Profit uplift modeling for direct marketing campaigns: approaches and applications for online shops

Daniel Baier () and Björn Stöcker ()
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Daniel Baier: University of Bayreuth
Björn Stöcker: BAUR Versand

Journal of Business Economics, 2022, vol. 92, issue 4, No 5, 645-673

Abstract: 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.

Keywords: Uplift modeling; Heckman sample selection model; Zero-inflated negative binomial regression; Random forests; Online shops (search for similar items in EconPapers)
JEL-codes: C01 C53 M31 M37 (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s11573-021-01068-3

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