Profit-based churn prediction based on Minimax Probability Machines

Sebastián Maldonado, Julio López and Carla Vairetti

European Journal of Operational Research, 2020, vol. 284, issue 1, 273-284

Abstract: In this paper, we propose three novel profit-driven strategies for churn prediction. Our proposals extend the ideas of the Minimax Probability Machine, a robust optimization approach for binary classification that maximizes sensitivity and specificity using a probabilistic setting. We adapt this method and other variants to maximize the profit of a retention campaign in the objective function, unlike most profit-based strategies that use profit metrics to choose between classifiers, and/or to define the optimal classification threshold given a probabilistic output. A first approach is developed as a learning machine that does not include a regularization term, and subsequently extended by including the LASSO and Tikhonov regularizers. Experiments on well-known churn prediction datasets show that our proposal leads to the largest profit in comparison with other binary classification techniques.

Keywords: Analytics; Churn prediction; Support vector machines; Minimax probability machine; Robust optimization (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (8)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:284:y:2020:i:1:p:273-284

DOI: 10.1016/j.ejor.2019.12.007

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