Ia Summit08 Wa Slides


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Web Use Recorders: The future of web analytics?

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  • Ia Summit08 Wa Slides

    1. 1. The future of web analytics: Inferring User Intent from Session Replays Dustin Chambers & Dick Horst UserWorks inc.
    2. 3. First source of web use data <ul><ul><li> - - [31/Oct/2005:18:15:16 -0500] &quot;GET /styles/style.css HTTP/1.1&quot; 200 5194 &quot;http://www.example.com/links/links.php?cat=css&quot; &quot;Mozilla/5.0 (Windows; U; Windows NT 5.1; en-US; rv:1.7.12) Gecko/20050915 Firefox/1.0.7&quot; </li></ul></ul>
    3. 4. Early attempts to visualize data
    4. 5. Dashboard
    5. 6. Page tagging
    6. 7. Google Analytics -Google Analytics
    7. 8. Heat Maps
    8. 9. Page Overlay -CrazyEgg
    9. 10. Hover Map
    10. 11. Session replay e.g.
    11. 12. Providers? Cost? <ul><li>RobotReplay – Free </li></ul><ul><li>www.robotreplay.com </li></ul><ul><li>ClickTale – Free - $99/month </li></ul><ul><li>www.clicktale.com </li></ul><ul><li>TeaLeaf - $20,000+ </li></ul><ul><li>www.tealeaf.com </li></ul>
    12. 13. How is this any better? How do we use it?
    13. 14. Alternative to eye-tracking? <ul><li>“ The data suggest that there is a very strong relationship between gaze position and cursor position.” </li></ul><ul><li>“ This implies that we can use an inexpensive and extremely popular tool as an alternative of eye-tracking systems, especially in web usability systems.” </li></ul><ul><li>-Carnegie Mellon, 2001 </li></ul>
    14. 15. Information Foraging Theory <ul><li>“ What information consumes is rather obvious: it consumes the attention of its recipients. Hence a wealth of information creates a poverty of attention, and a need to allocate that attention efficiently among the overabundance of information sources that might consume it.” </li></ul><ul><li>-Herb Simon (Nobel Prize Winner) </li></ul>
    15. 16. Consider the anecdotal evidence <ul><li>“ Just by looking at the way a mouse moves, I can tell whether you are reading a webpage or not.” “I can tell because when you read a webpage, you do one of two things. You either shove the mouse off to the right so it is out of the way, or you will walk down the page with your mouse.” </li></ul><ul><ul><li>Dr. Ted Selker, MIT Media Lab </li></ul></ul>
    16. 17. Ambiguity <ul><li>“ Slow and arched trajectories as users move their mouse would indicate an ambiguous state of mind.” </li></ul><ul><li>-MIT Media Lab </li></ul>
    17. 18. Are you experienced? <ul><li>“ People are extremely good at remembering graphic design. So when you act like you know where you are going on a place where you have no reason to know, then we know you have been there before.” </li></ul><ul><ul><li>Dr. Ted Selker, MIT Media Lab </li></ul></ul>
    18. 19. Web Analytics 2.0 -Avinash Kaushik
    19. 20. Web Analytics 2.0 <ul><li>Apply Context </li></ul><ul><li>Segment </li></ul><ul><li>Triangulate/Validate </li></ul>
    20. 21. Examples Inferring user intent using the latest web analytic technology with the latest theory
    21. 22. What’s next? Bleeding-edge technology Future Implications…
    22. 23. Integrated Triangulation <ul><li>Surveys </li></ul><ul><ul><li>Why are you here? </li></ul></ul><ul><ul><li>Where you able to complete? </li></ul></ul><ul><ul><li>Why not? </li></ul></ul><ul><ul><li>How satisfied? </li></ul></ul><ul><li>Unmoderated remote usability testing </li></ul>
    23. 24. Enhanced Segmentation <ul><li>Filter by: </li></ul><ul><ul><li>Task completion </li></ul></ul><ul><ul><li>Satisfaction rating </li></ul></ul><ul><ul><li>User intent </li></ul></ul><ul><ul><li>Problem encountered </li></ul></ul><ul><ul><li>Time on task </li></ul></ul><ul><ul><li>Search term </li></ul></ul>
    24. 25. Thanks for listening! <ul><li>Dustin Chambers </li></ul><ul><li>[email_address] </li></ul><ul><li>Dr. Dick Horst </li></ul><ul><li>dhorst@userworks.com </li></ul>