Modeling Online Browsing and Path Analysis Using Clickstream Data

Clickstream data provides information about the sequence of pages or the path viewed by users as they navigate a web site. 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 firstorder 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 web site. 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.