This whitepaper focuses on predicting customer churn in telecom using machine learning techniques. Retaining customers is critical, as acquisition costs are significantly higher than retention. The paper presents a predictive framework that analyzes customer behavior, such as top-ups, usage patterns, and inactivity, to identify users likely to leave within a defined period. It also incorporates explainable AI methods like SHAP to uncover the reasons behind churn, enabling targeted retention strategies. By transforming raw event data into actionable insights, operators can design personalized campaigns and intervene proactively. The approach enhances customer retention, improves revenue stability, and strengthens decision-making across telecom operations.

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