Web Metrics for Retailers

  • Maximilian Teltzrow
  • Oliver Günther
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2738)


In this study, we first propose a set of web usage metrics for multi-channel retailers. We then apply these metrics to data originating from a retailer who operates both an e-shop and a number of traditional stores. We focus in particular on the analysis of k-Means session clusters exhibiting an interest in offline purchases. Web usage metrics measuring the success of Web sites have been proposed before. In the domain of Web merchandizing, success is measured eventually by the number of purchases accomplished. Basic statistics such as conversion and traffic have been proposed to quantify this notion of success. However, when it comes to retailers with multiple distribution channels, there is a definite lack of standard metrics based on web log and transaction data. This paper tries to remedy this void.


Transaction Data User Session Sales Channel Online Customer Session Cluster 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Steinfield, C.: Understanding Click and Mortar E-Commerce Approaches: A Conceptual Framework and Research Agenda. Journal of Interactive Advertising 2(2) (2002) Google Scholar
  2. 2.
    BCG, Shop.Org: The State of Retailing Online 5.0. Press Release (2002) Google Scholar
  3. 3.
    Teltzrow, M., Kobsa, A.: Impacts of User Privacy Preferences on Personalized Systems - a Comparative Study. In: CHI-2003 Workshop "Designing Personalized User Experiences for eCommerce: Theory, Methods and Research, Fort Lauderdale, FL (2003)Google Scholar
  4. 4.
    Omwando, H.: Choosing the Right Retail Channel Strategy. Tech Strategy Report, Forrester Research (August 2002) Google Scholar
  5. 5.
    Doyle, J.I.: UCLA Internet Report. University of California, LA (February 2003),
  6. 6.
    Howard, J.A., Sheth, J.N.: The Theory of Buying Behavior. John Wiley & Sons Inc., New York (1969)Google Scholar
  7. 7.
    Cutler, M., Sterne, J.: E-Metrics - Business Metrics for the New Economy. Technical Report, Netgenesis Corp., (2000),
  8. 8.
    Kobsa, A., Koenemann, J., Pohl, W.: Personalized Hypermedia Presentation Techniques for Improving Customer Relationships. The Knowledge Engineering Review 16(2), 111–155 (2001)zbMATHCrossRefGoogle Scholar
  9. 9.
    Straub, D., Hoffman, D., Weber, B., Steinfield, C.: Measuring E-Commerce in Net-Enabled Organizations: An Introduction to the Special Issue. Information Systems Research 13(2), 115–124 (2002)CrossRefGoogle Scholar
  10. 10.
    Novak, T.P., Hoffman, D.L.: New Metrics for New Media: Toward the Development of Web Measurement Standards. World Wide Web Journal 2(1), 213–246 (1997)Google Scholar
  11. 11.
    Gomory, S., Hoch, R., Lee, J., Podlaseck, M., Schonberg, E.: Analysis and Visualization of Metrics for Online Merchandizing. In: Masand, B., Spiliopoulou, M. (eds.) WebKDD 1999. LNCS (LNAI), vol. 1836, pp. 126–141. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  12. 12.
    Spiliopoulou, M., Pohle, C., Teltzrow, M.: Modelling Web Site Usage with Sequences of Goal-Oriented Tasks. In: Multikonferenz Wirtschaftsinformatik, Nürnberg, Germany. Physika-Verlag, Heidelberg (2002)Google Scholar
  13. 13.
    Moe, W.: Buying, Searching or Browsing: Differentiating between Online Shoppers in-Store Navigational Clickstream. Journal of Consumer Psychology 13(1&2) (2001)Google Scholar
  14. 14.
    Berendt, B., Mobasher, B., Spiliopoulou, M., Wiltshire, J.: Measuring the Accuracy of Sessionizers for Web Usage Analysis. In: Proceedings of the Workshop on Web Mining at SIAM Data Mining Conference 2001, Chicago, IL, pp. 7–14 (2001)Google Scholar
  15. 15.
    Swerdlow, F.S., Deeks, J., Cassar, K.: In-Store Pickup Implement Last Mile Policies to Optimize Technology Investments, vol. 10, Jupiter Research (November 2002) Google Scholar
  16. 16.
    Shahabi, C., Zarkesh, A.M., Adibi, J., Shah, V.: Knowledge Discovery from User’s Web-Page Navigation. In: 7th IEEE International Conference On Research Issues in Data Engineering, pp. 20–29 (1997)Google Scholar
  17. 17.
    Fu, Y., Sandhu, K., Shih, M.: Asho: Generalization-Based Approach to Clustering of Web Usage Sessions. In: Masand, B., Spiliopoulou, M. (eds.) WebKDD 1999. LNCS (LNAI), vol. 1836, pp. 21–38. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  18. 18.
    Heer, J., Chi, E.H.: Separating the Swarm: Categorization Methods for User Access Sessions on the Web. In: ACM CHI 2002 Conference on Human Factors in Computing Systems, Minneapolis, MN. ACM Press, New York (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Maximilian Teltzrow
    • 1
  • Oliver Günther
    • 1
  1. 1.Institute of Information SystemsHumboldt-Universität zu BerlinBerlinGermany

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