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

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

Research output: Contribution to journalArticle

8 Citations (Scopus)

Abstract

Marketing messages are most effective if they reach the right customers. Deciding which customers to contact is 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. Studying the interplay between data-driven learning methods and their business value in real-world application contexts, the paper contributes to the emerging field of profit analytics and provides original insights how to implement profit analytics in marketing. The paper also estimates the degree to which profit-concious modeling adds to the bottom line. The results of a comprehensive empirical study confirm the business value of the proposed ensemble learning framework in that it recommends substantially more profitable target groups than several benchmarks.

Original languageEnglish
JournalInformation Sciences
DOIs
Publication statusE-pub ahead of print - 21 May 2019
Externally publishedYes

Fingerprint Dive into the research topics of 'Targeting customers for profit: An ensemble learning framework to support marketing decision-making'. Together they form a unique fingerprint.

Cite this