2025

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Browsing 2025 by Author "Nabeel Ahmad"

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    CUSTOMER CHURN PREDICTION OF PAKISTAN’S TELECOM INDUSTRY
    (UMT, Lahore, 2025) Nabeel Ahmad
    Identifying customer churn is a critical challenge in a highly competitive industry like telecom, where companies struggle to retain customers amidst market saturation, competitive pricing, and service dissatisfaction. Predicting and prevent churn is essential for the telecom providers to maintain revenue, optimize their operational costs, and enhance customer satisfaction leading to retaining of customers by turning churners to non-churners. This study aims to develop an accurate prediction model which is tailored to Pakistan’s telecom sector by leveraging machine learning techniques. This research employs an explanatory approach, a dataset of 9,760 customer records was taken from Kaggle. Key features for prediction include monthly usage, relationship duration, service type, and customer complaints. Machine learning models including Logistic Regression, Decision Trees, Random Forest, Gradient Boosting and Artificial Neural Networks (ANN)- were used and their performance was evaluated using accuracy, precision, recall, F1-score, and AUC-ROC. Results from the applied models indicate that deep learning model, ANN outperformed by achieving 97.4% accuracy, 100% recall, and 88.3% precision, which proves to be the most reliable model for churn prediction. Random forest also showed strong performance of 92.6% accuracy and 97.2% recall, balancing the interpretability with predictive power. This study highlights the importance of customer churns and also the importance of addressing the churns using SMOTE which improves model performance by making sure minority class are included. The results of research study shows that costumers who frequently use the service and have minimal relationship duration are more inclined to leave. This research offers practical insights to telecom organizations with recommendations for enhanced service quality, personalized retention methods and predictive analytics-based marketing campaigns