PREDICTIVE ANALYTICS IN MARKETING: ENHANCING CUSTOMER LIFETIME VALUE (CLV) THROUGH DATA-DRIVEN DECISIONS

Abstract

Predictive analytics has emerged as a powerful tool in modern marketing, enabling organizations to transform large volumes of customer data into actionable insights. This study explores the role of predictive analytics in enhancing Customer Lifetime Value (CLV) by supporting data-driven marketing decisions. By integrating historical transaction data, customer behavioral patterns, and advanced analytical techniques such as machine learning and statistical modeling, firms can accurately forecast customer purchase behavior, retention probability, and long-term profitability. The paper highlights how predictive models improve customer segmentation, personalization, retention strategies, and marketing resource allocation. Furthermore, it discusses implementation challenges related to data quality, model interpretability, and ethical considerations, including privacy and bias. The findings suggest that predictive analytics significantly contributes to sustainable competitive advantage by enabling marketers to proactively manage customer relationships and maximize long-term value.