Advertisement

Data Mining and Knowledge Discovery

, Volume 5, Issue 1–2, pp 85–114 | Cite as

Data Mining for Measuring and Improving the Success of Web Sites

  • Myra Spiliopoulou
  • Carsten Pohle
Article

Abstract

For many companies, competitiveness in e-commerce requires a successful presence on the web. Web sites are used to establish the company's image, to promote and sell goods and to provide customer support. The success of a web site affects and reflects directly the success of the company in the electronic market. In this study, we propose a methodology to improve the “success” of web sites, based on the exploitation of navigation pattern discovery. In particular, we present a theory, in which success is modelled on the basis of the navigation behaviour of the site's users. We then exploit WUM, a navigation pattern discovery miner, to study how the success of a site is reflected in the users' behaviour. With WUM we measure the success of a site's components and obtain concrete indications of how the site should be improved. We report on our first experiments with an online catalog, the success of which we have studied. Our mining analysis has shown very promising results, on the basis of which the site is currently undergoing concrete improvements.

web usage mining contact efficiency conversion efficiency web merchandizing web site analysis data mining e-commerce 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Alpar, P. 1999. Satisfaction with a web site. In 4th Internationale Tagung Wirtschaftsinformatik 1999, August-Wilhelm Scheer and Markus Nüttgens (Eds.), Physica Verlag: Heidelberg.Google Scholar
  2. Agrawal, R. and Srikant, R. 1995. Mining sequential patterns. In Proc. of Int. Conf. on Data Engineering, Taipei, Taiwan. Mar. 1995.Google Scholar
  3. Agrawal, R., Imielinski, T., and Swami, A. 1999. Mining association rules between sets of items in large databases. In SIGMOD'93, Washington DC, USA, May 1993, pp. 207–216.Google Scholar
  4. Büchner, A.G., Baumgarten, M., Anand, S.S., Mulvenna, M.D., and Hughes, J.G. 1999. Navigation pattern discovery from internet data. In (Masand and Spiliopoulou, 1999).Google Scholar
  5. Büchner, A.G. and Mulvenna, M.D. 1998. Discovering internet marketing intelligence through online analytical web usage mining. ACM SIGMOD RECORD, Dec. 1998, pp. 54–61.Google Scholar
  6. Berthon, P., Pitt, L.F., and Watson, R.T. 1996. Theworld wide web as an advertising medium. Journal of Advertising Research, 36(1):43–54.Google Scholar
  7. Berendt, B. and Spiliopoulou, M. 2000. Analysing navigation behaviour in web sites integrating multiple information systems. VLDB Journal, Special Issue on Databases and the Web, 9(1):56–75.Google Scholar
  8. Chen, J. Han, M.-S., and Yu, P.S. 1996. Data mining: An overview from a database perspective. IEEE Trans. on Knowledge and Data Engineering, 8(6):866–883.Google Scholar
  9. Chen, M.S., Park, J.S., and Yu, P.S. 1996. Data mining for path traversal patterns in a web environment. In ICDCS, 1996, pp. 385–392.Google Scholar
  10. Cooley, R., Mobasher, B., and Srivastava. J. 1997. Web mining: Information and pattern discovery on the world wide web. In 9th IEEE Int. Conf. on Tools with AI, Dec. 1997.Google Scholar
  11. Cooley, R., Mobasher, B., and Srivastava, J. 1999a. Data preparation for miningworld wide web browsing patterns. Journal of Knowledge and Information Systems, 1(1):5–32.Google Scholar
  12. Cooley, R., Tan, P.-N., and Srivastava, J. 1999b. WEBSIFT: The web site information filter system. In (Masand and Spiliopoulou, 1999).Google Scholar
  13. Drèze, X. and Zufryden, F. 1997. Testing web site design and promotional content. Journal of Advertising Research, 37(2):77–91.Google Scholar
  14. Eighmey, J. 1997. Profiling user responses to commercial web sites. Journal of Advertising Research, 37(2):59–66.Google Scholar
  15. Green, P. E. and Srinivasan, V. 1978. Conjoint analysis in consumer research. The Journal of Consumer Research, 5:103–122.Google Scholar
  16. Ho, J. 1997. Evaluating the world wide web: A global study of commercial web sites. Journal of Computer Mediated Communication, 3(1).Google Scholar
  17. Joachim, T., Freitag, D., and Mitchell, T. 1997. Webwatcher - a tour guide for the world wide web. In Proc. of IJCAI'97, pp. 770–777.Google Scholar
  18. Martin, D. 1999. IBM SurfAid project: Transactive analysis and prediction. Invited Talk in WEBKDD'99 (Masand and Spiliopoulou, 1999). See also http://surfaid.dfw.ibm.com/.Google Scholar
  19. Masand, B. and Spiliopoulou, M. editors. 1999. KDD'99 Workshop on Web Usage Analysis and User Profiling WEBKDD'99, San Diego, CA, Aug. 1999. ACM. Online archive of the extended abstracts at http://www.acm.org/sigkdd/proceedings/webkdd99/. Long version of the contributions in LNAI, vol. 1836, Springer Verlag, 2000.Google Scholar
  20. Perkowitz, M. and Etzioni, O. 1998. Adaptive web pages: Automatically synthesizing web pages. In Proc. of AAAI/IAAI'98, pp. 727–732.Google Scholar
  21. Parthasarathy, S., Zaki, M.J., Ogihrara, M., and Dwarkadas, S. 1999. Incremental and interactive sequence mining. In Proceedings of the Conference on Information and Knowledge Management.Google Scholar
  22. Spiliopoulou, M. 1999. The laborious way from data mining to web mining. Int. Journal of Comp. Sys., Sci. & Eng., Special Issue on “Semantics of the Web, ” 14:113–126.Google Scholar
  23. Spiliopoulou, M. and Berendt, B. 2000. Kontrolle der Präsentation undVermarktung vonGütern imWWWanhand von Data-Mining Techniken, in “Handbuch Data Mining im Marketing”, (on German), Vieweg, 2000.Google Scholar
  24. Spiliopoulou, M. and Faulstich, L.C. 1999. WUM: A Tool for Web Utilization Analysis. In extended version of Proc. EDBT Workshop WebDB'98. LNCS vol.1590, pp. 184–203, Springer Verlag.Google Scholar
  25. Spiliopoulou, M., Faulstich, L.C., and Winkler, K. 1999a. A Data Miner analyzing the Navigational Behaviour of Web Users. In Proc. of the Workshop on Machine Learning in User Modelling of the ACAI'99 Int. Conf., Creta, Greece. July 1999.Google Scholar
  26. Spiliopoulou, M., Pohle, C., and Faulstich, L.C. 1999b. Improving the effectiveness of a web site with web usage mining. In (Masand and Spiliopoulou, 1999).Google Scholar
  27. Sullivan, T. 1997. Reading reader reaction: A proposal for inferential analysis of web server log files. In Proc. of the Web Conference'97.Google Scholar
  28. Wexelblat, A. 1996. An environment for aiding information-browsing tasks. In Proc. of AAAI Spring Symposium on Acquisition, Learning and Demonstration: Automating Tasks for Users, Birmingham, UK: AAAI Press.Google Scholar
  29. Wu, K.-L., Yu, P.S., and Ballman, A. 1998. SpeedTracer: A web usage mining and analysis tool. IBM Systems Journal, 37(1):89–105.Google Scholar
  30. Zamir, O., Etzioni, O., Madani, O., and Karp, R.M. 1997. Fast and intuitive clustering of web documents. In KDD'97, Aug. 1997. Newport Beach, CA: AAAI Press, pp. 287–290.Google Scholar
  31. Zaïane, O., Xin, M., and Han, J. 1998. Discovering web access patterns and trends by applying OLAP and data mining technology on web logs. In Advances in Digital Libraries, Santa Barbara, CA. pp. 19–29.Google Scholar

Copyright information

© Kluwer Academic Publishers 2001

Authors and Affiliations

  • Myra Spiliopoulou
    • 1
  • Carsten Pohle
    • 2
  1. 1.Institute of Information SystemsHumboldt University BerlinBerlinGermany
  2. 2.Institute of Information SystemsHumboldt University BerlinBerlinGermany

Personalised recommendations