Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/287419 
Year of Publication: 
2021
Citation: 
[Journal:] Journal of Business Economics [ISSN:] 1861-8928 [Volume:] 92 [Issue:] 4 [Publisher:] Springer [Place:] Berlin, Heidelberg [Year:] 2021 [Pages:] 645-673
Publisher: 
Springer, Berlin, Heidelberg
Abstract: 
In order to select "best" customers for a direct marketing campaign, response models are widespread: a sample of customers receives an ad, a catalog, a sample pack, or a discount offer on a test basis. Then, their responses (e.g., website visits, conversions, or revenues) are used to build a predictive model. Finally, this model is applied to all customers in order to select "best" ones for the campaign. However, up to now, only models that reflect website visits, conversions, or revenues have been proposed. In this paper, we discuss the shortcomings of these traditional approaches and propose profit uplift modeling appoaches based on one-stage ordinary regression and random forests as well as two-stage Heckman sample selection and zero-inflated negative binomial regression for parameter estimation. The new approaches demonstrate superiority to the traditional ones when applied to real-world datasets. One dataset reflects recent discount offers of a large online fashion retailer. The other is the well-known Hillstrom dataset that describes two Email campaigns.
Subjects: 
Uplift modeling
Heckman sample selection model
Zero-inflated negative binomial regression
Random forests
Online shops
JEL: 
C01
C53
M31
M37
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version

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