Modeling online browsing and path analysis using Clickstream data

Modeling online browsing and path analysis using Clickstream data

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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: , , ,
Keywords: markov processes, e-commerce
Abstract:

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.

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