Volume 1 Issue 1 | 2024 | View PDF
Paper Id: IJMSM-V1I1P107
doi: 10.71141/30485037/V1I1P107

Predictive Analytics for Customer Retention in Telecommunications Using ML Techniques

Rami Reddy Kothamaram, Dinesh Rajendran, Venkata Deepak Namburi, Vetrivelan Tamilmani, Vaibhav Maniar, Aniruddha Arjun Singh Singh

Citation:
Rami Reddy Kothamaram, Dinesh Rajendran, Venkata Deepak Namburi, Vetrivelan Tamilmani, Vaibhav Maniar, Aniruddha Arjun Singh Singh, "Predictive Analytics for Customer Retention in Telecommunications Using ML Techniques" International Journal of Multidisciplinary on Science and Management, Vol. 1, No. 1, pp. 45-58, 2024.

Abstract:
The telecom sector places a premium on accurate customer churn prediction (CCP) since long-term revenue viability and competitive advantage depend on innovative retention measures. This study presents a comprehensive machine learning (ML) framework to accurately predict customer churn using the Light Gradient Boosting Machine (LGBM), with a systematic comparative analysis against existing algorithms. The methodology employs a complete end-to-end pipeline, including data preprocessing such as cleaning, duplicate removal, encoding, and min-max scaling optimization, alongside advanced feature engineering. The proposed LGBM model is evaluated using consistent metrics on telecom customer data and compared to XGBoost, Support Vector Machine (SVM), and Logistic Regression. An efficient division between training and testing made sure that performance evaluations were thorough. In comparison to XGBoost (89%), SVM (92%), and Logistic Regression (82%), the suggested LGBM model achieved much better performance in the experiments, with ACC of 98.07%, PRE of 97.6%, REC of 98.7%, and an F1-score of 99.2%. This dramatic increase in performance is proof that LGBM can successfully process category telecoms data and identify intricate patterns in consumer behavior. The systematic preprocessing pipeline and feature engineering enhance model reliability and efficiency. This study provides telecom companies with a scalable solution for real-time churn prediction, enabling customized interventions to minimize customer loss and improve operational sustainability.

Keywords: Customer retention, Telecom customer churn prediction dataset, Explainable Artificial Intelligence SHAP, Machine learning, LGBM, XGBoost, SVM, Logistic regression.

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