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  • 1. You toolbox for eBusiness success - Web Analytics Congress 2010 w/Sean Power & Jeroen Tjepkema
  • 2. I betook myself to linking...
  • 3. ...tis some visitor entreating entrance at my chamber door
  • 4. About you We won’t tweet about it. Honest.
  • 5. @seanpower @jeroentjepkema #wac2010
  • 6. What kind of site are you?
  • 7. About your company
  • 8. What’s your job? Webops (it’s up, and it’s fast) User experience (it’s easy to use) Community management and monitoring Market research (what people think and why) Support Other
  • 9. What we’ll cover Analytics, interaction, UX, Voice of the Customer EUEM, synthetic tests, RUM Online communities, internal communities Competitive analysis Integrating data sources
  • 10. Which pretty much means We’re going to waste some of your time.
  • 11. What’s complete web monitoring?
  • 12. Ries, Mclure, and Blank are often misquoted.
  • 13. They never said “fail faster”
  • 14. Instead:
  • 15. Learn and adapt.
  • 16. Waterfall, agile, and lean Three approaches for three situations
  • 17. Waterfall methodologies Know the problem and the solution
  • 18. Known ways to Known set of satisfy them requirements Spec Build Test Launch
  • 19. Known ways to Known set of satisfy them requirements Spec Build Test Launch
  • 20. Agile methodologies Know the problem, iterate on the solution
  • 21. Unclear how Known set of to satisfy them requirements Problem statement Build Test Viable? Launch Sprints Adjust
  • 22. Unclear how to Unknown set satisfy them of requirements Problem statement Build Test Viable? Launch Iterations & pivots Redefine problem, business
  • 23. Most new startups don’t know even know what problem they solve.
  • 24. Possible viable offering You are Trial startup t here vo Pi Possible Possible Possible viable Trial startup problem Trial startup viable offering space offering Trial startup Possible viable offering
  • 25. As we become more agile, we need to be more aware.
  • 26. Startups 101: as seen by Eric Ries & Sean Ellis ps: the concepts in the next two slides are full of awesome. Look Sean, Eric and Dave up. IDEAS Learn  Faster Code  Faster LEARN Growth BUILD Unit  Tests Split  Tests Customer  Interviews Transition to Usability  Tests Customer  Development Growth Con7nuous  Integra7on Five  Whys  Root  Cause  Analysis Incremental  Deployment Customer  Advisory  Board Free  &  Open-­‐Source  Components Falsifiable  Hypotheses Product/Market Fit Cloud  Compu7ng by: Sean Ellis Cluster  Immune  System Product  Owner  Accountability Customer  Archetypes Just-­‐in-­‐7me  Scalability DATA CODE Refactoring Cross-­‐func7onal  Teams Semi-­‐autonomous  Teams Developer  Sandbox Smoke  Tests Measure  Faster MEASURE Split  Tests Funnel  Analysis Clear  Product  Owner Cohort  Analysis Con7nuous  Deployment Net  Promoter  Score Usability  Tests Search  Engine  Marke7ng Real-­‐7me  Monitoring Real-­‐Time  Aler7ng Customer  Liaison Predic7ve  Monitoring
  • 27. !"# !"#$"%&'()*!+',-(,( !"# !" !"#$%&' !"#$%&' ()*+",-. !"#$% !"#$%&'( !""#$%$&'()*+# !"#$%&'()$*'()+ !"#$% !"#$"%&'()*!+',-(,( !"#$%&' 1.  ACQUISITION RAL FER 4.  RE Emails  &  Alerts 2.  A !"#$%&'$()(*&+,-+'(.&'$ ctiv !"#$%&'(')$*+,-& atio ON NTI !"#$%&'( E TE )*+'%"*, 3.  R n System  Events  &  Time-­‐based   Features Blogs,  New  Content !"#$%&' !"#$%&'("%)'*$% +,-#./01203*#$%'2. 5.  R ev e Website.com nue  $$$ AARRR! by Dave McClure
  • 28. Complete Web Monitoring The big picture
  • 29. Users do what we wanted Enrolment: They sign up Purchases: They buy stuff Invitations: They tell their friends Stickiness: They stay for longer Loyalty: They come back Contribution: They add content
  • 30. What could we watch? What we’d like to know Tool set How much did visitors benefit my business? Internal analytics Where is my traffic coming from? External analytics What’s working best (and worst?) Usability testing How good’s my relationship with my market? Customer surveys, community How healthy is my infrastructure? Performance monitoring How am I doing against my competitors? Search, external testing Where are my risks? Search, alerting What are people saying about me? Search, community monitoring How is my content being used elsewhere? Search, external analytics
  • 31. How much did visitors benefit my business? Internal analytics Conversion and Billing and account use abandonment Click-throughs Offline activity User-generated content Subscriptions
  • 32. Where’s my traffic coming from? External analytics Referring websites Inbound links from social networks Visitor motivation
  • 33. What’s working best (and worst)? Usability testing, A/B testing Site effectiveness Trouble ticketing and escalation Upselling effectiveness Content popularity Ad and campaign effectiveness Usability Findability and search User productivity effectiveness Community ranking and rewards
  • 34. How good is my relationship with my market? Customer surveys, community monitoring Loyalty Enrollment Reach and rewards
  • 35. How healthy is my infrastructure? Performance monitoring Availability and Impact of performance performance on outcomes SLA compliance Content delivery Capacity and flash traffic
  • 36. How am I doing against my competitors? Performance monitoring Site popularity and ranking How are people finding my competitors? Relative site performance Competitor activity
  • 37. Where are my risks? Search, alerting Trolling and spamming Copyright and legal liability Fraud, privacy, and account sharing
  • 38. What are people saying about me? Search, community monitoring Site reputation Trends Social network activity
  • 39. How is my content being used elsewhere? Search, external analytics API access and usage Mashups, stolen content, and illegal syndication Integration with legacy systems
  • 40. The difference between accounting and optimization
  • 41. http://www.flickr.com/photos/roryfinneren/65729247
  • 42. Chair rentals per day 50 37,5 25 12,5 0 1 2 3 4 5 6 7 8 9 10 http://www.rvca.com/anp/wp-content/plugins/wp-o-matic/cache/57226_07+proof+1a+hb+beach+day.jpg
  • 43. http://www.imdb.com/media/rm3768753408/tt0073195
  • 44. http://www.flickr.com/photos/kapungo/2287237966
  • 45. Ice cream and drownings 10000 1000 100 10 1 Ice cream consumption Drownings
  • 46. http://www.flickr.com/photos/25159787@N07/3766111564
  • 47. http://www.flickr.com/photos/wheressteve/3284532080
  • 48. http://www.flickr.com/photos/wtlphotos/1086968783
  • 49. True causality 10000 1000 100 10 1 Jan Feb Mar Apr May Jun Jul Aug Sept Oct Nov Dec Ice cream consumption Drownings Temperature
  • 50. http://www.flickr.com/photos/stuttermonkey/57096884
  • 51. http://www.flickr.com/photos/germanuncut77/3785152581
  • 52. http://www.flickr.com/photos/fasteddie42/2421039207
  • 53. Everybody has goals. http://www.flickr.com/photos/itsgreg/446061432/
  • 54. Organic Ad Campaigns search network $ 1 1 1 Advertiser site Visitor 2 O er 3 $ 8 Upselling 4 Abandonment Reach 5 Purchase step $ Mailing, alerts, Purchase step $ 9 promotions $ Conversion $ Disengagement 7 Enrolment 6 Impact on site $ Positive $ Negative
  • 55. Bad $ 4 content Social Search Invitation network link results 4 Good content 1 $ 1 1 Collaboration site 2 Visitor Content creation Moderation $ 3 Spam & trolls $ Engagement 5 Viral 6 Social graph spread 7 Disengagement $ Impact on site $ Positive $ Negative
  • 56. Enterprise subscriber $ 1 End user (employee) $ Refund $ 2 Renewal, upsell, SLA reference SaaS site violation Performance Good Bad 3 Helpdesk Support 5 $ Usability escalation costs 7 4 Good Bad Productivity Good Bad 6 Churn $ Impact on site $ Positive $ Negative
  • 57. $ Media site Enrolment Targeted 2 embedded ad 5 $ 6 1 Ad Visitor network 4 3 5 Advertiser $ Departure $ site Impact on site $ Positive $ Negative
  • 58. Analytics is the measurement of movement towards those goals. http://www.flickr.com/photos/itsgreg/446061432/
  • 59. ATTENTION ENGAGEMENT CONVERSION NEW VISITORS SEARCHES GROWTH CONVERSION PAGES TIME RATE TWEETS NUMBER OF VISITS PER ON x MENTIONS VISIT SITE GOAL ADS SEEN LOSS VALUE BOUNCE RATE
  • 60. http://www.flickr.com/photos/itsgreg/446061432/ Lots of moving parts.
  • 61. “Hard” data Analytics Usability Performability (what did they (how did they (could they do do on the interact with what they site?) it?) wanted to?) Complete Web Monitoring VoC Communilytics Competition (what were (what were (what are they their they saying?) up to?) motivations?) “Soft” data
  • 62. “Hard” data Analytics Usability Performability (what did they (how did they (could they do do on the interact with what they site?) it?) wanted to?) Complete Web Monitoring VoC Communilytics Competition (what were (what were (what are they their they saying?) up to?) motivations?) “Soft” data
  • 63. http://www.d-9.com/
  • 64. These people drive nicer cars than us. :/ Source: http://www.webanalyticsdemystified.com/sample/Web_Analytics_Demystified_RESEARCH_-_March_2007_-_Salary_Survey.pdf
  • 65. Hits
  • 66. http://bit.ly/5H5Xc6
  • 67. Hits Pages
  • 68. http://www.cs.cmu.edu/~jasonh/blog/evolution-big.png
  • 69. Hits Pages Sessions
  • 70. Hits Pages Sessions Visitors
  • 71. Hits Pages Sessions Visitors Segments
  • 72. e ar e ts es en Th gm se
  • 73. (You can make your own.)
  • 74. http://www.human20.com/who- owns-your-voice-online/ ?utm_source=abowyer &utm_medium=twitter &utm_content=communication &utm_campaign=post
  • 75. Who would you rather have sending a message?
  • 76. Old analytics: report the news http://www.flickr.com/photos/thomasclaveirole/538819881/
  • 77. http://www.flickr.com/photos/23883605@N06/2317982570/sizes/l/
  • 78. Old analytics: New analytics: report the news optimize goals http://www.flickr.com/photos/thomasclaveirole/538819881/ http://www.flickr.com/photos/sanchom/2963072255/
  • 79. blah blah blah ... A unique visitor arrives at your website, possibly after following a link that referred them. They land on a web page, and either bounce (leave immediately) or request additional pages. In time, they may complete a transaction that’s good for your business, converting them from a mere buyer into something more—a customer, a user, a member, or a contributor—depending on the kind of site you’re running. On the other hand, they may abandon that transaction and ultimately exit the website. That visitor has many external attributes—such as the browser they’re using, or where they’re surfing from—that let you group them into segments. They may also see different offers or pages during their visit, which are the basis for further segmentation. The goal of analytics, then, is to maximize conversions by optimizing your website, often by experimenting with different content, layout, and campaigns, and analyzing the results of those experiments on various internal and external segments.
  • 80. Find the site The three stages of a Use the site unique visit Leave the site
  • 81. Find the site: How did they get there?
  • 82. “Direct” traffic isn’t. Type-In Traffic Bookmarking JavaScript redirect Browser Inconsistencies Bots, Spiders and Probes
  • 83. source: the conversation prism by Brian Solis and JESS3 http://www.theconversationprism.com
  • 84. Use the site: What did they do?
  • 85. Landing page: Task: View one story Create account Task: Log in Pick name Place: View stories Check if free Enter credentials Vote up Next 25 Set Password Verify Vote down Last 25 CAPTCHA Recovery Send mail Place: Read Get confirm poster comments Vote up Next 25 Task: Vote down Last 25 Forward a story Task: Submit Enter recipients a new story Place: My Enter message Enter URL account Send Describe Change My address comments Deduplicate Change PW See karma Post it
  • 86. Landing page: Create acct. Create acct. View one story Form uptime Place: View stories Task: Log in # started Place: View stories Bad form Stories/visit # up/down Place: Read # CAPTCHA poster comments Time/story Mail uptime Top stories Task: Forward a story Task: Submit Refresh time Mail bounced Views/page a new story Place: My Confirm & return account Return 3x
  • 87. Places Efficiency matters How quickly, how many, productivity Learning curve OK Leave when they’re bored Collect “aha” feedback A/B test content for pages/session, exits
  • 88. Tasks Effectiveness matters Completion, abandonment Intuitiveness rules Leave when they change their mind or it breaks Collect “motivation” feedback A/B test layouts for conversion
  • 89. Now suppose that you have a specific goal, such as a visitor filling out a survey on your website. You can analyze how many people completed that goal over time and measure the success of your business in a report like the one in
  • 90. Leave the site: Parting is such sweet sorrow
  • 91. Pages per visit Time on site :-D :-) 16 2,1 15 1,6 Minutes 14 1,1 13 0,5 12 0 September October September October Email opt-outs Days between visits :-| O_o 26.000 5 19.500 3,75 13.000 2,5 6.500 1,25 0 0 September October September October
  • 92. “Hard” data Analytics Usability Performability (what did they (how did they (could they do do on the interact with what they site?) it?) wanted to?) Complete Web Monitoring VoC Communilytics Competition (what were (what were (what are they their they saying?) up to?) motivations?) “Soft” data
  • 93. How did they do it? Web Interaction Analytics
  • 94. http://www.flickr.com/photos/trekkyandy/189717616/
  • 95. Yes Seen False (perceptible) Perceptual information affordance affordance (did I see it?) Unseen Correct (hidden) rejection affordance No No Affordance Yes (was I supposed to interact with it?) Adapted from Gaver (1991)
  • 96. http://www.flickr.com/photos/americanlady/3118301118 consume http:// give data navigate
  • 97. Usability issue 1: Visitors don’t see what you wanted them to.
  • 98. Your mileage will vary.
  • 99. Usability issue 2: Visitors don’t interact as you intended.
  • 100. Usability issue 3: Visitors don’t input data
  • 101. “Hard” data Analytics Usability Performability (what did they (how did they (could they do do on the interact with what they site?) it?) wanted to?) Complete Web Monitoring VoC Communilytics Competition (what were (what were (what are they their they saying?) up to?) motivations?) “Soft” data
  • 102. Voice of the customer Why did they do it?
  • 103. People on the internet do weird things
  • 104. So what’s this “VOC” thing? Get new ideas Evaluate things you can’t collect in other ways Evaluate sentiment Collect demographics data
  • 105. http://4.bp.blogspot.com/_0iHpQZ3MU1E/SnJxr-HYeoI/AAAAAAAAAAw/pnMWYdWi75A/s320/oldlady.jpg
  • 106. http://threeminds.organic.com/virtual%20online%20community2.jpg
  • 107. “Hard” data Analytics Usability Performability (what did they (how did they (could they do on the interact with do what they site?) it?) wanted to?) Complete Web Monitoring VoC Communilytics Competition (what were (what were (what are they their they saying?) up to?) motivations?) “Soft” data