Customer Retention of MCDR using 3SCDM Approaches

Abstract: In telecommunication industry satisfying customers� needs plays a vital role to retain them within their network for longer duration of time. A well-known fact in the telecommunication industry is that the competition among industries is very fierce. The acquisition of new and resourceful customers has become difficult and often very expensive. Subsequently customer retention has become more and more important. Data Mining can determine characteristic customer clusters on the basis of collected historic data points from customers - such as for instance the frequency and timely distribution of customer�s usage of services (calls, text messages, MMS, navigation, mail exchange). For each of these customer patterns the company can then offer tailored customer life cycle messages and offers. Implementing the Three-Stage Classifier based Data Mining (3SCDM) approach, an operator can predict churn, incentives may be offered to the customers for successful retention. The proposed system is evaluated by implementing Chi-Square (Chi2) Feature Reduction method along with 3SSCDM approach. Combination of Bayesian Network � RBFNet � RT, Bayesian Network � RBFNet � J48 and Bayesian Network � RBFNet � MLP classifiers are used in Three-Stage Classifier (TSC). On comparing the performance based on accuracy and time taken, Bayesian Network � RBFNet � RT with Chi-Square method performs well by 89.31% and 8.03 secs respectively. This inference can be used for identifying the prospective 3G customers in the network.

Keywords: Three-Stage Classifier based Data Mining, PAKDD 2006, MCDR, Bayesian Network, RBFNet, RT, J48, MLP, Chi-Square, and WEKA.