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Using Web Analytics in the Library Kate Marek

Library Technology R

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�Using Web Analytics in the Library Kate Marek

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Volume 47, Number 5 �Using Web Analytics in the Library ISBN: 978-0-8389-5833-9

American Library Association 50 East Huron St. Chicago, IL 60611-2795 USA alatechsource.org 800-545-2433, ext. 4299 312-944-6780 312-280-5275 (fax)

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About the Author Kate Marek, Ph.D., is a library educator, trainer, and consultant. She is a Professor at the Graduate School of Library and Information Science, Dominican University, where she teaches in the areas of technology and policy. She is a leader in online teaching design and delivery. Kate has worked in the LIS profession for over 25 years as a professional librarian, a teacher, a trainer, and a consultant. Kate has a B.A. from the University of Texas at Austin, an MLIS from Dominican University, and a Ph.D. in Library and Information Management from Emporia State University. She is fully certified in online synchronous course design and delivery through InSync Training, Inc.

Abstract As libraries deliver an increasing proportion of their services through the web, the need to accurately and comprehensively track the use of library websites, online resources, and services is more important than ever. This issue of Library Technology Reports offers an explanation of web analytics and how these tools can be applied in a library setting. The author provides a comprehensive overview of web analytics that defines the different types of data that can be tracked and their application. The report presents a detailed guide to implementing Google Analytics, one of the most popular web analytics tools, and offers case studies detailing how web analytics have been applied in a variety of library settings.

Tim Clifford, Production Editor Karen Sheets de Gracia, Manager of Design and Composition

Library Technology Reports (ISSN 0024-2586) is published eight times a year (January, March, April, June, July, September, October, and December) by American Library Association, 50 E. Huron St., Chicago, IL 60611. It is managed by ALA TechSource, a unit of the publishing department of ALA. Periodical postage paid at Chicago, Illinois, and at additional mailing offices. POSTMASTER: Send address changes to Library Technology Reports, 50 E. Huron St., Chicago, IL 60611. Trademarked names appear in the text of this journal. Rather than identify or insert a trademark symbol at the appearance of each name, the authors and the American Library Association state that the names are used for editorial purposes exclusively, to the ultimate benefit of the owners of the trademarks. There is absolutely no intention of infringement on the rights of the trademark owners.

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Contents Chapter 1—Web Analytics Overview

What Is Web Analytics? Combining Quantitative with Qualitative Data Website Goals and Implementing Web Analytics Analytics Data and Insights Web Analytics Tools A Note About User Privacy Google’s Approach to Privacy with Google Analytics Privacy and Service Resources Notes

Chapter 2—Getting to Know Web Analytics Web Tracking Basics: Data Collection Mechanisms GA Basics Possible Actions Resources Notes

5 5 6 6 7 7 8 8 9 9 9

11 11 13 15 16 16

Chapter 3—Installing and Configuring Google Analytics 17 Getting Started Configuration Best Practices Setting Up Goals and Funnels in Google Analytics Resources Notes

17 22 22 25 25

Chapter 4—Reporting and Analysis

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Chapter 5—Case Studies

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General Overview of Metrics and Reporting Options First-Level Metrics Viewing Reports Your Dashboard Google Analytics Reports Backing Up and Exporting Data Access to Reports Benchmarking Data Visualization Conclusion Resources Notes Additional Case Studies Social Media Metrics Summary Notes

26 26 27 27 28 30 30 31 31 31 31 32 40 50 54 54

Chapter 1

Web Analytics Overview

Abstract This chapter of Using Web Analytics in the Library provides an overview of Web Analytics, the types of services available, and the type of data that they can provide.

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What Is Web Analytics? Web analytics is a process through which statistics about website use are gathered and compiled electronically. An analytics program can be used as a tool to help you get to know your users—who they are, where they are coming from, and how they use your site. Ultimately, having access to information about your users helps you to make appropriate decisions about your site, whether those decisions apply to major redesigns of your site or to ongoing tweaking and minor changes reflective of shifts in customer usage or in your own current programs and services. The origins of web analytics are from commercial website design, where tracking users’ behaviors and actions directly relates to consumers’ purchasing behavior. However, web analytics can be just as valuable for a nonprofit website as for an e-commerce site. One approach is to monetize your website goals, applying a dollar figure to certain activities such as user visits or online program registrations. Essentially, �Using Web Analytics in the Library  Kate Marek

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e live in an age of accountability. For libraries, that means it is no longer enough just to inform our stakeholders that the work we do is worthwhile, is good for the community, and is a responsible use of the taxpayers’ (or owners’) money. Instead, we must justify our services, from both financial and logistical points of view. Librarians must operate with a “return on investment” mindset, learning to demonstrate value offered for both public and private dollars. At the same time, we have been shifting to a userfocused paradigm for the past decade. While our traditional way of conducting business has been one-directional, where we have made decisions for programs and services based on our own professional perspectives, we have recently become aware of the value of listening to patrons and of crafting programs and services based in large part on their input. With the growth of social networking tools being a major facilitator of societal change, users have become partners in numerous aspects of libraries’ daily operations. Nowhere is this more evident than in the library’s digital branch,1 or its Web presence. The library website has become vital as a community outreach tool and as an ongoing source of information to its users. However, rather than a one-directional, electronically posted version of a flyer or newsletter, the contemporary website is truly a place of community interaction among its users. The website can extend the services available at the brick and mortar library and

offer a diverse set of interactive tools such as book discussions, live chat, print resources for download, video instruction and support for research, and blogs that invite user participation. All of these initiatives heighten engagement among the community. Thus librarians are faced with the challenge of creating innovative tools in a changing society, while at the same documenting responsible use of our resources. In addition, we want to demonstrate within our own organizations that our website is constantly responding to trends and user needs. Finding tools to assist with these challenges is essential. Web analytics is just such a tool.

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however, librarians should simply translate the theories of profit-based customer usage into the goals and situations that are specific to your organization. No matter the kind of organization, the website should seek to understand the visitors’ experience and be responsive to their needs.

Combining Quantitative with Qualitative Data Perhaps the best known name in the field of web analytics is Avinash Kaushik. In addition to two books, Kaushik writes an excellent blog, Occam’s Razor, and has numerous videos online regarding best practices of web analytics use. As mentioned previously as typical of web analytics literature, much of Kaushik’s work focuses on e-commerce, as his own history in the field is from work with several major commercial companies. Today he is the Analytics Evangelist for Google.

Occam’s Razor

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www.kaushik.net/avinash

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Kaushik’s enthusiasm for web analytics is contagious. It’s difficult to read his books, read his blog, or watch any of his numerous web instruction videos without also becoming passionate about web analytics. But one of Kaushik’s overriding philosophies about web analytics is that the raw data tells only a portion of the story: the “what.” To get at the “why,” and even the “how,” you must get into the mindset of the user. As Kaushik says, you must ask them (the users), using methods such as electronic surveys, focus groups, and user testing. One method of asking for users’ feedback is to automatically direct users to an online survey, using a tool such as SurveyMonkey or Zoomerang. Kaushik writes that three open-ended questions are enough: 1. “What was the purpose of your visit to our website today?” 2. “Were you able to complete your task today?” 3. “If . . . not, why not?”2

SurveyMonkey www.surveymonkey.com

Zoomerang www.zoomerang.com

If the visitors can compete your survey in just a few short minutes, they are much more likely to offer you �Using Web Analytics in the Library  Kate Marek

feedback about their website visit. The answers to these questions will provide you with a wealth of information and will put your analytics data in context. For a more intensive user feedback project, consider a formal usability study. Two books by Steve Krug are wonderfully helpful in introducing both the purpose and the execution of user studies: Don’t Make Me Think: A Common Sense Approach to Web Usability and Rocket Surgery Made Easy.3 Another excellent tool is Usability Testing for Library Websites: A HandsOn Guide, by Norlin and Winters.4 Each of these tools can help you get familiar with a process whereby you observe actual users in real time trying to complete tasks on your website. The information you gather is invaluable in learning about user behavior, and the observations (often surprising) provide critical information for web designers.

Website Goals and Implementing Web Analytics Do you have goals for your website? Before you ever start to collect and review data, you should have key performance goals in mind for your digital branch. Do you have a strategy for your website and its role in your overall strategic plan and your mission? First, establish your website goals, based on your library’s mission and strategic plan. Build the website to reflect that mission and that plan. With clear goals in mind, you can use the data to help you make decisions about your website. Avinash Kaushik calls these “actionable insights.”5 Kaushik sees no reason to collect data if there is no direct match to a potential decision for improving your website. He also stresses the 10/90 rule, which states that 10 percent of an organization’s cost for web analytics is for the tool itself and its setup, and 90 percent of the expenditure is for a person (or team of persons) to analyze the data for actionable insights. It’s not just about the numbers; analysis is key, and that analysis is the most challenging part of the process. As you consider implementing web analytics, it is important that everyone has a firm understanding of the critical role of savvy personnel in regards to data analysis for your specific organization. A strong culture of data will include both the numbers and the people-generated actionable insights. Another caveat about data collected from web analytics tools is that the numbers themselves are never 100 percent accurate. Much of this has to do with how the analytics tools work, which will be explained at various points in this report. Whatever the source of the odd inaccuracies, they are ultimately not particularly significant. What are important are trends over time (six months to two years), patterns of usage (such as seasonal spikes or dips), and insights you can draw through advanced tools such as user segmentation (for

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example, finding out what your Polish language users are seeking). Slight discrepancies in the numbers are normal and should not be considered worrisome.

Analytics Data and Insights Are your visitors doing what you expect them to do, or what you want them to do? Are they following the path you thought they would follow when you designed your menu system? Are visitors to your Digital Library page finding the link to historical photos of your city or the university’s archival images? These are some of the questions you should be able to answer by using an analytics program. You can follow a user’s clickstream, find out which pages get the most visits, gather numbers about how long users stay on each page and where they come from. You could in theory track every visitor to your site, and each click of each of those visitors. Kaushik refers to “the paradox of data”: there is so much data, but so many barriers to good insights.6 With so many metrics available, what should you be looking for, and what conclusions can you draw from those metrics? These questions will be investigated in more detail throughout this report (see especially chapter 2 for a longer glossary), but for now I will offer a just a few examples. As you look at these examples, consider how you could use these statistics to make changes in your website content and design, and how you could also use the statistics to justify website personnel and support.

When getting started, concentrate on a few effective metrics for your own site and follow those. As you build experience and confidence with those metrics, you will begin to add more dimension to your analysis. Start small, as the amount of data can be absolutely overwhelming unless approached with planning. Web analytics need not be an in-house example of information overload.

Web Analytics Tools There has been an exponential growth in the web �Using Web Analytics in the Library  Kate Marek

Library Technology Reports  alatechsource.org  July 2011

• Page views: This is just what it sounds like: number of times an individual page is viewed. You can watch the numbers in this category for trends over time, seasonal spikes and dips, and comparisons with other pages in your website. You can tell from the various page view rates within your website which pages are most popular, and which pages are rarely viewed. Another way to use these figures is to compare your total page views per month with your walk-in numbers. Thus, what percentage of your total use is from your digital branch? This comparison will of course be an estimate, since many customers use the website and also visit in person, but it is nevertheless an extremely useful comparison. • Time on page: You can study this metric in conjunction with the purpose of the page. For example, you may not want a visitor to spend much time on an opening page with basic information and descriptive links. A short time on page average could indicate that people are finding what they want quickly. However, another page may offer richer content, such as bibliographic citation helps or blog posts. In such cases, you would prefer a longer time on page.

• Unique visits and returning visits: The unique visits metric indicates the number of visits to your site by different users, while returning visits indicates users who return from at least one previous visit. Since return visits are measured by cookies, users who turn off cookies will be counted as unique rather than return visitors. This is an example of how the metrics numbers may not be 100 percent accurate. Obviously you want to have an increasing number of visitors over time, but in general website visits will trend upward, and this fact alone will not give you a lot of actionable insights. Partner this metric with other information, such as increasing numbers of return visits to the YA blog when the YA librarian makes more frequent entries, demonstrating increased engagement with the target community. • Bounce rate: This metric counts visitors who left your site after visiting only one page. A high bounce rate is often considered undesirable, and you will see this assumption in the literature. However, bounce rate is another example of a metric that should be compared to the actual page. If visitors are finding exactly what they want when they land on a page in your site, either at the opening page or some other file within the site, they may indeed leave after viewing only that individual page. A high bounce rate for e-commerce would be considered bad since the customer did not continue to browse and eventually make a purchase. In information websites, analysis of bounce rate is more complex. • Visitor information: Many metrics can give you insights about your users, including geographic location, browser used, operating system used, and what path they took through your site. The more you know about your visitors, the more you can adapt your site to meet their needs. For example, if you find that many people are using mobile devices to view your site, you will know that designing for mobile interfaces should be a priority for your web development team.

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analytics field in the last decade. Google launched its free tool, Google Analytics, in 2006, and the field has continued to boom since then. Many commercial tools exist, including Coremetrics, Adobe’s Omniture, and WebTrends.7 Open source tools are also available, including Piwik, which is billed as an open source alternative to Google Analytics. Google Analytics has become an extremely popular tool among libraries, due not only to its tremendous power, but also to its free availability, its ease of use, its flexibility, and its clear reporting mechanisms. This report will use Google Analytics as a model for library implementation. The various chapters of the report will look at details of the Google Analytics program, how to install and set up the program for your own library, and how to interpret the reports. Individual case studies from libraries will show real-world examples of Google Analytics in use, where librarians have used Google Analytics to help them better understand their users’ website behaviors and understand how they can continually improve their customers’ website experience.

Piwik http://piwik.org

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A Note About User Privacy

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As you begin to learn more about web analytics and its capabilities, you will see that a tremendous amount of data can be collected about your end users. This behind-the-scenes data collection may make many librarians uncomfortable. However, the reality is that virtually all websites collect some user data through the operation of server logs. While completely eliminating data capture is an unrealistic goal, intentionally adding a tracking tool such as Google Analytics to collect personal information about the library’s website visitors may seem to be the antithesis of our service philosophy. How can we reconcile the priority of personal privacy with the organizational need to examine website usage and statistics? Essentially, what is important to libraries is that our users operate with complete anonymity at all times and that they also maintain ultimate control of their data. The ability to opt out of any data collection is key and should be clearly offered as an option to our website visitors. In addition, we must be thoughtful about what data to collect and how it will be used. Libraries must strike a balance between user privacy and organizational effectiveness, with the scales always being tipped in favor of user choice. The excellent TechSoup article “Site Statistics and User Privacy for Nonprofit Websites,” by Elliot

�Using Web Analytics in the Library  Kate Marek

Harmon, discusses at length the issue of nonprofit organizations’ struggle with website end-users’ privacy. Ultimately Harmon concludes that clear communication with the site’s users from the outset is the best approach. When TechSoup’s own website added Google Analytics, the leaders posted an entry to their blog explaining the role of analytics in their overall communication strategy, the potential implications of adding the analytics tool, and clear reasons for their decision. Harmon quotes library consultant Jessamyn West as saying, “Have good reasons [for using web analytics]. If you don’t have good reasons, that sends the message that you really haven’t thought about the implications of these tools.”8 If you don’t already have a privacy policy for your website, beginning to use an analytics program is an excellent time to move this to the top of your priority list. A privacy statement should include information for your visitors about what personal data they may be asked to provide, either as registered users or as guests, what information may be collected without their explicit knowledge (such as their geographic location), how you will store and use the information, and what third parties (such as the analytics program host) may have access to it.9 Finally, you will also want to offer your users clear instructions of how they might opt out of as much as the data collection as possible.

Google’s Approach to Privacy with Google Analytics The approach to privacy at the other end, the host company for the analytics tool itself, is a key part of the decision process in regards to using web analytics. (Some librarians prefer open source analytics programs run from their own local servers for just this reason.) However, just as libraries choose automated circulation systems and online public access catalogs with patron privacy built in, so too can we select analytics tools that build in sensitivity to user anonymity. Google Analytics (GA) is a good model in this respect, as the company is clear, direct, and intentional in its privacy policies. GA has its own internal policies that strongly support end-user privacy. It actually includes a privacy statement in its licensing agreement, and encourages full disclosure and individual privacy statements for all users of their tools. Brian Clifton, a GA expert and author of Advanced Web Metrics with Google Analytics, says Google made an intentional decision not to collect user information from the beginning of its analytics program development. GA works through the use of cookies, a prevalent tool in web analytics. When you install GA, you add a string of code to each webpage for which you

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will be gathering statistics. That code in turn communicates with the user’s page by sending it a cookie, or tracking code, when it visits your site. (See chapter 2 for more technical information about how GA works.) All data collected and then reported with GA, however, is in aggregate and anonymous form.10 In addition, as mentioned above, the company requires adherence to its own privacy policy for all clients of its analytics tool. The privacy statement is issued as part of its Terms of Service agreement and imposes strict limits on GA clients in regards to third-party access to collected data. GA clients must also agree to post and abide by a clear privacy policy of their own, informing their website visitors of the presence of the analytics tracing program and use of cookies.11 See the GA Terms of Service for more information. In addition, Clifton offers a sample “Best Practice Privacy Statement When Using Google Analytics.”12 Google itself offers a user opt-out tool for some browsers (see http://tools .google.com/dlpage/gaoptout). The link to this optout tool should be posted on your website or within your privacy statement.

Google Analytics Terms of Service www.google.com/analytics/tos.html

Privacy and Service

Resources Clifton, Brian. Advanced Web Metrics with Google Analytics, 2nd ed. Indianapolis, IN: Wiley Publishing, 2010. Electronic Frontier Foundation. www.eff.org.

Google Analytics Terms of Service. www.google.com/ analytics/tos.html. Harmon, Elliot. “Site Statistics and User Privacy for Nonprofit Websites: Learn the Facts Before You Install Analytics Tools.” TechSoup, Oct. 26, 2009. Available online at www.techsoup.org/ learningcenter/webbuilding/page12238.cfm. Accessed March 31, 2011.

Kaushik, Avinash. Occam’s Razor. www.kaushik.net/ avinash.

———. Web Analytics: An Hour a Day. Indianapolis, IN: Wiley Publishing, 2007. ———. Web Analytics 2.0. Indianapolis, IN: Wiley Publishing, 2010. King, David Lee. “Building the Digital Branch: Guidelines to Transform your Website.” Library Technology Reports 45, no. 6 (Aug./Sept. 2009).

Morgan, Joe. Google Analytics for Libraries. Available online at http://josephsandersmorgan.com/home/ web-analytics. Accessed March 31, 2011.

Quinn, Laura S. “A Few Good Web Analytics Tools.” TechSoup, April 29, 2007. Available online at www .techsoup.org/learningcenter/internet/page6760.cfm. Accessed March 11, 2011. Web Analytics Association. www.webanalytics association.org.

Notes 1. David Lee King, “Building the Digital Branch: Guidelines to Transform Your Website,” Library Technology Reports 45, no. 6 (Aug./Sept. 2009). 2. Avinash Kaushik, “The Three Greatest Survey Questions Ever.” Occam’s Razor, April 10, 2007, www .kaushik.net/avinash/2007/04/the-three-greatest -survey-questions-ever.html (accessed March 25, 2011). 3. Steve Krug, Don’t Make Me Think: A Common Sense Approach to Web Usability, 2nd ed. (Berkeley, CA: New Riders, 2006); Steve Krug, Rocket Surgery Made Easy (Berkeley, CA: New Riders. 2010). 4. Elaina Norlin and C. M. Winters, Usability Testing for Library Websites: A Hands-On Guide (Chicago: ALA, 2002). 5. Avinash Kaushik, Web Analytics: An Hour a Day (Indianapolis, IN: Wiley Publishing, 2007), 15. 6. Avinash Kaushik, Web Analytics 2.0 (Indianapolis, IN: Wiley Publishing, 2010), 4. 7. Kaushik, Web Analytics: An Hour a Day, 5.

�Using Web Analytics in the Library  Kate Marek

Library Technology Reports  alatechsource.org  July 2011

Ultimately, citizens make constant decisions about their own comfort level with sharing personal data online. Individuals may choose to participate—or not—in social networking sites, online banking, online credit card use for purchases, online surveys, and casual Internet browsing for personal interest. What is important for libraries is that our users are fully informed of the parameters of their relationship with our digital branch, including what information we collect about their visits, how that information is collected, and how it is used. Our users should find those details clearly posted on our websites, and information about how they can take personal control of the tracking mechanism should be easy to find. Creating a thoughtful rationale that considers both patron privacy and organizational effectiveness will help end users and librarians alike feel comfortable with website analytics.

Google Analytics Opt-Out Browser Add-On (Beta as of March 31, 2011). http://tools.google.com/dlpage/ gaoptout.

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8. Elliot Harmon, “Site Statistics and User Privacy for Nonprofit Websites,” TechSoup, Oct. 29, 2009, www.techsoup.org/learningcenter/webbuilding/ page12238.cfm (accessed March 12, 2011). 9. Ibid.

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10. Brian Clifton, Advanced Web Metrics with Google Analytics, 2nd ed. (Indianapolis, IN: Wiley Publishing, 2010), 61. 11. Clifton, Advanced Web Metrics, 60–62. 12. Ibid., 63.

Chapter 2

Getting to Know Web Analytics

Abstract This chapter of Using Web Analytics in the Library provides guidelines for selecting and implementing web analytics tools in a library context. The author examines different variables that determine which tool might be appropriate and offers suggestions determining which tools meet different needs.

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Web Tracking Basics: Data Collection Mechanisms It’s ALL clickstream data. . . . When we typically hear about collecting a Web user’s clickstream data, this term can actually refer to different mechanisms used to track and store users’ activities while on the Web. There are a variety of tools used to capture user information, with the two main approaches being log file data capture and page tagging. Log File Data Web log files were the original method of capturing and storing information about visitors to individual

Page Tagging The other common method of web analytics used today is often referred to as page tagging. This method of collecting data involves inserting tags, or lines of JavaScript code provided by an analytics program, into the source code of a webpage (mylibrary.org). �Using Web Analytics in the Library  Kate Marek

Library Technology Reports  alatechsource.org  July 2011

ne of the first things you will need to do as you consider implementing web analytics is to select the appropriate tool for your library. In this report, I will focus specifically on Google Analytics (GA) to illustrate various aspects of web analytics. But before we get into the basics of any one program, it is useful to have some foundational knowledge of how the tools work, what programs are out there and how to choose one, and what the most used standard metrics are.

websites. Typically a request for your website comes to your server, and the server creates an electronic file entry in the log for that request. Web logs capture information such as the page name, IP address and browser of the visitor, and date and time stamps.1 Web servers collect data and create logs as part of their regular activity, independent of the user’s browser, which makes the data readily available. Relying on this method of data collection for user analysis has several disadvantages. For one thing, the data may be hard to access. If your library contracts with an external web hosting service for your website, you must work with the service to access server log file information. If your server is controlled locally, log files will probably be maintained by the information technology (IT) department rather than the website design department. Analysis and use of this information would require close collaboration with IT. In addition, log files are primarily intended for the capture of technical information. While this information is useful in the overall analysis of information technology resources, web logs are not the most effective way to capture and analyze website visitor behaviors. Ultimately, server log information can help you evaluate traffic numbers in regards to your server load and capacity, but it tells you very little about your users or the effectiveness of your site in relation to your goals.

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The tag code collects data from the visitor’s browser and sends it to the analytics program’s remote host computer, where reports are available to the mylibrary.org owners. Page tagging has become quite popular, as both implementation and management of the analytics tool are much easier than through using the log file method. Many of the analytics tools available today are a service of a third-party vendor, which frees the mylibrary.org owners from having to develop an internal technical analytics infrastructure. Advantages of Page Tagging as An Analytics Tool

Library Technology Reports  alatechsource.org  July 2011

In his book Web Analytics: An Hour a Day, Avinash Kaushik points out a number of advantages to using page tagging:2

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• Tagging is extremely easy to implement. Once you sign up with an analytics program vendor, you add a few lines of code to the section of each HTML page. The analytics program immediately begins to capture vast amounts of data from its visitors. • A tagging solution is possible for those without access to their servers. Analytics data gathered through JavaScript tags is made available through the third-party analytics program host, and thus you do not need to rely on server access. • As a webmaster or a design team, you have significant local control over data review and analysis. While typically a tremendous amount of data is available, you can easily select the data that is most relevant to your own specific goals and outcomes. • Similarly, you can create administrative reports that clearly highlight data relevant to your library’s priorities. • Innovation in web analytics is focused on page tagging. Using this data collection mechanism keeps you closer to ongoing upgrades and revisions in the field. There are, however, some concerns with page tagging as an analytics tool. The tagging process involves the use of cookies, and some web users turn off both cookies and JavaScript. These visitors will be invisible to you. In addition, as mentioned in chapter 1, the use of cookies raises some privacy issues for our customers who do not turn off cookies in their Web use. These issues should be clearly discussed both within the library and with our website visitors. Cookie Basics All cookies are small chunks of data that are sent from a website you visit to your hard drive so that your �Using Web Analytics in the Library  Kate Marek

computer can provide information to your browser as you use the Internet. There are various distinctions to keep in mind: session cookies versus persistent cookies, and first-party cookies versus third-party cookies. Session Cookies and Persistent Cookies A session cookie is stored only as long as your visit lasts within a particular website, stores your browsing history for as long as you stay at that website, and is erased when you close your browser. A persistent cookie, on the other hand, stays stored on your hard drive as long as the file is programmed to stay there, or until you manually remove it.3 First-Party and Third-Party Cookies Most web analytics programs, including Google Analytics (GA), use only first-party cookies. First-party cookies are set by the host website itself and are used by that host website to store a user’s data so that person can return to the site without starting all over as a new customer. In addition, the first-party cookies used by GA allow GA to analyze things such as which keywords and referring sites bring visitors to your site.4 Third-party cookies are set from a domain outside the one shown on the user’s address bar, usually without the knowledge of the user, and are typically used by advertisers to collect and store an individual’s browsing habits. These cookies are much more likely to be turned off by web users, as they are considered more invasive and thus more objectionable. Other Web Data Capture Methods Additional methods of data capture include web beacons and packet sniffing. Web beacons are 1 × 1 pixel GIF images placed in webpages, usually hosted by a third-party server. Packet sniffing captures all of the user’s data, including passwords. Both methods are typically used with commercial websites so that advertisers can track the effectiveness of their ads through number of views, user behavior, and so forth. To a great extent these methods have fallen out practice, due mostly to privacy concerns and the fact that a vast number of users (some estimates as high as 40 percent) turn off third-party cookies. Many fewer people turn off first-party cookies, as they are much less invasive, they collect only anonymous data, and it is very difficult to browse the Web if you turn them off. Choosing a Program Shopping for a web analytics program is quite similar to shopping in general: you thoughtfully consider what is important to you, and then you examine the options within your budget. Analytics considerations

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include the cost, the level of sophistication for data collection and analysis, the reporting options, and the program’s overall accessibility. You may also consider advanced options such as segmentation of users, depth of analysis for the individual metrics, and host-level support. GA is an excellent choice for most libraries, as it offers tremendous data collection, ease of use and customization, and support. And there is no cost for its implementation. GA, as a free service, has been so successful that the commercial services have had to continually refine and specialize their products. Examples of commercial products include Omniture, WebTrends, ClickTracks, and CoreMetrics. These programs can cost thousands of dollars per year, but usually include a range of services along with the analytics program and may be good alternatives for the largest library systems. For a thorough discussion of what to look for in a commercial analytics vendor, see Avinash Kaushik’s blog post “Web Analytics Tool Selection: Three Questions to Ask Yourself” from January 30, 2007, and chapter 2 in his book Web Analytics 2.0.

Web Analytics Tool Selection: Three Questions to Ask Yourself www.kaushik.net/avinash/2007/01/web-analytics-toolselection-three-questions-to-ask-yourself.html

At the other end of the spectrum are open source alternatives. Piwik and OWA, or Open Web Analytics, are GPL-licensed, downloadable systems that use page tagging. AWStats is a log file–based GPL-licensed product. There are quite a few comparison sites available via a simple web search for “open source web analytics.”

http://piwik.org

Open Web Analytics www.openwebanalytics.com

AWStats http://awstats.sourceforge.net

Although there are various reasons for an individual organization’s ultimate choice of a web analytics program, for the purposes of clarity I will use a single program for illustration and description throughout the rest of this report. While not specifically endorsing GA or rejecting other programs, I will use GA as a frame for library-specific descriptions of how to use website user data to analyze your website goals and customer satisfaction.

Like the page tagging analytics programs described earlier in this chapter, GA begins with the insertion of JavaScript code tags in your webpages. The GA program is available to anyone who has a basic Google account. Once you are a registered Google account holder, you can very easily sign up for the analytics program. Details of the step-by-step process are outlined in chapter 3. But what will the program measure? How will you use the various metrics to help you understand more about your website customers, and whether your website is successful? This section will outline some of the basic analytics terms as defined by the Web Analytics Association, along with some comments regarding their relevance to library usage. Definitions from the Web Analytics Association are noted with the (WAA) designation, and are taken as posted in Occam’s Razor.5 • Page: “A page is an analyst definable unit of content” (WAA). This is an individual HTML file within your site, with page tagging in the section of the source code. An example is mylibrary.org/youthservices.html. • Page views: “The number of times a page (an analyst-definable unit of content) was viewed” (WAA). As mentioned in chapter 1, important things to watch are trends over time (six months to two years), dips and spikes according to your own program calendar, unexpected dips and spikes that may need observation and reflection, comparisons within your site among pages, and overall total views as compared to your total walk-in numbers. • Visits/sessions: “A visit is an interaction, by an individual, with a website consisting of one or more requests for an analyst-definable unit of content (i.e. “page view”)” (WAA). Again, you can watch for trends over time and unexpected changes. • Unique visitors: “The number of inferred individual people (filtered for spiders and robots), within a designated reporting timeframe, with activity consisting of one or more visits to a site. Each individual is counted only once in the unique visitor measure for the reporting period” (WAA). • New visitor: “The number of Unique Visitors with activity including a first-ever Visit to a site during a reporting period” (WAA). Don’t forget that if a user has turned off cookies, she will be counted as a new visitor whether she has been to your site before or not. Nevertheless, you can watch for trends over time to see if your site is gaining ground in your target market. You can also use this metric to watch for upward jumps after a �Using Web Analytics in the Library  Kate Marek

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Piwik

GA Basics

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significant redesign, a large marketing program, or other library events where increasing your Web presence has been a goal or turns out to be an organic but unplanned side effect. If this increase in new visitors happens as a pattern in parallel with significant library events, you can begin to plan for the potential incoming website traffic by intentionally updating your site to coordinate with those events. For example, make sure your content is fresh, add notices about other upcoming events, rev up activity within your social networking sites, and so on. Repeat visitor: “The number of Unique Visitors with activity consisting of two or more Visits to a site during a reporting period” (WAA). Again, partner this information with library events, such as summer reading for public libraries or research paper time in an academic library. Another tool that can be extremely useful is segmenting users, or looking deeper (drilling down) into an individual segment of your user population. I will include examples of segmentation in chapter 4. Return visitor: “The number of Unique Visitors with activity consisting of a Visit to a site during a reporting period and where the Unique Visitor also Visited the site prior to the reporting period” (WAA). The return visitor has been to your site before, but has not been there as often as the repeat visitor. Again, user segmentation would be beneficial to understand more about these customers and what they are looking for, and then hopefully to improve their website experience and their success rate with the library’s online resources. Entry page: “The first page of a visit” (WAA). With analytics tools, you can tell where the website visitor comes from on the Web, such as through a search engine, a specific web address (URL), or a link from an outside blog. (See also referrer, below.) You may assume that your homepage will have the highest count as the customers’ entry page, but it would be interesting to see if this is the case. For example, one of your departments may attract quite a few customers directly to its own page, or a construction update page for a renovation or new building project may be extremely popular. As with the other metrics, noticing these figures helps you to gauge your customers’ interests and information needs—and in general, helps you to get to know them better. Landing page: “A page intended to identify the beginning of the user experience resulting from a defined marketing effort” (WAA). Although some literature does use this term almost interchangeably with entry page, the WAA defines it separately. When used in e-commerce, the landing page’s ultimate purpose is to easily facilitate a purchase. The customer would come in at the

�Using Web Analytics in the Library  Kate Marek

entry page, and then “land” when he is ready to take action. If using the term within this framework, a landing page for libraries might be a page where a user can register for a program, join a particular online discussion group, or RSVP for a library instruction session. • Exit page: “The last page on a site accessed during a visit, signifying the end of a visit/session” (WAA). Kaushik says this is where you can look for “leakage” in your site, determining where the highest percentage of users leave your website.6 However, Kaushik also states that the metric actually tells you very little about the success or failure of your site or the individual page. For example, 52 percent of your visitors may leave your site from the Upcoming Events page, but this could be either good (they are finding just what they want) or bad (they are annoyed that there is nothing for them)—or some of both! It’s extremely difficult to make judgments about the exit page by using the numerical result alone. Rather, combine it with surveys or other mechanisms for user feedback. • Visit duration: “The length of time in a session. Calculation is typically the timestamp of the last activity in the session minus the timestamp of the first activity of the session” (WAA). Normally you want your website to be engaging enough to hold customers’ interest for at least several minutes. A longer visit duration is considered desirable in e-commerce sites where the goal is to have the customer find multiple products for purchase. However, library websites will have some pages where a speedy result is desirable (a successful in-and-out catalog search for a specific title), and some situations where a longer visit is desirable (engagement with a digital image collection). Don’t attempt to lump this result as one individual metric across your site and make any assessments based on the single figure. Visit duration is also categorized as time on site and time on page. One thing to keep in mind when reviewing these particular metrics is that the time being measured is the number of minutes the site or page is open, which does not necessarily equal the amount of time the visitor is actually engaged with your content. The clock will time out after some inactivity, frequently 29 minutes. • Referrer: “The referrer is the page URL that originally generated the request for the current page view or object” (WAA). Another way to say this is that the referrer is the URL from which your visitor clicks to get to your site. The referrer can be internal, coming from within your website, or external, coming from outside your site. Related terms are search referrer, which indicates whether the visitor came via a search engine, and source of visit, which is the origin of the referrer,

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including specific search engines or another specific website.7 • Conversion: “A visitor completing a target action” (WAA). This term originated with e-commerce, and in that environment, it usually means a visitor completes a purchase. For libraries, a conversion can be much more general, and the WAA definition is in keeping with the broader interpretation. Examples for libraries could be any goal or target action you designate in your analytics configuration, such as placing a hold on a catalog item, adding a comment to a blog, or clicking on a link to a special collection page. Here are some useful terms in addition to those defined by the WAA:

For more information, see a complete list of terms in the Google Analytics Glossary.

Google Analytics Glossary www.google.com/support/googleanalytics/bin/topic .py?topic=11285

Possible Actions Looking at these metrics, you can begin to see some possible actions libraries could take as a result of an analysis of their website analytics data. Here are just a few examples: • If you find low page view: • Remove the page and free that space or personnel time for something else. • Relocate the link so more people can find the content, particularly if you think it is important. • Redesign or rewrite the page itself to make the content more appealing. • Perform a user study to make sure the page works the way you think it does, such as digital images that should be easily found and used. • If you find unexpected click density: • Reorganize your menu structure • Relocate your menu, making sure its placement is consistent throughout your site. • Change the names of your links. (Frequently libraries rely on professional jargon for link names, and users don’t understand those terms. Use your users’ vocabulary, not your professional jargon.) • If you find unexpectedly high traffic in one department: • Drill down further in the reports to analyze use; expand content and services in response to user demand. • If you find source of visit data that shows a consistent increase in visitors using mobile devices: • Put more effort into designing pages for the mobile systems. Chapter 3 will provide specific information about setting up GA and will begin to show how you can identify these metrics from the GA graphs and reports. �Using Web Analytics in the Library  Kate Marek

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• Google Analytics Tracking Code (GATC): The GATC is the snippet of code that is added to each of the webpages using GA. This code, or the page tag, is what enables the data tracking and collection. • Bounce rate: As described in chapter 1, the bounce rate indicates the number of visitors who left your site after visiting only one page. A high bounce rate is generally considered undesirable, since e-commerce sites prefer visitors to browse and ultimately buy. However, just as with visit duration, bounce rate can be either good or bad depending on the purpose of an individual page and your user’s expectations. • Click density: Click density refers to the number of times a link was clicked by a visitor. You can view this and view the typical path of the customers by using the analytics tool’s site overlay feature. As Avinash Kaushik says, this feature enables you to walk in the shoes of the visitors, assessing whether they follow the path you’d like them to follow or indeed the path you think they will follow.8 For libraries this might mean that visitors rarely use your departments’ named links, but instead rely heavily on the search bar. Are people clicking on the link to the director’s blog? If not, you might want to find a more prominent placement for that link. Click density analysis is considered one of the most powerful tools for actionable insights, as the site overlay literally lets you look at your website and your actual customers’ use. • Funnel: The funnel is the set path a visitor takes before a goal conversion. In e-commerce, this would be a path a user takes to the checkout process, but a funnel can also be a path toward a different kind of goal. For example, we can envision a specific funnel, or visitor path, for a catalog search or an interlibrary loan request. • Key Performance Indicators (KPIs): KPIs are key factors, specific to your organization, that measure success.9 These goals will be defined by

your library’s mission and thus the mission of your digital branch. Do you have a percentage of your user population that you want to have visit each month? Do you want to see an increase in program attendance based on website advertisement? You could define goals based on usage trends, participation in social networking tools, and any conversion rates you define.

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Resources All About Cookies.org. “About Cookies: Are All Cookies the Same?” www.allaboutcookies.org/ cookies/cookies-the-same.html. Accessed March 14, 2011.

Clifton, Brian. Advanced Web Metrics with Google Analytics, 2nd ed. Indianapolis, IN: Wiley Publishing, 2010.

Google Analytics. “Glossary.” www.google.com/ support/googleanalytics/bin/topic.py?topic=11285. Accessed March 31, 2011. Kaushik, Avinash. Web Analytics: An Hour a Day. Indianapolis, IN: Wiley Publishing, 2007.

Kaushik, Avinash. “Web Analytics Standards: 26 New Metrics Definitions.” Occam’s Razor. Aug. 23, 2007. www.kaushik.net/avinash/2007/08/web-analytics -standards-26-new-metrics-definitions.html. Accessed March 31, 2011. Tonkin, Sebastian. “Top Ten Myths About Google Analytics.” Google Analytics blog. May 28, 2009. http://analytics.blogspot.com/2009/05/top-ten -myths-about-google-analytics.html. Accessed March 31, 2011.

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Web Analytics Association. “Standards Committee Deliverables.” www.webanalyticsassociation.org/ ?page=standards. Accessed March 31, 2011.

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Notes 1. Avinash Kaushik, Web Analytics: An Hour a Day (Indianapolis, IN: Wiley Publishing, 2007), 26. 2. Kaushik, Web Analytics, 32. 3. “About Cookies: Are All Cookies the Same?” www .allaboutcookies.org/cookies/cookies-the-same.html, All About Cookies.org website (accessed March 14, 2011). 4. Sebastian Tonkin, “Top Ten Myths about Google Analytics,” May 28, 2009, Google Analytics blog, http:// analytics.blogspot.com/2009/05/top-ten -myths-about-google-analytics.html (accessed March 13, 2011). 5. Jason Burby, Angie Brown, and WAA Standards Committee, Web Analytics Definitions (Washington DC: WAA, Aug. 16, 2007), as quoted in Avinash Kaushik, “Web Analytics Standards: 26 New Metrics Definitions.” Occam’s Razor, Aug. 23, 2007, www.kaushik .net/avinash/2007/08/web-analytics-standards-26new-metrics-definitions.html (accessed March 18, 2011). See also Web Analytics Association, “Standards Committee Deliverables,” www.webanalytics association.org/?page=standards. 6. Avinash Kaushik, “Standard Metrics Revisited: #2: Top Exit Pages,” Occam’s Razor, Dec. 27, 2006, www .kaushik.net/avinash/2006/12/standard-metrics-revisited-top-exit-pages.html (accessed March 18, 2011). 7. Brian Clifton, Advanced Web Metrics with Google Analytics, 2nd ed. (Indianapolis, IN: Wiley Publishing, 2010), 6. 8. Kaushik, Web Analytics, 10. 9. Clifton, Advanced Web Metrics, 11.

Chapter 3

Installing and Configuring Google Analytics

Abstract This chapter of Using Web Analytics in the Library provides a step-by-step guide to installing and configuring a web analytics platform using Google Analytics—one of the most popular platforms—as an example.

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his chapter will use Google Analytics (GA) as the illustration and model for a web analytics implementation in your library. I will walk step-bystep through the initial registration and setup, and will look at some specific aspects of configuration that can be useful for library website analysis.

Getting Started

Google Analytics signup www.google.com/analytics/sign_up.html

At this stage, you are ready to begin the signup process. Make sure you select the proper geographic region (found in the upper right-hand corner under the phrase Change Language), as that affects not only the language but also the appropriate terms of service. Once you are registered, the system will welcome you and will generate a snippet of code specific to your website. This unique page tag, the Google Analytics Tracking Code (GATC), is the key to making GA work (see figure 4). Copy and paste this same GATC directly into the source code of every page in your website. Currently, the recommendation is to place the GATC immediately before the tag (figure 5). This is called the asynchronous snippet. �Using Web Analytics in the Library  Kate Marek

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The initial registration and start-up with Google Analytics (GA) is extremely easy. All you need for your first step is to set up a basic account with Google. (Since setting up a Gmail account is just one activity once you have that basic Google account, you do not need to have Gmail to get started with Google Analytics.) When you register with GA, you can use any e-mail address. Keep in mind that the e-mail account you use for you library’s Google account registration becomes the login that you use to register for GA and will then be associated with your library’s analytics account and the reports. Thus, for this purpose use an e-mail account specifically associated with your library rather than your personal account. In my example, I use [email protected] (see figures 1 and 2). You are, of course, still free to create additional personal and professional accounts with Google. Once you have created an account, you are ready

to register for GA. In the GA support material you will see references to AdWords accounts; this fee-based service from Google is associated with online advertising. The installation process of GA differs depending on whether or not you subscribe to the AdWords service. The GA versions are the same, but the free version (called the standalone version) is limited to five million page views per month, or approximately three thousand visits per day.1 Larger libraries will want to consider this as you consider analytics options. If you do decide to use AdWords, check the GA documentation for special instructions for getting started and for installation. The following examples are based on the standalone version of GA (see figure 3).

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Figure 1 Signing up for a Google account.

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The practice up until recently was to place the code just above the tag, and if you are already using GA, your pages probably reflect that practice. GA refers to the previous placement as the “traditional” GATC placement. The current GA recommendation is that you update your code to the asynchronous snippet by locating the Edit link next to your profile on the Analytics Settings page, retrieving the asynchronous code, and making the placement changes throughout your site. The rationale for the new asynchronous snippet and its placement is that by placing the code in the , browsers will continue to render the visitor’s webpage as the GATC loads, thus more quickly capturing the data from that visit. With the traditional placement immediately above the tag, the page load itself is not delayed, but data capture will not kick in as quickly. See the Google Analytics Help article “Set Up the Tracking Code (Asynchronous)” online for more information on the GATC and its current placement. The webpage “Asynchronous Tracking Usage Guide” compares the asynchronous and traditional snippets and how they work. �Using Web Analytics in the Library  Kate Marek

Set Up the Tracking Code (Asynchronous) www.google.com/support/analytics/bin/answer. py?answer=174090

Asynchronous Tracking Usage Guide http://code.google.com/apis/analytics/docs/tracking/ asyncUsageGuide.html

Explaining the GATC As Brian Clifton explains in his book Advanced Web Metrics with Google Analytics, there are three components of the GA tracking code:2 • The call of a master JavaScript file from Google servers. This master file, ga.js, is exactly the same for all GA accounts. It contains the code necessary for data collection and is cached by the visitor’s browser either from a previous visit to your site or to a visit to another site which also uses GA. This portion of the GATC also includes code detecting whether to load ga.js as a standard

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Figure 2 Google account confirmation.

request or via an encrypted protocol for any secure areas of your site. • An individual account ID with the format UAXXXX-YY. This is the portion of the page tag that is unique to your account. If not used exactly as quoted to you in the setup window, your data will go to another account. Since the Web operates in an open source environment, your unique ID will

be visible to anyone who views your HTML source code. To protect your data, Clifton strongly suggests that you set up a custom profile filter that tells the program to capture only your website’s traffic in the GA results.3 Thus, if someone either makes a mistake or deliberately copies your GATC and pastes it into their own webpages, GA will still report only traffic to mylibrary.org and any �Using Web Analytics in the Library  Kate Marek

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Figure 3 Google Analytics signup without an AdWords account.

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Figure 4 Google Analytics Tracking Code (GATC), sent upon signup.

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Figure 5 GATC placement in HTML code.

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Figure 6 The checkmark lets you know the program is active. The “View report” link takes you to your Dashboard and your GA reports.

There are many modifications and customizations to the GATC available according to your individual needs. Check the GA online documentation as well as support materials such as Brian Clifton’s book for additional technical details about GATC setup. In addition, if you are using a content management system (CMS) such as Drupal or Joomla!, check its documentation for GATC installation instructions. Testing Your GATC Remember that your Google Analytics Tracking Code must be pasted on all of your pages in order for the program to capture complete data across your website. You should begin to see results in your account views within 24 hours of your page tag installation. Brian Clifton stresses the importance of accurately tagging all the pages in your site, noting that

a page not tagged is a page not counted. “Missing tags equals no data for those pageviews.”5 Aim for 98 percent or more of your pages being tagged with the accurate GATC. Depending on your web server, you may be able to find loadable modules that will help with automatic page tagging. For example, Clifton suggests mod_layout for Apache servers, available at TangentOrg.6 If you are using a CMS, watch out for pages that do not use your standard template and be sure they are tagged along with the rest of your pages.

TangentOrg http://tangent.org

To see data capture results, log in to your account page. In the meantime, identify the Check Status section at the top of the Profile Settings page. Once the system detects that your code is in place, you should see a green check in that status area (figure 6). You can also view reports from your main screen and watch for new results to populate the forms within the first day of installation. If you are unsure if you’ve placed the code correctly and you are using a web authoring system such as Dreamweaver, make sure that you did not place the GATC in the Design window of that program’s interface; double check your HTML, �Using Web Analytics in the Library  Kate Marek

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directories and subdomains you specify. Setting up filters will be explained in more detail later in this chapter. • The call of the JavaScript routine _track Paveview(). Clifton calls this the “workhorse” of GA.4 This part of the code collects the data, including the URL of your visitor’s pageview, the browser type, language setting, and timestamp. Cookies are also read and set, and then communicated back to the Google servers.

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and make sure the GATC does not display on your pages via your browsers.

Configuration Best Practices Creating Profiles When you sign up for a GA account, a profile is automatically created; at least one profile is required per account in order to view data collected from your visitors. In many cases, libraries may want to keep a single profile for data collection and reporting. There are some instances, though, when separate profiles would be desirable. An example typically used in the literature is to create separate profiles for website visitors from the United States and for European visitors. Another example of separate profiles is when the organization has different departments or divisions, and separate reports can be beneficial when those departments have very different goals and responsibilities. A possible library application would be a large, multicampus university where each campus library would need to generate its own reports. The downside for most libraries to creating separate profiles is that no aggregate reports are available for those separate profiles. See chapter 4 for a more detailed discussion of GA reports.

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Setting Goals

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One of the most important things you can do when planning for website analysis, both from a big-picture, organizational perspective and from the perspective of your website’s specific role within the library, is to set goals. You do this first conceptually, based on your library’s mission, and then technically within GA. As mentioned in chapter 1, a key part of website planning is first knowing its role in the overall mission of the library. What is the purpose of your website? Why does it exist? If your website has been clearly defined by the library as its digital branch, what exactly does that mean for design and content? How will you evaluate its success? A goal as set within GA is a key strategy that helps accomplish your overall website priorities. For libraries, setting goals within GA becomes easier once you begin to consider some examples. • An academic library’s overall mission is to support education through the provision of informational resources. One website goal is to increase use of instructional support, both through face-to-face instruction sessions and through student use of online instruction tutorials. You can identify the following goals: • Online registrations for face-to-face sessions �Using Web Analytics in the Library  Kate Marek

• Page views and time in page for your online tutorials • LibGuide page views • A goal for a public library could be to increase the page views and time on page for the director’s blog. Remember when analyzing success with these goals, you will start with a baseline figure that you capture at a certain point in time. Then, make assessments in coordination with your knowledge of what else is going on in the library: is it a peak instructional time in the library? Is there a big concurrent marketing plan that includes promoting the director’s blog? Conversions A conversion, then, is when the visitor completes the action that comprises your goal. Looking again at the goal of increasing the use of online tutorials, you may have set your goal as a certain number of minutes on specific pages that include the tutorials. A conversion is tallied when a visitor completes that defined action. Thus, your success is measured by the number of conversions for each goal. Funnels A funnel is a specific, predefined path toward a conversion. This is another metric that is most appropriate for e-commerce, where an efficient step-by-step path toward the moment of purchase is critical to potential profit. The funnel visualization will show the analyst where the customer abandons his or her cart, and thus that’s the area that the designer knows to fix. (Think of the potential airline passenger who ditches the ticket purchase at the last minute because the credit card form will accept only American Express.) Using a predefined funnel can be helpful in certain situations for libraries as well. For example, if one of your key goals is to increase usage of your digital collection, you may try to streamline the menu system that leads to that collection. Setting up a funnel path and then analyzing the visitors’ paths can be very useful toward taking action for continuous improvement of the process.

Setting Up Goals and Funnels in Google Analytics Find the Goals section in the GA Administration basic left-hand side menu (figure 7). Follow the link on the next welcome screen to Set Up Goals and Funnels (figure 8). Select Add Goal (figure 9). Add a goal title. Then select the type of goal from URL Destination, Time on Site, or Pages/Visit. The URL Destination goal would be used when you are

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shooting for a higher number of visits for the director’s blog. The Pages/Visits would be a goal where you would like to go over a certain number of pages viewed per visit, so 15 might be an appropriate threshold for this category. In the example in figure 10, I have used Time on Site as a goal for the online instruction session, with a five-minute time threshold. (I’m assuming the instruction videos are five minutes long.) I selected Time on Site rather than URL Destination

because I don’t want to know just that the students are going to the page, but rather that they are viewing the entire tutorial. (For information about tracking use of Shockwave/Flash tutorials, see chapter 4.) If you would like to work with Funnel Visualization, set up the funnel path when you identify your goal. Find the report area under the expanded Goals menu (figure 11). Goal conversions will be displayed in graph form, also through the expanded Goals menu. Each profile in GA can be configured for up to 20 goals. You can expand this number, however, by using wildcards in your goal settings, such as specifying *.pdf rather than one individual PDF file name in the URL Destination. And again to stress a caveat: all data, including goal conversions, should be analyzed in collaboration with a variety of relevant players in the library structure. The more people, the greater the potential for insights; the more insights, the more potential actions toward continual improvement. Monetizing Goals

Creating Filters Figure 7 Goals section from the left-hand GA menu.

A filter is applied to your incoming data such that you

Figure 8 Link to Set up goals and funnels.

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One suggestion from the primarily e-commerce oriented analytics literature is that non e-commerce sites consider monetizing goals. This would involve assigning a dollar figure to each conversion. You will notice in figure 10 that an opportunity does exist at the bottom of the goal setup screen to enter a Goal Value. This might be an interesting research project, particularly for libraries where return on investment (ROI) discussions have taken on critical importance. Assigning a dollar value to each conversion, such as each program registration or each completed ILL request, could become a key part of your budget justification. For more information on monetizing goals, see Clifton’s book Advanced Web Metrics with Google Analytics. Avinash Kaushik also blogs about this issue frequently, and I would suggest a basic Web search for various articles on monetizing non–e-commerce website goals.

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Figure 9 Adding a goal or a filter.

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the use of profile filters is to eliminate data counting visits from your own employees or from library computers that automatically open to your home page. This you can do by selecting individual IP numbers or ranges of IP numbers and filtering out that data capture. Brian Clifton lists two common profile filters that are relevant to libraries:8

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• Include only your website’s traffic. Clifton recommends applying this to all your profiles, if indeed you have more than one profile. As discussed earlier, people could use your unique GATC snippet either by mistake or deliberately. This could cause corruption of your data unless you filter in only data from mylibrary.org. • Exclude certain known visitors. Examples of known visitors are your own employees and public access computers that open to your home page. These visitors will create a high number of visits that will skew your metrics and your conversion rates. (Note: for filtering a range of IP addresses, see the article “How Do I Exclude Traffic from a Range of IP Addresses?”) Figure 10 Add arrow to Goal Name, type of goal, and specific minutes of this goal.

can create a specific accurate report. For example, you can exclude specific information or IP addresses, or report from a specific subdomain or directory. GA has predefined filters (such as the two just listed), as well as custom options.7 For example, one way to eliminate “noise” through

�Using Web Analytics in the Library  Kate Marek

How Do I Exclude Traffic from a Range of IP Addresses? http://tinyurl.com/googleanalytics-ip

To create a filter, click Edit next to the profile name where you’d like to add the filter. Then scroll down to the Add New Filter for Profile link. Give the

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Figure 12 Create a new filter.

(as well as its Conversion University website found at www.google.com/support/conversionuniversity/) for more details and more technical information. Chapter 4 will look more specifically at the various ways you can sift, parse, highlight, and segment data for reports to help you find actionable insights.

Resources

Figure 11 Funnel Visualization.

How Do I Create a Predefined Filter? www.google.com/support/analytics/bin/answer. py?answer=55496.

That’s the basics of the GA setup. There is a great deal more sophistication and customization available, but this will give you a good start. Remember to use Brian Clifton’s book and the Google Analytics Help

Google Analytics. Filters Help Pages. www.google .com/support/analytics/bin/topic.py?topic=11091 and www.google.com/support/analytics/bin/answer .py?answer=55496. Accessed March 31, 2011.

Google Analytics. GATC Help Pages. www. google.com/support/analytics/bin/answer. py?answer=174090 and http://code.google.com/ apis/analytics/docs/tracking/asyncUsageGuide.html. Accessed March 31, 2011.

Notes 1. Brian Clifton, Advanced Web Metrics with Google Analytics, 2nd ed. (Indianapolis, IN: Wiley Publishing, 2010), 132. 2. Ibid., 134–136. 3. Ibid., 238. 4. Ibid., 135. 5. Ibid., 26 6. Ibid., 137. 7. Google Analytics Help, “Filters,” www.google.com/ support/analytics/bin/topic.py?topic=11091 (accessed March 21, 2011). 8. Clifton, Advanced Web Metrics, 238.

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new filter a name, complete the remaining inputs, and Save Changes. Figure 12 shows the settings for adding a filter to include only your website traffic. The Google Analytics Help screens provide clear instructions for setting filters, including the method used to exclude traffic from a domain or an IP address or range of addresses, and to include only traffic to a subdirectory. See “How Do I Create a Predefined Filter?” For a more in-depth study of filters, see also Brian Clifton’s book, Advanced Web Metrics with Google Analytics.

Clifton, Brian. Advanced Web Metrics with Google Analytics. 2nd ed. Indianapolis, IN: Wiley Publishing, 2010.

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Chapter 4

Reporting and Analysis

Abstract Web analytics tools provide huge amounts of data, and the prospect of sorting through that data to determine what is useful and what is not can be daunting. This chapter of Using Web Analytics in the Library offers descriptions of different metrics and types of data, as well as guidelines for finding the data most important for a given situation.

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he literature suggests again and again that you choose a few key web analytics metrics, selected directly from your library’s mission, goals, and objectives and the resulting key performance indicators to analyze your website’s success. The vast amount of unused data in your analytics program may bother you because you feel it’s wasted, but remember that if you are not using the data for actionable insights, it is actually worthless and literally wastes your time. Concentrate instead on as few as five key metrics that will align very specifically with your predefined goals and objectives. This chapter will review the process of selecting metrics and reports that are appropriate to your goals and help you measure your website’s success. Also, keep in mind, as you begin to scan for numbers, that the data from your Google Analytics (GA) program should be combined with other kinds of evaluative tools (surveys, requests for feedback, and formal usability studies) so that you can constantly be working to improve customer satisfaction. As you begin to review metrics and to think about reporting, keep that purpose firmly in mind; give your administrators information, not just data. �Using Web Analytics in the Library  Kate Marek

General Overview of Metrics and Reporting Options The amount of information you will begin to receive from GA can seem overwhelming at first. It helps to start with a basic understanding of some of the most used metrics and how they might help you. See chapter 2 for some definitions and descriptions; this chapter will focus specifically on how to use metrics to measure success.

First-Level Metrics In his book Advanced Web Metrics with Google Analytics, Brian Clifton identifies initial visitor data that will give you a baseline as you begin to understand your traffic, your users, and potential concerns. Looking at these numbers will start to show you how and by whom your library’s website is being used. A hands-on review of these first-level metrics inside your own GA account will also provide you with an excellent introduction and practice for navigating the GA interface, and through this practice you will begin to discover the many options of the program. Initial visitor metrics include the following:1 • How many daily visitors do you receive? • What is your average conversion rate? (See chapter 3 for setting goals that produce conversion rates specific to your library.) • Which of your pages are most visited? • What is the average time on site, and how often do visitors come back?

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• What is the average visit page depth (how many pages are being viewed within your site), and how does this differ by referrer? • What is the geographic distribution of your visitors, and what languages do they use? • How “sticky” are your pages? Do visitors stay on your site, or do they leave after viewing only one page? As mentioned above, if you are new to GA, these questions can help you feel more comfortable with the interface and the metrics that will matter to you. As you begin to navigate through the screens, be aware that GA gives you many, many chances to drill down further with individual bits of data. Always look for the standard blue text signaling deeper links to more information about that topic or data. GA’s many opportunities for fine-tuned discoverability are a great strength of the program.

Viewing Reports A point of information as you begin to look at the GA screens is the program’s use of two key vocabulary terms: dimensions and metrics. As you will see as

Your Dashboard The Dashboard is the first section you see when viewing your reports. You will see various abridged reports as small images, brought together on one screen for an initial visual administrative overview. You can customize your Dashboard to your own specifications so that the reports that interest you are organized and accessible on your opening screen. Up to 12 reports can be removed, added, and reordered according to your desired layout. From the Dashboard, you can begin to click on individual reports, and then individual metrics, to access your data. As you drill down with mouse clicks, the left-hand text menu of the GA screen will also expand, and your breadcrumbs (upper left) will also reflect your path and current location in the site. While the menus are quite helpful as points of reference, GA is designed so that the drill-down feature is the recommended path toward viewing the metrics in context. As each user can arrange the Dashboard to individual specifications, each one looks different. Figure 13 shows one example. Working clockwise from the upper right-hand corner, you see an Advanced Segments option (figure 14). You can choose to view All Visits, or click the triangle from that drop-down menu to get to the options shown in figure 15. Back to the Dashboard, you next see the current date range. The default date range in GA is the last 30 days. However, there is a drop-down menu option where you can change that date range depending on

Figure 14 Advanced Segments selection on the Dashboard.

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Library Technology Reports  alatechsource.org  July 2011

Figure 13 Google Analytics Dashboard.

you begin to look at the reports, the dimensions in GA are the “text strings,” often words or parts of URLs, that identify and describe an item. The metric for each dimension is the corresponding count for that dimension. GA tables list the dimensions to the left and the metrics to the right. Also, the GA report screens include many embedded help notices, including sparklines, or small, dense pop-up explanations at different hotspots on the screen. To view your reports, click the View Report link in the opening GA screen (see figure 6). This link is your basic overall access to GAreports, and it takes you to your Dashboard screen.

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Figure 15 Expanded Advanced Segments menu.

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Figure 16 Date View options.

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the particular period you’d like to view (figure 16). You can select the monthly, weekly, or daily view; changing that option will also change your graph. When selecting date ranges, be savvy in your comparisons with previous periods. For example, April visits may reasonably be lower than May visits simply because April is one day shorter. The date range you select can be a powerful way to track website usage in coordination with special events or activities in the library. For example, you may want to change to the weekly view to track usage leading up to and immediately following a major author visit, exam week activities, or library gaming night. Simply noticing that website usage went up is helpful to demonstrate the importance of the website as a component of your overall marketing and communication structure. But zeroing in on usage surrounding a specific event can help you learn even more about how your visitors might have been different during the week. For example, you can look at the geographic sources of visits during the week of the special event to see if you attracted a wider audience, and if so, where the new visitors came from. Can you look at bounce rates differently that week to see if perhaps people left your site without finding information about the event? As you become more familiar with the GA report screens, these are the kinds of clues you find �Using Web Analytics in the Library  Kate Marek

to help you discover more than just the popularity of your site. Again back to the Dashboard screen, find the various metrics organized under the mid-screen graphic category Site Usage. There you will find quite a few of the standard metrics, such as overall number of visits, time on site, pages viewed (depth of visit), and percent new visits. (Again, percent new visits would be a good metric to watch in the days surrounding a special event.) Beneath this section are the report snapshots you have selected for your basic view. Beneath this section are the report snapshots you have selected for your basic view. The Dashboard layout in figure 13 includes the Site Usage, Visitors Overview, and the Traffic Sources Overview. Moving back to the left-hand side of the screen, you find more of the basic menu items. Remember that each item in the basic menu can be expanded via mouse clicks, and the left menu items also expand automatically to reflect your drill-down mouse actions in the graphic views.

Google Analytics Reports The reports available through GA cover all the basic metrics mentioned throughout the previous chapters,

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including total number of visits, unique or returning visitors, time on site, depth of visit, time on page, referrer, and bounce rate. In addition, GA offers some additional useful reports. Here are some examples, with the main menu item shown in parenthesis after the individual report item:

your site. These numbers are growing dramatically and are predicted to continue to grow, and they can help you watch trends among your own users as you plan future site development.

• Map Overlay (found under the Visitors menu). • Languages (found under the Visitors menu). • Visitor Trending and Visitor Loyalty (found under the Visitors menu). • Mobile (found under the Visitors menu). • Traffic sources overview, with its various options. • Site Overlay, or In-Page Analytics (found under the Content menu). • Segmentation View

The Traffic Sources Overview item expands to offer you an extensive list of reporting options. In addition to the Overview report, it includes information about direct traffic, referring sites, search engines, and keywords visitors used to find you. Knowing these keywords can be particularly helpful in understanding your users, although of course we don’t know what keywords they used that did not result in a successful search. Qualitative metrics such as user studies would help here.

Traffic Sources Overview

Map Overlay The Map Overlay report is a great visual view of your visitors’ geographic locations. You can easily see volume and quality (visit depth, etc.) of your visitors, and you can zoom to city level. There are a variety of uses for this tool for libraries, depending on your type of library and the different collections you are promoting through your online presence. Identifying your users and then drilling down to see what pages they viewed can be another way to understand whom you are serving and how you could better meet their information needs. Languages

Visitor Trending and Visitor Loyalty Libraries tend to have a very loyal following of core users. In addition, we hope to be continually recruiting users to the digital branch. Using the Visitor Trending and Visitor Loyalty report can help you see where you stand with return and new visitors, and how those metrics shift in parallel with other events and trends at the library. Mobile The Mobile report will show you which mobile devices and which mobile carriers visitors are using to access

The Site Overlay feature loads an actual page from your website and then adds a visual layer including key metrics for each link on that page. You will see small boxes superimposed on top of your webpage that indicate the number of clicks at that item. This can be an extremely beneficial visual snapshot of what’s happening with your menu structure and with your visitors’ overall behavior on that webpage. Many people find, however, that the Site Overlay feature is unreliable and full of bugs. The new In-Page Analytics, currently available in Beta, is seen as a preferable alternative.2 Site Search Site Search gathers metrics from your internal search engine, if you have one. Customers tend to rely more and more on this feature, and it’s extremely beneficial to know the terms they use within the search box. Every time visitors type a term in your search box, they are telling you exactly what they expect to find on your site.3 This information can be very useful for librarians, as we tend to be stuck in our own professional lingo and often don’t realize that ours is not a language clearly understood by our users! And, in general, when we know what our customers are looking for, we can consider that a community wish list, and act accordingly. Segmentation Advanced segmentation allows you to separate out a user group in GA to learn more about their website behavior. Figure 15 shows the built in segmentation options in GA. In addition, various spots within the program also offer segmentation options. See the Visitors item on the left-hand menu, and see how you can segment users from that screen (figure 17). �Using Web Analytics in the Library  Kate Marek

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The Languages report can be useful to libraries of all types, as we all find we are serving diverse populations both locally and beyond. Noting which geographic area our visitors come from is not enough to fully understand the first-choice languages of the people we are serving. Understanding their languages of choice can help in developing face-to-face programs and services, collection development, and staffing, not to mention providing appropriate language options within the website itself.

Site Overlay or In-Page Analytics

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Figure 17 Segmentation options from the Visitors menu.

Figure 19 Setting up e-mail reports.

Figure 18 Export and Email options from the Dashboard.

Custom Reports The Custom Reports feature creates your own report, depending precisely on the information you want to see, organized to your own specifications. GA provides a drag-and-drop feature to select the metrics and create multiple levels of subreports. The menu item for Custom Reports is on the left-hand side of the Dashboard, under the general menu.

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Google Analytics Intelligence Reports

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Google Analytics Intelligence is currently a Beta service that monitors common metrics, looks for significant variations, and sends you automatic alerts as a result of those variations. Going beyond even the basic Custom Reports option, the Intelligence reports aim to alert you to things you would otherwise miss— the “unknown unknowns,” as Avinash Kaushik calls them. You can find the Intelligence menu item in the main left-hand Analytics menu section, and can use either automatic reports or custom reports. Many of these reports are associated with e-commerce, such as a custom Intelligence report that would watch for a revenue drop in a specific geographic area. But you may find uses for your specific library as you become more aware of your organization’s goals and how the website helps toward those objectives. For example, if you have just started a Twitter account or some other social networking service, Intelligence reports can tell you if there is a significant and unusual surge in usage you otherwise might miss. �Using Web Analytics in the Library  Kate Marek

Backing Up and Exporting Data Google Analytics commits to storing data for up to 25 months, although so far Google has shown no evidence of removing data from accounts older than that. Nevertheless, you may choose to back up your data and also to manually export report data. You can modify your GATC so that the script simultaneously sends data to the GA servers and also to your own web server log files. For details about this process, see Brian Clifton’s excellent book, Advanced Web Metrics Using Google Analytics.4 To export copies of reports, choose either the Export or Email option in the left-hand corner of the center dashboard screen (figure 18). You can e-mail reports in a variety of formats, including PDF, XML, and CSV. In addition, you can set up regular periodic e-mails of specific reports using the same buttons on the report screen (figure 19).

Access to Reports You can add users to your account in order to make GA reports accessible to others in your organization (figure 20). You can add users through the User Manager link, found in the center of your Analytics Settings screen. You may select View Reports Only as the access type. You may also add additional administrators to your account from this screen. And, as noted above, you may set up automatic distribution of reports via e-mail to yourself and to others. It’s important to consider who among the members of your staff might benefit from seeing your analytics reports. Various department heads and managers who receive your website reports could

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Figure 20 Adding users to an account.

offer valuable insights. Consider making these conversations a regular feature in organizational meetings.

Benchmarking

Data Visualization Sharing your data in unexpected formats can create impacts beyond the typical printed report. Data visualization is one way to communicate your report results with greater impact. For example, consider the Keyword metric, where the GA program generates a list of keywords that visitors have used in search engines to find you. Rather than simply sharing that list, which people may or may not spend time analyzing, dump the words and numbers instead into an open source word cloud tool such a Wordle. In general, get to know a little more about the different rationales for using bar charts, pie charts, ad tables. Data visualization has been around for quite some time—consider mind maps and the classic work of Edward Tufte. However, it is being discussed as an important way to communicate

Wordle http://wordle.net

Conclusion In general, look for trends, not single numbers. Start with a few metrics that relate directly to the goals for your library’s website, and build from there. Keep in mind that you are looking for actionable insights, and if the data does not help you to make decisions, it is virtually useless. Combine the information from these Google Analytics reports with insights you receive from qualitative sources, such as user studies, online surveys, and focus groups. Use the continuous improvement process of organizational goal setting, data collection, data analysis, action, and evaluation as a framework for web analytics. The next section will describe some library case studies, where librarians’ use of Google Analytics has helped them gain insights into their websites and their users.

Resources Booth, Char and Paul Signorelli. “Library Analytics: Inspiring Positive Action through Web User Data.” ALA TechSource webinar, Jan. 20 and 27, 2011.

Clifton, Brian. Advanced Web Metrics with Google Analytics, 2nd ed. Indianapolis, IN: Wiley Publishing, 2010. Instant Cognition. http://blog.instantcognition.com.

Kaushik, Avinash. “Beginner’s Guide to Web Data Analytics: Ten Steps to Love & Success.” Occam’s Razor. Nov. 15, 2010. www.kaushik.net/avinash/2010/11/ beginners-guide-web-data-analysis-ten-steps-tips-best -practices.html. Accessed March 22, 2011. �Using Web Analytics in the Library  Kate Marek

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Through benchmarking, GA compares your data with website usage data from websites in similar markets. This process involves anonymously sharing aggregate data from other GA customers. You can opt in or opt out of the service at any time by editing your user profile, although when you opt in, the data stored in that period remains in the GA system. This feature allows you to put your metrics into broader perspective. All identifying information is removed from the data of organizations that choose to participate. Check this report once a quarter or once a year, simply to get some comparison data about your site.5 Benchmarking is found under the Visitors menu category, and will show as an option only if you choose to share your information anonymously. Find the data-sharing options under your account settings. Choose to benchmark by size and by industry category list. Find the options by following the menu path Visitors > Benchmarking > Open Category List.

web metrics as well. Paul Signorelli reviewed the potential of this method of data sharing in the January 2011 ALA TechSource webinar on web analytics.6

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Notes

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1. Brian Clifton, Advanced Web Metrics with Google Analytics, 2nd ed. (Indianapolis, IN: Wiley Publishing, 2010), 8. 2. Char Booth and Paul Signorelli, “Library Analytics: Inspiring Positive Action through Web User Data.” ALA TechSource webinar, Jan. 2011. 3. Clifton, Advanced Web Metrics, 122. 4. Ibid., 139–141. 5. Ibid., 108.

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�Using Web Analytics in the Library  Kate Marek

6. Char Booth and Paul Signorelli, “Library Analytics: Inspiring Positive Action through Web User Data,” ALA TechSource webinar, Jan. 20 and 17, 2011. See also Instant Cognition, http://blog.instantcognition .com, a blog about web analytics and data visualization, and Avinash Kaushik, “Beginner’s Guide to Web Data Analytics: Ten Steps to Love & Success,” Occam’s Razor, Nov. 15, 2010, www.kaushik.net/ avinash/2010/11/beginners-guide-web-data-analysisten-steps-tips-best-practices.html (accessed March 22, 2011).

Chapter 5

Case Studies

Abstract This chapter of Using Web Analytics in the Library provides several examples of how different types of libraries in different contexts have successfully used web analytics to enhance their online presence.

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Wheaton College David Malone The Wheaton College Archives and Special Collections is a division of Buswell Memorial Library at Wheaton College in Illinois. The library’s mission is to support the college’s liberal-arts curriculum. As a department of the library, the Archives and Special Collections supports that curriculum through monographic, manuscript, and media holdings, including collections from authors, artists, evangelical organizations, social scientists, and members of Congress.

Buswell Library website http://library.wheaton.edu

Archives and Special Collections catalog http://archon.wheaton.edu

Archives and Special Collections legacy site www.wheaton.edu/learnres/ARCSC

Getting Started with GA Our computing department had been using an analytics program that it administered locally. We found that certain bits of information were either not being tracked or were being stripped out because of the way �Using Web Analytics in the Library  Kate Marek

Library Technology Reports  alatechsource.org  July 2011

he previous chapters have defined various Google Analytics (GA) metrics and how they can be used in website analysis. This section will highlight some specific examples of libraries where GA has been implemented, how those metrics have been used, and how librarians have formed actionable insights based on their reviews of the GA reports. In addition, I will include some information about gathering metrics from your library’s social media tools, which while not strictly a part of mainstream web analytics, is nevertheless a powerful part of your overall website analysis and thus relevant to this report. I will start with an extensive case study from the Archives and Special Collections from Wheaton College in Illinois. Their department head, David Malone, is a seasoned user of GA and describes his extensive use of the program to form actionable insights about the website on a regular basis, enabling him to tweak the site to continually improve the visitors’ experiences. I will continue with two more examples based on shorter interviews, both of which continue to inform us regarding various insights from GA and making decisions for website or service improvement based on those statistics. Next, I will include a case study from the literature which also illustrates many of the points made throughout this report. Finally, I will include information on tracking social media activity on your site using tools other than Google Analytics. Many thanks to those librarians who took the time to

share their experiences with using both GA and social media metrics.

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Figure 21 Google Analytics profiles at Wheaton College.

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Figure 22 Basic information about site usage.

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the computer network on campus was installed or utilized, or the software itself was stripping it out. We wanted to track who was using our resources, but that program was not providing the information. Some of the things we wanted to track were these: Where are people coming from? What are they viewing? How long are they staying on our site? We are running six different profiles (figure 21) because I want to track the various websites we are running right now. I include numerals at the beginning of the profile name so that they sort by order of importance rather than alphabetically when GA displays the profile list. I am most interested in our Archon site. Next is our static legacy website. Also included as one of our profiles is our online encyclopedia, which is a wiki of sorts, and our exhibits page. I do use filters to exclude local traffic, including site visits from my computer and my staff’s computers so that as we’re working on the sites our visits don’t �Using Web Analytics in the Library  Kate Marek

get counted by GA. Here is some basic information about site usage from the GA report (see figure 22). On our Archon site, we’ve had 2,974 visits, and the average time on site was 4 minutes and 31 seconds. The bounce rate is 52 percent. And, in this last month, these usage statistics are 37.31 percent lower than they were the month before. That’s helpful just as a quick pulse check. I’m going to look at that—why do we have 37 percent fewer users this month? It could be that we’re on semester break, Christmas break—a significant amount of our user population is gone, and that could account for it. Here again is an example of where we started off simple, and now we’re stepping it up. We’re seeing different ways we can make this more useful for ourselves. By clicking in this report and going deeper, I’m presented again with a number of bits of information that were in that previous screen. I find that of

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Figure 23 Referring sites.

2,974 visits, the users looked at 12,422 pages. So visitors looked at four pages per visit—that’s good, in my mind. If they are navigating to various pages on our site, that means we have staying power—validated by the 4 min 31 seconds time on site. On the Internet, even 30 seconds on site can be considered a long time. Scrolling down, I can see where our users are coming from (figure 23). I can see that about a third of our users are being referred to us, a third are coming from search engines, and a third are coming from direct traffic—they may have a bookmark or an e-mail with a link. They are coming straight to us. So let me dive a little into this. This information is now broken down a little further (figure 24). My top traffic sources are from visitors who come from a direct link, such as a bookmark.

The next highest source is from Google itself. Then I notice that something is coming from a particular blog. I navigate there and find that they were referencing some of the digital content we have on our site. I may want to consider contacting the author of the blog. I can also go to a full report, and I can see that I actually have 80 lines of information (figure 25). So I’m going to expand this further so I can see as much information as possible all at once. And here I can get a sense of the various sites that are coming to us. One of our collections is the National Association of Evangelicals (figure 26)—we have their records. So people are coming to us from their website. People are coming to us from our blog—23 people in this last month jumped from our blog to our archival resources (figure 27). That’s a positive. You can see �Using Web Analytics in the Library  Kate Marek

Library Technology Reports  alatechsource.org  July 2011

Figure 24 More information from referring sites.

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Figure 25 Expanded referral sources.

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people are coming from Bing; they’re coming from Yahoo. So—some people are coming from some other blogs. Let’s go back and look at what the keywords are that people are searching when they’re out on the Web. Find the keywords listing by starting at the main menu on the left, then going to Traffic sources, to Keywords, and to See All (figure 28). I can drill down to see which search engines are being used for each keyword, and I can look at search terms and see how long visitors stay on the page they come to. The thing that jumps out to me about this list of results was the term “glastost,” which I have never heard of. I have heard of glasnost, the political philosophy that grew out of the Soviet Union. We have a number of collections that deal with Soviet Union religious freedom. After looking further at this, I realize we had 36 hits based on a misspelling. One of the things using Google Analytics has helped me do is to find a misspelling, something I certainly never anticipated! I then take “glastost” and jump in to Google and search glastost site:wheaton.edu (figure 29). I find that our Anita and Peter Deyneka Papers have that misspelling. I can go to that item and search within the page to find the specific appearance of that misspelling, and then can verify whether it is indeed �Using Web Analytics in the Library  Kate Marek

misspelled in our index. I never anticipated that our analytics tool could help us find misspellings, but that’s what it’s doing. If you’re not reviewing the search logs, you’re missing out on a lot of important information about how your users function and how your users search. I’m finding, for example, that some of our visitors are using quotes. Good—we’re telling them to use quotes on the front page and the introduction! Figure 30 shows an example of looking at traffic sources. Clicking on Visitors, I can begin to see who the people are who are coming to our site. Of 2,974 visits, I see that 2,094 of them are unique visitors. To me, this is fantastic information. This means that we don’t just have a small number of visitors who keep coming back and back and back. We have a much larger user population. Much of what we have here was on the GA report’s front page. But here I’m able to know what browsers people are using; by keeping track of this information, I have a better idea of our users’ experience when they come to our site. For example, if I see I have BlackBerry users, I need to know what our site looks like on a BlackBerry—especially if I notice a consistent growing trend here. What are the screen resolutions (figure 31)? The traditional VGA screen is 800 × 600—and

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Figure 26 Additional referral sources shown.

now I know that only 0.64 percent of our users are coming in at that resolution. Our blog is set up as a template at 800 × 600. So in the back of my mind as a to-do item is to edit our template to move up to a 1024 × 768 environment. I can see from this that the vast majority of our users are coming in with a wide screen. So let’s take

advantage of that—let’s not be constrained by a limited desktop as we’re developing our site (see figure 32). I can find out additional information such as what kind of flash versions and how many screen colors our visitors have, so if we have a Flash resource, I’m sure we’re meeting the most recent standards. We can also see the connection speeds of our user �Using Web Analytics in the Library  Kate Marek

Library Technology Reports  alatechsource.org  July 2011

Figure 27 Referrals from our blog.

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Figure 28 Keywords being used by our visitors to find us.

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Figure 29 Finding “glastost” misspelling.

population. Just about 1.5 percent of our user population is on dialup (figure 33). What does that tell me? That tells me I’m going to spend very little time worrying about our download speed. We have to make choices whether to spend time developing our site for the 98.5 percent with high-speed access or the 1.5 percent with dialup. Figure 34 shows a good example of a significant jump in site traffic and how it can be explained and analyzed. There was an 8,226 percent jump in this statistic last month. The difference between February 8 and February 9 is significant. February 9 was the day �Using Web Analytics in the Library  Kate Marek

that an audiovisual slide show was posted on the main page of the college library. The page had 14,000 page views, 12,000 of those unique, most likely all from that one exhibit (figure 35). That to me is phenomenal—the average time on that page is 5 minutes and 5 seconds, which is just about how long that video is. So we have reached over 12,000 people with this one video in this last week. To me that says I need to make more of those slides shows and to work with our staff in the library to have more of those videos cycling through. This becomes a point of discussion within the staff in the archives: Do we have any content that can be delivered as an audiovisual slide

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Figure 30 Visitors Overview.

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Figure 31 Screen resolutions.

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Figure 32 Wasted space on the left.

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Figure 33 Dialup users.

show rather than as a static webpage? Static clickthrough exhibits should be repackaged as a four- to five-minute videos to introduce to a lot more people to our holdings. Administrative and Staff Reports I review Google Analytics reports several times a week, and I do sometimes bring up some of the trends I’m seeing at our staff meetings. We may start structuring more times for us to incorporate this as a regular part of our meetings, as I’m incrementally growing some of the staff into this. In reporting to our administration, we were able �Using Web Analytics in the Library  Kate Marek

to justify spending time developing our blog entries through the Google Analytics data. We post entries two to three times a week, which is an investment in staff time. I’m making a choice to do that, and as a result we’re not processing materials at the same rate. I was able to show through using GA that 1,500 individual users per month who visit the blog are equivalent to 1,500 people coming in and looking at an exhibit at our building. So when we use GA data in reports to the administration, we are not necessarily trying to justify more staff, but rather trying to look at the best ways to use our staff time. We need to make sure that the collections that we have are not hidden—we know how people are finding them through using GA. We are making decisions about what we are and are not doing in order to best meet our users’ needs and to best leverage our collections.1

Additional Case Studies The next two case studies are shorter and more focused. Anthony Molaro, formally from Messenger Public Library in North Aurora, contributed information about how that library used Google Analytics for insights toward a major redesign of their website. Sharon Streams from WebJunction describes how another type of information organization uses Google Analytics on a regular basis to gauge their users’ experience and the overall effectiveness of their website. Next, I provide a synopsis of a case study published in 2007 by Wei Fang about the library redesign at

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Figure 34 Jump in traffic after posting an online video.

Figure 35 Video page views.

Messenger Public Library Anthony Molaro Messenger Public Library, named for longtime librarian Emeline Messenger, serves a community of approximately 16,000 in North Aurora, Illinois, about 35 miles west of Chicago. According to its 2010 technology plan, “The chief technology product of the Messenger Public Library is its website. The library has almost as many virtual visitors to the library as in-person. We, therefore, believe that the library website should match the welcoming and warm environment found within our walls and to use the digital space to help users find what they seek.”2

Anthony Molaro, former head of Technical Services and Automation at Messenger Public Library, placed a high priority on the library’s website as stated in the library’s technology plan. He spoke about the library’s use of the Google Analytics program as a tool for ongoing improvement and for a major site redesign. Our library set up Google Analytics and began using it in 2009. When I started at Messenger Public Library in January 2010 as head of Technical Services and Technology, I decided to continue using GA because of the depth of information it provided. I checked GA twice a month for basic library reporting purposes. I also monitored particular metrics, including traffic sources to determine where traffic was coming from and what keywords were being used to find us on the Web. This helped us to know how our public was searching for information. I also used Google Analytics in conjunction with our library’s website redesign. We relied heavily on several metrics in determining what pages to keep, delete, and create. For example, based on reports from GA, we could see that we had three top landing pages where people entered our site. In all likelihood, these �Using Web Analytics in the Library  Kate Marek

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Rutgers-Newark Law Library, where they also used Google Analytics for various actionable insights. Finally, I include an overview and some tips from independent library consultant Dawne Tortorella regarding data from your social networking tools.

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Figure 36 Finding pages with low page view rates.

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users relied on a bookmark to bring them to a specific page of ongoing interest. One of those top landing pages was the current job openings page at Messenger Public Library. Because this page received such heavy traffic, I decided to push it up to the front page of the new website. In other words, we didn’t want users to dig around for information that they thought was important. We did a similar study of exit pages. I believe that entrance and exit pages can tell you a lot about how people are using your website. For example, one of our top exit pages is our database page. This would make sense. The loyalty metric is super neat. At Messenger Public, 80 percent of our website users visit the library website two or more times within a year. That’s great, but we also wonder why people visit only once during a given year. What were they looking for that they could not find? What turned them off? While qualitative feedback such as surveys might help here and is the preferred method for this type of question, at this time we simply do not have the time or the resources to carry that out. There was discussion about creating a mobile version of our website. However, the metric on mobile users revealed that only about 1 percent of our traffic

�Using Web Analytics in the Library  Kate Marek

is from mobile devices. As a result of this metric we decided to not create a mobile site at that time. This particular report is monitored closely. Our plan is that when the percentage of mobile users hits 5 percent, we will design a mobile version of the website. We also set website goals using GA. For example, we heard through an online suggestion box that our users wanted to see staff reading selections. In response to that suggestion, the site redesign included a visual bookcase of staff selections in various formats. I set a goal within GA of at least 100 hits per month, which I determined as my threshold for justifying staff time to update the page. The first month of usage after the launch of the redesigned page had 108 hits. Hitting this goal in the very first month really demonstrated the value of the new page. As mentioned, I checked the reports twice a month for library reporting purposes. I also wrote a departmental report monthly that included visits and hits to the website. When working on plans for the website redesign, I viewed reports daily. I shared extremely basic information with the director of the library for monthly reporting purposes, and I also used GA to justify to other departmental managers why I moved, eliminated, or added a page. The screenshots in this section provide illustrations

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Figure 37 Top landing pages, Messenger Public Library.

frustrated and quit looking for something or if they find what they are looking for. The data showing the top few exit pages revealed that people found a library database or library catalog and left the site, hopefully after finding the item they were seeking. The visitor loyalty report (figure 40) revealed that 20 percent of our visitors come one time and never return. Some of this may be accounted for by their using a different computer, but the bigger question is what they are not finding such that they never return. The mobile devices report (figure 41) was used to determine whether we should develop a mobile version of our website. We decided that 1 percent of total traffic did not warrant a mobile site. However, we will continue to watch for growth in this area. I used the operating systems report (figure 42) to determine the need for the library to support Apple products. As a result of this data, I made the case that we needed to offer Mac computers to patrons as a growing number of our users were using Macs. My advice for using GA is simply to play and explore the rich data available in this wonderful tool. In terms of a redesign, GA takes a lot of guessing out of the equation because it provides hard data that helps you understand your users and that justifies action for improving your website.3

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of the GA reports that facilitated decisions about the website. Figure 36 shows how GA helped us determine whether we should keep a webpage on the new site. For us, the decision was that any page that received less than 10 hits per year did not serve a purpose and was deleted. We did not receive any complaints about content that had been removed. Top landing pages (figure 37) revealed how people entered the site on a particular page. Because the keyword report told us what people were searching to land on our site, we determined that the top three landing sites were accessed via bookmarks or direct links and not via searching. In the new design we used this data to determine where to locate a particular page and how many levels into the website it existed. The keyword report (figure 38) informed us how people are finding Messenger’s website. Item 132 showed us that some people had an interest in where the name Messenger came from. In response to this, we created a page under About Us with the history of Messenger Public Library and on founding librarian Emeline Messenger. The exit pages report (figure 39) report informed us from which pages people are leaving the site. In other words, the data here may tell us if people are

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Figure 38 Keyword report, Messenger Public Library.

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Figure 39 Exit pages report, Messenger Public Library. �Using Web Analytics in the Library  Kate Marek

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Figure 40 Visitor Loyalty Report, Messenger Public Library.

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Figure 41 Mobile devices report, Messenger Public Library. �Using Web Analytics in the Library  Kate Marek

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Figure 42 Operating systems report, Messenger Public Library.

WebJunction

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Sharon Streams

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WebJunction is an organization that provides online educational resources for libraries and librarians. On its website, library staff “discover resources, attend programs, take courses, and share their knowledge.”4 The following information about the WebJunction staff use of Google Analytics is from staff member Sharon Streams.

WebJunction www.webjunction.org

We use Google Analytics for basic web analytics functions: to track overall site traffic trends, to see which pages and sections of the site are most frequently visited, to measure how effectively our communications drive traffic to the website, and to identify other websites that are referring visitors to our website. Some of the particular metrics we find helpful are: • Top Traffic Sources: It’s useful to use to see the number of visits to the website that originate from �Using Web Analytics in the Library  Kate Marek

our newsletters or from social networking sites such as Twitter or Facebook. • Top Content: We watch which pages on our site are visited most frequently. • Visits/Visitors/Page Views per Month: We compare these numbers from month to month and from year to year, to track seasonal trends and overall growth or decrease in use of our website. • Site Search terms report: More recently, we implemented site search tracking so that we can see which terms users input into our search box. Here are some examples of insights we get from the GA reports that help us to make decisions: • From Search Terms report: We note the language people are using and adjust the terms we use on the website accordingly. We also note frequently searched-for content and look for ways to improve its findability. For example, we have a page named Technical Services but notice a lot of people searching for “cataloging.” We will need to do a better job of connecting people looking for cataloging-related content to the Technical Services pages, by either renaming or redescribing that page. • From Top Landing Pages: We look for pages that are getting hit on that are surprising, and look for

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Figure 43 Top search terms report, WebJunction.

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Figure 44 Traffic sources report, WebJunction.

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Figure 45 Newsletter Views, Monthly Comparisons, WebJunction.

outdated information on those pages and update if needed. For example, there is an old bookmark on some library computers that takes users to an outdated page of our WebJunction. We made sure that page clearly redirects users to more current information. • From the Top Traffic Sources and Top Content reports: We monitor how effectively we are using our newsletters and social networking channels to drive traffic to the website in general as well as to specific pages. As we initiate a new implementation of social networking, I can track its impact on site and page traffic. The report in figure 43 of top search terms used �Using Web Analytics in the Library  Kate Marek

on our website shows that cataloging is a frequently requested search. We will look at how to improve the findability of this content on the site in our navigation menus. Genealogy is also high on the list, and this points out a topic for which we do not have much content to offer. We will put that on our content wish list for future acquisitions efforts. The traffic sources report in figure 44 shows a spike on January 5 that coincides with the broadcast of our member newsletter. You can see that it is the top driver (3) to our website after Google. We also get visits that come from our post-event e-mail to webinar registrants (6) and from our events calendar (7). In the report in figure 45, we can compare the performance of our newsletter in January to the December

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broadcast and see that the January issue brought 58 percent more visits to the website than the previous month. We can then assess what could be the reasons for this, such as the holiday slow period, or the level of interest in the content of the newsletter, or a combination of the two. In general, the staff at WebJunction are able to use Google Analytics to understand their users better and to continually improve and update the website.5

Rutgers-Newark Law Library Wei Fang

• Based on their review of users’ connection speeds, screen resolutions, and browsers, the website staff kept the basic layout and design of the site. At the time (2006), enough visitors were still using lower connection speeds to demonstrate a need to keep heavy graphics at a minimum. Site testing in the browsers most visitors used revealed a good level of compatibility with the visitors’ use and the existing design, including JavaScript and CSS. • The staff added a Most Viewed Items section to the site’s right-hand menu. While GA reports showed that certain pages were routinely among the most visited, drilling down into the statistics of these reports showed that most people found those pages via search engines. By putting links to the popular pages on the site’s main menu, the librarians were better able to promote the content and enhance its use. At the same time, the librarians were able to target other digital collections that users frequently visited when they left the RNLL site (see the guide to the GA Event Tracking tool), so the staff added external links to these sites for their users. One menu item the staff had considered important was very rarely used, so they eliminated it and used the space differently. In general, the RNLL team reorganized and refurbished their menu system based on information about the users’ behavior coming in to their site, while on their site, and when leaving their site.

GA Event Tracking Guide http://code.google.com/apis/analytics/docs/tracking/ eventTrackerGuide.html

Statistics captured before and after the redesign showed success in many areas. Adding a link to the Most Viewed Items resulted in a 30 percent increase in traffic for those pages. Providing links to popular external content, such as the New Jersey Digital Legal Library �Using Web Analytics in the Library  Kate Marek

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An example of a GA case study from the literature is the excellent review by Wei Fang of his library’s 2006 website redesign.6 Fang, Digital Services Librarian at Rutgers-Newark Law Library (RNLL), directed an analysis of website usage using Google Analytics and made suggestions for the site’s redesign based on the findings. The goal of the case study was to prepare for the site’s redesign by tracking the usage and behaviors of the website visitors, to review the site’s existing menu system, and to make suggestions for improving the visitors’ website experience. In addition to information from GA, the digital services department collected some user feedback from paper surveys and an online survey. The Rutgers-Newark Law Library is a part of Rutgers School of Law-Newark, and the purpose of its website is to serve the educational and research needs of the school’s faculty and students. The website also provides services to users outside the library’s primary service community. Indeed, one benefit of using GA was that the library was able to more clearly identify its user population and thus the reach of its services. Google Analytics features used by RNLL started with the various visual summaries presented through the GA Dashboard’s weekly statistics, including how many visitors it had, how many pages those visitors had viewed, whether the visitors were new or returning, where they were coming from, and what search engine the visitors had used to find the library. Fang wrote, “This ‘digital dashboard’ feature greatly enhanced our productivity, since we didn’t need to spend a lot of time reading numbers and analyzing data. It also provided powerful evidence to convince other librarians and administrators of the necessity of making changes to the website.”7 Fang and his staff also looked at trends over time to compare data from different date ranges, as well as the Defined Funnel Navigation feature to gauge the number of people who followed the navigation path the librarians had set. Two more GA features Fang used were the Site Overlay and Visitor Segmentation. Fang could look at one of their most frequently viewed pages

and then drill down through GA to see the visitors’ geographic locations as well as the keywords those visitors had used to find the RNLL site. Fang also used the data export feature, noting that the exported data could be combined with other statistical software for further analysis and institutional comparisons. Findings from the GA data included the visitors’ typical connection speeds, browsers used, percentage breakdowns of screen resolutions, page layout issues associated with usage and access, visitors’ clickpaths, geographical segmentation of users, and overall data regarding specific page popularity. Wang and his staff reviewed and analyzed these data points to inform their site redesign. Here are some of the decisions they made based on the data:

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Figure 46 Accessing Facebook Insights

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Figure 47 Exporting Facebook data by date range

website, increased their referrals to that website by 23.4 percent. And increased usage statistics demonstrated that the overall changes to the menu system also proved to be highly successful. In general, the website had more new and returning visitors, and visitors viewed more pages while on the website (demonstrating greater engagement with the content). Fang reported broad satisfaction with GA and the ability of the Digital Services department to use the GA data for critical insights toward a successful redesign of the website.

Social Media Metrics An aspect of your digital branch usage that will not show up in your analytics report is the activity within your various social networking sites. This activity is an important �Using Web Analytics in the Library  Kate Marek

aspect of your web services program and should be monitored in much the same way your main website is monitored—looking for actionable insights. Watch for increases in activity associated with specific promotions and events, and include your social networking activities in your qualitative assessments and user studies. Look for ways to assess the quality of interaction with these social networking tools rather than just the quantity. You can use free third-party tools such as allfacebookstats.com and twittercounter.com to collect social media metrics. However, library technology expert Dawne Tortorella suggests instead using the more stable embedded metrics within the specific social networking sites: Measuring growth in Twitter followers, Facebook fans, YouTube channel subscribers, or

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Flickr contacts requires consistent monitoring of measurable data and noting significant activity which drives that data. Direct metrics from the site are important; you may gain information from your analytics program such as whether your visitors came to you from a social media site or whether they followed one of your links to a social media site, but will tell you little else. Collect metrics for those social media platforms you actively include

in your outreach, communication, and service activities. A good way to keep track of your social media activity is simply by creating a local spreadsheet and updating it on a regular schedule (preferably weekly, at a consistent time). Use a standard spreadsheet such as Excel as the tool allows for future analysis, chart creation, and trend analysis. In addition to the suggestions below for individual social metrics tools, keep a special comments �Using Web Analytics in the Library  Kate Marek

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Figure 48 Facebook User Data

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Figure 49 Facebook User Demographics breakdown

field where you note specific dates and significant actions that could have affected statistics, views, and interaction on your social media sites.8

Examples of data to collect and source for the data include the following:

page’s discussion board • Wall Posts—Wall posts to your page’s wall See the section “In Depth: Facebook” below for a more detailed description of gathering Facebook statistics.

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Twitter

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Facebook Data collected from Facebook Insights data download: • Unique Users—The number of people who have interacted with or viewed your page or its posts. This includes interactions from fans and non-fans • New Fans—The number of new people who have liked your page • New Unlikes—The number of people who unlike your page • Total Fans—The total number of people who have liked your page • Total Unsubscribers—The total number of users who have hidden your app or page in news feed • Likes—The number of likes made on news feed stories posted by your page • Comments—The number of comments made on news feed stories posted by your page • Discussion Posts—Discussions created on your �Using Web Analytics in the Library  Kate Marek

Data collected from Twitter feed: • Followers—The number of people following your account • Following—The number of accounts you are following • Tweets/Posts—The number of items posted • Mentions—The number of times your organization or Twitter account were mentioned (derived through a search and tally based on time period) • Retweets by you—The number of posts you retweeted • Your tweets, retweeted—The number of your posts that were retweeted • Twitter grade—Produced by Twittergrader.com YouTube Data collected from YouTube:

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• • • • •

Subscribers Comments Channel views Individual video views Total video count

Photostream” you will notice a chart graphic. Click on that graphic and then “Activate your stats.” Notice trends in tagging level and traffic to your images. The more organized, tagged, and fully described the photo, the more traffic it will attract.

Flickr

In Depth: Facebook

Statistics activated within Flickr:

The following screenshots illustrate the tools available within Facebook for tracking Facebook pages. All Facebook administrators of the page have access to “Facebook Insights.” Access Facebook Insights by logging in to your Facebook account and then visiting the Facebook page you administrate. Admins will see the link “View Insights” for exporting and viewing page statistics. Note: Facebook Insights are only activated

• Views • Most viewed images • Contacts By default, Flickr statistics are not activated, but can easily be turned on. To activate Flickr statistics, go to your Flickr home page. To the right of “Your

�Using Web Analytics in the Library  Kate Marek

Library Technology Reports  alatechsource.org  July 2011

Figure 50 Facebook Activity Statistics

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once a page has 30 fans, so you need a small fan base in place before you can use Insights. Insights provides a date range interface that can be modified for any recording period. Once selected, you can export the raw data for detailed analysis. The statistical charts and graphs are helpful for the big picture. The exported data is extensive and can uncover additional trends and usage of your Facebook page. The following screen view shows the active users of the page, as well as a count of fans. It indicates if the fan base has grown or declined. It further illustrates the engagement level of users including page views, comments, and likes. The user breakdown can provide helpful information in determining whether you are reaching your target audience. For example, a public library would hope to find a large population of people from the cities they serve. A view of the gender and age breakdown, and tracking over time, will identify changes in the Facebook user demographic. Activity statistics display page views, as well as unique page views. You will be able to determine exactly what area of the Facebook page is being accessed (wall, discussion boards, events, photos). Analyzing these individual views will identify targeted messaging. The external referrers identify the sources of traffic referral to your Facebook page. The synergy between your social media platforms and your web properties should become apparent when reviewing this data. In addition, this information can help identify good partner institutions and organizations within your community for cross-posting and sharing. In a nutshell, collection of social media analytics requires a combing of the data through either manual means (counting and recording) or through use of the statistical dashboards provided by the service. Knowing these actions and their impact, both in volume and in immediacy, will help in the analysis of your social media presence.9

�Using Web Analytics in the Library  Kate Marek

Summary All of these case studies taken together provide ideas, examples, and confirmation of the potential for library websites and their designers when using web metrics. Librarians can look at the overall mission of the library and its website, and begin to draw conclusions about whether that website is successful. The idea is to spend time with the data from the GA reports and from social networking sites, using the metrics to show trends over time and progress toward your goals. Partnering the quantitative with the qualitative can expand your understanding based on analytics data; as Avinash Kaushik says, if you don’t understand why your users are behaving in a certain way while on your website, ask them. In this way, by using web analytics programs combined with your users’ comments and suggestions, you can truly move from web analysis to web insights.

Notes

1. David Malone, personal correspondence with the author, March 2011. 2. Messenger Public Library, Messenger Public Library Technology Plan, 2010–2013 (North Aurora, IL: Messenger Public Library, 2011), 1. 3. Anthony Molaro, personal correspondence with the author, March 2011. 4. “About Us: Services for Library Staff,” WebJunction website, www.webjunction.org/about (accessed March 25, 2011). 5. Sharon Streams, personal correspondence with the author, March 2011. 6. Wei Fang, “Using Google Analytics for Improving Library Website Content and Design: A Case Study,” in “Libraries and Google,” special issue, Library Philosophy and Practice 2007 (June), www.webpages.uidaho. edu/~mbolin/fang.htm (accessed March 10, 2011). Material for this case study review comes solely from this publication. 7. Ibid., 5. 8. Dawne Tortorella, personal correspondence with the author, April 2011. 9. Ibid.

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Notes

Library Technology Reports Respond to Your Library’s Digital Dilemmas Eight times per year, Library Technology Reports (LTR) provides library professionals with insightful elucidation, covering the technology and technological issues the library world grapples with on a daily basis in the information age. Library Technology Reports 2011, Vol. 47 January 47:1

“Web Scale Discovery Services” by Jason Vaughan

February/ March 47:2

“Libraries and Mobile Services” by Cody W. Hanson

April 47:3 May/June 47:4

“Using WordPress as a Library Content Management System” by Kyle M. L. Jones and Polly Alida-Farrington “Librarians’ Assessments of Automation Systems: � Survey Results, 2007–2010” by Marshall Breeding and Andromeda Yelton

July 47:5

“Using Web Analytics in the Library” by Kate Marek

August/ September 47:6

“Re-thinking the Single Search Box” by Andrew Nagy

October 47:7 November/ December 47:8

“The Transforming Public Library Technology Infrastructure” by ALA Office for Research and Statistics “RFID In Libraries” by Lori Bowen-Ayre

alatechsource.org ALA TechSource, a unit of the publishing department of the American Library Association