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Modeling Online Browsing and Path Analysis Using Clickstream Data
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Personen und Körperschaften: | , , , |
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Titel: |
Modeling Online Browsing and Path Analysis Using Clickstream Data |
In: | Marketing Science, 23, 2004, 4, S. 579-595 |
veröffentlicht: |
Institute for Operations Research and the Management Sciences (INFORMS)
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Umfang: | 579-595 |
ISSN: |
0732-2399 1526-548X |
DOI: | 10.1287/mksc.1040.0073 |
Zusammenfassung: | <jats:p> 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. </jats:p> |
Format: | E-Article |
Quelle: | Institute for Operations Research and the Management Sciences (INFORMS) (CrossRef) |
Sprache: | Englisch |