consumer panel sector (see below) as part of the Europanel organization. As Kantar specializes in Western Europe, while the GfK Group focuses on Central and Eastern Europe and Italy, this cooperation makes it possible to supply pan-European data via Europanel. The German GfK Group has a clear focus on panel research (cf. www.gfk.com) but also offers ad hoc research. In retail panels for technical consumer goods, the company is active worldwide and is the market leader. Data from Germany, Italy, Benelux, and Northern and Eastern Europe are provided in the consumer panel segment. In addition, television audience panels are operated in various countries, including Germany. The US-American IRI Institute (cf. www.iriworldwide.com) has specialized entirely in panel research. It became known in the USA as a pioneer in scannerbased retail panel research and the inventor of the BehaviorScan test-markets. It is foreseeable that expenditure on panel research will continue to rise in the coming years: large countries—and India and China in particular—are developing in such a way that ever-larger sections of the population are accessible to Western-style branded goods. This development increases the demand for continuous and representative market data, which only panels can provide.

References ESOMAR. (ed). (2016). Global market research 2016 - An industry report. ESOMAR. (ed.). (2021). Global market research 2021 - An industry report.

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Institutional Panels

The roots of panel research go back to the USA in the 1920s. At that time, American companies in the consumer goods industry were concerned with how the different sales trends of their products could be explained. The American A. C. Nielsen was the first to recognize that their own production figures are not sufficient for such questions. An industry (category) must always be considered complete. In addition, a company is embedded in an economic environment. In this case, it is necessary to consider the environment in its entirety. The company’s sales control and market monitoring are not sufficiently informative to control the brick-and-mortar (stationary) sales channels. Even at that time, key figures from competitors and retail companies were needed. However, since these were not usually disclosed, A. C. Nielsen looked for ways to calculate approximate values and began collecting information at the POS (point of sale). These considerations led to the development of the retail panel, which can thus— historically speaking—be described as the first of its kind. In the meantime, many other additional panels have been developed, making it necessary to structure these different panel types according to survey subject and data collection type.

5.1

Classifications of Panels

The usual distinction is the panel division into the: 1. Retail panel (question: where are which goods sold) 2. Consumer (Shopper) panel (question: which consumer buys which goods) 3. Media panel (which media—TV, Internet, Radio—are used) Numerous new, innovative panels have been designed that have no place in this approach. In addition to expanding to other sectors of the economy, a variety of products has also been developed. Today, panels are established in the consumer goods industry and nonfood product groups and services (cf. Sect. 8.1). For # Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2022 M. Günther et al., Market Research with Panels, Springer Texts in Business and Economics, https://doi.org/10.1007/978-3-658-37650-5_5

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Research approach Institutional Panels / Joint Industry Committee

Category description Food / Nonfood / Media / Services

Type of data collection Scanner / Internet / Mobile phone / Calendar

Fig. 5.1 Classification of panels

example, in addition to financial services and travel, pharmaceutical products and textiles, product groups in the sanitary and furniture industry and all media goods sectors are also monitored. The importance of panels within market research has thus increased continuously, and they are now an integral part of quantitative market research. This expansion makes it necessary to use more comprehensive classification features to describe the overall complexity. A classification system that is generally valid today has three levels, as shown in Fig. 5.1: 1. Research approach (survey approach) 2. Description of categories 3. Type of data collection Level 1 (research approach (survey approach)) represents a general distinction between institutional and personal panels. This rather general level allows the assignment of new, innovative panel types. An example may serve to clarify this. If a managing director provides information about his purchasing behavior in the private sphere, this collected data will be assigned to a household panel food or nonfood within the institutional panels. If, however, the same person is asked about

5.1 Classifications of Panels

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Fig. 5.2 Research approach

the future economic developments of his company (industry), the data could be for an institutional panel, as shown in Fig. 5.2, or for a JIC (cf. Sect. 6.2.1): Level 2 (description of categories) represents the reported products. Both panel types of the described level 1 (research approach) have in common that they can report consumer goods and durables. The individual panels offer a third, additional product type that has been established for some years. These are the most diverse services, media, and “other product groups,” which can only be observed very well in the long term in their development with the help of individual panels. Level 3 is technical and describes data collection and/or transmission methodology, see Fig. 5.3. Data collection can be either “active” or “passive.” In the case of “passive” measurement, apps take over the recording and transmission of the desired information. The panel member thus no longer has to “actively” intervene in the process, often not even having to enter any additional information at all. This “passive” measurement uses laptop, tablet, or mobile devices. For such measurement and data collection to be legally compliant, the panel member must first agree to this type of measurement before the app is installed. In the case of “active” measurement, the traditional methods are still predominant. Here, the data is collected at the POS directly at the scanner cash register (retail panel) or in writing if such cash register systems are unavailable. This very timeconsuming data collection method is still carried out in some countries. There are numerous “active” survey options for the panel members in the consumer panel. One possibility is the use of a hand scanner which is established in many countries. The panel member scans the GTIN and makes the additional

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Data collection

activ

passiv

Scanning at POS Written methodology at POS

Apps, installed on mobile phone, tablet etc. record and transmit the requested data – and clickstreams

Receipt scanning Inhome scanning Inhome online data collection Written survey method

Fig. 5.3 Data collection

product- and shop-specific entries. This type of data entry is very time-consuming, especially for large purchases. For this reason, developments are being driven forward to reduce the panel member’s workload significantly. One promising method is the use of mobile phones—so-called “receipt scanning.” The panel member takes a photo of the receipt, which is transmitted as an image to the institutes. The desired information like product name, shop, date of purchase, and the price is automatically read out from the receipt. However, despite the progress of technology, some country-specific consumer panels still rely on the written survey method. Here, a so-called household calendar is sent out monthly, in which the panel members enter their purchases. Sent back to the institutes, these must then be processed (more or less) manually. A detailed overview of the different types of panels is provided in Fig. 5.4. Although the overall degree of complexity has increased considerably compared to the first panel approaches, the four general principles of a panel still apply. In addition, the advantages mentioned above of panel research also continue to apply. Variations arise according to: • • • •

The object of investigation The periodical intervals The data collection types and The survey locations

5.2 The Retail Panel: the Origin of Institutional Panels

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General Panel overview

Institutional Retail Panels

Institutional Consumer Panels

Passive measurement

Active measurement Durables

Durables

Consumer goods

Consumer goods

Media

Media Services

Fig. 5.4 General panel overview

5.2

The Retail Panel: the Origin of Institutional Panels

Until the late 1960s, the retail panel was regarded as the traditional way of obtaining information on the sales development of companies from outside their own company. It was referred to as “the retail panel” without further differentiation. What was meant was the collection of information for defined categories (product groups) in selected traditional retail outlets. In those years, the retail structure in Germany and many other European countries consisted mainly of medium-sized and small-sized shops. These had a wide range of articles with limited assortment depth. In food retailing, the demand for daily consumer goods could be satisfied, and in the area of specialists, the demand for consumer durables. However, the products are becoming more and more differentiated; additional manufacturers from a wide range of countries are entering the market, in some cases with identical or more advanced products and features. This change in the product range (increasing assortment depth of the product range) is also accompanied by a change in the breadth of the product range. Parallel to the change in the breadth and depth of the product range, there were also shifts in the retail landscape and the purchasing behavior of consumers. The traditional retailer was (is) being pushed back further and further. Therefore, completely new sales channels emerged.

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These developments have been incorporated into the original retail panel and have led to a high degree of diversification within the market research institutes. Today, a rough subdivision is generally made into the 1. Food and Near-food or FMCG or CPG panel and 2. Nonfood or SMCG panel. The main distinguishing criteria can be described as follows: Food and Near-Food Panels In the food panel, all product groups of the so-called FMCG product groups are surveyed and evaluated. FMCG stands for Fast Moving Consumer Goods, i.e., goods of the consumer goods industry. In Anglo-Saxon countries, in particular, the term CPG (Consumer Packaged Goods) is also used. In both cases, this refers to pre-packaged food and beverages. The definition includes all categories in the consumer goods industry with a high (fast) repurchase rate and the near-food product groups with an equally high repurchase rate. These include, in particular, the product groups of personal care products, detergents, household care, pet food, and baby food. Nonfood Panels In contrast to consumer goods, the so-called durable goods of the nonfood panel are characterized by longevity. The period between first purchase and repurchase can often be several years. Even if these articles have a lower purchasing frequency at the POS, the term SMCG (Slow Moving Consumer Goods), which contrasts with the food panel, has only partially become established to date. The big difference between the two panels can be found in the “use” and the longevity of the goods. If the food product groups are more for daily use and actual consumption, the durable goods categories are characterized by use, low purchasing frequencies, and late repurchase activity. For example, the repurchase rate in the food panel can be as low as 1 day (e.g., bread, butter, and milk, impulse items such as chewing gum and ice cream), whereas for durables (e.g., freezers), it can be as high as several years. A large number of product groups can be assigned by this description either to the food and near-food or nonfood area. However, a few product groups still do not meet any or even all of the specific criteria. An excellent example of this is the product group batteries. These are intended for (daily) use (nonfood) and enjoy relatively fast (high) repeat purchases (food). To which panels these categories are assigned to is regulated quite differently in the various European countries. In Germany, for example, batteries tend to be reported within the nonfood panel, whereas in England, they are part of the near-food categories.

5.2 The Retail Panel: the Origin of Institutional Panels

5.2.1

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Current Developments in the Retail Panel

GfK Nuremberg operates a Retail Panel Nonfood. The global market coverage is comprehensive, concerning a few countries, mainly in Africa, where the panel is not yet established. In a globalized world, GfK has succeeded in creating a global instrument for market observation in a wide range of countries, mentalities, and languages. It is no exaggeration to call this an international market panel. On the other hand, the Retail Panel Food tends to be national in scope and often reaches its limits for European reporting. In many countries, such a panel is operated by two providers. A.C. Nielsen (www.nielsen.com) and Information Resources (www.iriworldwide.com), offer such a panel. For many decades, the retail panel was operated using the method of stocktaking at the POS. This approach is still the only way to collect data for some retailers in some countries, as these refuse to forward data collected by scanner checkouts to the institutes. This form of collection is associated with manual effort and requires many field staff. In the meantime, the evaluation of the Retail Panel Food and the Retail Panel Nonfood cover all product groups included in the above definition. The reason for this lies in the data supplied by some retail organizations. These organizations supply to the panel institutes all articles sold and do not select (filter) the data. This enables the institutes to evaluate the entire product group. However, whether this happens in every case and continuously still depends on the existing order situation. It happens rarely, but some categories show delivery gaps again and again. On the one hand, the data collection methodology, the retail landscape on the other—these two poles currently form the area of tension for changes in the retail panel. The number of hypermarkets in Germany increased from 7930 outlets in 2006 to 8970 in 2015. Sales increased from € 51.0 billion to € 67.7 billion in the same period, growing from € 6.4 million to € 7.5 million per hypermarket. Similar developments can be found in numerous other countries. Equally, positive developments can be observed for the discounters. The number of stores increased from 14,800 in 2006 to 16,115 in 2015. The number of stores thus increased by 8.9% in 2006–2015. Sales have expanded from € 55.9 billion in 2006 to € 100.2 billion in 2015, which represents an increase in sales of 79.2%. In contrast, the grocery retail sector has been shrinking for years. From 26,870 stores in 2006, only 10,800 were still opened in 2014. In the same period, sales fell from € 18.4 billion to € 14.2 billion. These few examples illustrate the rapid development of outlets in the retail landscape. The small shops with a sales area of up to 200 m2 are continuing to decline and are thus losing importance. The discounters are gaining impact and should not be missing from a retail panel report despite all data collection difficulties. Another aspect is the development of entirely new business typologies or the Internet. These are to be integrated into the reporting if a considerable turnover of the product group is realized via this distribution channel. There is no general rule when

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such a new distribution channel should be reported separately. It is generally accepted that the sales share of such accounts should be at 5% or more.

5.2.2

Types of Data in the Retail Panel

The evaluations of a retail panel are composed of two different data sources. Whenever possible, the data from the scanner systems are used for calculations as data entry errors only occur occasionally. Some shops do not have scanner data or do not want to supply data to the institutions. There are two approaches to solving this problem. The necessary data can be collected by the institute’s field staff or integrated from the consumer panel into the retail panels. In summary, therefore, a distinction is made between two different types of data based on the data collection methodology. 1. Audit data: physically (manually) collected data at the POS. This type of data collection is now only performed in stores that cannot (or do not want to) provide scanner data. For these stores, sales are calculated using the formula: Sales units ¼ stock last period þ purchase during the reporting period  new stock at the end of the reporting period

ð5:1Þ

2. Scanning data: sales data collected directly at the POS via a scanner checkout. If a retailer delivers the data of all (its) stores in addition to the scanner data of a sample panel store, this is also referred to as “census data.” In this case, no extrapolation of the retailer is necessary.

5.2.3

Data Sources and Data Availability

Suppose a sales channel or a retailer supplies the scanner data described above. In that case, the data are often available to the institutes by the beginning of the following week at the latest. Audit data are collected manually. In this case, data is usually collected monthly. This type of collection is only carried out bi-monthly in a few special segments. It requires personnel input and additional time. Due to these circumstances, no daily reports can be prepared for the overall market. Even in the weekly reports, including all scanner data transactions, the overall market view is not possible due to the lack of “audit transactions.” This circumstance may be bearable; as already described, the data of the small and rather insignificant shops in terms of turnover are missing. A third problem is posed by those stores that neither provide scanner data nor allow field staff access to the stores. Here, neither daily nor monthly data are available for the retail panel. The sales channel can nevertheless be included in the analysis, and for individual product groups, it is a decisive sales opportunity. Data is

5.2 The Retail Panel: the Origin of Institutional Panels

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Fig. 5.5 Channels included in the Retail Panel Food

taken from the consumer panel and merged into the reports. In Germany in particular, the discounters Aldi, Norma, and Lidl are addressed here. For a precise overview of which sales channels are available, see Fig. 5.5.

5.2.4

Specific Complements to the Retail Panel Nonfood

At the beginning of the reporting, the focus was on the product groups “photo” and “DIY,” followed later by the product groups “electrical appliances.” The appliances for private use were divided into “brown and white goods,”1 “red goods” (heaters), and “gray goods” (information and communication electronics). This classification is now obsolete in colloquial language but is still frequently found in the lingua franca of industry and various statistics. The terms are derived from the original casing colors of the appliances. Refrigerators and washing machines were predominantly designed in white, and brown wooden frames were used for radios. Heating coils emit red radiation, and computer housings were primarily gray. Today, computer articles, mobile phones, and all consumer electronics articles are present in the evaluation. However, furniture, textiles, eyewear, and many other product groups are also observed in the various stores. In total, the GfK Retail Panel Nonfood now has more than 760 different product groups, which are observed in more than 120 different types of outlets.

Brown goods ¼ televisions, radios; white goods ¼ large appliances such as refrigerators and stoves, as well as small appliances such as razors, irons, and kitchen aids

1

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Trading conditions have also changed significantly for this nonfood panel. From the perspective of the retailer landscape, many small shops could no longer withstand the competitive pressure of the large specialist retail chains and gave up. This development is similar to the Retail Panel Food. Numerous new and changed sales channels have emerged in recent years. Special attention must be paid to the nonfood product groups in Internet ordering. In contrast to the food product groups, the Internet has assumed a much more significant role here and is always included. Today, at least the following segmentation is made in this panel2: 1. Specialty markets 1.1 Traditional specialist retailers (not affiliated with a purchasing cooperative) 1.2 Specialist stores (such as Media Markt, Saturn) 1.3 Purchasing cooperations 2. Do-it-yourself stores 3. Product group-specific specialists3 4. Hypermarkets—Cash & Carry 5. Department stores 6. Mail order 7. Other

5.3

Consumer Panels

The consumer panel is the second form of panel research. One of the first knowns was probably started in the UK by Attwood Statistics under “Attwood Random Panel.” In Europe, consumer panels are operated by the major institutes A.C. Nielsen and GfK SE. A large number of smaller institutes complete the range. The retail panel measures sales in the various sales channels. This data is becoming more and more precise through scanner checkouts, and the coverage reaches almost 100% in some product groups. This panel clarifies the question as to where products are sold. However, what remains unanswered is which groups of people purchased these products. This is precisely where the consumer panel comes in. The institutes started with a few product groups with a high repurchase rate. These product groups included coffee and detergents. Households were recruited in writing and instructed on the necessary activities for cooperation. These representatively selected households were asked to enter a few essential information about purchasing these products into a so-called household calendar.

2

The selection is very general, as, with over 120 sales channels, there is no genuinely universal description. 3 e.g., Mobile phone shops for the telecommunications product group - Specialist opticians’ shops for the product groups spectacle frames, spectacle lenses, contact lens care products

5.3 Consumer Panels

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The first type of consumer panel was a pure household panel. The so-called head of the household was supposed to enter all purchases intended for the entire household in this calendar. In addition to the date of purchase, in the early day’s questions were asked about the brand purchased, the shop, and the price. This household calendar is still used today in some countries but is more complex. More information is asked for with a higher level of detail. The further development of this panel occurs continuously in the horizontal and vertical directions. The horizontal view reveals the product groups to be reported. In addition to the initial high-frequency product groups of the food market, the lower-frequency product groups were integrated later. In the meantime, all product groups of the FMCG type are continuously monitored without exception. Today, many product groups from the non-food sector are reported solely with a consumer panel. Sweepstakes, travel, and donations are examples of these. The conversion of the retail panel to scanner-only technology makes data collection more difficult or even impossible for the products mentioned above groups (donations, raffles). Similar difficulties arise in reporting tickets for events and functions only offered for sale via the Internet. Even today, these products cannot be collected at all, neither online nor with the written calendar methodology. Theoretically, the panel member could write the information about the bought ticket into the calendar. A GTIN does not exist, so the scanner technology of the household panel cannot be used either. On the one hand, this is additional work (buying on the Internet and writing it into the calendar). On the other hand, panel members forget to include these products in their reporting. However, the consumer panel did not stop there. A little later, it was expanded to include a third area, services. For example, financial services such as insurance portfolios, new insurance contracts, building society contracts, and banking services are continuously surveyed and analyzed. This list does not claim to be exhaustive. However, it clarifies a further permanent development in the horizontal approach. On the one hand, this continuous process is driven by customer requirements and, on the other hand, by market developments. The vertical developments do not involve any changes in the content of this household panel but describe the expansion of the person reporting. Today, numerous articles, including groups of goods, are no longer intended for consumption by the entire household. Many products are purchased by an individual and “consumed” by that person alone. A good example is the “chewing gum.” In a household panel approach, the head of household cannot report this category entirely because not all household members will report every individual act of purchase to this “reporting instance,” the reporting person. As a result of this item individualization, an individual panel was installed in addition to the household panel. The methodology itself remains identical; only the product groups differ and are always clearly assigned to a panel. The main difference lies in the scope of reporting. Whereas in the household panel, the purchases of all household members are entered, in the individual panel, it is only the individual purchase records of the individual; the purchases of other household members are not included in the individual panel (Fig. 5.6).

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Fig. 5.6 Categories reported in a household and individual panel

5.3.1

Consumer Panels: Household Versus Individual Panel

The list of product groups surveyed is extensive for both the household and individual panel and should be easy to distinguish from one another “by definition.” Product groups with a high degree of family use belong to the household panel for survey purposes. However, with increasing individualization, a threshold is soon reached above which a product group should rather be surveyed in the individual panel. An example from the SMCG area: The product group watches are subdivided into grandfather clocks, wall clocks, and wristwatches. For the first two sub-groups, the benefit for the household and the family is clearly in the foreground. This argues in favor of a household panel survey. On the other hand, wristwatches are intended for personal use and should be surveyed by the individual panel. This now results in three survey variants: The complete survey in the individual or household panel – splitting the total category and thus partial survey in each panel type.

5.3.2

Possibilities of Data Collection in the Consumer Panel

The consumer panel is characterized by a variety of individual data collection methods. On the one hand, this considers the individual wish of the panel participant to choose the most convenient variant from different methodologies. On the other hand, it helps the panel management reduce the participants’ inhibitions because panel mortality (cf. Sect. 2.3.3) is considerably reduced by a high degree of individualization in data collection.

5.3 Consumer Panels

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Today, data are collected from panel participants in three fundamentally different ways, regardless of the household or individual panel approach.

5.3.2.1 Written Calendar Method The written data collection method in a panel is the oldest and most widely used methodology in the non-food consumer panel. It is still used in a few countries to survey FMCG and Near-food product groups. However, it is increasingly used for the survey of nonfood product groups. The panel household or the panel member enters their purchases in a so-called calendar, also known as a diary. These calendars are structured, specific fields are predefined with contents, open questions are as good as excluded. They are usually sent to the households at the beginning of the month—the necessary entries are made—and at the end of the month, this calendar is returned to the institute. 5.3.2.2 Scanning For all parties involved, institutes and panel members, the most convenient and secure way of data collection would be the scanner survey carried out in the retail panel. On the one hand, this would ensure that the correct articles are consistently named, and on the other hand, it would rule out data collection errors in the institutes. However, this scanner methodology has not yet been able to establish itself in all consumer panel areas fully. For several years, the Consumer Panel Food has been working with a scanneronly collection. The household receives a device with a built-in scanner and keyboard. The GTIN of each purchased product is scanned, and the keyboard is used to quickly fill in the missing information (date, shop, price). The advantages are obvious. The detailed product information is already stored on the product (in the GTIN). A panel member does not have to enter this information again in a calendar. However, not all articles are equipped with a GTIN code, even in FMCG product groups. A good example is the product group “fresh foods.” In addition to fruits and vegetables, this includes fresh meat (including cold cuts) and cheese. In the case of fruits and vegetables, intermediate receipts are often not4 (or no longer) created, here, pricing takes place directly at the checkout scales. Fresh meat and cheese, freshly packaged at the counter, are usually not marked with a generally valid GTIN. The “scanner slip” that is swiped over the checkout during the shopping process contains information about the product group and the price to be paid. Unfortunately, the product group is coded differently from store type to store type, often even defined differently within retail chains. This means that there is no unique GTIN for a product. For this reason, GfK has developed a so-called “codebook” for these categories. Printed in high quality, it contains an image of the product and GfK’s code.

4

A so-called intermediate voucher is created at some fruit and vegetable scales.

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The panel member must now select the correct article in this book, scan the GTIN shown and add the shop and the price via the keyboard. This ensures a complete scanner capture for the Consumer Panel Food. It has mainly been the older panel members that have opposed this type of data collection. On the one hand, they did not want to collect data via the Internet, and on the other hand, Internet penetration in these age groups was too low. In the meantime, both aspects have changed considerably.

5.3.2.3 Online Data Collection The described calendar has been converted into an online data collection form. After logging in, all categories are visible to the panel member in a menu selection with the user’s name and password. This selection contains the product groups that would otherwise have been reported in writing. The advantages for the institutes, just a few of them may be mentioned, are obvious: 1. Faster and more detailed data delivery 2. Mailing the data entry forms to the participant and back to the institutes is no longer necessary. It also eliminates printing and postage costs. In addition, the data is available directly after entry by the panel member and can be processed further. In these further steps, any necessary QC (Quality Control) activities take place. 3. It was shifting of data entry efforts to the panel member. 4. The panel member takes over the data acquisition by entering all necessary information instead of recording it in writing. This increases the overall quality because all information can be read directly from the product. Although there are automated document readers for the calendars available, reworks are always necessary today because either the writing was too unclear or entire passages were not filled in (Fig. 5.7). It has mainly been the older panel members that have opposed this type of data collection (as for the data collection via scanning). On the one hand, they did not want to collect data via the Internet, and on the other hand, Internet penetration in these age groups was too low. In the meantime, both aspects have changed considerably.

5.3.2.4 The Use of the Smartphone The smartphone is another possibility to capture the data quickly and as completely as possible. This type of data collection is advancing and might replace the written survey methodology described above. Smartphones are usually equipped with a camera that creates perfect resolution photos. This technology is used to 1. Get the data faster 2. Directly after the purchase, the receipt can be photographed and sent. The purchase data is then directly available 3. Obtain more complete data

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Fig. 5.7 Landing page for the online data collection at GfK Consumer Panel

4. All articles are automatically transmitted to the institute. The shop, the prices, and the corresponding quantities are completely available 5. Reduce the time and effort required by the panel members 6. The workload for the panel members is significantly reduced. Scanning a weekly bulk purchase or recording it online is significantly more time-consuming than taking a quick receipt photo. Each methodology has advantages and disadvantages when considered separately. It will be important to offer techniques that meet the needs of the panel participants. If the scanner on the kitchen table still serves as a reminder for recording daily purchases, the written questionnaire also has a certain reminder factor. However, the workload is very high with these methodologies, and thus small purchases may not even be reported. A photo is quickly taken and sent over to the panel institutes, but the receipt may be lost or badly damaged. Many points are carefully weighed here by the institutes. Different groups will presumably also want different recording methodologies in the future so that different options will (must) be offered in short to medium term.

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Institutional Panels

Reporting Cycles in the Consumer Panel

The product groups of the consumer panel show a similarly high degree of differentiation in the delivery cycles, as is the case in the retail panel. The shortest reporting period is a calendar week, followed by a calendar month. Two-month periods and quarters are also found in some product groups, as are tertials, 6-month (half-year) periods, or annual periods. The attempt to systematize all product groups according to their delivery rhythm turns out to be rather tricky. The systematization described above could be used as a delivery criterion: 1. 2. 3. 4. 5.

Data collection methodology FMCG (fast-moving consumer goods)—SMCG (slow-moving consumer goods) Household panel—individual panel Seasonality Number of cases (number of purchased items) per time unit

Survey Methodology An essential criterion for the periodicity of a consumer panel product group is the data collection methodology. The highest frequency product group cannot be reported weekly if the data collection is based on the written calendar methodology. Thus, it is clear that the speed of delivery and the periodicity of a category is directly related to the data collection methodology. Therefore, the more technical the data collection (and data entry), the higher the periodicity of a category tends to be. FMCG: SMCG The choice of words alone makes it clear that the FMCG product groups have a significantly higher purchasing frequency and, therefore, also reporting frequency than is the case with the SMCG product groups. Now, however, the product group separation is not always free of overlap, also for survey reasons, there are many leeways. Example: The product group Whisky is classified as FMCG. However, it is also obvious that the number of purchased items per defined time unit will be significantly lower than for Roasted Coffee. Some print media (newspapers and magazines) are surveyed for SMCG product groups. Here, the reverse is true—the frequency is significantly higher than for the product group Washing machines or Digital cameras. Total Store This panel approach combines GfK’s two household panels into a holistic research approach. The panel participants report in both samples, or rather, they report their purchases in both the FMCG and SMCG panels. On the one hand, this increases the burden on the participants, but on the other hand, it also significantly increases the evaluation options.

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Today, the FMCG panel in Germany reports approx. 330 different categories (product groups) at GTIN level—item by item. From the nonfood panel, another approx. 250 categories are added, which are reported at the category level. It means that linking food and nonfood purchasing behavior analyses has become possible with this research approach. Household Panel: Individual Panel Numerous product groups from both panel types show very high and very low purchase acts per period. Purchases of chewing gum (individual panel), for example, are significantly higher than purchases of books. Fresh products such as sausage, meat, or fruit (household panel) are purchased and entered more frequently than cosmetics (individual panel) of all kinds. Thus, this distinguishing criterion is also unsuitable for general systematization. Seasonality Product groups could also have different reporting frequencies for the year. A technically surveyed product group could require weekly reporting in the months with high sales and only monthly reporting in the months with low sales. A good example of this is the product group sparkling wine. While total sales are meager in spring and summer, they increase significantly in December. Mineral water shows a similar cyclical pattern. In the hot summer months, sales increase sharply—here, the number of cases for weekly reporting could be given. On the other hand, monthly reporting will be sufficient in the autumn and winter months. However, this again shows that seasonality is not suitable for general systematization. Number of Transactions (Number of Purchased Items) Per Time Unit The only remaining factor is the number of purchased items per time unit, which may be decisive as a suitable yardstick for the delivery frequency and thus the period selection of individual categories. The institute’s responsibility is to define suitable solutions for possible delivery rhythms here. The scope of reporting can also be defined variably from period to period. Due to the number of cases, for example, a weekly delivery of key figures for the entire market is possible—a differentiation by vendors or even on a brand level, however, may only be plausible and stable enough with the monthly delivery. Another data slice can represent the segment structure. Weekly delivery of the total market subdivided into North and South, then more detailed reporting at the monthly level, e.g., at the federal state level.

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Panels for Media Markets

6.1

The GfK Crossmedia Link Panel

The GfK Crossmedia Link Panel (GXL) is a panel for measuring media usage behavior. It enables analyses of online behavior—“clickstream”—in addition to crossmedia campaign analyses. Within a very complex technical approach, data is measured partly passively and linked with the purchasing data known from the consumer panel (single-source panel with partial imputation). The aim is to analyze purchasing data combined with data on media usage behavior and consumers’ advertising contacts to evaluate campaigns. In consequence, this means that this panel is only established in countries with a consumer panel. These data are available in Germany, Italy, Netherlands, and Russia. In total, four sources are brought together: The purchase data is taken from the GfK Household Panel. Here, the purchasing data of 30,000-panel participants are collected using various scanner methods and the online survey. The online behavior is collected through a technical measurement of browser and app usage. This involves measuring desktop@home and investigating smartphone and tablet use. Special software is installed on the devices of the panel participants, and the data is transferred to GfK. Advertising contacts are mapped via a GfK pixel, Facebook/Instagram via a direct data exchange (cleanroom match). The measurement of other walled gardens (e.g., YouTube) is measured via passive measurement combined with campaign performance data (Fig. 6.1). Furthermore, a particular, separate device enables the collection of TV data. It measures the TV usage via an “audio sound matching” process and assigns it to one of 21 recorded advertising-bearing stations. The next component is the print and radio measurement. Here, an annual written survey is conducted using a questionnaire based on the “ma” (https://www.agmammc.de/). Data collection is very complex, as different data need to be merged. The best is to recruit the panel participants from one panel. It is the case here, and so from the # Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2022 M. Günther et al., Market Research with Panels, Springer Texts in Business and Economics, https://doi.org/10.1007/978-3-658-37650-5_6

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Fig. 6.1 Panelstructure for the GfK Crossmedia Link Panel in Germany

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6.1 The GfK Crossmedia Link Panel

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Fig. 6.2 Example of the Crossmedia Link Panel

approach, this panel is a so-called “single source” panel. The basis for these different survey methods is the GfK Household Panel (a sample representative of the population), providing the purchase data. All other information mentioned is obtained by using sub-samples of this panel. This Crossmedia Link Panel structure provides insights into campaign reach and contacts. Effects on sales are measured in the FMCG household panel and various categories from the nonfood panel. Thus, this model forms a basis for calculating the ROI—the “return on investment”—of media spending. Since online usage behavior (incl. advertising contact) via various end devices is also included in the analysis, a platform is created for evaluating and optimizing activities in the areas of paid and owned media. Paid media: is the most commonly used form of advertising. TV commercials, online banner/video advertising, switched ads, paid search result placements, and sponsorships are the main manifestations. Owned media: all media presences that belong to the company itself. This includes the own home page, the Twitter channel, the Facebook page, and often also the own blog. Key Performance Indicators (KPIs) are provided for advertising effectiveness research to quantify this ROI. In addition to the ROI mentioned above, the KPI of this GXL panel is the “sales uplift.” It determines whether advertising campaigns generate sales and measures whether buying behavior changes are the same or greater with advertising contact. This is independent of the integrated medium because qualitative media weighting does not take place. The following applies: “One contact is one contact.” In Fig. 6.2, the quantity structures of TV and online contacts are analyzed. The evaluation is comparable to the consumer panel’s combination analysis (cf. Sect. 15.4).

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For example, a TV campaign reaches 21.1 million people, 31.4% of the defined population. Online achieves a reach of 15.5% and thus 10.4 million people. Overall, this adds up to 24.0 million contacts. The overlap of 7.5 million must be factored out to avoid double-counting of contacts (persons). Of course, the TV and the TV and online contacts are interesting. But also important: how many additional contacts could be generated exclusively online, in this case, 2.9 million, which corresponds to 4.3% of the population. These people only had contact with the campaign online. All further KPIs are usually based on gross advertising spend unless the client provides GfK with the net figures. The combination with the household panel now makes it possible to examine the relevant participants’ purchasing behavior. Contact is not the same as the act of buying; the aim is to find out the new, additional buyers: Who has not bought before, but who may have become a buyer through an online or TV contact?

6.2

Television Audience Panel

6.2.1

Special Features of the TV Audience Panel

Television audience research continuously records which households and persons use which channels and programs. In doing so, it pursues two main goals: 1. It provides the basis for analyzing and planning television programs. The number of viewers or specific target groups is essential for individual programs or entire channels. In the case of broadcasting genres where the director of the program has creative possibilities (e.g., shows or talks), it is also helpful to analyze how viewing figures develop over time. 2. TV audience research also provides the performance evidence for the ability of the advertising time offered by the broadcaster to reach a predefined target group quantitatively and qualitatively. Therefore, the data helps advertisers and their agencies to plan and control their investment in TV advertising and, in the process, helps broadcasters generate corresponding advertising revenues. In many countries, television audience research differs from retail or consumer panels in its organization and funding. Retail or consumer panels are usually set up and operated by panel institutes. The data first belong to the panel institute, which sells them to the interested companies. In the case of television audience research, it is partly the case that the demanders form Joint Industry Committees (JIC) and then—after an appropriate selection process—commission an institute to carry out the television audience research. The data then belong to the JIC, thus determining which data are reported. In Germany, the AGF Videoforschung has the role of the JIC. In Great Britain, it is the “Broadcaster Audio Research Board” or BARB for short, and in Austria, it is the Arbeitsgemeinschaft Teletest (AGTT). This solution has the advantage that the

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broadcasters’ commitment increases the recognition of the data as currency in the market. In addition, there is also the situation that a company measures on its account. This applies, for example, to Nielsen in the USA or Médiamétrie in France. The use of the data as a performance record for TV advertising shows that the data ultimately influences the broadcasters’ revenues. At the same time, there is no possibility of control, as there is, for example, in the consumer or retail panel, by comparing one’s sales with the volumes reported in the panel. It results in exceptionally high demands on the verifiability and accuracy of this form of television research.

6.2.2

Survey

The survey usually takes place in a sample representative of the population (cf. Sect. 2.3.1.1). Suppose smaller regional stations are to be mapped. In that case, the corresponding regions may be more strongly represented in the sample than in the population, which is then compensated for by extrapolation. Furthermore, the survey usually takes place through technical devices because surveys through queries have proven unreliable. Figure 6.3 shows such a device. These devices are connected to the TV set and record which channel was active at

Fig. 6.3 TV Meter that GfK uses in Germany and other countries

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what time. In the case of delayed use (e.g., programs stored on a hard disk), this is also recorded via video coding. Viewers log in via remote control with personal log-in buttons, and thus it is also recorded which persons are watching.

6.2.3

Important Facts

The most important facts of television research are to be explained using a fictitious and, for the purpose of clarity, highly simplified example. A projection cell is considered whose sample consists of ten households with three persons each. Of course, the relevant facts are extrapolated. However, extrapolation is not used here to present the facts because this makes the essential relationships easier to understand. In the example, two channels, A and B, are considered, simultaneously broadcasting a program of 100 min duration. For this example, it is further assumed that only these two stations exist. The situation shown in Table 6.1 is considered to have arisen. Therefore, the people in the household watched the program at least partly in parallel. Reach, Average Reach, Audience Rating, and Viewing Participation The absolute reach (or ratings) expresses the total use of TV overall, of a channel, or a program. It is given as a percentage and expresses which part of the maximum (possible use) was used. In the example, the total person-related use of channel A is 200 person minutes, and the corresponding household-related use is 130 household minutes. The relative personal reach or average reach results when this actual use is related to the total, theoretically possible use. The relative household range is calculated accordingly. Reach at the personal level is also called viewing participation; reach at the household level is also called viewing rate. In the above example, the relative person range for transmitter A is calculated as follows: The possible use of station A is ten households multiplied by three-person multiplied by 100 min. It results in 3000 person minutes. The actual use is 200 person minutes. The household reach is calculated from the total value of 1000 Table 6.1 Fictitious viewing behavior of two households

Household 1

Person 1 2 2 1 2 3 Viewing time persons Viewing time household

Viewing time Station A 30 20 50 0 100 200 130

Viewing time Station B 70 0 50 30 0 150 120

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household minutes (10 households times 100 min) and actual household use of 130 min at 13%. Suppose the relative reach is multiplied by the base (number of persons or households). In that case, the result is the absolute reach, which expresses how many households have used the corresponding TV offer on average at the same time. The net reach is derived from the (gross) reach presented so far by excluding from the calculation of the use of the TV offer those people who have watched it for less than 1 min continuously. This means those who only watched the respective channel for a very short time (e.g., when “zapping,” i.e., quickly switching through channels) are not included. Net reach can also be expressed as an absolute number by multiplying it by the underlying base. Average Viewing Time in Minutes The average viewing time in minutes expresses how long a person or a household in the population uses a TV service on average. In the example, the average personrelated viewing time for channel A is 200 minutes=ð10 households
3 personsÞ ¼ 6:7 minutes

ð6:1Þ

This figure can also be shown by person and by household. Market Share The market share of a broadcaster is an important indicator of its relative strength in a competitive environment. It is calculated as the share of personal television viewing in a time interval. Because this is related to the total television use in the time interval, it is independent of the absolute television duration. In the example, the market share of channel A is 200 person minutes/350 person minutes ¼ 57.1%. Seasonally lower use of a channel in summer can certainly co-move with higher market shares if the total use declines even more strongly than the use of the channel under consideration. Price Per Thousand (CPM) This measure is an essential indicator of the value for money of an advertising spot. To calculate it, the rate price (without taking discounts, among others, into account) of a 30-second commercial is related to the absolute number of viewers in thousands. Often, not the total number of viewers but the number of viewers in a previously defined target group (e.g., young adults) is used for assessment. A CPM of € 7.00 says that an advertiser who does not receive any discounts has to invest € 7.00 to reach 1000 people with a 30-second spot.

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Important Segments

Special segments result from the reception levels for a TV panel, i.e., how the household receives its TV signal, whether cable, satellite, or antenna. In principle, all characteristics that are queried in the survey of the panel households can be used in the definition of segments, such as head of household aged 25 to 55 who own a freezer. For example, when advertising for frozen food is to be controlled.

7

Special Panels

In addition to the classic consumer and retail panels, which have already been described in detail, many special panels are created for specific industries or concerning overarching questions. In addition, various institutions offer panels for research purposes. The following offerings for the pharmaceutical and agricultural sectors are presented as examples. The Innovation panel and the Mobility panel are then presented before two panels that can be used for research purposes are described.

7.1

Panels for Pharmaceutical Products

Due to its extensive regulations and large volume, the pharmaceutical market is a significant but particular market, which is analyzed closely within the framework of panel research. The American company IQVIA Inc., formerly Quintiles and IMS Health, Inc., offers, among other things, the required panel data. IQVIA is the most significant data science company in the healthcare market and a human data science technology leader (www.iqvia.com). The primary data sources for the panel analyses are the following three panels: • IMS ®Hospital Index (DKM®): Determination of the sales and revenue volume of the entire hospital market about the pharmaceuticals used. The extrapolation considers four-bed size categories, 15 medical specialties, and seven regions. The data collection is conducted monthly via the respective hospital pharmacies, whereby 480-panel hospitals were recorded in Germany in 2021. • IMS PharmaScope®: There is a separate mail order panel for the mail order sector. With the help of this panel, the dispensing of medicines by pharmacies can be mapped, whereby a distinction can be made between the SHI market (statutory health insurance funds), private prescriptions, and cash sales. The first group is represented by # Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2022 M. Günther et al., Market Research with Panels, Springer Texts in Business and Economics, https://doi.org/10.1007/978-3-658-37650-5_7

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the SHI settlements made by the pharmacy computer centers, the other two groups by a sample of 4000 pharmacies. • IMS® Consumer Report Pharmacies: This report continuously records the sales of over-the-counter medicines and non-prescription medicines/dietary foods and medical devices in Germany’s public and mail-order pharmacies. The public pharmacies are again based on 4000 pharmacies, while the data for the mail-order pharmacies are mapped by the IMS mail-order panel. Based on these data sources, the IQVIA Market Report continuously analyses the development of the German pharmaceutical market, whereby the hospital and pharmacy markets are also presented in addition to the overall pharmaceutical market. Beyond that, the SHI market is shown separately. The volume analysis is based mainly on the number of tablets, capsules, pre-filled syringes, and sachets sold. In contrast, the revenue analysis must consider the form in which the prices were calculated in each case. Different sales values result in whether manufacturer discounts and savings resulting from discount agreements are included in the calculation. The values are usually prepared every month, focusing mainly on sales, revenues, and the respective market shares. It is important to consider the number of working days when comparing monthly, as this value significantly influences the data. This effect gradually fades into the background when aggregating quarterly or (half-) yearly values. The following presents some findings from the 2020 Market Report1: • In 2020, the revenue of pharmaceuticals in the overall pharmaceutical market increased by 6.7%. In contrast, sales volume stagnated at the previous year’s level, with a total of 98 billion counting units (capsules, strokes, sachets) sold. Sales amounted to around 50 billion Euros. • Fifty-nine percent (4.0 billion Euros) of revenue in the inpatient sector is accounted for by only ten drug groups • Among the ten top-selling drug groups, the group of interleukins inhibitors showed the most substantial growth in revenue with +26%. • The influence of Corona is visible in the year 2020. In March, there were disproportionately high growth rates (compared to previous years) due to stock purchases, among other things, whereas massive declines were observed in April and May. • The pharmacy market achieved a revenue growth of 5% in 2020. • The market segment of prescription preparations grew by 7% in sales in 2020. In contrast, over-the-counter drugs had to accept a 5% decline in sales.

1

https://www.iqvia.com/-/media/iqvia/pdfs/germany/library/publications/iqvia-pharmamarktbericht-classic-jahr-2020.pdf?_¼1637519295881

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• OTC products via mail-order pharmacies significantly increased volume (+14%) and revenue (+16%). • In 2020, the statutory health insurance funds were able to record € 5.731 billion from manufacturer discounts and rebates from reimbursement amounts. This was an increase of +18% over the previous year. The private health insurance funds resulted in a saving of € 883 million, which corresponded to an increase of 13%. The panels shown offer a multitude of additional facts and segments. In the pharmaceutical market, the panel data form an indispensable basis for describing the market structures, their changes, and the development of individual drug groups. In the pharmaceutical market, which is in constant flux due to the continuously changing legal framework conditions, close monitoring of the markets must be ensured. Similar to Germany, such panels are operated in numerous other countries. Germany does not serve as a blueprint, but many developments are based on it.

7.2

Agriculture Panel

Agriculture also represents a special market. The previous provider of numerous agriculture panels, the Kleffmann Group from Lüdinghausen, has sold its market research division in 2019 to Kynetec, a British market research company focusing on agriculture (www.kynetec.com). Kynetec now covers almost all areas of agriculture and animal health, including: • • • • • • • • • • • • •

Animal Health Animal Nutrition & Pet Food Animal Health diagnostic Fertilizer Crop protection Seed and Seed treatment Machinery Plant Nutrition Biotechnology Digital agriculture Soil and Water Management Hops Tobacco

Based on the panel data, global analyses can be carried out, for example, on seeds, fertilizers, and crop protection (these three areas form the focus of the panel surveys) in relation to all major crops. More than 100,000 selected farmers worldwide are interviewed to describe their yearly activities. The focus is on the entire cultivation phase, from the sowing of the individual seed varieties to seed treatment, fertilization, and crop protection.

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Table 7.1 Facts reported in the agriculture panel Seeds Market shares, sowing strength Where to buy Decision time source of information exploratory farmland personal recommendation Prices Choice of varieties Pickling

Plant protection Market shares, sowing strength Where to buy Decision time source of information personal recommendation Prices

Product migrations

The following key data ensure the quality of the panel surveys: • • • • • •

Data based on over 100,000 interviews per year with farmers around the world Possible time-series analyses through annual repetition of the surveys Global compatibility Unique “culture for culture” methodology Representativeness of the results Standardized questionnaires allow for temporal and geographical compatibility and comparability of data. • Local agricultural experts guarantee market knowledge and expertise • Independent source of information • Extensive market coverage, around 350-panel surveys worldwide, representing approximately 70% of the global seed market and 52% of the global crop protection market value In addition to in-depth analyses to describe the seed and crop protection market, the panel data are also suitable for analyzing the following aspects, among others:

• Insights into the purchasing and usage behavior as well as product perception of farmers • Insight into cause–effect relationships of market mechanisms • Cross-analysis of quantitative and qualitative information with demographic characteristics • Enhanced knowledge by linking crop protection and seed data. Table 7.1 lists the most important facts collected for seed and crop protection. These facts are available for all relevant crop types such as winter and summer grain, maize, sugar beet. Based on the extensive panel data, the market shares of the individual seed or crop protection manufacturers, the different seed varieties, and the crop protection brands and crop protection products can be presented. On a local, regional and global level,

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it is possible to show how the purchasing behavior of farmers is developing, broken down by numerous demographic characteristics. Furthermore, trends and developments in the individual markets are regularly analyzed, and the usage behavior of farmers concerning seed (sowing rate) or crop protection (application rate) is made available to customers according to numerous segmentation criteria. Important insights are also gained for market participants by presenting brand loyalties, the images of the various products and manufacturers, and the analysis of pricing behavior.

7.3

Innovation Panel

The Innovation panel in Germany is conducted by ZEW—Leibnitz Centre for European Economic Research on behalf of the Federal Ministry of Education and Research. Cooperation partners are the Institute for Applied Social Sciences (infas) and the Fraunhofer Institute for Systems and Innovation Research (ISI) in Karlsruhe. Every 2 years, the Innovation panel is part of the Community Innovation Survey (CIS), conducted by the Statistical Office of the European Commission to produce European innovation statistics. The survey is designed as a panel (Mannheim Innovation panel), which means that broadly the same sample of companies is surveyed every year. The sample is refreshed every 2 years due to company closures, acquisitions, or industry changes. The 2020 survey included data from a total of 13,182 companies. This data was used to extrapolate to the entire companies or the individual segments. In 2020, the universe consisted of all legally independent companies based in Germany with five or more employees in 2019 and could be assigned to the relevant industries (www.zew.de). The Innovation panel is an essential basis for assessing the German economy’s technical performance and thus provides policymakers with an important information base for economic policy decisions to strengthen the innovative strength of German companies. However, due to the in-depth analysis possibilities, the panel is also an important source of information for national and international companies to estimate the innovation activity in the various sectors. In addition, there are also many indications of successful or failed innovation projects in the companies, which allows using the Innovation panel for somewhat coarser benchmarking. The panel is representative of most industrial sectors, knowledge-intensive services (publishing, film industry, data processing, information services, financial services), and other services (wholesale, transport, cleaning, personnel services). Numerous innovation and structural variables are collected to describe the innovation process. These are, for example: – Innovation variables • Product innovation • Process innovation • Market news

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• • • •

Special Panels

Cost-cutting process innovations Innovation expenditure R&D expenditure R&D employees

– Structural variables • Sales • Export • Personnel expenses • Cost of materials • ICT investments The data from the panel survey is made available for analysis in an anonymized form to external users for scientific, non-commercial purposes. To maintain data protection concerning the participating companies, various anonymization procedures are used so that it is not possible to conclude about individual companies. The fact that a query is also made for the following years means that the current status and the future development can be described so that politicians can take countermeasures if the results do not correspond to the government’s objectives. Typical questions that can be answered with the help of the panel are, for example: • How did innovation spending develop in the different industries? Which industries invest particularly heavily in the area of innovation? • How does innovation activity in SMEs (small and medium sized enterprises) compare with that in large companies? • How does the innovation intensity (innovation expenditure to turnover) develop in the individual economic sectors? • How does the innovator rate (proportion of firms that have introduced product or process innovations) compare to previous years? • What is the relationship between product and process innovations? • How high do product innovations account for the share of sales? • What is the importance of digitalization in the context of innovation activities? An interesting example from the 2020 Innovation Survey is the level of the 2019 innovator rate by industry, shown in Table 7.2. Innovators are defined as companies that have introduced at least one product or process innovation in the last 3 years. Table 7.2 shows that innovation activity varies significantly from sector to sector. While the leaders—chemicals/pharmaceuticals, information/communication, electrical industry, and mechanical engineering—have percentages between 73% and 75%, wholesale, transport, supply/disposal, and mining miss the 50% mark. The figure for vehicle manufacturing of “only” 59 percent is somewhat surprising, especially if we look at the share of sales achieved with product innovations in 2019 (cf. Table 7.3).

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Table 7.2 Innovator rate 2019 in percent

Industry Consumer goods industry Other. Materials processing industries Chemistry/pharmaceutical industry Plastics industry Metal industry Electrical industry Mechanical Engineering Vehicle construction Supply/disposal, mining Wholesale, Transport Information/Communication Financial services Technical services Consulting, advertising other business services

Innovators in percent 53 53 75 58 57 73 73 59 45 41 76 66 63 63 49

Table 7.3 Sales share of product innovations 2019 in %

Industry Consumer goods industry Other. Materials processing industries Chemistry/pharmaceutical industry Plastics industry Metal industry Electrical industry Mechanical Engineering Vehicle construction Supply/disposal, mining Wholesale, Transport Information/Communication Financial services Technical services Consulting, advertising other business services

Innovators in percent 8 8 15 11 8 28 16 45 5 6 19 12 14 8 8

In many industries, product innovations do not reach a sales share of 10%, which means the focus is on products or services already available on the market for a more extended period. Reasons could be that many companies have primarily implemented process innovations to reduce costs, increase efficiency and consider product innovations less important. This can be attributed, for example, to customer behavior in these industries. Another driver of product innovation is also competition. If the market expects innovations regularly, companies must deliver to avoid competitive disadvantages.

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The Innovation panel offers numerous possibilities for analysis to show the cause–effect relationships. The annual indicator reports on the innovation surveys provide numerous suggestions for this (www.zew.de).

7.4

Mobility Panel

The topic of mobility is one of the major challenges of the future. The relationship between individual transport and local and long-distance public transport is just as much in focus here as the question of the environmental compatibility of current and future mobility behavior. How can mobility be designed so that all needs can be covered as well as possible? Reliable data can only be obtained if time-series analyses are possible within the framework of a panel study. This has been achieved in Germany with the help of the mobility panel. The German Mobility panel has been conducted regularly once a year since 1994 under the direction of the Karlsruhe Institute of Technology to map mobility behavior in Germany. The panel focuses on the everyday mobility of people and the use (mileage, fuel consumption) of passenger cars (https://mobilitaetspanel.ifv. kit.edu). Members aged 10 years and older of representatively selected households are asked to keep a travel diary for 1 week to collect the data. The following facts are to be recorded in this diary: • • • •

Location changes with start and finishes time Purpose of the change of location (shopping, work, visiting friends) Means of transport used (bicycle, car, public transport) Distance traveled

Furthermore, information on the respondent’s person (e.g., age, occupation) and their household (e.g., car ownership, number of household members) are asked for as structuring characteristics. Participants who own a car are asked to keep a fuel diary for 8 weeks to record their car mileage and fuel consumption. The refueling transactions with quantities, prices, and the type of fuel refueled are to be recorded. Furthermore, the mileage at refueling and the date are requested. Finally, car characteristics (e.g., year of manufacture, engine capacity, brand) are recorded. Key factors for describing everyday mobility in the context of the studies are: • • • • • •

Driving license possession Car availability Traffic participation Mobility time Traffic volume Transport performance

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Table 7.4 Driving license ownership 2015:2019 in Germany

Age group 18–25 years 26–35 years 36–50 years 51–60 years 61–70 years over 70 years Total

2015 77% 94% 94% 92% 85% 75% 87%

2019 87% 95% 96% 94% 92% 83% 91%

Table 7.5 Car availability by age group 2015:2019

Age group 18–25 years 26–35 years 36–50 years 51–60 years 61–70 years over 70 years Total

2015 37% 60% 72% 59% 53% 44% 56%

2019 44% 60% 72% 69% 60% 59% 63%

• Means of transport (walking, cycling, motorized private transport, public transport) For the question on possession of a driving license, the 2019 figures are shown in Table 7.4, compared with the 2015 figures. Table 7.4 shows that the proportion of people with a driving license has increased again in recent years. In 2019, 91% of respondents indicated that they had a driver’s license and were authorized to drive a passenger vehicle. The largest increase occurred in the youngest age group. In 2019, 87% of 18–25-year-olds had a driver’s license, a 10%-point increase over 2015. Second place in terms of increasing rates is achieved by the over 70s, for whom there was a + 8% point increase when comparing 2019:2015. The 61- to 70-year-old group joined this leading group with +7% points. Now, having a driving license does not mean that the person in question also has access to a car. Table 7.5 therefore also summarizes car availability by age group and compares 2015 to 2019. Table 7.5 shows that the availability of a car has also increased from 2015 to 2019. Sixty-three percent of respondents said they have regular access to a car, with only 56% making this statement in 2015. The oldest age group made the most significant jump in this question; this resulted in 59%, whereas in 2015, only 44% of this age group answered yes to the question about having a car available. There were also big positive changes in the 51–60 age group (+10 percentage points) and the 61–70 and 18–25 age groups, where an increase of 7 percentage points was achieved in each case.

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Table 7.6 KPIs of mobility Mobility benchmark Driving licence possession Car fleet Traffic participation Traffic volume Transport performance Mobility time Path length

Unit Adult driving licence ownership rate (%)

2015 87

2019 91

Cars per inhabitant Proportion of mobile persons per day (%) Journeys per person and day (number) Kilometres per person per day (km) Time of all trips per person and day (h: min) Average path length (km)

0.525 91.2 3.37 40.9 1:22

0.545 89.3 3.15 40.9 1:20

12.1

13

When comparing the two tables, Tables 7.4 and 7.5, it becomes clear that overall, the opportunity for individual mobility with the help of a car increased noticeably from 2015 to 2019. Although the proportion of driving license holders increased by “only” four percentage points, the availability of a car increased by seven percentage points. Based on the structure of the mobility panel, numerous studies can be carried out that allow deep insights into the mobility behavior of the population. These are essential for all planning in a “future-oriented” mobility design. Finally, Table 7.6 summarizes key mobility KPIs comparing 2015 to 2019. The comparison in Table 7.6 shows little difference in the KPIs from 2015 to 2019. The proportion of mobile people per day and the trips per person per day have decreased somewhat, with no impact on the kilometers per person and per day. This value also reached 40.9 kilometers in 2019. The benefit of the panel arises primarily for transport or mobility planners. On the other hand, all companies from the mobility sector are also seen as customers since data on ordinary bicycles, e-bikes, and other means of transport are also collected. Only a brief overview of this panel should be given here.

7.5

Socio-Economic Panel

The Socio-Economic Panel (SOEP) provides microdata for research questions from basic social, behavioral, and economic sciences. Furthermore, these data are used for social reporting and policy advice. The SOEP is a representative repeat survey of private households in Germany that has been conducted annually since 1984. As far as possible, the same families and individuals are surveyed each year, which allows for robust time-series analyses. The SOEP is located at DIW Berlin and is funded by the German federal and state governments (www.diw.de). About 30,000 persons from about 15,000 households are interviewed per year, with the same persons always being interviewed as far as this is possible. Children living in the households are included in the survey after their 16th birthday.

7.6 EBDC Business Panel

85

Topics covered by the SOEP include: • • • • • •

Demography, population, and life expectancy Work and employment Family and social networks Education and qualification Health Integration and migration

SOEP data are used by more than 500 researchers from all over the world. All results obtained with the help of SOEP are published in the literature documentation system SOEPlit of the DIW.

7.6

EBDC Business Panel

The EBDC Business Panel is provided by the LMU-ifo Economics & Business Data Center (EBDC). The EBDC was founded in 2008 as a cooperation between the LMU Munich and the Ifo Institute for Economic Research to provide new fields for economic research. In addition to the data from the regular ifo surveys, data sets from surveys and external balance sheet data from the company databases Amadeus, Orbis, and Hoppenstedt are also taken into account. Currently, three panels are offered: • EBDC Business Expectations Panel (focus: economic factors and balance sheet data) • EBDC Investment Panel (focus: investments and balance sheet data) • EBDC Business Innovation Panel (focus: innovations and balance sheet data) As already briefly mentioned, the database consists of four main sources: 1. ifo Survey data The ifo Institute’s Business Panel consists of the ifo Business Survey, the ifo Investment Survey, the ifo Innovation Survey, and the World Economic Survey (www.ifo.de). 2. Company database Amadeus The database is provided by Bureau van Dijk, A Moody’s Analytics Company, and contains business and financial information on approximately 21 million companies across Europe. The focus is on unlisted companies. The company financial data is prepared to make a cross-border comparison of the companies possible. In addition to the company financial data, creditworthiness indicators, share prices of listed companies, and news about the companies, including their board members, directors, and managers, are also provided (www.bvdinfo.com). 3. Company database Orbis

86

7

Special Panels

Orbis is also offered by Bureau van Dijk, A Moody’s Analytics Company, and provides information on more than 400 million companies worldwide, with detailed financial data available for 40 million (www.bvdinfo.com). 4. Balance sheet database Hoppenstedt Hoppenstedt-Firmeninformationen GmbH offers this database. The balance sheet database, which is evaluated and processed daily, comprises more than 3.5 million financial statements from around 1 million German companies in the industry, trade, services, insurance, and banking (www.hoppenstedtfirmendatenbank.de) The data sets are made available for research purposes, but for data protection reasons, the data can only be used on the premises of the Institute. The data will be made anonymous and accessible with a one-year time lag.

7.7

Test-Panels

Test-panels are set up and operated to test a specific question. Since their operation is expensive, they usually question where wrong decisions are costly. One such question is whether or not to introduce a new product. However, test-panels have also been and are still being used to answer questions about the right approach in general. When does TV advertising make sense? Should advertising be continuous or pulsating? When is sample distribution promising? Such questions are answered by test-panels, which are mainly operated for the FMCG markets, i.e., the fastmoving consumer goods markets. The earliest test-panels were store tests and regional test-markets. In-store tests, test stores were recruited and stocked with the new product. In the following 12 weeks, it was recorded how well the new product sold. In most cases, a control group was considered in parallel, in which the product was not introduced. It is also possible to implement different in-store marketing activities in the test stores, such as placements and pricing. In the regional test stores, the sales of the new products were recorded. In the regional test-markets, an additional retail panel was recruited in an area as representative as possible of the overall market. The introduction and success of the products in these stores were observed. This was much more expensive because many more products had to be produced, but it had the advantage of additionally testing the receptiveness of the retailers. The disadvantage of both panels is that only retail data is available. The long-term success in the FMCG markets is on repeat purchases, which require purchasing data from households. This led to regional test-markets, where test stores were recruited with households shopping in them. These ERIM panels were developed in France in the 1970s and quickly introduced by GfK in Germany. In the USA, BehaviorScan test stores were introduced slightly later by IRI. Nielsen pursued similar objectives with Telerim testmarkets. These had two improvements over the ERIM panels: First, entire cities were recruited with as many stores as possible and a household panel from those

7.8 Conclusion and Outlook

87

cities. In addition, the cable network (Nielsen changed the antenna signal) changed the television programming so that commercials could be overlaid with test commercials. This also made it possible to test the success of TV advertising, which was even more important at the time. Mediametrie also adopted this model in France and GfK in Germany, which operated a BehaviorScan test-market in Haßloch (southwest Germany) from 1985 to 2021. The test-markets grew competition early on in the simulated test-markets, where consumers were invited into a test studio, given money, and used it to buy a product. In addition, they received another product to go home with their previous favorite brand and the test brand and try both. A second studio or even an interview was used to gauge repurchase willingness. This approach led to faster results, and the test product could be kept secret better. However, the depth of analysis and the prediction accuracy was also lower than, for example, with BehaviorScan. Examples of such instruments include Bases from Burke as the market leader, Nielsen later bought, and TeSi by GfK. The increasing spread of online shops facilitated the test-market simulation, so the importance of test-market panels has decreased significantly. For instance, store tests are offered, e.g., in Germany by the company Yagora (Markttest/Storetest— Test measures in advance in real markets (yagora.com), regional test-markets by Bonsai GmbH (cf. Test Market & Sales Support—Bonsai Research (bonsairesearch.com).

7.8

Conclusion and Outlook

The above explanations have made it clear that many panels outside the “classics” of consumer and retail panels can be used to describe and analyze special markets or even global changes. However, looking at the development of panel research, it must be noted that several special panels have already been discontinued in recent years. On the one hand, this is due to the high costs of conscientious panel maintenance, and on the other hand, to customer behavior. Companies increasingly prefer data from the Internet, which has certain qualities but is usually not suitable for an accurate time-series analysis. A change in thinking would be advisable, although this may be rather unlikely. In many countries, other panels describe individual markets, but not all can be presented here.

8

Product and Period Description

Quantitative information needs for diverse business areas can be satisfied by different panels. Depending on the specific question, the answer can be given using retail or consumer panel data. The statement of a panel type is based on the object of analysis. This differs from panel to panel. However, the basic principles (cf. Sect. 1.2) are identical for all types of panels. The following definitions behind these reporting types will be explained in detail. Every panel lives from its content, the actual data. These data are obtained from a wide variety of sources. Generally, the two most important types of data collection are the scanner checkouts for the retail panel and hand scanners and smartphones for the consumer panel: • Retail panel: Retail companies directly supply sales data recorded via scanner checkouts to the respective institutes. • Consumer panel: Consumers scan the purchases using a hand-held scanner. In the process, questions about the place of purchase and the price are also answered via a miniature keyboard. An alternative is to capture purchases using a smartphone. The total receipt is photographed and sent to the institute. However, there are some other survey methods. In the few cases in which individual stores are not equipped with scanner tills, the inventory methodology continues to be used for the retail panel (cf. Sect. 2.4.1). The same applies to store types that carry a special assortment equipped with few GTINs. Some articles are not (yet) equipped with a GTIN (cf. Sect. 8.1.2) (e.g., flowers, cinema tickets, textiles, financial services) and cannot be scanned. Therefore, online registration is used in the consumer panel. Here, the participant is guided through a questionnaire that asks for a variety of product characteristics in addition to the pure purchase data. In particular, for the consumer panel, the issue of data collection is of enormous importance. The participant is asked to record every purchase, which involves considerable effort. It is essential to reduce this effort as much as possible so that # Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2022 M. Günther et al., Market Research with Panels, Springer Texts in Business and Economics, https://doi.org/10.1007/978-3-658-37650-5_8

89

90

8 Product and Period Description Basic segments of the retail and consumer panel

Segments

- Regions - Shop types - Retailer organization

Additional segments reported within the consumer panel - Sociodemographic features (Age, Income, etc.) - Lifeworlds - Buyer segmentation

Total market Beverages Washing maschines Period

Financial services January February March

April May June

July August September

October November December

Facts - Measures: Sales value Sales volume (units, packs) Ø price Number of buyers Penetration Loyalty

Category A

January

Fig. 8.1 Dimensions of panel data

panel participants report their purchases. The survey methodology is an essential tool to reduce panel mortality and recruit younger participants—to encourage them to participate. When taking a closer look at these different data input channels, the generally valid statement can be made that a reported data point contains: • • • •

The individual articles (products), services, or media data a product group A certain point in time A defined shop Certain technical conditions

Therefore, a panel number has so-called four-dimensions with the following characteristics: • • • •

Article (e.g.: GTIN code) Period (time) Segment (point of sale—point of purchase—household characteristic) Facts—Measures (key figures)

Depending on the panel type, these dimensions (characteristics) differ in parts significantly. While the articles have a specific identity across all panels,1 the reporting rhythm of the panels determines the periodicity. The location of the data collection (retail panel) or the purchase (consumer panel) forms the components of the segments (Fig. 8.1). 1

On the one hand: A coffee machine of a certain type remains a coffee machine, regardless of the panel type, consumer panel, or retail panel. On the other hand, however, there may be different product group definitions across institutes so that items with identical content are reported in different product groups.

8.1 Product: Article Description

91

The type of information determines the characteristics and contents of the different measures. These different information components are defined and described in more detail below.

8.1

Product: Article Description

8.1.1

Definition of a Product Group: Category

The range of the institutes’ product groups under observation covers almost the entire spectrum of articles from the consumer goods industry. A detailed list of the food categories surveyed today can be found in the appendix. Consideration of this range of articles results in an initial classification according to the distinguishing criterion of consumer goods, services, and media. However, this is by far not sufficient to achieve a customer-specific market observation for its assortment. First, groups of products have to be formed, being observed together in a product group (also called category or category basket). The grouping of individual articles (GTINs) leads to a brand. One or also several brands lead to the manufacturer level. Those brands that are similar in essential characteristics are referred to as a product group. The renewed aggregation of categories results in a so-called merchandise category basket or category baskets. The aggregation of all category baskets leads to the “product world.” This structure is illustrated in Fig. 8.2. A clear and, if possible, simple definition of a product group is important. This is the only way to distinguish it from other, similar product groups. As a rule, product groups are defined individually from one institution to another, considering customer Article pyramid

Product world

All FMCG articles

Category basket

Category

Label owner

Brand

Article

Beverages Coffee Tea Bear Mineral water

Mondelez Tchibo

Jacobs Krönung Tchibo

Jacobs Krönung 250 Gramm Jacobs Krönung 500 Gramm Tchibo Feine Milde 500 Gramm

Fig. 8.2 Structure of the article pyramid

White Goods Washing machine Refrigerators Tumble dryer

All SMCG articles

Category basket

Category

Bosch Miele Liebherr Siemens

Label owner

Bosch Miele Liebherr Siemens

Brand

Bosch WAJ 24060 Miele G6770 SCVi Liebherr MRFvd 3511-20 Siemens WT44 W5WO

Article

92

8 Product and Period Description

requirements. This circumstance alone can lead to a different market assessment of a product group. Therefore, a good and, if possible, simple product group definition should include: • • • •

The detailed definition of a category Separating features within this product group (product groups) The nature and scope of the articles observed Nature and scope of the excluded articles

8.1.2

The GTIN Code

The GTIN (Global Trade Item Number) is managed worldwide by GS1 and is an international, unique number to identify products. The formerly common designation EAN (European Article Number) or UPC (Unique Product Code) was replaced by the designation GTIN in 2009. There are five main versions of this GTIN: • GTIN-8, also called GTIN short number, consists of a country code, article number, and a check digit. This GTIN short number is intended for products on which GTIN-13 cannot be printed for space reasons. • GTIN-12 goes back to the American UPC. This GTIN-12 is compatible with the GTIN-13. By simply adding a leading zero, a GTIN-12 becomes a GTIN-13. These GTINs contain an indicator digit (country code), company prefix, item reference number, and a check digit. • GTIN-13: Like GTIN-12, this number can be used to identify almost any item (Fig. 8.3). • GTIN-14 is a special case. This coding is used for containers that themselves contain articles with their GTIN. • SGTIN is also referred to as serialized GTIN. In addition to a GTIN, this code contains a serial number of the marked product. Thus, the code identifies the type

Fig. 8.3 Elements of a GTIN

Indicator digit

GS1 Company Prefix

Item Reference Number

Check Digit

8.1 Product: Article Description

93

of product and each (individual) product. In most cases, this SGTIN is used in so-called RFID tags.

8.1.2.1 The Check Digit When purchasing or scanning products at the checkout, it must be ensured that the correct number has been entered or scanned. The last digit of both GTIN codes (8 or 13 digits) represents the check digit. A check digit is required wherever no additional check routine can be connected to the actual data entry (e.g., repeated check of the entry). In practice, various methods for check digit calculation have been developed and tested. However, there was no choice for the check digit of the GTIN. The check algorithms of the American UPC system had to be adopted. This was necessary to ensure that both systems were compatible and guaranteed a smooth exchange of goods. This UPC system is based on weighting the sequence of digits to be checked. The two digits 3 and 1 are multiplied alternately (important: starting from the right) with the GTIN, these values are added, and the total sum is divided by 10. The “remainder” is subtracted, and the result corresponds to the check digit. 8.1.2.2 The Determination of the Check Digit The check digit for the above example can be calculated as follows: GTIN: Weighting: Multiplication: Sum:

40 13 40 4+0+

13,600 13,131 19600 1+9+6+0+0+

01111 31313 01313 0+1+3+1+3 ¼ 28

Dividing this sum by the modulo 10 gives 2 “remainder” 8. The modulo 10 minus the remainder represents the check digit (10–8), in this case, 2. This method applies to the determination of the necessary check digit. The actual check of the check digit, i.e., the check for the correctness of the entire GTIN, is carried out analogously. The factor for the check digit is 1. If the entire number is entered correctly, the “remainder” in the last step of the division must always be a “0” according to the procedure described. If this is not the case, an error has occurred. GTIN: Weighting: Multiplication: Sum: Division:

40 13 40 4+0+ 30/10 ¼ 3 “Remainder” 0

13600 13131 19600 1+9+6+0+0+

01111 31313 01313 0+1+3+1+3+

2 1 2 2

¼30

94

8.1.3

8 Product and Period Description

Instore Codes

The so-called instore codes2 often cause major difficulties for the institutions. The problem is individuality. Identical articles can have a different bar code from retailer to retailer because they are individually assigned. This will be explained using the example of fresh foods. In addition to fruit and vegetables, fresh food includes sausages, meat, and cheese from the counters. In the past, the customers themselves often weighed fruit and vegetables; a symbol on the scales provided the customer with the necessary support. The weighing slip produced was stuck onto the packaging. Customers operated the scales incorrectly or added goods after the weighing process. Many grocery stores have disposed of this system. In fact, in many countries, a unique code indicating the price for these categories is no longer printed when the products are taken out. Instead, the goods are weighed and priced directly at the checkout. While this system is possible for fruits and vegetables, fresh sausage and cheese cannot necessarily be inspected and weighed at the checkout. The operators still need to print legible labels at the relevant counters here. Such an “instore code” contains much information about the actual product and the price in the last digits. This ensures pricing, but seeing which sausage, meat, or cheese was purchased at the service counter is not always possible. In addition, sausage products are equipped with different bar codes at the retailers. It is not guaranteed across retailers that coincidentally identical barcodes have the same meaning. Therefore, these codes have to be processed manually by the institutes in the subsequent processing steps and are very time-consuming to handle.

8.1.4

The ISBN and ISSN Code

In addition to the GTIN, other standardization methods have become established. In particular the ISBN—International Standard Book Number and the ISSN—International Standard Serial Number must be mentioned here. The ISBN code designates a machine-readable identification feature for a unique book worldwide. In addition to the ordering system for books, it is an essential feature for the international literature supply of libraries. It ensures intra-library cooperation across national borders and language barriers.

2 The indicator digits 20–29 are used for “in-store codes.” Within this number range, each labeler is free to assign his numbers.

8.1 Product: Article Description

95

Book production is not numbered centrally worldwide, but an ISBN is assigned nationwide. To avoid duplicate numbering, the ISBN has been divided into four parts. The different parts are either separated from each other by hyphens or spaces. An unstructured representation of the 10-digit number is not permitted. At the same time, a sequence of numbers in ISBN format is not considered an International Standard Book Number if it is not preceded by the letters ISBN. Thus, a valid ISBN, which is divided into four parts, has the following structure: Example: ISBN 3-404-11896-0 Part 1—Group number—Example here 3 The group number indicates geographical, national, or linguistic groups. Three stands for the German-speaking area in the above example: the Federal Republic of Germany, Austria, and German-speaking Switzerland. Part 2—Publisher number—Example here 404 The publisher number is variable and can be 3, 4, or even five digits. Part 3—Title number—Example here 11896 A title number is an article number assigned by the publisher for each article. Depending on part 2 of the ISBN, publishers have different article numbers at their disposal. While 100,000 article numbers can be assigned with a 3-digit publisher’s number, only 1000 internal article numbers are available with a 5-digit publisher’s number. Part 4—Check digit—Example here 0 The check digit is always a single digit here as well. The calculation is based on modulo 11 with the weighting 10 to 2. ISBN: Weighting: Multiplication: Total: Division:

3 10 30 30+ 176/11 ¼ 16 “Remainder” 0

4 9 36 36+

0 8 0 0+

4 7 28 28+

1 6 6 6+

1 5 5 5+

8 4 32 32+

9 3 27 27+

6 2 12 12 ¼ 176

The ISSN is used for publications of serial publications. This includes journals, yearbooks, annual reports, series, loose-leaf publications, DVDs, and publications that do not have a pre-planned conclusion and are published in successive parts. An ISSN number consists of 8 digits. Separated by a hyphen, these are divided into two groups of 4 digits each. As with the ISBN, this distinction between groups suggests a language area or a publisher’s code. However, this cannot be read from an ISSN. In terms of content, the first seven digits represent the ISSN, and the eighth digit is the check digit as the result of the modulo 11 calculation.3 3

The concrete calculation of this check digit is described in detail in DIN ISO 3297.

96

8 Product and Period Description

The ISSN is assigned via national centers. In Germany, it is attached to the German National Library in Frankfurt/Main; the international ISSN center is based in Paris.

8.2

Periods

The periods describe the time and thus the rhythm of continuous reporting by the institutions. The reporting cycles range from daily data delivery to once-a-year reporting. The media panels have the shortest periodicity until the end of 2020. GfK’s TV audience panel delivers the previous day’s data at 10.00 a.m. every working day. One day more—two working days—are required for the daily reporting of the scanner panel Music and Books. The consumer panel reports some selected product groups every week. Categories with a high penetration (purchased by many households) and numerous purchase acts are eligible here. The data are delivered on Tuesday of the following week. The retail panel also reports weekly in many countries, but in some cases only after the end of a month—i.e., retrospectively weekly. However, most retail and consumer panel categories are compiled using monthly data or even longer period types. Typically, a full year is the most extended aggregation level. It is worthwhile to mention that an entire year does not have to be the same as a calendar year. For example, in Germany, Austria, and Switzerland, the brewing industry’s production year differs from the calendar year and runs from October 01 to September 30.

8.2.1

Base Period Week

At first glance, the description of a week seems trivial. There is a uniform calendar worldwide, and calendar weeks are defined uniformly in all countries. The calendar week three is designated identically in every country. However, one question remains and is handled differently from country to country. When does the respective calendar week begin? Does it start on Sunday or only on Monday? This is irrelevant for purely local reporting. However, if reports are prepared internationally, the accrual should be consistent across national borders. After all, there are many countries where stores are open 7 days a week, while in others purchases can only be made 6 days a week.

8.2 Periods

8.2.2

97

Aggregated Periods

Monthly Periods A similar problem as with calendar weeks arises with monthly periods. A reporting period is assumed to be identical to the calendar month and requires no further explanation. However, many different monthly periods have become established. The retail companies supply their scanner data for the food and nonfood panels weekly and even daily. Of course, complete data delivery also includes the day of the sale so that the retail data can be precisely adjusted to the calendar month. The same applies to consumer panels—here, the panel member notes the exact day of purchase. Thus, the source data would be entirely possible to offer calendar-month (day-by-day) reporting. However, this day-by-day approach has not yet established itself among market researchers in the industry. So even today, a monthly view, with reporting of individual weeks, is the standard for reports. According to the Retail Food Panel, A.C. Nielsen reports internationally in the so-called 5/4/4 methodology. The first month of each quarter always consists of 5 weeks (calendar weeks 01–05 represent month 1 of the first quarter, and calendar weeks 14–18 represent the first month of the second quarter). The following 2 months each consist of 4 weeks. It adds up to 13 weeks per quarter and 52 weeks per calendar year. Information Resources (IRI) takes a different approach. International reporting is based on the 4/4/5 method. The first month of each quarter has 4 weeks, and the last month is 5 weeks. In total, the quarters of both institutes are identical, but the individual months are not directly comparable. Both methods have weaknesses inherent in the system. Depending on the definition of the calendar weeks, the reporting periods are more or less identical to the calendar month. In the example of Fig. 8.4, calendar week 01 of “Year 1” begins on January 03, and January would end on February 06 according to the 5/4/4 methodology. After all, five sales days of February are added to January. February ends on March 06, and the quarter ends on April 03. In this particular case, the 4/4/5 methodology would be more closely aligned with the calendar. January ends on the 30th, and only the 31st would be assigned to February. The end of February is also more apparent. February ends with the 27th, and only the two methodologies meet again at the end of March. The 4/4/4 methodology gives an entirely different definition of a monthly period. Independent of any calendar, 4 weeks always describe a reporting month (calendar week 01–04, 05–08). In total, with 52 weeks per year, this results in 13 reporting months. This is rarely encountered but should nevertheless not go unmentioned. Regardless of all the methodologies described above, they are based on a 52-week rhythm per year. There is particular explosiveness in years with 53 weeks. What should be done if the entire year consists of 53 weeks? This question is continuously debated in institutes and is often solved pragmatically. The “additional week 53” week is “accommodated” where best positioned according to the calendar. Here, best always means achieving a high overlap of a reporting month with the calendar month. Therefore, there are many approaches to this problem,

98

8 Product and Period Description

Fig. 8.4 Calendar

which creates a further obstacle to the comparison of individual periods between institutes. This problem is not necessarily the case with the consumer panels; the reporting is based on the calendar, and reports can be made from the month’s first to the last day. This sounds sensible, as it avoids data fluctuations simply because a calendar week

8.2 Periods

99

belongs to a particular reporting period. However, caution should still be exercised when making direct monthly comparisons across years. The number of sales days can vary and thus influence the purchased quantity. At the same time, monthly reports are also compiled for the product groups of the Consumer Panel Food based on calendar weeks. Thus, the freedom of choice between the 5/4/4 and the 4/4/5 methodology is given here. Two-Month Periods In a few product groups of the retail panel, this periodicity is still found today as a typical data delivery frequency. On the one hand, it affects sectors with little or no GTIN penetration, and on the other hand, it affects countries in which scanner checkouts have not yet been installed. This is the case in numerous African and Asian countries or even South America. Data collection is then carried out according to the inventory methodology in both cases. The 2-month period usually aggregates individual months and can comprise 8 or 9 weeks. Quarterly Periods Consumer panel’s report quarterly periods are exactly according to the calendar. Quarter 1: January 1–March 31 Quarter 2: April 1–June 30 Quarter 3: July 1–September 30 Quarter 4: October 1–December 31 The aggregation of 13 weeks leads approximately to the quarterly periods for the scanner-based retail panel categories. These are directly comparable internationally, as the institutes have established a 13-week rhythm for the quarters. Quarter 1: Week 1–13 Quarter 2: Week 14–26 Quarter 3: Week 27–39 Quarter 4: Week 40–52 If data is only supplied bimonthly for a product group, quarterly periods cannot be generated; the same applies to the 4/4/4 method described above. Tertial Periods They combine two reporting periods from the bimonthly retail panel with the inventory methodology results in one tertial period. Four individual months are combined into one tertial for all other product groups from the retail and consumer panel.

100

8 Product and Period Description

Tertial periods are not comparable between the institutes. While the first tertial at A.C. Nielsen consists of 5/4/4/5 (18 weeks), at Information Resources, only 17 weeks (4/4/5/4) are included. The household panels (consumer panels) follow the calendar and report the first tertial from January 01 to April 30. Half-Year Periods A half-year period accumulates six individual monthly or three two-monthly periods. If the company’s financial year corresponds to the calendar year, January– June and July–December result. If a differently defined fiscal year is needed, the two periods, October–March and April–September, could result4 in half-year periods. Annual Periods This type of period is directly influenced by the half-yearly values described above. The periods January–December or, depending on the financial year, e.g., October– September, are included in the examination and analysis. Year-to-Date (YTD) The period type accrued year describes the aggregation of periods from the start period determined for the annual view. In the above example with orientation to the calendar year, this would be January–May, if May was the last—the current— period. In the differently defined fiscal year view of the brewing year, this would be October–May. Therefore, the periods from the so-called start period (January for the calendar year or October for the differently defined fiscal year) to the current period are considered. At the beginning of the year, the accrued year corresponds to the first reporting period, after three (2-month) deliveries to the half-year period and at the end of the year to the cumulated annual period. If comparisons are made with previous years on this basis, periods of the same length are always considered. Moving Annual Total (MAT) Total year values are formed backward from the current period with the rolling year. Twelve months are aggregated to form a new full year. In the above example (May would be the last—the current—period), this is the period June–May (calendar year and fiscal year). Here, too, comparisons with the corresponding period of the previous year are permissible since the same number of base periods are aggregated to form a new period to be analyzed. Here, too, comparisons with the corresponding period of the previous year are permissible since the same number of base periods are usually aggregated to form a new period to be analyzed. Caution is only required when comparing 52-week and 53-week years.

4

As an example, consider the brewing year. It begins on October 01 and ends on September 30.

9

Shops and Household Characteristics

The dimension shops, outlets, or channels primarily describe the location where goods are sold (retail panel) or purchased (consumer panel). For the retail panel, these are also the places of data collection and form the basis for the segmentation possibilities of the different product groups. In the Retail Panel Non-food, other shops (shopping locations) are shown with a different level of detail than in the Retail Panel Food. Some shopping centers cannot be reported in the retail panel at all, either because no data is supplied or because data collection by the institutes’ field staff is prohibited. For many countries, some discounters have to be mentioned in particular—these neither supply data to the institutes nor allow data collection at the point of sale (PoS). In this case, data from the consumer panel is helpful as the households participating in the consumer panel report their purchases from all visited outlets. On the other hand, some shops are missing within the consumer panel, e.g., in the Cash & Carry segment, as these are not geared to private but to commercial sales.

9.1

General Segmentations in the Retail and Consumer Panel

For both panel types, a first segment split is performed according to: • Distribution channel Was the product sold via the classic stationery shop, via mail order, or was it sold online? • Shop type size of the shop in terms of square meters and turnover, the range of products, etc. are taken into account. • Organization To which organization does a retailer (shop) belong? • Region # Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2022 M. Günther et al., Market Research with Panels, Springer Texts in Business and Economics, https://doi.org/10.1007/978-3-658-37650-5_9

101

102

9 Shops and Household Characteristics

General shop split

Distribution channel

Shop type

Organization

Region

Brick&Mortar

Traditional Grocery

FMCG Accounts

Country

Click&Mortar

Hypermarket

(SMCG Accounts)

North – South – East - West

Pure Player

Discounter

Region 1

Specialist shops

Region n

Fig. 9.1 General shop split

In which (geographical or political) region of the respective country was the product sold? (Fig. 9.1)

9.1.1

Distribution Channel

The distribution channel differentiates between pure-player and brick-and-mortar. If both channels are served, it is called click-and-mortar. Brick-and-mortar—offline only stores This is the classic stationery retailer. They do not sell products via the Internet. They often have a homepage but no online shop. Pure-player—pure online business The products are only sold online—the stationary segment is not served. Specialists, but also full-range suppliers, offer the products here. Click-and-mortar—online and offline shops This segment has the above-described distribution channels, stationery outlets, and online shops (Fig. 9.2).

Almost all articles being sold over the counter are now also available online. This is not only relevant for the SMCG sector; in fact, this distribution channel is also growing steadily in the FMCG sector. Delivery Hero, Lieferando, and Just-Eat are

9.1 General Segmentations in the Retail and Consumer Panel

103

Distribution channels

Brick-and-mortar

Offline Stationary supplier

Click-and-mortar

Pure-player

Online E-Commerce

Fig. 9.2 Distribution channels

just a few companies offering food products of all kinds and, in addition, complete meals.

9.1.2

Shop Types

The shop types differ significantly between the observed product groups in the food and non-food sectors. Electronic stores are of minor importance for food product groups.1 The relevant outlets are clustered into traditional grocery, hypermarkets, discounters, and specialist shops when differentiating the shops by shop type. Traditional food suppliers are not taken into account for electrical goods groups. Nevertheless, there are commonalities reported in all panels. Traditional grocery contains all stores that do not belong to hypermarkets or discounters but predominantly offer a range of goods consisting mainly of groceries. Excluded are specialist shops (e.g., confectionery retailers, bakeries), which only offer a peripheral food range. A further subdivision of traditional grocery is usually done by sales size class, as in the case of hypermarkets. Regional subdivisions are not offered in every country. Traditional grocery total – Up to 199 sqm – 200–399 sqm – 400–799 sqm

1

One exception is coffee, sold through consumer electronics stores in various countries.

104

9 Shops and Household Characteristics

Sales area

Fig. 9.3 Segmentation of hypermarkets

Hypermarkets Total

Region 1

1,500 – 2,499 sqm

Region 2 . . Region N

5,000 sqm +

1 2 3 4 5 6 7 8 9 10

Hypermarkets Total

800 – 1,499 sqm 2,500 – 4,999 sqm

Table 9.1 Top 10 discounters in Europe. Source: https://www. lebensmittelzeitung.net/ galerien/Die-groesstenDiscountbetreiber-inEuropa-795

Regions

Lidl Aldi Netto Brand Discount Penny Biedronka Rema 1000 Slide NET Kiwi Norma

Hypermarkets are self-service retail stores with a sales area of at least 800 sqm. A. C. Nielsen defines hypermarkets as having a sales area of 1000 sqm or more. FMCG products are offered in categories and consumer goods for short- and medium-term needs. The large hypermarkets with a 5000 sqm and more sales area are also called selfservice department stores (department stores). These have an assortment similar to that of a department store, analogous to the definition of a hypermarket, but with the addition of consumer durables for short- and long-term needs (e.g., televisions). All hypermarkets usually have a central checkout and ample customer parking. The location is often in suburban areas. The institutes further subdivide this segment into different size classes and often also regions (Fig. 9.3). Discounters are self-service stores that carry a narrowly limited assortment with a predominant share of food. The stores’ equipment is kept relatively simple, focusing on low prices. Important representatives of this group in Europe are the companies Aldi, Lidl, Netto, and PENNY. All retail panel institutes have significant problems fully representing this type of business. It is still the case that the important representatives of this group do not supply any data to the market research institutes and do not allow any survey by the respective field service (Table 9.1). The retail panel figures are therefore incomplete for the discount sector. On a geographical basis, the institutes only break down the discounters quite roughly into the discounters of the possible regions.

9.2 Special Shops of the FMCG Product Groups

105

Specialist stores generally offer a very narrow range of products. Consumer electronics stores are reported in the Retail Panel Nonfood; in the FMCG sector, bakeries and butchers, for example, fall into this category.

9.1.3

Organization

Organization—Retail companies often belong to a specific organization, which in Europe is predominantly reported in the FMCG sector. This view can be a group view and also a purchasing organization view. However, the institutes cannot give a consistent definition over the years because the structures of the groups and the purchasing members vary.

9.1.4

Regions

A country is not differentiated by FMCG or SMCG areas but by specific regions. Depending on the country, more minor, regional, or political units are possible and the location (e.g., North, South). Therefore, the area descriptions also apply within one country across panels.

9.2

Special Shops of the FMCG Product Groups

The segments described in Sect. 9.1 are included in the standard delivery of the institutes for almost all product groups. However, in many categories, this type of subdivision is not sufficiently meaningful and must be more detailed. If segments such as drugstores and perfumeries are included in the personal care product groups, a comprehensive observation of the beverages categories must also include special beverages stores.

9.2.1

Drugstore/Perfumery

A drugstore is a retail outlet that mainly sells drugstore products. As a rule, a strongselling (fast-moving) range of branded goods is also sold in self-service. This additional assortment is subject to the discount principle (small assortment width and depth, relatively low-priced). The complex area of (drugstore)/perfumery retailing comprises several individual segments. In addition to the specialist stores and drugstores, the department stores’ drugstore and/or perfumery departments are included.

106

9.2.2

9 Shops and Household Characteristics

Beverage Specialty Stores

Beverages can be non-alcoholic (water—soft drinks) and alcoholic (beer, wine, spirits). The type of packaging ranges from single bottles to cartons and crates. Those shops, which realize the main turnover with the sale in beverages, are summarized here.

9.2.3

Department Stores

Department stores are large-scale retail businesses in central city locations. They offer a broad and deep range of products that focus on specific sectors like clothing, textiles, household products, and food.

9.2.4

Convenience Channels

This channel includes petrol stations and “impuls” shops. Petrol stations are divided into motorway petrol stations (petrol stations with exit and entrance on federal motorways), roadside petrol stations, and motorway service stations. They must have a shop that is accessible and recognizable from the outside. The “impulse” channel subsumes three business types: • Bakeries that are recognizable as such by their name and also by the range of goods on offer. • Kiosks are small shops (less than 100 sqm) with a wide range of goods. Often beer, ice cream, drinks, sweets, and magazines are offered. • Grocery