Measuring Web Portal Utilization

1. Measuring Web Portal Utilization Mario Christ Humboldt University Berlin, Germany

2. Measuring Web Portal Utilization Agenda • Objectives of the study • Related work • Description of data source and method • Results • Implications for Electronic Commerce and Public Policy • Conclusions and Future Work

3. Objectives of the study • Measure Web Portal Utilization of Individuals • Identify demographic characteristics that determine Web Portal Utilization • Draw conclusions for Electronic Commerce and Public Policy

4. Web Portals: Introduction • Content-Aggregating • Transition from simple search engines to portal sites • Additional Services, such as: • News • Maps/driving directions • Chat rooms • Email service • Examples include Yahoo!, Excite!, Lycos!

5. Web Portals Popularity / brand recognition high low low high Visit frequency

7. Yahoo! • One of the first online navigational directories in the Web • Strong brand recognition • growth in page views and ads placed

8. Services at Yahoo! • How successful are additional services? • Search (search.yahoo.com) • Headlines (headlines.yahoo.com) • Finance/business (biz.yahoo.com or finance.yahoo.com) • Maps/driving directions (maps.yahoo.com) • Yellow pages (yp.yahoo.com) • Chat (chat.yahoo.com) • Local services (la.yahoo.com or ny.yahoo.com) • Utilization rates of these services influence how suited they are for ads

9. Related Work • Digital Divide • Refers to those members of the society who are unable to benefit from the Internet due to: • their lack of access to it, or • their inability to make full use of it • Saturation of lay Internet usage • Individual Web usage saturates out over time • Work on churn in Web sites visited • Loyalty in the Web is low, churn in Web sites visited is high

10. Data Source:The HomeNet Project • Understanding people’s use of the Internet at home • Provided families with hardware and internet connections • Number of users: n=139 • Period of observation: 2 years (95-97)

11. Data • Clickstream data taken from the HomeNet project at CMU • 1,187,325 http requests • 139 residential users • 11-6-1995 … 4-28-1997 • Demographic data from questionnaires • Age, gender, family role, race

12. Method • Count distinct Yahoo! services visited by individuals • Observe relationship between characteristics of users and service count: • Informally (draw conclusions from tables) • Formally (regression analysis)

13. Advancing the research on Web use • Addressing specifically the issue of portal utilization • Data sample that is closely representative of the general population • Long period of observation (2 years)

14. Results: User Profiles

15. Age / Service Count

16. Age

17. Gender

18. Race

19. Family Role

20. Formal Analysis: Poisson regression

21. Poisson estimates Poisson regression Number of obs = 139 LR chi2(3) = 6.49 Prob > chi2 = 0.0901 Log likelihood = -184.80 Pseudo R2 = 0.0173 ------------------------------------------------------- portutil | Coef. St.Err. z P>|z| [90%Conf.Int] -------------+----------------------------------------- female | -.304 .1586 -1.92 0.055 -.5652 -.0434 white | .080 .18283 0.44 0.659 -.2200 .3814 elder | .292 .168 1.74 0.082 .0157 .5687 _cons | .193 .19005 1.02 0.309 -.1193 .5058 -------------------------------------------------------

22. Portal utilization and overall Web utilization • Does the use of content aggregating sites lead to less intense use of other sites? • Correlation between overall Web utilization and portal utilization is actually > 0.

23. Conclusion • The majority of users does not use the additional services offered • Only 23% of the users use more than 1 service • Marketing problem? • Services may not be easily realizable to users or just not attractive to them • Demographic factors the determine portal utilization • Age, gender

24. Implications for Electronic Commerce • Demographic characteristics of users have implications for Internet marketing • High utilization portal users tend to be over 40 years and male • Existence of content aggregating portals does not decrease number of visits to other sites • Portals are no ‘one-stop-surfing sites’

25. Implications for Public Policy: Digital Divide • Overall Web Usage • Percentage of minorities: • Non-users: 39.7% • Moderate users: 19.8% • Heavy users: 15.2% • Very heavy users: 16.7% • Percentages of females: • Non-users: 61.6% • Moderate users: 54.2% • Heavy users: 39.4% • Very heavy users: 16.7% • Heavy users tend to be younger • Portal Usage • Percentage of minorities: • Non-users: 26.6% • Moderate users: 41.4% • Very heavy users: 19.7% • Heavy users: 27.8% • Percentages of females: • Non-users: 51.8% • Moderate users: 65.5% • Heavy users: 48.7% • Very heavy users: 33.3% • Heavy users tend to be older

26. Future Work • Longer period of observation to capture truly long term patterns • Use more recent data • Identity of services used • Find more subtle measures of utilization: Measure portal utilization not only by number of distinct services visited but by page impressions

27. Thanks. Questions? Comments? christ@wiwi.hu-berlin.de

28. Acknowledgements HomeNet is funded by grants from Apple Computer, AT&T, Bell Atlantic, Bellcore, Intel, Carnegie Mellon University’s Information Networking Institute, Interval, the Markle Foundation, the NPD Group, the U.S. Postal Service, and US West. Farallon Computing and Netscape Communications contributed software The work of Daniel S. Nagin was supported by the National Science Foundation under Grant No. SBR-9513040 to the National Consortium on Violence and also by separate National Science Foundation grants SBR-9511412 and SES-9911370. Mario Christ was supported by the German Research Society, Berlin-Brandenburg, Graduate School in Distributed Information Systems (DFG grant no. GRK~316). This research was also supported by the TransCoop program of the Alexander von Humboldt Foundation, Bonn, Germany. The work of Ramayya Krishnan was funded in part by NSF grant CISE/IIS/KDI 9873005.