Fuzzy clustering for effective customer relationship management in telecom industry

Abstract

Data mining is the process of extracting interesting patterns from data. Data mining is recently proving very effective in business decision making and is becoming a widely used strategy to improve CRM (Customer Relationship Management). CRM is the process of managing a good relationship with customer and improving the profitability of their interactions with the customer. Data mining is widely used particularly in handling large data sets as in telecom sector. Clustering is a popular mining strategy that separates those data into subsets called clusters. This research work focuses on comparing the two main approaches of clustering soft clustering and hard clustering namely the Kmeans and Fuzzy C Means (FCM) clustering algorithms on large telecom data to determine the churn ratio as a measure to enhance CRM. It is observed that FCM outperforms Kmeans in estimating churn ratio accurately and is more effective in supporting CRM. © 2011 Springer-Verlag Berlin Heidelberg.