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Modeling Online Browsing and Path Analysis Using Clickstream Data

Author

Listed:
  • Alan L. Montgomery

    (Tepper School of Business, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, Pennsylvania 15213)

  • Shibo Li

    (Rutgers University, 228 Janice Levin Building, 94 Rockafeller Road, Piscataway, New Jersey 08854)

  • Kannan Srinivasan

    (Tepper School of Business, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, Pennsylvania 15213)

  • John C. Liechty

    (Pennsylvania State University, 710 M Business Administration Building, University Park, Pennsylvania 16802)

Abstract

Clickstream data provide information about the sequence of pages or the path viewed by users as they navigate a website. We show how path information can be categorized and modeled using a dynamic multinomial probit model of Web browsing. We estimate this model using data from a major online bookseller. Our results show that the memory component of the model is crucial in accurately predicting a path. In comparison, traditional multinomial probit and first-order Markov models predict paths poorly. These results suggest that paths may reflect a user's goals, which could be helpful in predicting future movements at a website. One potential application of our model is to predict purchase conversion. We find that after only six viewings purchasers can be predicted with more than 40% accuracy, which is much better than the benchmark 7% purchase conversion prediction rate made without path information. This technique could be used to personalize Web designs and product offerings based upon a user's path.

Suggested Citation

  • Alan L. Montgomery & Shibo Li & Kannan Srinivasan & John C. Liechty, 2004. "Modeling Online Browsing and Path Analysis Using Clickstream Data," Marketing Science, INFORMS, vol. 23(4), pages 579-595, November.
    • Handle: RePEc:inm:ormksc:v:23:y:2004:i:4:p:579-595
    DOI: 10.1287/mksc.1040.0073
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    References listed on IDEAS

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