Profit optimizing customer churn prediction with Bayesian network classifiers

Authors:
Thomas Verbraken;Wouter Verbeke;Bart Baesens
Affiliations:
Department of Decision Sciences and Information Management, Katholieke Universiteit Leuven, Leuven, Belgium;Department of Decision Sciences and Information Management, Katholieke Universiteit Leuven, Leuven, Belgium;Department of Decision Sciences and Information Management, Katholieke Universiteit Leuven, Leuven, Belgium and School of Management, University of Southampton, Southampton, UK and Vlerick Leuven- ...
Venue:
Intelligent Data Analysis - Business Analytics and Intelligent Optimization
Year:
2014

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Abstract

Customer churn prediction is becoming an increasingly important business analytics problem for telecom operators. In order to increase the efficiency of customer retention campaigns, churn prediction models need to be accurate as well as compact and interpretable. Although a myriad of techniques for churn prediction has been examined, there has been little attention for the use of Bayesian Network classifiers. This paper investigates the predictive power of a number of Bayesian Network algorithms, ranging from the Naive Bayes classifier to General Bayesian Network classifiers. Furthermore, a feature selection method based on the concept of the Markov Blanket, which is genuinely related to Bayesian Networks, is tested. The performance of the classifiers is evaluated with both the Area under the Receiver Operating Characteristic Curve and the recently introduced Maximum Profit criterion. The Maximum Profit criterion performs an intelligent optimization by targeting this fraction of the customer base which would maximize the profit generated by a retention campaign. The results of the experiments are rigorously tested and indicate that most of the analyzed techniques have a comparable performance. Some methods, however, are more preferred since they lead to compact networks, which enhances the interpretability and comprehensibility of the churn prediction models.