Bank Customer Churn Prediction Based on Support Vector Machine: Taking a Commercial Bank's VIP Customer Churn as the Example

Jing Zhao · Xinghua Dang

31 citations0/0 files storedInternational Conference on Wireless Communications, Networking and Mobile Computing

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Customer churn and segmentation Customer Service Quality and Loyalty Data Mining and Machine Learning Applications

Abstract

Customer churn analysis and prediction play an important role in customer relationship management and improve benefit of enterprise. According to the bank's customer churn data which is large scale and imbalance, this paper presented a support vector machine model to predict customer churn. The method was compared with artificial neural network, decision tree, logistic regression and naive Bayesian classifier regarding customer churn prediction for a commercial bank's VIP customers. It is found that the method has the best accuracy rate, hit rate, covering rate and lift coefficient, and provides an effective measurement for bank's customer churn prediction.

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References

1996Why Satisfied Customers Defect 2004Determinants of subscriber churn and customer loyalty in the Korean mobile telephony market 2000Predicting subscriber dissatisfaction and improving retention in the wireless telecommunications industry 2007Customer Perception, Customer Satisfaction, and Customer Loyalty Within Chinese Securities Business

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2016Churn Prediction in Customer Relationship Management via GMDH-Based Multiple Classifiers Ensemble 2020Churn Prediction for Savings Bank Customers: A Machine Learning Approach 2010Data Mining Applications in Customer Churn Management 2020Churning of Bank Customers Using Supervised Learning 2017Towards Accurate Predictions of Customer Purchasing Patterns

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