Data Mining as a tool to Predict the Churn Behaviour among Indian bank customers

Abstract

The socio economic growth of the country is mainly dependent on the services sector. The financial sector is one of these services sector. Data mining is evolving into a strategically important dimension for many business organizations including b anking sector. The churn problem in bankin g sector can be resolved using data mining techniques. The customer churn is a common measure of lost customers. By minimizing customer churn a company can maximize its profits. Companies have recognized that existing customers are most valuable assets. Customer relationship management (CRM) can be defined as the process of acquiring, retaining and growing profitable customer which requires a clear focus on service attributes that represent value to the customer and c reates loyalty. Customer retention is c ritical for a good marketing and a customer relationship management strategy. The prevention of customer churn through customer retention is a core issue of Customer relationship management. Predictive data mining techniq ues are useful to convert the meani ngful data into knowledge. In this analysis the data has been analyzed using probabilistic data mining algorithm Naive Bayes, the decision trees algorithm (J48) and the support vector machines(SMO).