Developing a prediction model for customer churn from electronic banking services using data mining

Background: Given the importance of customers as the most valuable assets of organizations, customer retention seems to be an essential, basic requirement for any organization. Banks are no exception to this rule. The competitive atmosphere within which electronic banking services are provided by different banks increases the necessity of customer retention. Methods: Being based on existing information technologies which allow one to collect data from organizations’ databases, data mining introduces a powerful tool for the extraction of knowledge from huge amounts of data. In this research, the decision tree technique was applied to build a model incorporating this knowledge. Results: The results represent the characteristics of churned customers. Conclusions: Bank managers can identify churners in future using the results of decision tree. They should be provide some strategies for customers whose features are getting more likely to churner’s features.