Abstract :

Customer Churn Is A Critical Challenge In The Telecommunications (telco) Industry, Where Intense Competition And Low Switching Costs Make Customer Retention Essential For Business Sustainability. Accurately Predicting Churn And Understanding Customer Segments Can Help Telecom Companies Design Targeted Retention Strategies And Improve Overall Customer Satisfaction. This Study Proposes An Integrated Framework That Combines Churn Prediction And Customer Segmentation Using Machine Learning Techniques To Provide Actionable Insights For Telco Businesses. The Proposed System Utilizes Customer Data Such As Demographics, Service Usage Patterns, Billing Information, And Customer Support Interactions. Data Preprocessing Techniques Including Cleaning, Normalization, And Feature Engineering Are Applied To Improve Data Quality. For Churn Prediction, Supervised Machine Learning Algorithms Such As Logistic Regression, Decision Trees, Random Forest, And Gradient Boosting Are Implemented To Classify Customers As Likely To Churn Or Not. For Customer Segmentation, Unsupervised Learning Techniques Such As KMeans Clustering Are Used To Group Customers Based On Similar Behavior And Characteristics. By Integrating Both Approaches, The System Enables A Deeper Understanding Of Customer Behavior And Identifies High-risk Segments That Require Targeted Interventions. Experimental Results Demonstrate That Ensemble Models Such As Random Forest And Gradient Boosting Achieve Higher Accuracy In Churn Prediction, While Clustering Techniques Effectively Identify Meaningful Customer Segments. The Combined Framework Allows Businesses To Not Only Predict Churn But Also Understand The Underlying Reasons And Patterns Associated With It. This Leads To More Personalized Marketing Strategies And Improved Customer Retention. Overall, The Proposed Framework Provides A Scalable And Efficient Solution For Telco Companies To Enhance Customer Loyalty And Reduce Churn Rates.

Published:

08-4-2026

Issue:

Vol. 26 No. 4 (2026)

Page Nos:

1837-1843

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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