AN INTELLIGENT MACHINE LEARNING FRAMEWORK FOR PREDICTING CUSTOMER CHURN AND ENHANCING RETENTION IN SUBSCRIPTION-BASED BUSINESS MODELS

Urwa Javed; Arooj Arshad; Salahuddin

doi:10.71146/kjmr760

Authors

Urwa Javed Department of Computer Science , Alhamd Islamic University, Pakistan. Author
Arooj Arshad Department of Computer Science, University of Engineering and Technology (UET), Lahore. Author
Salahuddin Department of Computer Science, NFC Institute of Engineering and Technology, Multan, Pakistan. Author

DOI:

https://doi.org/10.71146/kjmr760

Keywords:

Customer Churn Prediction, Predictive Analytics, Machine Learning (ML), Subscription-Based Models, Random Forest SMOTE (Synthetic Minority Oversampling Technique), Data Preprocessing, Ensemble Learning, Customer Retention Strategy

Abstract

This study presents a machine learning-based framework designed to predict customer churn in subscription-based businesses and help organizations understand why customers discontinue their services. The research investigates the performance of several classification algorithms, including Logistic Regression, Random Forest, and Gradient Boosting, using the dataset obtained from the thesis data. Particular attention is given to data preprocessing and feature engineering, as these steps are essential for improving data quality and developing reliable predictive models. The experimental results demonstrate that ensemble learning techniques deliver the strongest overall performance, achieving an accuracy of 88.7% and a ROC-AUC score of 0.95.

These findings suggest that the proposed approach can effectively distinguish between customers who are likely to remain subscribed and those who may leave the service. The feature importance analysis identifies customer age, active membership status, and the number of products held by each customer as the most influential variables associated with churn. Understanding these factors can help businesses recognize customer behavior patterns and identify potential dissatisfaction at an early stage.

Furthermore, the framework provides valuable insights into the characteristics of customers who are more likely to discontinue their subscriptions. Instead of relying solely on historical reports, organizations can use the model to anticipate possible customer losses and take timely action. For example, businesses may offer personalized incentives, improve customer support, or introduce targeted engagement campaigns for customers identified as being at risk. The integration of predictive analytics into customer relationship management can also support more informed decision-making and improve the allocation of retention resources. Another important aspect of the proposed framework is its potential adaptability to different subscription-based environments, including telecommunications, digital platforms, online services, and membership programs. With appropriate retraining and validation, the model can be updated to reflect changing customer preferences and evolving business conditions. The combination of strong predictive performance and interpretable feature insights makes the framework useful not only for forecasting churn but also for understanding the factors associated with customer departure.

However, the effectiveness of the system in real-world applications will depend on data quality, customer behavior changes, and validation across different business settings. Overall, this research demonstrates how machine learning can transform customer churn analysis from a reactive process into a proactive decision-support mechanism. By identifying at-risk customers earlier and supporting evidence-based retention strategies, the proposed framework can help organizations strengthen customer relationships, improve retention efforts, and increase long-term customer lifetime value.

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