Modeling Online Browsing and Path Analysis Using Clickstream Data

Alan L. Montgomery (), Shibo Li, Kannan Srinivasan () and John C. Liechty ()
Additional contact information
Alan L. Montgomery: Tepper School of Business, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, Pennsylvania 15213
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

Marketing Science, 2004, vol. 23, issue 4, 579-595

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.

Keywords: personalization; multinomial probit model; hierarchical Bayes models; hidden Markov chain models; vector autoregressive models (search for similar items in EconPapers)
Date: 2004
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (110)

Downloads: (external link)
http://dx.doi.org/10.1287/mksc.1040.0073 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:inm:ormksc:v:23:y:2004:i:4:p:579-595

Access Statistics for this article

More articles in Marketing Science from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().