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Customer Attrition Analytics in Banking
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In an era of mature markets and intensive competitive pressure, more and more companies realise that their most precious asset is their existing customer base. This realisation has resulted in a rise in emphasis on customer relationship management, in order to retain customers. This is a major area on which banks need to concentrate. Banks tend to be reactive to customer attrition, and many times it is too late to retain a customer. Customer attrition needs to be minimised, and loyal customers need to be rewarded.
The objective of this study to identify the factors affecting customer attrition of trust accounts for a leading American financial services company. The company realised that its trust accounts were getting closed after a period of seven to twelve years. Initially, the company tried to identify the ischolar_main cause using a small set of data, but they were unable to do so. This triggered the use of analytics to build a model to predict customer churn, and come up with strategies to retain customers. This was achieved by applying data mining techniques to the transactions history of the accounts that closed down as against those that remained active.
Keywords
- Au, W., Chan, C. C., & Yao, X. (2003). A novel evolutionary data mining algorithm with applications to churn prediction. IEEE Transactions on Evolutionary Computation, 7, 532-545.
- Datta, P., Masand, B., Mani, D. R., & Li, B. (2001). Automated cellular modelling and prediction on a large scale. Issues on the Application of Data Mining, 485-502.
- Gray, J. B., & Fan, G. (2008). Classification tree analysis using TARGET. Computational Statistics & Data Analysis, 52, 1362-1372.
- Hadden, J., Tiwari, A., Roy, R., & Ruta, D. (2006). Churn prediction using complaints data. Transactions on Engineering, Computing and Technology, Enformatika 13, 158-163.
- Hwang, H., Jung, T., & Suh, E. (2004). An LTV model and customer segmentation based on customer value: A case study on the wireless telecommunications industry. Expert Systems with Applications, 26, 181-188.
- Jacob, R. (1994). Why some customers are more equal than others. Fortune, 149-154.
- Ma, G., & Li, S. (1994). Applications of the survival analysis techniques in modelling customer retention.
- Workbook for the 5th Advanced Research Techniques Forum, American Marketing Association.
- Mozer, M. C., Wolniewicz, R., Grimes, D. B., Johnson, E., & Kaushansky, H. (2000). Predicting Subscriber Dissatisfaction and Improving Retention in the Wireless Telecommunications Industry. IEEE Transactions on Neural Networks 11, 690-696.
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