Abstract: Method(s) and System(s) for predicting Customer Lifetime Value (CLV) based on segment level churn includes segmenting the customers into multiple segments based on weighted RFM scores associated with data within a dataset. The data is representative of purchasing behavior of customers over a predefined time period. The segmenting is performed such that customers with similar and close weighted RFM scores are placed in one segment. Further, the method includes computing a churn value for each of the customer segments based on the buying behavior of the customers within each segment. The churn value is associated with transaction characteristics associated with customers corresponding to the data in each segment. Expected lifetime period in years for the customers is then predicted from the calculated segment level chum values. Thereafter, CLV, that indicates profitability associated with customers, is predicted for each customer based on their expected lifetime value in years.

Abstract: Method(s) and System(s) for predicting Customer Lifetime Value (CLV) based on segment level churn includes segmenting the customers into multiple segments based on weighted RFM scores associated with data within a dataset. The data is representative of purchasing behavior of customers over a predefined time period. The segmenting is performed such that customers with similar and close weighted RFM scores are placed in one segment. Further, the method includes computing a churn value for each of the customer segments based on the buying behavior of the customers within each segment. The churn value is associated with transaction characteristics associated with customers corresponding to the data in each segment. Expected lifetime period in years for the customers is then predicted from the calculated segment level chum values. Thereafter, CLV, that indicates profitability associated with customers, is predicted for each customer based on their expected lifetime value in years.