Title :

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

Author(s) :

Lessmann, Stefan [Auteur]
Coussement, Kristof [Auteur]
Lille économie management - UMR 9221 [LEM]
de Bock, Koen W. [Auteur]
Audencia Recherche
Haupt, Johannes [Auteur]

Journal title :

Information Sciences

Publisher :

Elsevier

Publication date :

2019-05-21

ISSN :

0020-0255

HAL domain(s) :

Sciences de l'Homme et Société/Gestion et management
Statistiques [stat]/Machine Learning [stat.ML]
Sciences de l'Homme et Société/Méthodes et statistiques

English abstract : [en]

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 ...
Show more >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.Show less >

Language :

Anglais

Popular science :

Non

Collections :

• Lille Économie Management (LEM) - UMR 9221

Source :

Harvested from HAL