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Leveraging artificial intelligence for predictive customer churn modeling in telecommunications: a framework for
Mohamed G Abdelhady1, Karim A Mohamed2
1Madina Higher Institute for Administration and Technology, Giza, Egypt. Mohamed_gamal182@yahoo.com.
Scientific Reports
|December 12, 2025
Summary
This study uses Artificial Intelligence (AI) in Customer Relationship Management (CRM) to predict telecom customer churn with 95.13% accuracy. The AI model identifies at-risk customers for proactive retention strategies.
Area of Science:
- * Artificial Intelligence (AI) and Machine Learning (ML) in business analytics.
- * Customer Relationship Management (CRM) system optimization.
- * Data science applications in the telecommunications sector.
Background:
- * Customer churn significantly impacts telecommunications industry profitability and customer lifetime value.
- * Proactive customer retention strategies are essential for sustained business growth.
- * Integrating AI within CRM systems offers a novel approach to managing customer relationships.
Purpose of the Study:
- * To develop and evaluate an AI-driven framework for proactive customer churn identification and retention within CRM systems.
- * To assess the predictive performance of a Random Forest model in identifying high-risk telecom customers.
- * To explore the link between explainable AI insights and actionable CRM strategies for customer engagement.
Main Methods:
- * Implementation of a Random Forest classifier on a telecom customer dataset (N=2,668).
- * Application of data balancing techniques, including SMOTE (Synthetic Minority Over-sampling Technique) and class weighting, to address a 14.6% churn rate.
- * Comparative analysis against XGBoost, Support Vector Machine (SVM), and Artificial Neural Network (ANN) models.
Main Results:
- * The Random Forest model achieved high predictive accuracy (95.13%) and Area Under the Curve (AUC) of 0.89.
- * Feature importance analysis identified 'total day minutes', 'total day charge', and 'customer service calls' as key churn predictors.
- * The proposed AI framework demonstrated superior or comparable performance to other evaluated ML models.
Conclusions:
- * AI-driven frameworks integrated with CRM systems are effective for proactive customer churn prediction and retention in telecommunications.
- * Explainable AI insights provide actionable data for targeted customer engagement and retention campaigns.
- * The study offers a robust methodology for enhancing customer retention through intelligent CRM operationalization.
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