Customer Lifetime Value Estimation Using Transactional Data

Customer Lifetime Value (CLV) estimation plays a pivotal role in enhancing customer management and long-term business planning. However, current methodologies often suffer from significant constraints, including strong dependence on context-specific variables, substantial data requirements, and potential threats to user privacy. This work introduces a versatile and privacy-conscious machine learning approach to CLV prediction that relies solely on variables extracted from transaction timestamps and monetary amounts. The proposed model was evaluated across five distinct datasets, each with varying structures and behavioral patterns, and benchmarked against well-known models from the literature. Results demonstrate that the proposed solution delivers robust predictive accuracy, achieving up to a 34% reduction in CLV error in some cases, while offering adaptability across a wide range of business scenarios.