<![CDATA[The high churn rate in the telecommunications industry remains a persistent challenge affecting customer retention, revenue stability, and long-term competitiveness. Despite extensive research, most customer churn management (CCM) studies in the telecom sector focus narrowly on improving model accuracy, overlooking organizational, strategic, and adaptive dimensions essential for effective management. This paper presents a systematic literature review (SLR) of academic publications from 2020 to 2025, analyzed through the Input–Process–Output (IPO) framework, to synthesize state-of-the-art developments in CCM from an Information Systems perspective. Twenty high-impact studies were coded across industries, emphasizing telecommunications, to examine data inputs, analytical processes, outputs, and feedback or retraining mechanisms. The findings reveal a strong bias toward predictive modelling using ensemble machine learning techniques (e.g., Random Forest, XGBoost, LightGBM) and limited exploration of explainable AI tools (SHAP, LIME), adaptive retraining, and business validation. This imbalance highlights the need for a holistic, adaptive framework integrating analytical intelligence with managerial decision-making. The study contributes by proposing a synthesized reference model and future research agenda for developing adaptive, information-systems-based churn management frameworks in the telecommunications industry.]]>
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