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Modeling Online Browsing and Path Analysis Using Clickstream Data

  • 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)

Registered author(s):

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.

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File URL: http://dx.doi.org/10.1287/mksc.1040.0073
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Article provided by INFORMS in its journal Marketing Science.

Volume (Year): 23 (2004)
Issue (Month): 4 (November)
Pages: 579-595

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Handle: RePEc:inm:ormksc:v:23:y:2004:i:4:p:579-595
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