An application of support vector machines for customer churn analysis: credit card case

Authors:
Sun Kim;Kyung-shik Shin;Kyungdo Park
Affiliations:
College of Business Administration, Ewha Womans University, Seoul, Korea;College of Business Administration, Ewha Womans University, Seoul, Korea;College of Business Administration, Ewha Womans University, Seoul, Korea
Venue:
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part II
Year:
2005

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Abstract

This study investigates the effectiveness of support vector machines (SVM) approach in detecting the underlying data pattern for the credit card customer churn analysis. This article introduces a relatively new machine learning technique, SVM, to the customer churning problem in attempt to provide a model with better prediction accuracy. To compare the performance of the proposed model, we used a widely adopted and applied Artificial Intelligence (AI) method, back-propagation neural networks (BPN) as a benchmark. The results demonstrate that SVM outperforms BPN. We also examine the effect of the variability in performance with respect to various values of parameters in SVM.