Integrated approach of RFM, clustering, CLTV and machine learning algorithm for forecasting

Abstract

Since the increment in competition .firms have to manage and evaluate customer interactions. Here customer segmentations plays a vital role and which is necessary for this competitive environment. The most common way to segregate one customer to another is to promote a group of customers into standard and premium customers. This segregation will helps to enhance customer relationship management. Here manually segmented data is analyzed. with the analyzed data firms starts identifying the customers true value and loyalty . since customers loyalty and value can provide basic data to focus more on personalized marketing. Customer life time value (CLTV) is known as an important concept in marketing and management of organization to increase the captured profitability. Total revenue generated by a customer during His/hers lifetime is named customer lifetime value. The generated value can be calculated through different methods. Each method considers different parameters. Due to the industry , firms , companies the parameters of CLTV may vary. In this article we review most presented models of calculating CLTV.