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Benchmarking sampling techniques for imbalance learning in churn prediction

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  • Bing Zhu
  • Bart Baesens
  • Aimée Backiel
  • Seppe K. L. M. vanden Broucke

Abstract

Class imbalance presents significant challenges to customer churn prediction. Many data-level sampling solutions have been developed to deal with this issue. In this paper, we comprehensively compare the performance of several state-of-the-art sampling techniques in the context of churn prediction. A recently developed maximum profit criterion is used as one of the main performance measures to offer more insights from the perspective of cost–benefit. The experimental results show that the impact of sampling methods depends on the used evaluation metric and that the impact pattern is interrelated with the classifiers. An in-depth exploration of the reaction patterns is conducted, and suitable sampling strategies are recommended for each situation. Furthermore, we also discuss the setting of the sampling rate in the empirical comparison. Our findings will offer a useful guideline for the use of sampling methods in the context of churn prediction.

Suggested Citation

  • Bing Zhu & Bart Baesens & Aimée Backiel & Seppe K. L. M. vanden Broucke, 2018. "Benchmarking sampling techniques for imbalance learning in churn prediction," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 69(1), pages 49-65, January.
    • Handle: RePEc:taf:tjorxx:v:69:y:2018:i:1:p:49-65
    DOI: 10.1057/s41274-016-0176-1
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    Cited by:

    1. Koen W. de Bock & Kristof Coussement & Arno De Caigny & Roman Slowiński & Bart Baesens & Robert N Boute & Tsan-Ming Choi & Dursun Delen & Mathias Kraus & Stefan Lessmann & Sebastián Maldonado & David , 2023. "Explainable AI for Operational Research: A Defining Framework, Methods, Applications, and a Research Agenda," Post-Print hal-04219546, HAL.
    2. Andreea Dumitrache & Monica Mihaela Maer Matei, 2019. "Churn Analysis in a Romanian Telecommunications Company," Postmodern Openings, Editura Lumen, Department of Economics, vol. 10(4), pages 44-53, December.
    3. Matthias Bogaert & Lex Delaere, 2023. "Ensemble Methods in Customer Churn Prediction: A Comparative Analysis of the State-of-the-Art," Mathematics, MDPI, vol. 11(5), pages 1-28, February.

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