Customer Churn Prediction in the Telecommunications Sector Using Explainable Artificial Intelligence

XGBoost Algorithm for Telecom Customer Churn Prediction and Its Business Implications

Predicting customer loss is a critical challenge for telecommunications companies, as identifying at-risk customers is key to reducing financial losses and formulating effective retention strategies. Conventional statistical methods and basic ensemble models often perform poorly on telecom datasets due to their inability to capture complex feature relationships and handle class imbalance. This study applies the Extreme Gradient Boosting (XGBoost) algorithm to telecom customer churn prediction, comparing its performance with Logistic Regression and Random Forest using the public Telco Customer Churn dataset (7,043 records, 21 features). A rigorous data preprocessing pipeline was implemented, including missing value handling, categorical encoding, and feature standardization, with an 80/20 train-test split that preserved class distribution. Hyperparameter tuning for XGBoost addressed the 26.5% churn rate imbalance. Evaluation metrics (accuracy, precision, recall, F1- score, AUC) showed XGBoost outperformed benchmark models: 82.1% accuracy, 78.3% churn recall, 59.2% precision, 67.4% F1-score, and 0.869 AUC. Feature importance analysis identified customer tenure, contract type, monthly charges, and fiber optic internet service as the primary churn drivers. XGBoost also captured complex non-linear interactions unrecognized by other models.

Predicting Telecom Customer Churn and Win‑Back Probability: A Stacked Ensemble Approach

An Interpretable Machine Learning Framework for E-Commerce Customer Churn Prediction Using SHAP

In the digital economy, the cost of customer acquisition for e-commerce platforms has been rising steadily. Customer retention has become an essential force for achieving sustainable profitability. Although the machine learning models exhibit superior performance in predicting customer churn, the black-box nature of some models limits their interpretability in practical applications, which in turn hampers their performance in real-world scenarios. This paper proposes a hybrid framework that combines advanced ensemble learning algorithms with interpretable artificial intelligence (XAI) techniques to bridge the gap caused by this limitation in machine learning. Multiple classification models were evaluated in this paper, including Logistic Regression, Random Forest, and XGBoost, in a high-fidelity synthetic e-commerce dataset. In order to bridge the gap between the model and business utility, the Shapley Additive exPlanations (SHAP) method is adopted to interpret the output of the optimal model. The results show that the proposed system can not only achieve stable predictive performance but also reveal the key drivers of customer churn, such as "engagement score" and "days since last purchase". Hence, the system can provide actionable, data-driven business strategies for customer retention.

Customer Churn Prediction Using Machine Learning Models: A Comparative Study Using R Software

This study compared explainable machine learning models for predicting customer churn using the IBM Telco Customer Churn dataset in R and found Logistic Regression achieved the best performance, with an accuracy of 82.30% on this dataset.

An Intelligent Machine Learning Framework for Customer Churn Prediction in CRM Systems

In today's highly competitive market environment, organizations face significant challenges in retaining customers due to increasing competition, evolving customer expectations, and unpredictable churn behavior. In order to address these concerns and predict customer turnover using the Telco Customer turnover dataset, this article suggests a CRM system that relies on clever machine learning techniques. To ensure high-quality data for model training, the proposed system incorporates thorough data pretreatment stages such ADASYN class balancing, missing value management, label encoding, and z-score normalisation. Build and test two supervised ML models, XGBoost and Random Forest (RF), to see how well they perform. The trials showed that the Proposed RF Model achieved 97.1% accuracy (ACC), 97.6% precision (PRE), 97.4% recall (REC), and 97.2% F1-score (F1), whereas the Model Proposed XGBoost Model generated 96.9% ACC, 96.7% PRE, 96.6% REC, and 96.3% F1-score. The suggested method outperforms state-of-the-art machine learning and deep learning models for predicting customer attrition. With the help of the suggested framework, businesses may pinpoint consumers who are likely to churn, which in turn allows for more proactive retention measures, happier customers, and more profits in the long run.

A Binary Firefly Optimized Stacking Ensemble Model for Customer Churn Prediction in the Telecommunications Industry

Experimental results demonstrate that the proposed framework achieved strong and balanced predictive performance and demonstrated improvements in several classification metrics compared with the previously developed BFA-based Hybrid TabNet-DNN model.