ABSTRACT
The telecommunications industry faces a persistent challenge in retaining customers, given the dynamic nature of consumer preferences and the competitive landscape. Customer churn, the phenomenon of subscribers discontinuing services, poses significant financial implications and underscores the importance of effective churn prediction models. This comprehensive review explores the evolution, methodologies, challenges, and future trends in customer churn prediction within the telecommunications sector. The significance of customer churn is highlighted, emphasizing its impact on revenue, customer loyalty, and market competitiveness. Objectives of the review encompass examining existing models, evaluating their effectiveness, and identifying avenues for improvement. It scrutinizes the historical overview of churn prediction, detailing the progression from early methods to contemporary data-driven approaches. Key metrics and indicators crucial for effective churn prediction are analyzed, offering insights into the factors signaling potential churn. Traditional statistical models such as logistic regression and decision trees are compared with machine learning algorithms, including random forests and neural networks. Ensemble models, blending multiple algorithms, are explored for enhanced accuracy. Evaluation metrics for model performance, including accuracy, precision, recall, and ROC-AUC, are detailed, providing a comprehensive framework for comparing different models. The challenges inherent in customer churn prediction, such as imbalanced datasets and model interpretability, are critically examined. The paper concludes with an exploration of future trends and innovations in churn prediction, including the integration of explainable AI, advanced feature engineering techniques, real-time prediction, and the incorporation of external data sources.
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