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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| Veröffentlicht in: | Marketing science (Providence, R.I.) R.I.), 2004-10, Vol.23 (4), p.579-595 |
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| creator | Montgomery, Alan L Li, Shibo Srinivasan, Kannan Liechty, John C |
| description | 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. |
| doi_str_mv | 10.1287/mksc.1040.0073 |
| format | Article |
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| ispartof | Marketing science (Providence, R.I.), 2004-10, Vol.23 (4), p.579-595 |
| issn | 0732-2399 1526-548X |
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| source | Jstor Complete Legacy; Informs; RePEc; EBSCOhost Business Source Complete |
| subjects | Analysis Autoregression (Statistics) 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 Markov processes 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 |
| title | Modeling Online Browsing and Path Analysis Using Clickstream Data |
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