Marrying Web Analytics and User Experience
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Marrying Web Analytics and User Experience

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

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  • Full Name Full Name Comment goes here.
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  • Wonderful presentation but pity can not open it in Windows machine. The.key format format is compatible with Mac and not Windows.
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  • Very helpful and nicely presented.

    Thanks for sharing.
    Parcel delivery from http://www.clickandsendparcel.com
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  • Thanks! But there's actually a more up-to-date (and I think better) version here: http://www.slideshare.net/lrosenfeld/beyond-user-research
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  • Simply Superb
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  • I've recently volunteered to take on the analytics area of our usability team, and I've been struggling with how my role will work with our existing analytics team (who is not part of ux). This presentation was a huge help. It confirmed some ponderings I was already having and started to put some practical ideas around them. Thanks for sharing this!
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  • http://www.nif.or.jp/eng/graph/M7.gif <br /> http://interactions.acm.org/i/XV/wine.jpg
  • Camille Jordan: <br /> http://myoops.fgu.edu.tw/twocw/mit/NR/rdonlyres/Mathematics/18-700Fall-2005/4AC2EE51-AA81-45EA-AB73-1935A7F3BAFC/0/chp_jordan2.jpg <br /> <br /> Arthur Rimbaud: <br /> http://www.stevesilberman.com/celestial/rimbaud/rimbaud.jpg <br /> <br /> Those dreaming eyes: are the looking upon the same thing?
  • Avinash Kaushik: &#x201C;Trinity: A Mindset & Strategic Approach&#x201D; (http://www.kaushik.net/avinash/2006/08/trinity-a-mindset-strategic-approach.html)
  • Analysis versus synthesis comes from Lindsay Ellerby article in UXMatters: http://www.uxmatters.com/mt/archives/2009/04/analysis-plus-synthesis-turning-data-into-insights.php
  • Analysis versus synthesis comes from Lindsay Ellerby article in UXMatters: http://www.uxmatters.com/mt/archives/2009/04/analysis-plus-synthesis-turning-data-into-insights.php
  • Analysis versus synthesis comes from Lindsay Ellerby article in UXMatters: http://www.uxmatters.com/mt/archives/2009/04/analysis-plus-synthesis-turning-data-into-insights.php
  • Analysis versus synthesis comes from Lindsay Ellerby article in UXMatters: http://www.uxmatters.com/mt/archives/2009/04/analysis-plus-synthesis-turning-data-into-insights.php
  • Analysis versus synthesis comes from Lindsay Ellerby article in UXMatters: http://www.uxmatters.com/mt/archives/2009/04/analysis-plus-synthesis-turning-data-into-insights.php
  • Analysis versus synthesis comes from Lindsay Ellerby article in UXMatters: http://www.uxmatters.com/mt/archives/2009/04/analysis-plus-synthesis-turning-data-into-insights.php
  • Bottom line and &#x201C;top line&#x201D;
  • This is how I would do it
  • This is how I&#x2019;d do it
  • This is how I&#x2019;d do it
  • This is how my co-author would do it
  • Start with KPI, then add data
  • Feedback loop
  • Start with KPI, then add data
  • The reports are often as far as we go <br /> But they&#x2019;re often useless <br /> &#x2022; No deep, custom analysis (top-down) <br /> &#x2022; No exploratory data analysis (bottom-up)
  • The reports are often as far as we go <br /> But they&#x2019;re often useless <br /> &#x2022; No deep, custom analysis (top-down) <br /> &#x2022; No exploratory data analysis (bottom-up)
  • &#x201C;The center can not hold!&#x201D; <br /> <br /> You&#x2019;ll notice this isn&#x2019;t a canned report <br /> <br /> This all means putting pressure on commercial analytics apps to change
  • &#x201C;The center can not hold!&#x201D; <br /> <br /> You&#x2019;ll notice this isn&#x2019;t a canned report <br /> <br /> This all means putting pressure on commercial analytics apps to change
  • &#x201C;The center can not hold!&#x201D; <br /> <br /> You&#x2019;ll notice this isn&#x2019;t a canned report <br /> <br /> This all means putting pressure on commercial analytics apps to change
  • &#x201C;The center can not hold!&#x201D; <br /> <br /> You&#x2019;ll notice this isn&#x2019;t a canned report <br /> <br /> This all means putting pressure on commercial analytics apps to change
  • Start with KPI, then add data
  • you can do this, regardless of how you feel about data <br /> <br /> note that it&#x2019;s in Excel
  • you can do this, regardless of how you feel about data <br /> <br /> note that it&#x2019;s in Excel
  • you can do this, regardless of how you feel about data <br /> <br /> note that it&#x2019;s in Excel
  • Yes, data can tell stories <br /> <br /> And sometimes stories make a better case than reports
  • Actually, both sides (Bowman&#x2019;s and Google&#x2019;s) are valid <br /> But while it won&#x2019;t always be possible to combine WA and UX (in some orgs, one perspective is far dominant--e.g., engineering at Google), you&#x2019;ve got to come halfway <br /> <br /> But... weren&#x2019;t Page and Brin designers of a sort when they started out?
  • Actually, both sides (Bowman&#x2019;s and Google&#x2019;s) are valid <br /> But while it won&#x2019;t always be possible to combine WA and UX (in some orgs, one perspective is far dominant--e.g., engineering at Google), you&#x2019;ve got to come halfway <br /> <br /> But... weren&#x2019;t Page and Brin designers of a sort when they started out?

Marrying Web Analytics and User Experience Marrying Web Analytics and User Experience Presentation Transcript

  • Marrying Web Analytics and User Experience Louis Rosenfeld • 5 August 2009 Delve NYC • Brooklyn 1
  • Web Analytics? User Experience? 2
  • Code “DELVE” for 25% off at rosenfeldmedia.com 3
  • My recent struggle 4
  • CONTRASTING WEB ANALYTICS AND USER EXPERIENCE 5
  • Who we are How we do our work What data we use How we use that data CONTRASTING WEB ANALYTICS AND USER EXPERIENCE 5
  • WHO WE ARE ARE THE STEREOTYPES TRUE? 6
  • VIVE LA DIFFÉRENCE! (FROM MARKO HURST) 7
  • !"#$%&"'()*+),%(-).(%("-&/)0(1/*$%) 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
  • !"#$%&"'()*+),%(-).(%("-&/)0(1/*$%) 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
  • HOW WEB ANALYTICS PEOPLE SEE THEIR WORK (FROM AVINASH KAUSHIK) 9
  • HOW WEB ANALYTICS PEOPLE SEE THEIR WORK (FROM AVINASH KAUSHIK) 9
  • The data that drives our decisions 10
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • Not much use to know what is happening if you don’t know why 11
  • 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
  • The ways we analyze our data 12
  • The ways we analyze our data 12
  • The ways we analyze our data 12
  • 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
  • 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
  • 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
  • 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
  • Analyzing data the UX way: play with the data, look for patterns, trends, and outliers
  • Analyzing data the UX way: play with the data, look for patterns, trends, and outliers So what’s being measured?
  • 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
  • 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
  • Before data analysis: why are we here? ★ Commerce ★ Lead Generation ★ Content/Media ★ Support/Self-Service 17
  • Before data analysis: why are we here? ★ Commerce ★ Lead Generation ★ Content/Media ★ Support/Self-Service Data supports metrics 17
  • Analyzing data the WA way: start with metrics, benchmark and measure performance
  • Analyzing data the WA way: start with metrics, benchmark and measure performance But you can’t measure what you don’t know
  • WA: Top-down analysis UX: Bottom-up analysis 19
  • what WA: Top-down analysis UX: Bottom-up analysis 19
  • what WA: Top-down analysis UX: Bottom-up analysis why 19
  • INTEGRATING WEB ANALYTICS AND USER EXPERIENCE 20
  • Integrating methodologies: What, then why 21
  • Common queries can drive task analysis 22
  • 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
  • Query data can augment personas 23
  • Query data can augment personas “What Steven Searches” added to existing persona (from Adaptive Path) 23
  • Looking ahead ★ How do we improve other qualitative methods with data? ★ How do qualitative data impact quantitative analyses? 24
  • Methodology takeaways: ★ Qualitative research is expensive ★ Start with quantitative research to identify where/when to use qualitative methods 25
  • Changing how we analyze: Moving away from the middle 26
  • 27
  • 28
  • What’s in the middle? 28
  • What’s in the middle? Your analytics app’s canned reports 28
  • Netflix moved away from the middle 29
  • Netflix moved away from the middle 29
  • Netflix moved away from the middle 29
  • Netflix moved away from the middle 29
  • Netflix moved away from the middle 29
  • 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
  • Changing our thinking: Getting comfortable with the other 31
  • UX people need to get comfortable with measuring the unmeasurable 32
  • Can you measure your content’s quality? Systems can help us objectify the subjective 33
  • Subjective evaluations... Can you measure your content’s quality? Systems can help us objectify the subjective 33
  • Subjective evaluations... ...lead to Can you measure objective decisions your content’s quality? Systems can help us objectify the subjective 33
  • UX people need to get comfortable with numbers (but just a little) 34
  • This is not statistics 35
  • This is not statistics This is not difficult 35
  • This is not statistics This is not difficult This is very useful 35
  • This is not statistics This is not difficult This is very useful (and this is in MS Excel) 35
  • WA people need to get comfortable with stories 36
  • WA people need to understand the value of intuition and mistakes 38
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  • 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
  • UX and WA people need to talk together about project goals 41
  • 42
  • 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
  • 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
  • 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
  • Do we have a choice? An individual often uses only half their brain Effective teams and organizations use both halves 46
  • 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
  • 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