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Marrying Web Analytics and User Experience

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Keynote at:
• JBoye Conference; Philadelphia, PA, USA (May 7, 2009)
• IA Konferenz; Hamburg, Deutschland (May 16, 2009)
• Delve NYC; Brooklyn, NY, USA (August 5, 2009)

Published in: Technology, Design

Marrying Web Analytics and User Experience

  1. Marrying Web Analytics and User Experience Louis Rosenfeld • 5 August 2009 Delve NYC • Brooklyn 1
  2. Web Analytics? User Experience? 2
  3. Code “DELVE” for 25% off at rosenfeldmedia.com 3
  4. My recent struggle 4
  5. CONTRASTING WEB ANALYTICS AND USER EXPERIENCE 5
  6. Who we are How we do our work What data we use How we use that data CONTRASTING WEB ANALYTICS AND USER EXPERIENCE 5
  7. WHO WE ARE ARE THE STEREOTYPES TRUE? 6
  8. VIVE LA DIFFÉRENCE! (FROM MARKO HURST) 7
  9. !"#$%&"'()*+),%(-).(%("-&/)0(1/*$%) Behavioral / Eyetracking Data Mining/Analysis A/B (Live) Testing Usability Benchmarking (in lab) / Data Source Usability Lab Studies Online User Experience Assessments (“Vividence-like” studies) Ethnographic Field Studies mix Diary/Camera Study Message Board Mining Participatory Design Customer feedback via email Focus Groups Desirability studies Intercept Surveys Attitudinal Phone Interviews Cardsorting Email Surveys mix Qualitative (direct) Approach Quantitative (indirect) Key for Context of Product Use during data collection Natural use of product De-contextualized / not using product © 2008 Christian Rohrer Scripted (often lab-based) use of product Combination / hybrid 20 HOW USER EXPERIENCE PEOPLE SEE THEIR WORK (FROM CHRISTIAN ROHRER) 8
  10. !"#$%&"'()*+),%(-).(%("-&/)0(1/*$%) Behavioral / Eyetracking Data Mining/Analysis A/B (Live) Testing Usability Benchmarking (in lab) / Data Source Usability Lab Studies Online User Experience Assessments (“Vividence-like” studies) Ethnographic Field Studies mix Diary/Camera Study Message Board Mining Participatory Design Customer feedback via email Focus Groups Desirability studies Intercept Surveys Attitudinal Phone Interviews Cardsorting Email Surveys mix Qualitative (direct) Approach Quantitative (indirect) Key for Context of Product Use during data collection Natural use of product De-contextualized / not using product © 2008 Christian Rohrer Scripted (often lab-based) use of product Combination / hybrid 20 HOW USER EXPERIENCE PEOPLE SEE THEIR WORK (FROM CHRISTIAN ROHRER) 8
  11. HOW WEB ANALYTICS PEOPLE SEE THEIR WORK (FROM AVINASH KAUSHIK) 9
  12. HOW WEB ANALYTICS PEOPLE SEE THEIR WORK (FROM AVINASH KAUSHIK) 9
  13. The data that drives our decisions 10
  14. The data that drives our decisions Web Analytics User Experience behavioral attitudinal quantitative qualitative high fidelity artificial high volume high quality This data is about WHAT This data is about WHY 10
  15. The data that drives our decisions Web Analytics User Experience behavioral attitudinal quantitative qualitative high fidelity artificial high volume high quality This data is about WHAT This data is about WHY 10
  16. The data that drives our decisions Web Analytics User Experience behavioral attitudinal quantitative qualitative high fidelity artificial high volume high quality This data is about WHAT This data is about WHY 10
  17. The data that drives our decisions Web Analytics User Experience behavioral attitudinal quantitative qualitative high fidelity artificial high volume high quality This data is about WHAT This data is about WHY 10
  18. The data that drives our decisions Web Analytics User Experience behavioral attitudinal quantitative qualitative high fidelity artificial high volume high quality This data is about WHAT This data is about WHY 10
  19. The data that drives our decisions Web Analytics User Experience behavioral attitudinal quantitative qualitative high fidelity artificial high volume high quality This data is about WHAT This data is about WHY 10
  20. Not much use to know what is happening if you don’t know why 11
  21. Not much use to know what is happening if you don’t know why Hard to know why things are happening if you don’t know what is happening 11
  22. The ways we analyze our data 12
  23. The ways we analyze our data 12
  24. The ways we analyze our data 12
  25. XXX.XXX.X.104 - - [10/Jul/2006:10:25:46 -0800] "GET /search? access=p&entqr=0&output=xml_no_dtd&sort=date%3AD%3AL %3Ad1&ud=1&site=AllSites&ie=UTF-8&client=www&oe=UTF-8&proxysty lesheet=www&q=lincense+plate&ip=XXX.XXX.X.104 HTTP/1.1" 200 971 0 0.02 XXX.XXX.X.104 - - [10/Jul/2006:10:25:48 -0800] "GET /search? access=p&entqr=0&output=xml_no_dtd&sort=date%3AD%3AL %3Ad1&ie=UTF-8&client=www&q=license+plate &ud=1&site=AllSites&spell=1&oe=UTF-8&proxystylesheet=www&ip=XX X.XXX.X.104 HTTP/1.1" 200 8283 146 0.16 XXX.XXX.XX.130 - - [10/Jul/2006:10:24:38 -0800] "GET /search? access=p&entqr=0&output=xml_no_dtd&sort=date%3AD%3AL %3Ad1&ud=1&site=AllSites&ie=UTF-8&client=www&oe=UTF-8&proxysty lesheet=www&q=regional+transportation+governance +commission&ip=XXX.XXX.X.130 HTTP/1.1" 200 9718 62 0.17 13
  26. XXX.XXX.X.104 - - [10/Jul/2006:10:25:46 -0800] "GET /search? access=p&entqr=0&output=xml_no_dtd&sort=date%3AD%3AL %3Ad1&ud=1&site=AllSites&ie=UTF-8&client=www&oe=UTF-8&proxysty lesheet=www&q=lincense+plate&ip=XXX.XXX.X.104 HTTP/1.1" 200 971 0 0.02 XXX.XXX.X.104 - - [10/Jul/2006:10:25:48 -0800] "GET /search? access=p&entqr=0&output=xml_no_dtd&sort=date%3AD%3AL %3Ad1&ie=UTF-8&client=www&q=license+plate &ud=1&site=AllSites&spell=1&oe=UTF-8&proxystylesheet=www&ip=XX X.XXX.X.104 HTTP/1.1" 200 8283 146 0.16 XXX.XXX.XX.130 - - [10/Jul/2006:10:24:38 -0800] "GET /search? access=p&entqr=0&output=xml_no_dtd&sort=date%3AD%3AL %3Ad1&ud=1&site=AllSites&ie=UTF-8&client=www&oe=UTF-8&proxysty lesheet=www&q=regional+transportation+governance +commission&ip=XXX.XXX.X.130 HTTP/1.1" 200 9718 62 0.17 14
  27. XXX.XXX.X.104 - - [10/Jul/2006:10:25:46 -0800] "GET /search? access=p&entqr=0&output=xml_no_dtd&sort=date%3AD%3AL %3Ad1&ud=1&site=AllSites&ie=UTF-8&client=www&oe=UTF-8&proxysty lesheet=www&q=lincense+plate&ip=XXX.XXX.X.104 HTTP/1.1" 200 971 0 0.02 XXX.XXX.X.104 - - [10/Jul/2006:10:25:48 -0800] "GET /search? access=p&entqr=0&output=xml_no_dtd&sort=date%3AD%3AL %3Ad1&ie=UTF-8&client=www&q=license+plate &ud=1&site=AllSites&spell=1&oe=UTF-8&proxystylesheet=www&ip=XX X.XXX.X.104 HTTP/1.1" 200 8283 146 0.16 XXX.XXX.XX.130 - - [10/Jul/2006:10:24:38 -0800] "GET /search? access=p&entqr=0&output=xml_no_dtd&sort=date%3AD%3AL %3Ad1&ud=1&site=AllSites&ie=UTF-8&client=www&oe=UTF-8&proxysty lesheet=www&q=regional+transportation+governance +commission&ip=XXX.XXX.X.130 HTTP/1.1" 200 9718 62 0.17 Q “What were the most common searches?” 14
  28. XXX.XXX.X.104 - - [10/Jul/2006:10:25:46 -0800] "GET /search? access=p&entqr=0&output=xml_no_dtd&sort=date%3AD%3AL %3Ad1&ud=1&site=AllSites&ie=UTF-8&client=www&oe=UTF-8&proxysty lesheet=www&q=lincense+plate&ip=XXX.XXX.X.104 HTTP/1.1" 200 971 0 0.02 XXX.XXX.X.104 - - [10/Jul/2006:10:25:48 -0800] "GET /search? access=p&entqr=0&output=xml_no_dtd&sort=date%3AD%3AL %3Ad1&ie=UTF-8&client=www&q=license+plate &ud=1&site=AllSites&spell=1&oe=UTF-8&proxystylesheet=www&ip=XX X.XXX.X.104 HTTP/1.1" 200 8283 146 0.16 XXX.XXX.XX.130 - - [10/Jul/2006:10:24:38 -0800] "GET /search? access=p&entqr=0&output=xml_no_dtd&sort=date%3AD%3AL %3Ad1&ud=1&site=AllSites&ie=UTF-8&client=www&oe=UTF-8&proxysty lesheet=www&q=regional+transportation+governance +commission&ip=XXX.XXX.X.130 HTTP/1.1" 200 9718 62 0.17 Q “What were the most common searches?” 14
  29. Analyzing data the UX way: play with the data, look for patterns, trends, and outliers
  30. Analyzing data the UX way: play with the data, look for patterns, trends, and outliers So what’s being measured?
  31. XXX.XXX.X.104 - - [10/Jul/2006:10:25:46 -0800] "GET /search? access=p&entqr=0&output=xml_no_dtd&sort=date%3AD%3AL %3Ad1&ud=1&site=AllSites&ie=UTF-8&client=www&oe=UTF-8&proxysty lesheet=www&q=lincense+plate&ip=XXX.XXX.X.104 HTTP/1.1" 200 971 0 0.02 XXX.XXX.X.104 - - [10/Jul/2006:10:25:48 -0800] "GET /search? access=p&entqr=0&output=xml_no_dtd&sort=date%3AD%3AL %3Ad1&ie=UTF-8&client=www&q=license+plate &ud=1&site=AllSites&spell=1&oe=UTF-8&proxystylesheet=www&ip=XX X.XXX.X.104 HTTP/1.1" 200 8283 146 0.16 XXX.XXX.XX.130 - - [10/Jul/2006:10:24:38 -0800] "GET /search? access=p&entqr=0&output=xml_no_dtd&sort=date%3AD%3AL %3Ad1&ud=1&site=AllSites&ie=UTF-8&client=www&oe=UTF-8&proxysty lesheet=www&q=regional+transportation+governance +commission&ip=XXX.XXX.X.130 HTTP/1.1" 200 9718 62 0.17 16
  32. XXX.XXX.X.104 - - [10/Jul/2006:10:25:46 -0800] "GET /search? access=p&entqr=0&output=xml_no_dtd&sort=date%3AD%3AL %3Ad1&ud=1&site=AllSites&ie=UTF-8&client=www&oe=UTF-8&proxysty lesheet=www&q=lincense+plate&ip=XXX.XXX.X.104 HTTP/1.1" 200 971 0 0.02 XXX.XXX.X.104 - - [10/Jul/2006:10:25:48 -0800] "GET /search? access=p&entqr=0&output=xml_no_dtd&sort=date%3AD%3AL %3Ad1&ie=UTF-8&client=www&q=license+plate &ud=1&site=AllSites&spell=1&oe=UTF-8&proxystylesheet=www&ip=XX X.XXX.X.104 HTTP/1.1" 200 8283 146 0.16 XXX.XXX.XX.130 - - [10/Jul/2006:10:24:38 -0800] "GET /search? access=p&entqr=0&output=xml_no_dtd&sort=date%3AD%3AL %3Ad1&ud=1&site=AllSites&ie=UTF-8&client=www&oe=UTF-8&proxysty lesheet=www&q=regional+transportation+governance +commission&ip=XXX.XXX.X.130 HTTP/1.1" 200 9718 62 0.17 Q “Are we converting license plate renewals?” 16
  33. Before data analysis: why are we here? ★ Commerce ★ Lead Generation ★ Content/Media ★ Support/Self-Service 17
  34. Before data analysis: why are we here? ★ Commerce ★ Lead Generation ★ Content/Media ★ Support/Self-Service Data supports metrics 17
  35. Analyzing data the WA way: start with metrics, benchmark and measure performance
  36. Analyzing data the WA way: start with metrics, benchmark and measure performance But you can’t measure what you don’t know
  37. WA: Top-down analysis UX: Bottom-up analysis 19
  38. what WA: Top-down analysis UX: Bottom-up analysis 19
  39. what WA: Top-down analysis UX: Bottom-up analysis why 19
  40. INTEGRATING WEB ANALYTICS AND USER EXPERIENCE 20
  41. Integrating methodologies: What, then why 21
  42. Common queries can drive task analysis 22
  43. Common queries can drive task analysis “Can you find a map of the campus?” “What study abroad options are available to students?” “When is the last home football game of the season?” 22
  44. Query data can augment personas 23
  45. Query data can augment personas “What Steven Searches” added to existing persona (from Adaptive Path) 23
  46. Looking ahead ★ How do we improve other qualitative methods with data? ★ How do qualitative data impact quantitative analyses? 24
  47. Methodology takeaways: ★ Qualitative research is expensive ★ Start with quantitative research to identify where/when to use qualitative methods 25
  48. Changing how we analyze: Moving away from the middle 26
  49. 27
  50. 28
  51. What’s in the middle? 28
  52. What’s in the middle? Your analytics app’s canned reports 28
  53. Netflix moved away from the middle 29
  54. Netflix moved away from the middle 29
  55. Netflix moved away from the middle 29
  56. Netflix moved away from the middle 29
  57. Netflix moved away from the middle 29
  58. Analysis takeaways ★ Canned reports are only a starting point ★ Move up, move down ★ Be prepared to “roll your own” ★ Demand better ad hoc reporting from analytics apps 30
  59. Changing our thinking: Getting comfortable with the other 31
  60. UX people need to get comfortable with measuring the unmeasurable 32
  61. Can you measure your content’s quality? Systems can help us objectify the subjective 33
  62. Subjective evaluations... Can you measure your content’s quality? Systems can help us objectify the subjective 33
  63. Subjective evaluations... ...lead to Can you measure objective decisions your content’s quality? Systems can help us objectify the subjective 33
  64. UX people need to get comfortable with numbers (but just a little) 34
  65. This is not statistics 35
  66. This is not statistics This is not difficult 35
  67. This is not statistics This is not difficult This is very useful 35
  68. This is not statistics This is not difficult This is very useful (and this is in MS Excel) 35
  69. WA people need to get comfortable with stories 36
  70. WA people need to understand the value of intuition and mistakes 38
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  74. Tom Chi: “Think of your designer as a guide in this multi-variate optimization process. A good designer has been all over parts of the territory a dozen times on various projects and has studied the design patterns and techniques that help in different problems/situations. Because of this, he or she has intuition on how to approach a problem, just as an experienced software architect has intuition on software design approaches that provide different benefits/drawbacks.” 40
  75. UX and WA people need to talk together about project goals 41
  76. 42
  77. Vanguard and the quantification of search Target Oct 3 Oct 10 Oct 16 Mean distance from 1st 3 13 7 5 Median distance from 1st 2 7 3 1 Count: Below 1st 47% 84% 62% 58% Count: Below 5th 12% 58% 38% 14% Count: Below 10th 7% 38% 10% 7% Precision – Strict 42% 15% 36% 39% Precision – Loose 71% 38% 53% 65% Precision – Permissive 96% 55% 72% 92% Note: quantification, not monetization
  78. Changing thinking takeaways ★ Most things can be quantified ★ Stories and emotions can make stronger cases than data, and for data ★ We need more talking, and more listening 44
  79. Challenges: how do we... ★ Bridge cultural gaps? ★ Get different groups to speak the same language? ★ Design and manage integrated teams? ★ Find better, more open tools? ★ Develop a unified methodology? 45
  80. Do we have a choice? An individual often uses only half their brain Effective teams and organizations use both halves 46
  81. Some day my book will come... Search Analytics for Your Site: Conversations with Your Customers Louis Rosenfeld & Marko Hurst Rosenfeld Media, 2009. rosenfeldmedia.com/books/searchanalytics 48
  82. Until then... Louis Rosenfeld 457 Third Street, #4R Brooklyn, NY 11215 USA lou@louisrosenfeld.com www.louisrosenfeld.com www.rosenfeldmedia.com Twitter: @louisrosenfeld @rosenfeldmedia This presentation @ http://www.slideshare.net/lrosenfeld

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