From prediction to proactive retention: AI-enabled dynamic and individualised customer churn management

Abstract

This paper examines the transformative shift in AI-driven churn management toward dynamic, real-time and individualised interventions enabled by advanced predictive analytics platforms. By analysing recent industry trends and empirical case evidence, the study highlights how organisations leverage high-dimensional behavioural, transactional and contextual data to uncover micro-segments and emergent patterns that inform precise retention strategies. Central to effective AI implementation are robust data infrastructure, explainable and transparent models, and ethical governance frameworks that ensure responsible data stewardship and cross-functional collaboration. The paper further discusses emerging technologies such as reinforcement learning, multi-modal data integration and federated learning, which promise to enhance personalisation and adaptive retention efforts. Finally, it offers practical recommendations for researchers and practitioners to advance the efficacy and ethical deployment of AI in churn management, emphasising continuous model adaptation, interdisciplinary cooperation and the systematic evaluation of business outcomes. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/.