Abstract
The purpose of this study is trying to find high-value revolvers and analyze their demographic characteristics using credit card data collected from a real Chinese bank instead of a survey. Due to the unique character of credit card, we develop RFM model to establish a new model, called RFMCT. The SOM neural network clusters the revolvers based on RFMCT and the revolvers are divided into high-value, potential-value and low-value based on the clustering results. In addition, demographic characteristics are analyzed by logistic regression. The results show education has negative relationship with high-value revolver and we find female, younger, high income, works in non-government organizations and non-state-owned enterprises have a higher probability of being high-value revolver.
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Acknowledgments
This work is supported by National Natural Science Foundation of China (No. 71071101). Ministry of Education in China Youth Project of Humanities and Social Sciences (No. 13YJC630249), Research Fund for the Doctoral Program of Education of the Ministry of Education of China (No. 0120181120074).
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He, C., Zhang, M., Zheng, J., Li, X., Du, D. (2014). Customer-Value Based Segmentation and Characteristics Analysis of Credit Card Revolver. In: Xu, J., Cruz-Machado, V., Lev, B., Nickel, S. (eds) Proceedings of the Eighth International Conference on Management Science and Engineering Management. Advances in Intelligent Systems and Computing, vol 281. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-55122-2_76
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DOI: https://doi.org/10.1007/978-3-642-55122-2_76
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