The Role of Data Integration and Context in Measuring User Engagement
1. The Role of Data Integration and
Context in Measuring User
Engagement
Pracitioner Web Analytics, My 25th, 2010
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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
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3. Trend I (Technical)
Increased connectivity, public data, data centers
Integrated devices and services
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4. Trend II (behavioral)
Strong dependence on computing
On-off line convergence, real time, anywhere
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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
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.
23. 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%
24. Example I
Observations
Most people that “search” for images are
actually browsing!
What is the right engagement metric here?
Impact on experience design…
25. 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)
26. Example II
Example Queries
“Wagner”
“Lindsey”
“Hal”
27. 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)
28. Example II
Observations
Lots of public data unexplored
Information flows? Profiles?
Business strategy…
29. 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
30. User Experience
Browsing
Discovery
Personalization
New services?
Eye tracking
Focus group user studies