Machine Learning for Telecom Customer Retention and Growth

• Author(s): Kethavath Hanmanthu; K. Balakrishna Maruthiram
• Paper ID: 1710393
• Page: 29-33
• Published Date: 02-09-2025
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 9 Issue 3 September-2025

Abstract

Customer churn is one of the most pressing problems for telecom operators, directly impacting revenue and long-term profitability. This paper presents a comprehensive machine learning framework for churn prediction using a publicly available telecom dataset. We describe data preprocessing, feature engineering, imbalance handling, model training, hyperparameter tuning, and evaluation. Algorithms evaluated include Logistic Regression, Decision Trees, Random Forest, Support Vector Machine (SVM), and XGBoost. Experiments use stratified 5-fold cross-validation and metric-based assessment (accuracy, precision, recall, F1-score, and AUC). Results show that ensemble techniques, particularly Random Forest and XGBoost, outperform simpler models, achieving the best balance between precision and recall on imbalanced data. We discuss practical deployment considerations for telecom providers, limitations of the current study, and directions for future work including online learning, explainability, and cost-sensitive retention strategies.

Keywords

Customer churn, Telecom, Machine learning, Random Forest, XGBoost, Imbalanced data, Retention

Citations

IRE Journals:
Kethavath Hanmanthu, K. Balakrishna Maruthiram "Machine Learning for Telecom Customer Retention and Growth" Iconic Research And Engineering Journals Volume 9 Issue 3 2025 Page 29-33 https://doi.org/10.64388/IREV9I2-1710393-6842

IEEE:
Kethavath Hanmanthu, K. Balakrishna Maruthiram "Machine Learning for Telecom Customer Retention and Growth" Iconic Research And Engineering Journals, 9(3) https://doi.org/10.64388/IREV9I2-1710393-6842