Predicting Customer Churn by Utilizing Analytical Datamining

Abstract

The rapid growth of the market in every sector is leading to a bigger subscriber base for service providers. More competitors, new and innovative business models and better services are increasing the cost of customer acquisition. Service providers realized the importance of the existing customers. Providers are putting more and more efforts for prediction and prevention of customer churn. This task gives the usually utilized information digging procedures for the recognizable proof of stir. In view of chronicled information these systems attempt to discover examples which can bring up conceivable churners. For performing these calculations on the provided data set we have developed a windows platform application. The final outcome of this project is a Business process model which will analyze the customer behavior on the basis of their past records and can be used by several enterprises for making decisions. It focuses on data analyzing, data pre-processing, and feature extraction and after all that a classification algorithm which will provide improved churn prediction rate.