TITLE

Modeling Online Browsing and Path Analysis Using Clickstream Data

AUTHOR(S)
Montgomery, Alan L.; Shibo Li; Srinivasan, Kannan; Liechty, John C.
PUB. DATE
September 2004
SOURCE
Marketing Science;Fall2004, Vol. 23 Issue 4, p579
SOURCE TYPE
Academic Journal
DOC. TYPE
Article
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 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.
ACCESSION #
15199618

 

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