Investigating factors affecting customer churn in electronic banking and developing solutions for retention

Abstract

Due to the competitive environment in electronic banking services, banks need to understand situations that lead to customer churn. Hence identification of factors affecting customer churn and developing programs to retain customer is important for banks. In this study, binomial logistic regression technique is used to identify factors affecting customer churn in electronic banking. Then affecting factors are employed in decision tree and artificial neural network methods to predict customer churn. Bagging technique is used to solve class imbalance problem and improves accuracy. The results show that the length of customer association, customer's age, customer's gender and the number of mobile banking transactions influence customer churn. Comparison of the prediction methods shows that performance of decision tree classifier is better than artificial neural network. Finally, based on the findings, some solutions are recommended to prevent customer from churning. In addition, some methods are suggested to build customer churn prediction model.