Modeling online browsing and path analysis using clickstream data

Alan L. Montgomery, Shibo Li, Kannan Srinivasan, John C. Liechty

Research output: Contribution to journalArticlepeer-review

302 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)579-595+630
JournalMarketing Science
Volume23
Issue number4
DOIs
StatePublished - Sep 2004

All Science Journal Classification (ASJC) codes

  • Business and International Management
  • Marketing

Fingerprint

Dive into the research topics of 'Modeling online browsing and path analysis using clickstream data'. Together they form a unique fingerprint.

Cite this