期刊
INFORMATION SCIENCES
卷 557, 期 -, 页码 286-301出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2019.05.027
关键词
Profit analytics; Marketing decision support; Machine learning; Business value
The study highlights the importance of targeting the right customer groups in marketing campaigns and proposes a profit-conscious ensemble selection modeling framework that integrates statistical learning principles with business objectives, contributing to the field of profit analytics.
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. (C) 2019 Elsevier Inc. All rights reserved.
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