Marketing Science 2004

Modeling Online Browsing and Path Analysis Using Clickstream Data

Alan L. Montgomery¹; Shibo Li²; Kannan Srinivasan¹; John Liechty³

¹ Carnegie Mellon University · ² Rutgers, The State University of New Jersey · ³ Pennsylvania State University

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.

DOI

10.1287/mksc.1040.0073

Sources

openalex

Cite

APA 7 MLA 9 Chicago (author–date) Harvard Elsevier Harvard (JFE, JBF…) The Accounting Review Academy of Management American Marketing Association INFORMS (Management Science) Copy citation Copied ✓