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Prasad Kantamneni Eye-Tracking At Yahoo! Overview of the Method Bay-CHI: Eye-Tracking Birds of feather meeting. June 22, 2...
About Me <ul><li>Research Scientist at Honeywell Labs </li></ul><ul><li>Research Engineer at WSU </li></ul><ul><li>Princip...
The Big Picture <ul><li>“  When you do something on Yahoo! Search 54M people will see it by the end of the week” </li></ul...
The Big Picture – Eye Tracking <ul><li>Running ET for about 4 years at Yahoo! </li></ul><ul><li>Deployed on multiple produ...
Why are we doing what we are doing?  <ul><li>In 2005 Traditional measurement methods were falling short of measuring and u...
History <ul><li>Round 1 of Eye-tracking studies in early 2006 with the standard Tobii software – to understand the value o...
History <ul><li>Round 2 of Eye-tracking studies in late 2006 - 2008 </li></ul><ul><ul><ul><li>Changed Software to Eyetools...
High resolution image seen by the Fovea Reduced visual acuity experienced by the parafovea Progressively reducing visual a...
Users use parafoveal preview to identify the parts most likely to have relevant information based on the location of boldf...
Familiar summary patterns draw user attention and clicks Users are relatively blind to unfamiliar summary patterns.
Learned how to model and extrapolate <ul><li>Users use  bolding  in  titles  to  rapidly scan  the SRP.  </li></ul><ul><li...
Learned to test Models with click logs Design A Conversational title style Design B To the point title (query term – prope...
Learned when and how to introduce changes into a UI  <video> Yahoo! Presentation
Learned when not to introduce change Rapidly Evaluating new ideas, and predicting user behavior    Launched with Keywords...
Learned how to quantifying the effectiveness of a UI and optimize for learning Old Yahoo! Y! with Search Assistance
Learned how to predict learning and better interpret logs <ul><li>Once you optimize for learning, give time for people to ...
Learned how to make money <ul><li>Rich advertisements help advertisers convert better, and command a premium in the market...
History <ul><li>Round 2 of Eye-tracking studies in late 2006 - 2008 </li></ul><ul><ul><li>By the end of the program we had...
History <ul><li>Round 3: 2009 - date </li></ul><ul><ul><li>Bring the cool back </li></ul></ul><ul><ul><li>Wanted to scale ...
History <ul><li>Round 3  - 2009 - date </li></ul><ul><ul><li>Redesigned Labs to support concurrent data collection </li></...
The future <ul><li>Eye Tracking is a stepping stone to generating finer behavioral models. </li></ul><ul><li>These models ...
Thought Exercise Which is better?
Why? Which is better?
Intent is Key
Prasad <ul><li>Kantamneni </li></ul><ul><li>Principal Architect –  Human Perception Center of Excellence </li></ul><ul><li...
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Overview Of Eye Tracking At Yahoo

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I presented this deck at the Bay-CHI Birds of feather meeting at Yahoo on June 22, 2010.

The deck provides a brief history of eye-tracking and the the kinds of decisions we make using the method.

Overview Of Eye Tracking At Yahoo

  1. 1. Prasad Kantamneni Eye-Tracking At Yahoo! Overview of the Method Bay-CHI: Eye-Tracking Birds of feather meeting. June 22, 2010
  2. 2. About Me <ul><li>Research Scientist at Honeywell Labs </li></ul><ul><li>Research Engineer at WSU </li></ul><ul><li>Principal architect of the Human Perception Center of Excellence at Yahoo!. </li></ul>Yahoo! Presentation
  3. 3. The Big Picture <ul><li>“ When you do something on Yahoo! Search 54M people will see it by the end of the week” </li></ul><ul><li>2.6 Billion visits on the Yahoo! home page alone </li></ul><ul><li># 1 or #2 in in Mail, Sports, Finance, Search… </li></ul><ul><li>More than 2x the users of any of the other mail providers </li></ul><ul><li>~5000 customers come through the user research labs in any given year (US only). </li></ul><ul><li>Use a range of techniques from ethnographic studies to click metrics to understand customer needs, and define UX. </li></ul>Yahoo! Presentation
  4. 4. The Big Picture – Eye Tracking <ul><li>Running ET for about 4 years at Yahoo! </li></ul><ul><li>Deployed on multiple products such as Search, Advertising, Mail, Finance, Sports, Shopping, Intl etc. </li></ul><ul><li>Usually run around ~ 15 - 20 ET studies a year </li></ul><ul><li>With significant increase in UX and Revenue. </li></ul><ul><li>New ET facilities designed for large scale collection of data </li></ul><ul><li>12 eye-trackers, with custom built software. </li></ul>Yahoo! Presentation
  5. 5. Why are we doing what we are doing? <ul><li>In 2005 Traditional measurement methods were falling short of measuring and understanding user behaviors. </li></ul><ul><li>Unable to get to the Why </li></ul>Yahoo! Presentation
  6. 6. History <ul><li>Round 1 of Eye-tracking studies in early 2006 with the standard Tobii software – to understand the value of the method </li></ul><ul><ul><ul><li>Pros: </li></ul></ul></ul><ul><ul><ul><ul><li>Cool factor </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Get engineers to come in and attend the studies </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Easier to build a case </li></ul></ul></ul></ul><ul><ul><ul><li>Cons: </li></ul></ul></ul><ul><ul><ul><ul><li>Too much data to make sense of </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Easy to bias: more sensitive instrument means more susceptible to biases </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Quality of information not significantly different from what you would get from traditional studies </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Starting to understand that certain decisions are made subconsciously – yet unable to quantify or measure them </li></ul></ul></ul></ul>Yahoo! Presentation
  7. 7. History <ul><li>Round 2 of Eye-tracking studies in late 2006 - 2008 </li></ul><ul><ul><ul><li>Changed Software to Eyetools </li></ul></ul></ul><ul><ul><ul><li>Fine tuned protocols to minimize Biases </li></ul></ul></ul><ul><ul><ul><li>Pros: </li></ul></ul></ul><ul><ul><ul><ul><li>Strong Quantitative analysis capabilities </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Reduced biases </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Allowed for solid experimental design </li></ul></ul></ul></ul>Yahoo! Presentation
  8. 8. High resolution image seen by the Fovea Reduced visual acuity experienced by the parafovea Progressively reducing visual acuity from the periphery of the retina
  9. 9. Users use parafoveal preview to identify the parts most likely to have relevant information based on the location of boldfaced terms
  10. 10. Familiar summary patterns draw user attention and clicks Users are relatively blind to unfamiliar summary patterns.
  11. 11. Learned how to model and extrapolate <ul><li>Users use bolding in titles to rapidly scan the SRP. </li></ul><ul><li>Bolding in scan path is critical to making users notice a result. </li></ul><ul><li>If a result is not bolded here, it is not noticed, and hence cannot be judged as relevant . </li></ul>
  12. 12. Learned to test Models with click logs Design A Conversational title style Design B To the point title (query term – property)
  13. 13. Learned when and how to introduce changes into a UI <video> Yahoo! Presentation
  14. 14. Learned when not to introduce change Rapidly Evaluating new ideas, and predicting user behavior  Launched with Keywords instead of image thumbnails -- despite more positive response to thumbnails – because of cognitive overhead.
  15. 15. Learned how to quantifying the effectiveness of a UI and optimize for learning Old Yahoo! Y! with Search Assistance
  16. 16. Learned how to predict learning and better interpret logs <ul><li>Once you optimize for learning, give time for people to learn the new experience. </li></ul>Yahoo! Presentation
  17. 17. Learned how to make money <ul><li>Rich advertisements help advertisers convert better, and command a premium in the marketplace </li></ul>Yahoo! Presentation
  18. 18. History <ul><li>Round 2 of Eye-tracking studies in late 2006 - 2008 </li></ul><ul><ul><li>By the end of the program we had </li></ul></ul><ul><ul><ul><li>Redefined the Search box experience with the Launch of the Search Assistance in 2007 </li></ul></ul></ul><ul><ul><ul><ul><li>Is now the default expectation with search boxes </li></ul></ul></ul></ul><ul><ul><ul><li>Supported significant Market Share and revenue increases </li></ul></ul></ul><ul><ul><ul><li>Enabled product teams to teams launch new features in 11 weeks compared to 1+ years for other teams </li></ul></ul></ul><ul><ul><ul><li>Demand outstripped our ability to deliver </li></ul></ul></ul><ul><ul><li>Cons: </li></ul></ul><ul><ul><ul><li>No cool factor – was too mechanical </li></ul></ul></ul><ul><ul><ul><li>Not scalable </li></ul></ul></ul><ul><ul><ul><ul><li>Time to manage panels </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Time to collect data </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Time to analyze </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Availability of trained researchers </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Time to roll out to other domains. </li></ul></ul></ul></ul>Yahoo! Presentation
  19. 19. History <ul><li>Round 3: 2009 - date </li></ul><ul><ul><li>Bring the cool back </li></ul></ul><ul><ul><li>Wanted to scale up our eye-tracking capacity </li></ul></ul><ul><ul><li>Support innovation </li></ul></ul><ul><ul><li>Reduce skills gap </li></ul></ul><ul><ul><ul><li>Make it easier </li></ul></ul></ul><ul><ul><ul><li>Phased training </li></ul></ul></ul><ul><ul><li>Automated metrics to further speed up analysis </li></ul></ul><ul><ul><li>Expand to other domains </li></ul></ul><ul><ul><li>Cons: </li></ul></ul><ul><ul><ul><li>There is no software to support the needs </li></ul></ul></ul>Yahoo! Presentation
  20. 20. History <ul><li>Round 3 - 2009 - date </li></ul><ul><ul><li>Redesigned Labs to support concurrent data collection </li></ul></ul><ul><ul><ul><li>Can collect data from 130 people in 3 days </li></ul></ul></ul><ul><ul><li>Custom Software built by Eye-Square </li></ul></ul><ul><ul><li>Decomposition of tasks to allow for graceful learning </li></ul></ul><ul><ul><ul><li>Researchers not thrown into the deep end in the first round </li></ul></ul></ul><ul><ul><li>Open access to data </li></ul></ul><ul><ul><ul><li>A large repository of data to mine and build on </li></ul></ul></ul><ul><ul><li>Automated process to correlate ET data with click logs </li></ul></ul>Yahoo! Presentation
  21. 21. The future <ul><li>Eye Tracking is a stepping stone to generating finer behavioral models. </li></ul><ul><li>These models will significantly enrich the way we design experiences </li></ul><ul><ul><ul><li>Reduced guess work </li></ul></ul></ul><ul><ul><ul><li>Faster design cycles – with the team buy-in </li></ul></ul></ul><ul><ul><ul><li>Deeper understanding of user behaviors </li></ul></ul></ul><ul><ul><ul><li>Quantification and Prediction of UI performance </li></ul></ul></ul>Yahoo! Presentation,
  22. 22. Thought Exercise Which is better?
  23. 23. Why? Which is better?
  24. 24. Intent is Key
  25. 25. Prasad <ul><li>Kantamneni </li></ul><ul><li>Principal Architect – Human Perception Center of Excellence </li></ul><ul><li>[email_address] </li></ul>

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