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Modeling online browsing and path analysis using clickstream data
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AI
DOI：10.1287/mksc.1040.0073.png)
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Abstract
En 中文
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 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.
Keywords:
personalization
multinomial probit model
hierarchical Bayes models
hidden Markov chain models
vector autoregressive models
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10.1
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3.4K
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2.2W
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