Abstract
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. 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. The results represent the characteristics of churned customers. 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.
Highlights
Given the importance of customers as the most valuable assets of organizations, customer retention seems to be an essential, basic requirement for any organization
Customer churn represents a basic problem within the competitive atmosphere of banking industry
The results proved support vector machines (SVM) to be a simple classification method of high capability yet good precision
Summary
Given the importance of customers as the most valuable assets of organizations, customer retention seems to be an essential, basic requirement for any organization. The competitive atmosphere within which electronic banking services are provided by different banks increases the necessity of customer retention. Many competitive organizations have realized that a key strategy for survival within the industry is to retain existing customers. Customer churn represents a basic problem within the competitive atmosphere of banking industry. According to Nie et al (2011), a bank can increase its profits by up to 85 % by improving the retention rate by up to 5 %. Customer retention is seen as more important than in the past. This survey seeks to identify common characteristics of churned customers in order to build a customer churn prediction model
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