The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Modeling Online Browsing and Path Analysis Using Clickstream Data
Abstract
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.
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Self-citationThomaz, F., Salge, C., Karahanna, E., & Hulland, J. (2020) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-019-00704-3 · Cited by 204
“The resulting Digital, Social Media, and Mobile (DSMM) ecosystem has been considered as a source of intelligence (Lamberton & Stephen 2016) for observing, analyzing, and predicting behavior (Bucklin & Sismeiro 2003; Chatterjee, Hoffman, & Novak 2003; Montgomery et al. 2004).”
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Last‐mile supply chain efficiency: an analysis of learning curves in online ordering
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The effects of mobile promotions on customer purchase dynamics
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Predicting online shopping cart abandonment with machine learning approaches
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Metadata
- Authors
- Montgomery, A. L., Li, S., Srinivasan, K., & Liechty, J. C.
- Venue
- Marketing Science
- Published
- 2004-11
- Volume
- 23
- Issue
- 4
- Pages
- 579-595
- ISSN
- 0732-2399 (ISSN-L)
- 1526-548X
- OpenAlex
- W2125441033
- Cited by
- 545
- References
- 5
- FWCI
- 44.58
- Type
- journal article
- Language
- en
- Open access
- closed
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