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Organizations earns huge amount of money by providing the different services to their customers. In today‟s world of competition, organizations need to focus on customer relationship management. Retaining the existing customers is as much important as attracting the new customers for an organization. For this purpose, organizations use data mining techniques for segmenting the churn customers and loyal customers so that special offers can be provided to churn customers to retain them as customers are the most valuable asset for organizations. The aim of this paper is to provide a customer churn prediction model using a standard CRISP-DM methodology based on RFM and Boosted Trees Technique. To enhance the performance of the technique, hybrid approach for building classifiers is used. There is also a comparison between the performances of both techniques. Results show that enhanced boosted trees technique performs better than existing boosted tree technique. Proposed approach is then implemented on the cloud environment to provide the cloud facilities for mining the data.

A Novel Approach for Providing the Customer Churn Prediction Model using Enhanced Boosted Trees Technique in Cloud Computing