Article ID: | iaor20061606 |
Country: | United States |
Volume: | 23 |
Issue: | 4 |
Start Page Number: | 579 |
End Page Number: | 595 |
Publication Date: | Sep 2004 |
Journal: | Marketing Science |
Authors: | Srinivasan Kannan, Montgomery Alan L., Liechty John C., Li Shibo |
Keywords: | markov processes, e-commerce |
Clickstream data provide information about the sequence of pages or the path viewed by users as they nagivate 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.