A hierarchical multiple kernel support vector machine for customer churn prediction using longitudinal behavioral data

Zhen-Yu Chen, Zhi-Ping Fan and Minghe Sun

European Journal of Operational Research, 2012, vol. 223, issue 2, 461-472

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 (search for similar items in EconPapers)
Date: 2012
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Citations: View citations in EconPapers (26)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:223:y:2012:i:2:p:461-472

DOI: 10.1016/j.ejor.2012.06.040

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