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A hierarchical multiple kernel support vector machine for customer churn prediction using longitudinal behavioral data
Authors:Zhen-Yu Chen  Zhi-Ping Fan  Minghe Sun
Institution:1. Department of Management Science and Engineering, School of Business Administration, Northeastern University, Shenyang 110819, China;2. Department of Management Science and Statistics, College of Business, The University of Texas at San Antonio, San Antonio, TX 78249-0632, USA
Abstract:The availability of abundant data posts a challenge to integrate static customer data and longitudinal behavioral data to improve performance in customer churn prediction. Usually, longitudinal behavioral data are transformed into static data before being included in a prediction model. In this study, a framework with ensemble techniques is presented for customer churn prediction directly using longitudinal behavioral data. A novel approach called the hierarchical multiple kernel support vector machine (H-MK-SVM) is formulated. A three phase training algorithm for the H-MK-SVM is developed, implemented and tested. The H-MK-SVM constructs a classification function by estimating the coefficients of both static and longitudinal behavioral variables in the training process without transformation of the longitudinal behavioral data. The training process of the H-MK-SVM is also a feature selection and time subsequence selection process because the sparse non-zero coefficients correspond to the variables selected. Computational experiments using three real-world databases were conducted. Computational results using multiple criteria measuring performance show that the H-MK-SVM directly using longitudinal behavioral data performs better than currently available classifiers.
Keywords:Data mining  Customer relationship management  Customer churn prediction  Support vector machine  Multiple kernel learning
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