Humboldt-Universität zu Berlin - High Dimensional Nonstationary Time Series

IRTG1792DP2018 012

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

Stefan Lessmann
Kristof Coussement
Koen W. De Bock
Johannes Haupt

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

Marketing Decision Support, Business Value, Profit-Analytics, Machine Learning

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