An Analysis of Customer Lifetime and Design of an Early Prediction of Customer Churn Using Decision Tree Approach with HMM Classifier (CUDA)

R. Manivannan and Dr.R. Saminathan

The revenue of a retail industry (stores) primarily depends on customer as its asset. Customer and their product buying behavior are considered as a major asset of any retail market and it is used for analysis purpose. Understanding the product buying behavior and consumption rate of a product suggests the demand of any related products in market. Customer increases the demand for a product which defines the interest towards buying the product. Any change in interest towards buying the product defines customer churn. Any churn of customer leads to loss of customer, hence the primary aim of this research work is to predict an early churn of customer towards buying the product. Hence, the Customer Life time Value (CLV) needs to be improved. This research work surveys on analytic research which focuses only on customer outcome variables such as customer churn, time taken to churn, and analytical suggestions behind churn with support for earlier prediction.

Volume 11 | 04-Special Issue

Pages: 618-628