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

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.