Predictive Modeling Insights for Customer Lifetime Value: Unlocking Future Value for Actionable Decision-Making

Abstract

The purpose of this book chapter is to create a simple, resilient Predictive Modeling (PM) process for predicting Customer Lifetime Value (CLV) in a business environment. The study is grounded in the theoretical foundations of Customer Relationship Management (CRM), Customer Value (CV), and Predictive Analytics (PA). It integrates concepts from CLV Models, Customer Segmentation, and Machine Learning (ML) Techniques to capture the multidimensional nature of customer behavior and its impact on long-term profitability. Qualitative insights are gathered through an empirical review of Articles published in Ivy League Journals, critical analysis of impactful Journal Articles, and a systematic and exhaustive survey of research. By understanding CLV, businesses can deliver more personalized experiences thereby improving customer satisfaction and loyalty. The summary and conclusions are demonstrate the effectiveness of the proposed PM approach in estimating CLV with a high degree of accuracy.