Modeling Online Browsing and Path Analysis Using Clickstream Data

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 booksel...

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Bibliographic Details
Published inMarketing science (Providence, R.I.) Vol. 23; no. 4; pp. 579 - 595
Main Authors Montgomery, Alan L, Li, Shibo, Srinivasan, Kannan, Liechty, John C
Format Journal Article
LanguageEnglish
Published Linthicum INFORMS 01.10.2004
Institute for Operations Research and the Management Sciences
SeriesMarketing Science
Subjects
Bayesian method
Bookstores
Computers
Consumer behavior
Customization
Data analysis
Data collection
Electronic mail systems
hidden Markov chain models
hierarchical Bayes models
Home pages
Hyperlinks
Information search and retrieval
Internet
Internet service providers
Marketing
Markov models
Markovian processes
Mathematical models
Modeling
Monte Carlo simulation
multinomial probit model
Personal computers
personalization
Servers
Shopping
Stock conversion ratios
Studies
Vector autoregression
vector autoregressive models
Websites
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Summary: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.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:0732-2399
1526-548X
DOI:10.1287/mksc.1040.0073