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

Arbeitspapier

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: IRTG 1792 Discussion Paper ; No. 2018-012

Classification and Topics

• Classification: Wirtschaft ; Mathematical and Quantitative Methods: General
• Subject: Marketing Decision Support ; Business Value ; Profit-Analytics ; Machine Learning

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Lessmann, Stefan ; Coussement, Kristof ; De Bock, Koen W. ; Haupt, Johannes
• Event: Veröffentlichung
• (who): Humboldt-Universität zu Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series"
• (where): Berlin
• (when): 2018

Further information

• Handle: http://hdl.handle.net/10419/230723
• Last update: 10.03.2025, 11:42 AM CET