Targeting customers for profit: An ensemble learning framework to support marketing decision making

Stefan Lessmann, Kristof Coussement, Koen W. De Bock and Johannes Haupt

No 2018-012, IRTG 1792 Discussion Papers from Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series"

Abstract: Marketing messages are most effective if they reach the right customers. Deciding which customers to contact is thus an important task in campaign planning. The paper focuses on empirical targeting models. We argue that common practices to develop such models do not account sufficiently for business goals. To remedy this, we propose profit-conscious ensemble selection, a modeling framework that integrates statistical learning principles and business objectives in the form of campaign profit maximization. The results of a comprehensive empirical study confirm the business value of the proposed approach in that it recommends substantially more profitable target groups than several benchmarks.

Keywords: Marketing Decision Support; Business Value; Profit-Analytics; Machine Learning (search for similar items in EconPapers)
JEL-codes: C00 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (22)

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Persistent link: https://EconPapers.repec.org/RePEc:zbw:irtgdp:2018012

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