<![CDATA[The high churn rate in the telecommunications industry remains a persistent challenge affecting customer retention, revenue stability, and long-term competitiveness. This study aims to identify, classify, and synthesize the current state of customer churn management (CCM) research through a systematic lens. Despite growing interest in churn prediction, most studies focus narrowly on improving model accuracy, overlooking the organizational, strategic, and adaptive aspects essential for effective churn management. To address this gap, this paper conducts a systematic literature review (SLR) covering publications from 2020 to 2025, analyzed through the Input–Process–Output (IPO) framework. Twenty high-impact papers were coded across various industries to examine data inputs, modelling processes, output insights, and feedback or retraining mechanisms. The findings reveal a strong bias toward predictive modelling and performance metrics, with limited attention to adaptive retraining, business impact assessment, and decision-support feedback loops. This imbalance highlights the need for a more holistic approach to churn management that integrates analytical and managerial dimensions. The study concludes by proposing a synthesized reference model and future research agenda to guide the development of adaptive, information-systems-based churn management frameworks for dynamic business environments.]]>
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