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Erscheinungsjahr: 
2018
Schriftenreihe/Nr.: 
IRTG 1792 Discussion Paper No. 2018-012
Verlag: 
Humboldt-Universität zu Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series", Berlin
Zusammenfassung: 
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.
Schlagwörter: 
Marketing Decision Support
Business Value
Profit-Analytics
Machine Learning
JEL: 
C00
Dokumentart: 
Working Paper

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