Data-Driven Telecom Marketing Optimization: A Machine Learning-Based Churn Prediction and Customer Segmentation Framework
2026-07-11 • Machine Learning
Machine Learning
AI summaryⓘ
The authors address the problem of customers leaving telecom companies by combining machine learning to predict who might leave with categorizing customers based on their value and risk. They tested three advanced models and picked CatBoost for best performance in predicting churn using a specific dataset. Then, they grouped customers into segments and designed tailored marketing strategies for each group, estimating potential financial benefits. They built an easy-to-use app to help marketing teams use these insights. Their work shows that mixing prediction with value-based segments leads to better marketing choices.
customer churnmachine learningCatBoostgradient boostingcustomer segmentationK Means clusteringSHAP valuesreturn on investment (ROI)customer lifetime value (CLV)cross validation
Authors
Nada Ali, Lina Ahmed, Tahani Abdalla Attia Gasmalla
Abstract
Customer churn is a major challenge for telecommunication companies, directly eroding revenue and long term customer relationships. Traditional retention programs rely on generic, not personalized incentives and lack the precision to identify high risk customers before they leave. This paper presents a data driven marketing optimization framework integrating machine learning based churn prediction, customer segmentation combining churn risk with customer value, and tailored, segment specific marketing and Return on Investment ROI strategies. Using the IBM Telco Customer Churn dataset with 7043 customers and 21 features, three gradient boosting ensembles, XGBoost, LightGBM, and CatBoost, were trained and tuned via randomized search with stratified 5 fold cross validation, class weighting, and F1 score driven decision threshold optimization to counter a class imbalance of 73.4% versus 26.6%. CatBoost was selected as the deployment model, achieving 77.68% accuracy, an F1 score of 0.6366, a PR AUC of 0.6553, and a ROC AUC of 0.8403 on the held out test set. Customers were partitioned with K Means clustering, validated via the Elbow method and visualized with Principal Component Analysis, into High, Medium, and Low Value segments, cross tabulated against churn risk labels to define four actionable clusters. Segment specific retention, upsell, and engagement strategies were designed for each cluster, and a theoretical ROI and CLV framework quantifies the financial impact of the proposed interventions. The pipeline was operationalized in an interactive Streamlit web application allowing marketing teams to upload data, filter by segment, visualize churn drivers via SHAP, and download automated segment reports. Results confirm that combining predictive churn modeling with value aware segmentation yields more actionable and profitable marketing decisions than churn prediction alone.