Customer Lifetime Value Measurement using Machine Learning Techniques

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Author(s): Tarun Rathi (Indian Institute of Technology Kharagpur, India)and Vadlamani Ravi (Institute for Development and Research in Banking Technology (IDRBT), India)
Copyright: 2014
Pages: 9
Source title: Encyclopedia of Business Analytics and Optimization
Source Author(s)/Editor(s): John Wang (Montclair State University, USA)
DOI: 10.4018/978-1-4666-5202-6.ch051

Keywords: Business & Management / Business Information Systems / Business Intelligence / Business Science Reference

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Abstract

Customer Lifetime Value (CLV) is an important metric in relationship marketing approaches. There have always been traditional techniques like Recency, Frequency and Monetary Value (RFM), Past Customer Value (PCV) and Share-of-Wallet (SOW) for segregation of customers into good or bad, but these are not adequate, as they only segment customers based on their past contribution. CLV on the other hand calculates the future value of a customer over his or her entire lifetime, which means it takes into account the prospect of a bad customer being good in future and hence profitable for a company or organization. In this paper, we review the various models and different techniques used in the measurement of CLV. Towards the end we make a comparison of various machine learning techniques like Classification and Regression Trees (CART), Support Vector Machines (SVM), SVM using SMO, Additive Regression, K-Star Method and Multilayer Perception (MLP) for the calculation of CLV.

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Customer Lifetime Value Measurement using Machine Learning Techniques

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