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The Role of Data Integration and Context in Measuring User Engagement
The Role of Data Integration and Context in Measuring User Engagement
The Role of Data Integration and Context in Measuring User Engagement
The Role of Data Integration and Context in Measuring User Engagement
The Role of Data Integration and Context in Measuring User Engagement
The Role of Data Integration and Context in Measuring User Engagement
The Role of Data Integration and Context in Measuring User Engagement
The Role of Data Integration and Context in Measuring User Engagement
The Role of Data Integration and Context in Measuring User Engagement
The Role of Data Integration and Context in Measuring User Engagement
The Role of Data Integration and Context in Measuring User Engagement
The Role of Data Integration and Context in Measuring User Engagement
The Role of Data Integration and Context in Measuring User Engagement
The Role of Data Integration and Context in Measuring User Engagement
The Role of Data Integration and Context in Measuring User Engagement
The Role of Data Integration and Context in Measuring User Engagement
The Role of Data Integration and Context in Measuring User Engagement
The Role of Data Integration and Context in Measuring User Engagement
The Role of Data Integration and Context in Measuring User Engagement
The Role of Data Integration and Context in Measuring User Engagement
The Role of Data Integration and Context in Measuring User Engagement
The Role of Data Integration and Context in Measuring User Engagement
The Role of Data Integration and Context in Measuring User Engagement
The Role of Data Integration and Context in Measuring User Engagement
The Role of Data Integration and Context in Measuring User Engagement
The Role of Data Integration and Context in Measuring User Engagement
The Role of Data Integration and Context in Measuring User Engagement
The Role of Data Integration and Context in Measuring User Engagement
The Role of Data Integration and Context in Measuring User Engagement
The Role of Data Integration and Context in Measuring User Engagement
The Role of Data Integration and Context in Measuring User Engagement
The Role of Data Integration and Context in Measuring User Engagement
The Role of Data Integration and Context in Measuring User Engagement
The Role of Data Integration and Context in Measuring User Engagement
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The Role of Data Integration and Context in Measuring User Engagement

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  • 1. The Role of Data Integration and Context in Measuring User Engagement Pracitioner Web Analytics, My 25th, 2010 1
  • 2. The Media Revolution [A historical perspective] Future Super 8 mm Applications Film 2050+ Cartridges VCR Lithography 1798 1965 TIVO 1972 TV Anytime Photography 1860s Late 1990s User Digital Activity Cameras Twitter Brownie camera 1990s 1900 YouTube Flickr Social Media! Time 2
  • 3. Trend I (Technical) Increased connectivity, public data, data centers Integrated devices and services 3
  • 4. Trend II (behavioral) Strong dependence on computing On-off line convergence, real time, anywhere 4
  • 5. Trend III (Socio-economic) Spread of technology, “Flat” world 5
  • 6. Why do we care?
  • 7. Yahoo! is ranked among the top 3 sites in 26 key categories and #1 in 12 of them Yahoo! Monthly Unique Visitors (000) Noted in Yellow E-mail Autos Entertainment News Local/Maps Groups Home Pages 1. Yahoo! Mail 1. eBay Motors U.S. 1. omg! 1. Google Maps 1. Facebook Groups 1. Yahoo! HP 2. Win. Live Hotmail 2. Yahoo! Autos 2. TMZ 2. Mapquest 2. Yahoo! Groups 2. Google HP 3. Google Gmail 3. KBB.com 3. People 3. Yahoo! Maps 3. Google Groups 3. Facebook HP Y! UVs: 106,166 Y! UVs: 6,972 Y! UVs: 20,428 Y! UVs: 13,163 Y! UVs: 7,654 Y! UVs: 118,011 Games Search Finance TV News IM 1. Yahoo! Games 1. Google Search 1. Yahoo! Finance 1. Yahoo! TV 1. Yahoo! News 1. Yahoo! Messenger 2. EA Games 2. Yahoo! Search 2. AOL Money & Finance 2. AOL TV 2. CNN 2. AIM.com/AIM App 3. Nickelodeon 3. ASK Network 3. MSN Money 3. MSN TV 3. MSNBC 3. MSN Msngr Y! UVs: 18,797 Y! UVs: 90,191 Y! UVs: 21,671 Y! UVs: 15,085 Y! UVs: 48,433 Y! UVs: 38,140 Why do we care? Movies Travel Music Sports Portals My (Custom)* 1. IMDB.com 1. TravelAd Network 1. Yahoo! Sports 1. Yahoo! Sites 1. AOL Music 1. My Yahoo! 2. Yahoo! Movies 2. Tripadvisor 2. ESPN 2. Microsoft Sites 2. MySpace Music 2. iGoogle 3. Moviefone 3. Yahoo! Travel 3. Fox Sports on MSN 3. AOL LLC 3. Yahoo! Music 3. My MSN Y! UVs: 17,942 Y! UVs: 10,167 Y! UVs: 30,718 Y! UVs: 156,506 Y! UVs: 21,889 Y! UVs: 24,644 Shopping (Comparison) Careers Reference Personals Photos Real Estate 1. Yahoo! Shopping 1. Careerbuilder 1. Wikimedia 1. Yahoo! Personals 1. Facebook.com Photos 1. Move Network 2. Shopzilla.com 2. Yahoo! HotJobs 2. Yahoo! Answers 2. SingelsNet 2. Photobucket 2. Yahoo! Real Estate 3. Shopping.com 3. Monster 3. Answers.com 3. PlentyofFish 3. FLICKR.COM 3. AOL Real Estate Y! UVs: 22,901 Y! UVs: 16,697 Y! UVs: 43,164 Y! UVs: 3,330 Y! UVs: 24,686 Y! UVs: 7,525 Source: comScore Media Metrix, July 2009 !"#$%&'()&*+,+&-"."/&01.$%&213$4"5$#&6#&71.&"&.8"-6.617"9&:".$518;&67&:13,:18$<#&#$8=6:$ *Not Shown: Yahoo! Green ranks #2 in Environment, Yahoo! Health ranks #3 in Health Monthly figures unless otherwise indicated ACM RecSys 2009 Courtesy of Todd Beaupre (Y!) ! 5
  • 8. Interaction Experience (Video of angry PC user..) 8
  • 9. Is he engaged? 9
  • 10. Or enraged..
  • 11. But what do we see? A click!
  • 12. We have…   More data than at any time in history   Better tools to store it, access it, process it   Better (sometimes) technology for our day to day   Overly complex systems, information overload   Larger diversity of “users” New business models Patterns, Data Mining, Interaction
  • 13. But… It is not about the data…
  • 14. Human-Centered Analytics! User Data Experience analysis Human aspects Machine Learning, Context, User Modeling, Engagement 14
  • 15. Human-Centered Analytics Social-cultural Psychological Context Economic Key enabler 15
  • 16. Key Differentiator? Analytics for customer experience innovation …. that is what should be optimized for .…
  • 17. But how? Know your “users” •  What, how, and why? •  Who?
  • 18. Two examples…
  • 19. Example I Image Search 1.4 Billion anonymous search queries (75 M unique queries) 100K most frequent queries
  • 20. Example I Results I 100 most frequent queries account for 5.8% of query volume 57 of celebrities (52 female) 5 fictional (Spongebob, Hello Kitty, Santa..) 6 tattoo related (e.g., tribal tattoo) 2 “functional” (xmas wall paper)
  • 21. Example I Results II (top 100K queries) 7% Entertainment_&_Music 8% Arts_&_Humanities 31% Sports 9% Science_&_Mathematics 9% Beauty_&_Style Travel 14% 9% Society_&_Culture 13% Cars_&_Transportation
  • 22. Example I Results III (top 100K queries) initial initial next page next page 2% 11% 2% 2% 2% 15% 15% 11% more specific more specific more generic more generic 5% 5% minor rewrite minor rewrite major rewrite major rewrite 65% 65%
  • 23. Example I Observations Most people that “search” for images are actually browsing! What is the right engagement metric here? Impact on experience design…
  • 24. Example II Web Search [weber & Castillo SIGIR ‘10] Anonymized Profiles of 28 million users (birth year, gender, ZIP) US census data Data aggregated (not per user)
  • 25. Example II Example Queries “Wagner” “Lindsey” “Hal”
  • 26. Example II Examples Female Male (Wagner=composer) (Wagner=spray painter) Hal Lindsey: American evangelist and Christian writer Hal Higdon: American writer and runner (above average education areas)
  • 27. Example II Observations Lots of public data unexplored Information flows? Profiles? Business strategy…
  • 28. Social Media   Clickstream   Favorites   Purchases   Social network analysis   Communities, influence, propagation, & dynamics   Interest/activity-based user modeling   Social “Network” of objects-people-interests   Trend spotting   Psycho-socio-cultural-economic perspectives
  • 29. User Experience   Browsing   Discovery   Personalization   New services?   Eye tracking   Focus group user studies
  • 30. Design Process Algorithm Interface User
  • 31. Analytics and Design Interaction design Analytics Human abilities, needs Implementation Socio-cultural context
  • 32. Human-Centered Analytics User Data Experience analysis Human aspects Machine Learning, Context, User Modeling, Personalization 33
  • 33. Thank you! Alex Jaimes ajaimes@yahoo-inc.com © 2010 A. Jaimes. No portion of these slides can be reproduced without permission. Personal opinions, not of Yahoo! Inc.

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