1. Improving the User’s Experience through Web Analytics* SAA Research Forum Chicago, Illinois August 28, 2007 Christopher J. Prom Assistant University Archivist University of Illinois at Urbana-Champaign prom@uiuc.edu *Special thanks to Beth Yakel, Aprille McKay, and Helen Tibbo!

2. What is Web Analytics Web Measurement: gathering and parsing the data Web Analytics: interpreting measurement reports so that some action can be taken – Eric T. Peterson, Web Site Measurement Hacks, p. 3 “the measurement, collection, analysis and reporting of Internet data for the purposes of understanding and optimizing Web usage”-- Neil Mason, “The Four Parts of Web Analytics,” http://www.google.com/analytics/cu/ac_the_four_parts.html http://www.google.com/analytics/cu/ac_the_four_parts.html

3. Why use it? Most archival user study involve intervention or surveys/interviews Both are useful But... –All interventions affect behavior –What people say they do is notoriously inaccurate Allows unobtrusive observation of actions (NOT motives or initiative) May provide basis for action or further study Daniel Russell, regarding Google’s Eyetracking studies: “ people behave differently here, they want to make researcher happy, have been given a free lunch”

4. Internal Log Data 138,041 hits on collection records 6,721 hits on “browse by digital content” 117,468 hits on search page 14,210 hits on “browse by provenance” (12,737 non-staff) 1,855 hits on full finding aid 2,287 hits on “browse by subject” 1,255 hits on “browse by title” 449 hits on “PDF/Deep Search”

5. Step 1: Analyze—Overview of UIUC ‘non-virtual’ use in 2005/06

6. Step Two—Understand: Current Website Structure/Purpose Structure Facilitate Contact Provide Descriptive Information About us; Link to Programs Mediate use Promote Services Archival Website Goals: Provide information on services? Provide access to descriptive information? Drive on-line or on-site use? Facilitate research, service, learning? (Developing Archival Metrics http://www.si.umich.edu/ArchivalMetrics/ )http://www.si.umich.edu/ArchivalMetrics/ More Complex

7. Step Three—Inform: Develop a Privacy Policy Google http://www.google.com/analytics/tos.html http://www.google.com/analytics/tos.html –COLLECTS what they term “anonymous traffic data” (includes IP Address) –REPORTS summary information only –Requires use of a privacy policy UIUC Archives http://web.library.uiuc.edu/ahx/about/privacy.php http://web.library.uiuc.edu/ahx/about/privacy.php

8. Step 4—Plan and limit scope: What I tried to measure Q1:How do people get to our site? Q2: What are the most popular pages/groups of content? Q3: What are most popular searches? Q4: How do users move through the net and our site toward 4 ‘goals’? –Use search form –View record series description –View full finding aid –Send email Better

9. My ‘Goal Conversion Funnel’ User visits or uses materials remotely! Caveat: Does not include direct ‘hits’ via Google, or other external referrers Caveat: Does not include emails sent directly to the address illiarch@uiuc.edu User enters site after Google Search - or -

10. Question One: “Referrers”

11. Google as “Referrer”

12. Question Two: Top Content Areas

13. Top Content:Homepage July 4Weekends 6.3%

14. “Archon” (Holdings Database) Area Lesson: the Holdings database is the heart of our web presence

15. Views Record Series Descriptions Hmmm….

16. Question Three: What do users search for? Can measure both external and internal searches Drill down to see ‘clickstream’ and exit pages

17. Google Keywords (Searches)

18. 4. Movement toward Goals

19. Goals Overview Lesson: Most users view our collection information at some point

20. Goal 1: Views Record Series Description Re-running searches to analyze how users get to and leave the site

21. “Clara Hamilton” YIKES!!!

22. Wow! Deadend!

23. “Daily Illini”

24. 17 (!) in Google Result Set Lesson We MUST provide links to digital content (where it exists) or create it (where it does not exist and users are trying to find it).

25. “Strip Mines and Illinois”

26. Clicked! Lessons Importance of controlled subjects area element usage Optimize ‘landing page’ It’s the digital content, stupid

27. Conclusion 1: Beware Making Assumptions about Online Use That users enter our main page, then search That users see our homepage and use instructional/policy materials linked from it That users want to walk a prescribed path to the physical records ( DON’T assume visitor intent, Peterson, p. 25 ) That digital content is a ‘value added’ function (in reality it is essential)

28. Conclusion 2: Google ranks digital content ‘higher’ Images Full Description

29. Conclusion 3: Tailor Site Improvements to Revised Goals Add more contextual information to landing pages (e.g. subject) Identify materials for digitization (keyword analysis) Improve Google page rank by integrating better information into title (66 character rule) Improve user experience by being concise and putting most important information first

30. Conclusion 4: Emphasize content, not description GA provides metrics that can drive decision making Use keyword analysis to identify content for digitization

31. Improving the User’s Experience through Web Analytics* SAA Research Forum Chicago, Illinois August 28, 2007 Christopher J. Prom Assistant University Archivist University of Illinois at Urbana-Champaign prom@uiuc.edu *Special thanks to Beth Yakel, Aprille McKay, and Helen Tibbo!