A Systematic Review of Machine Learning Approaches for Customer Lifetime Value Prediction in E-Commerce

Abstract

Customer lifetime value is an important measure for understanding long term profitability in e-commerce, where firms depend on effective customer retention and development in highly competitive digital markets. Traditional statistical approaches have supported early customer lifetime value estimation, but they often rely on assumptions that limit their ability to represent complex and changing customer behavior. This systematic review examined existing studies that applied machine learning methods to customer lifetime value prediction in e-commerce. Guided by the PRISMA framework, the review searched Scopus and IEEE Xplore for studies published between 2020 and 2026, and 12 studies met the inclusion criteria. The findings show that ensemble and deep learning approaches dominate recent research, with stacking, hybrid, and recurrent neural models frequently reporting strong predictive performance. However, the evidence also reveals important limitations, including restricted data accessibility, limited interpretability, high computational demands, and inconsistent evaluation methods, which reduce comparability across studies. The strong predictive performance of these models suggests their potential to support more effective customer segmentation and improved allocation of marketing resources. Future progress in customer lifetime value prediction will depend not only on improved model performance, but also on more transparent methods, publicly available benchmark datasets, and stronger attention to interpretability and practical applicability.