Modeling Online Browsing and Path Analysis Using Clickstream Data
2004, Marketing Science
Alan L. Montgomery, Shibo Li, Kannan Srinivasan, John C. Liechty
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 purchases 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.
Montgomery, Alan L., Shibo Li, Kannan Srinivasan, and John C. Liechty (2004), “Modeling Online Browsing and Path Analysis Using Clickstream Data,” Marketing Science, Vol. 23, No. 4, pp. 579-595.
Finalist, John D. C. Little Award, 2004 John A. Howard AMA Doctoral Dissertation Award, 2004 William Cooper Dissertation Competition Award, 2003