How can Artificial Intelligence (AI) be used to manage Customer Lifetime Value (CLV)—A systematic literature review

Customer Lifetime Value (CLV) represents the total worth of a customer to a company over time, aiding businesses in resource allocation and tailored marketing for profitability. This literature review fills a research gap by examining how customer risk factors are integrated into CLV calculations. We conducted a systematic literature review across databases, adhering to strict criteria for relevance and quality. The review analyzed CLV methodologies and outcomes, highlighting the use of mean–variance analysis to optimize customer portfolios, with customer income fluctuations identified as a major risk factor. The study also explores the evolution of CLV research, particularly in the application of Machine Learning (ML) for risk-adjusted CLV. Our findings offer a comprehensive overview, laying the groundwork for future research and helping businesses refine risk management strategies, identify high-risk customers, and enhance customer value through more dynamic, data-driven models. • We provide the first literature review related to Risk-Adjusted CLV models. • We summarize and analyze methods for integrating risk into CLV models. • We identify the most important sources of risks for distinct customers’ segments. • This research serves as the primary guide for new studies on CLV models.