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Consumers on the Web: Identification of usage patterns Consumers on the Web: Identification of usage patterns by Nina Koiso-Kanttila

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Presentation on theme: "Consumers on the Web: Identification of usage patterns Consumers on the Web: Identification of usage patterns by Nina Koiso-Kanttila"— Presentation transcript:

1 Consumers on the Web: Identification of usage patterns Consumers on the Web: Identification of usage patterns by Nina Koiso-Kanttila http://www.firstmonday.org/issues/issue8 _4/koiso/index.html

2 Research Study on Internet Usage Behavior of Consumers Nielsen/Net Ratings Internet Panel 4 months: Nov. 2000 through Feb. 2001 65 panelists

3 Three interrelated usage patterns Mosaic of the Web maintains individual preferences/tastes — 40% of personal favorites not among most popular sites Higher frequency users concentrate on fewer sites Means of navigation between sites are balanced between links, search queries, bookmarks — % of method used varies with usage intensity and age

4 Method: Observation of Clicks Nielsen/Net Ratings Internet Panel chose 65 Fins among voluntary survey participants  From an ordered list ranked according to time spent online chosen by random interval  Not demographically representative because basis is usage intensity  Studies:  averages & variances  time spent  numbers visited  means of navigation  age & gender

5 Real Time meter installed on users’ home computers Log in to Log out 89% of online time is viewing time 2.7 k sessions = 5k sites = 80 k pages = 82 k data records

6 Transferability to other countries Internet penetration  54% Fins 15 to 74 yo --> 59% Americans online Content types  5 hrs./mo Fin --> 10 hrs./mo Americans Cellular penetration  text messaging and content displays  80% of Fins

7 Data interpreted to provide 3 most viewed sites per panelist  content type comprises 55% of favorite sites  the choice of content seems to be guided by personally perceived value  ebanking Vs music http://www.firstmonday.org/issues/issue8_4/koiso/figure2.gif

8 Data (Continued) Page views and time spent on top 3 compared to total per individual  top sites account for lower percentage of total visited http://www.firstmonday.org/issues/issue8_4/koiso/figure3.gif

9 Data (Continued) The shorter the viewing time the fewer sites visited  20% of the subjects tend to account for 80% of a measure's volume Favorite site emphasis of the most active users... relates to share of customer building Consumers explore several sites...but that a smaller number of sites attract them to stay longer“

10 Log in data: 25% non portal start page – additional 6.7 pages requested -- more men 15% non portal start page – shortest viewing times –.5 additional pages 23% portal start page – 3.4 additional pages – frequent returns -- older on avg. 37% portal start page –.7 additional page requests

11 Session data Variance in online time does not effect total pages viewed  Gender not a factor  Younger = more page views  Content = more page views  Avg. 39 pages at 6.4 web sites http://www.firstmonday.org/issues/issue8_4/koiso/figure4.gif

12 Mid Session Navigation Bookmarks are relied upon for navigation among favorite sites Links from other sites are heavy factor in non-favorite sites Search engines used when in larger, less familiar terrain, or knew what they were looking for (teens search twice as often) http://www.firstmonday.org/issues/issue8_4/koiso/figure5.gif

13 The Mosaic a pattern formed by various and unrestricted shapes, colors, and sizes of tiles study eliminates generalizations about content or age related to viewing times  no one content type distinguishes activity  10 most frequent users different ages

14 Concentration and Exploration top sites account for lower percentage of total visited The shorter the viewing time the fewer sites visited Portals and search engines serve as common navigation modes

15 Future Variety will be central to attract users to mobile environments Usage concentration on favorite sites key to advertising content Navigation through bookmarks and cross- linking applies to useability and device design Research needed for exceptional users and socioeconomic factors


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