Customer churn is the term used to describe customers who terminate their relationship with a company. Since churn means a loss of revenue to a company, it is important to identify customer churn and provide incentives to them in order to retain them to the company. This paper aims to design methodologies for the customer churn prediction problem in wireless telecommunications industry. Since the number of features in customer churn dataset is rather large, the performance of decision trees is significantly degraded if inappropriate features are selected and used for building decision trees. This paper finds features that have high effect on customer churn, and to design methodologies that will cope with high dimensionality of dataset and the customer churn prediction problem. This paper provides experimental results of a set of data mining schemes and feature selection methods for customer churn datasets.
Keywords: customer churn, classification model, data mining, decision tree