Optimizing Telecom Operations with Segmentation and Churn Prediction

Client turnover is a significant issue for large organizations, particularly within the telecom sector, where it has a direct impact on profitability. To address this, telecom providers are increasingly focusing on developing predictive models to forecast customer churn and reduce its adverse effects. Identifying the key factors influencing churn is critical for implementing effective retention strategies. This research presents the development of a churn prediction model designed to help telecom companies identify customers at a high risk of leaving. The study involves exploring various data analysis techniques and presenting the results through graphical visualizations. Using machine learning methods, the model leverages large-scale datasets and introduces an innovative approach to feature design and selection. Historical customer data from previous months was utilized for training, evaluation, and validation of the model. Four machine learning algorithms were tested: Logistic Regression, XGBoost, Gradient-Boosted Machines (GBM), Random Forest, and Decision Trees. To further improve model accuracy, parameter tuning was applied during the training phase. The primary objective of this work is to enhance telecom operations by optimizing customer segmentation and churn prediction, ultimately improving both operational efficiency and customer retention.