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Analytical challenges in customer acquisition
Abstract
Returns are a significant problem for many direct marketers. New models to more accurately explain and predict returns, as well as models that will allow accurate scoring of customers and merchandise for return propensity, would be useful in an industry where returns can exceed 20 percent of sales. We offer a split adjusted hazard model as an alternative to simple regression of return times. We explain why the hazard model is robust and offer an example of its estimation using data of actual returns from an apparel direct marketer.
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“Also the Tobit II model has not been used very often, but see Hansotia and Wang (1997) for an exception.”
From Modeling CLV: A test of competing models in the insurance industry (2007) · cites this paper as Hansotia and Wang (1997)
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- Authors
- Hansotia, B. J., & Wang, P.
- Venue
- Journal of Direct Marketing
- Published
- 1997
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- Hansotia, B. J., & Wang, P. (1997). Analytical challenges in customer acquisition. Journal of Direct Marketing. https://doi.org/10.1002/(sici)1522-7138(199721)11%3A2
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
