Predictive Analytics for Customer Lifetime Value in Subscription-Based Digital Service Platforms

Abstract

Subscription-based digital service platforms have revolutionized customer relationships, shifting from transactional interactions to continuous engagement models. Understanding and forecasting Customer Lifetime Value (CLV) has emerged as a central challenge in this context. Predictive analytics enables organizations to anticipate customer behavior, optimize retention strategies, and maximize revenue streams. This paper provides a comprehensive literature-driven investigation into the methodologies, challenges, and opportunities surrounding predictive modeling of CLV within subscription ecosystems. Leveraging studies from machine learning, marketing analytics, and behavioral economics, we explore how contemporary approaches from logistic regression to deep learning architecture enhance predictive precision. Furthermore, we discuss the strategic significance of integrating predictive CLV models into pricing, personalization, and service delivery. Ethical considerations, model interpretability, and real-world deployment challenges are critically analyzed. Finally, we propose a future research agenda aimed at building robust, explainable, and ethically aligned predictive CLV systems for the next generation of subscription platforms.