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Modeling Online Browsing and Path Analysis Using Clickstream Data


Author Info

  • 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)

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    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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    Bibliographic Info

    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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    Related research

    Keywords: personalization; multinomial probit model; hierarchical Bayes models; hidden Markov chain models; vector autoregressive models;


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    Cited by:
    1. Sam Hui & Eric Bradlow, 2012. "Bayesian multi-resolution spatial analysis with applications to marketing," Quantitative Marketing and Economics, Springer, vol. 10(4), pages 419-452, December.
    2. Cecere, Grazia & Le Guel, Fabrice & SouliƩ, Nicolas, 2012. "Perceived Internet privacy concerns on social network in Europe," MPRA Paper 41437, University Library of Munich, Germany.
    3. G. Verstraeten & D. Van Den Poel, 2006. "Using Predicted Outcome Stratified Sampling to Reduce the Variability in Predictive Performance of a One-Shot Train-and-Test Split for Individual Customer Predictions," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 06/360, Ghent University, Faculty of Economics and Business Administration.
    4. J. Burez & D. Van Den Poel, 2008. "Handling class imbalance in customer churn prediction," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 08/517, Ghent University, Faculty of Economics and Business Administration.
    5. Garrow, Laurie A. & Hotle, Susan & Mumbower, Stacey, 2012. "Assessment of product debundling trends in the US airline industry: Customer service and public policy implications," Transportation Research Part A: Policy and Practice, Elsevier, vol. 46(2), pages 255-268.
    6. Hui, Kai-Lung & Png, Ivan P. L., 2005. "The Economics of Privacy," Working paper 272, Regulation2point0.
    7. J. Burez & D. Van Den Poel, 2005. "CRM at a Pay-TV Company: Using Analytical Models to Reduce Customer Attrition by Targeted Marketing for Subscription Services," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 05/348, Ghent University, Faculty of Economics and Business Administration.
    8. Oliver Rutz & Randolph Bucklin, 2012. "Does banner advertising affect browsing for brands? clickstream choice model says yes, for some," Quantitative Marketing and Economics, Springer, vol. 10(2), pages 231-257, June.
    9. Benjamin Reed Shiller, 2013. "First Degree Price Discrimination Using Big Data," Working Papers 58, Brandeis University, Department of Economics and International Businesss School, revised Sep 2013.
    10. Prasad Naik & Michel Wedel & Lynd Bacon & Anand Bodapati & Eric Bradlow & Wagner Kamakura & Jeffrey Kreulen & Peter Lenk & David Madigan & Alan Montgomery, 2008. "Challenges and opportunities in high-dimensional choice data analyses," Marketing Letters, Springer, vol. 19(3), pages 201-213, December.
    11. Bauer, Hans H. & Falk, Tomas & Hammerschmidt, Maik, 2006. "eTransQual: A transaction process-based approach for capturing service quality in online shopping," Journal of Business Research, Elsevier, vol. 59(7), pages 866-875, July.


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