Predicting customer churn using targeted proactive retention

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With the advent of innovative technologies and fierce competition, the choices for customers to choose from have increased tremendously in number. Especially in the case of a telecommunication industry, where deregulation is at its peak. Every year a new company springs up offering fitter options for its customers. This has turned the concentration of the business doers on churn prediction and business management models to sustain their places. Businesses approach churn in two ways, one is through targeted customer retention and through cause identification strategy. The literature of this paper provides a comprehensible understanding of the so far employed techniques in predicting customer churn. From that, it is quite evident that less attention has been given to the accuracy and the intuitiveness of churn models developed. Therefore, a novel approach of combining the models of Machine Learning and Big Data Analytics tools was proposed to deal churn prediction effectively. The purpose of this proposed work is to apply a novel retention technique called the targeted proactive retention to predict customer churning behavior in advance and help in their retention. This proposed work will help telecom companies to comprehend the risk associated with customer churn by predicting the possibility and the time of occurrence.