ABSTRACT To have an effective customer relationship management, it is essential to have information about the different segments of the customers and predict the future profit of them. For this reason companies can use customer lifetime value that consists of three factors-current value of customers, potential value, and customer churn. Potential value of customers focuses on the cross-selling opportunities for current customers. Therefore, cross selling models are built on the total customers of the database that is not interesting. To overcome this, we presented a framework that estimates the current value and churn probability for the customers and then segmented them base on these two elements and select the most profitable segment for the cross-selling models. In this study we predict the customer churn base on logistic regression as a case study on the insurance database.
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