Cluster & Rough Set Theory Based Approach to Find the Reason for Customer Churn

Data mining is the nontrivial process of extraction of interesting, implicit, potentially and previously unknown knowledge from large databases. There are many techniques used in data mining like: Statistical Analysis, Decision Tree, Neural Network, Clustering, Association Rule, Genetic Algorithms, Fuzzy Logic, and Rough Sets. Rough Set theory (RST), is a technique for dealing with uncertainty and for identifying cause-effect relationship in databases as a form of data mining and database learning. Customers become “churners” when they discontinue their subscription and move their business to a competitor. That is, churning is the process of customer turnover. This is a major concern for companies with many customers who can easily switch to other competitors. Examples include credit card issuers, insurance companies and telecommunication companies. This paper presents a mechanism to find the reason behind the churn. The proposed mechanism is based on the Clustering and Rough Set Theory. The objective of this chapter is to find the reason why a customer left the service provider. By knowing the reason behind the customer churn, service provider can take some preventive step to retain the customer.