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.
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Overview Of Eye Tracking At Yahoo
- 1. Prasad Kantamneni Eye-Tracking At Yahoo! Overview of the Method Bay-CHI: Eye-Tracking Birds of feather meeting. June 22, 2010
- 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. Users use parafoveal preview to identify the parts most likely to have relevant information based on the location of boldfaced terms
- 10. Familiar summary patterns draw user attention and clicks Users are relatively blind to unfamiliar summary patterns.
- 12. Learned to test Models with click logs Design A Conversational title style Design B To the point title (query term – property)
- 13. Learned when and how to introduce changes into a UI <video> Yahoo! Presentation
- 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. Learned how to quantifying the effectiveness of a UI and optimize for learning Old Yahoo! Y! with Search Assistance
- 22. Thought Exercise Which is better?
- 23. Why? Which is better?
- 24. Intent is Key
Hinweis der Redaktion
- We did a number of different Experiments including changing the way certain terms are bolded -- showed significant double digit % increase in clicks, and user engagement – which helped us quantify the impact, and understand more about why’s
- People make a number of subconscious decisions. In this case this abstract would have significantly eroded perception of relevance, and increased user frustration.
- Even though users said that they liked the design with the image thumbnails – the actual behavior indicated that users tended to avoid the images because they thought of them as ads or irrelevant content. Additionally there is a certain amount of cognitive overhead associated with switching contexts from scanning text to scanning images – as a result we decided not to try to change habits -- because in this case the behavior is hardwired into users.
- We were able to quantify the effectiveness of a user interface using eye-tracking. Senior leadership was not interested in launching search assistance, until we were able to prove a 14% increase in page effectiveness – at which point the Search organization quickly agreed to support a launch. Yahoo launched Search assistance in 2007 to rave reviews. This significantly improved user experience and market share. Google followed a year later. Search assistance is now a standard feature for anybody searching
- Eye tracking predicted a 6-8 week learning curve for search assistance. Once launched click logs confirmed the findings. The Click log findings on learnability were presented at SIGIR 2008 ( A longitudinal study of real-time search assistance adoption, Peter Anick, Raj Gopal Kantamneni ) Planning for learnability is critical for the web because the cost for switching is minimal. If something takes 20 weeks to learn users are likely to migrate to a different product rather than learn the new experience even if it will eventually be a better experience. At Yahoo! I usually shoot for learnability to be under 6 -10 weeks for search. Other products have different thresholds depending on the nature of user interaction.
- The rich advertisement program increased ad conversions (not clicks!) significantly. Currently this program commands a premium in the Yahoo! ad marketplace.
- The same heat map will mean different things depending on the user intent. This heatmap on the yahoo shopping experience is bad. As a result the page was redesigned to better match user inent.