Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/287419 
Erscheinungsjahr: 
2021
Quellenangabe: 
[Journal:] Journal of Business Economics [ISSN:] 1861-8928 [Volume:] 92 [Issue:] 4 [Publisher:] Springer [Place:] Berlin, Heidelberg [Year:] 2021 [Pages:] 645-673
Verlag: 
Springer, Berlin, Heidelberg
Zusammenfassung: 
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.
Schlagwörter: 
Uplift modeling
Heckman sample selection model
Zero-inflated negative binomial regression
Random forests
Online shops
JEL: 
C01
C53
M31
M37
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Dokumentart: 
Article
Dokumentversion: 
Published Version

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