Modeling Online Browsing and Path Analysis Using Clickstream Data
AbstractClickstream 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 InfoArticle provided by INFORMS in its journal Marketing Science.
Volume (Year): 23 (2004)
Issue (Month): 4 (November)
personalization; multinomial probit model; hierarchical Bayes models; hidden Markov chain models; vector autoregressive models;
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- 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.
- 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.
- 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.
- Cecere, Grazia & Le Guel, Fabrice & Soulié, Nicolas, 2012. "Perceived Internet privacy concerns on social network in Europe," MPRA Paper 41437, University Library of Munich, Germany.
- 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.
- Fok, D. & Paap, R. & HorvÃ¡th, C. & Franses, Ph.H.B.F., 2005. "A Hierarchical Bayes Error Correction Model to Explain Dynamic Effects of Price Changes," Research Paper ERS-2005-047-MKT, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus Uni.
- 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.
- 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.
- 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.
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