The paper presents a framework for integrating explainable AI into customer churn prediction for telecommunications. It benchmarks four classifiers—Logistic Regression, Random Forest, XGBoost, and LightGBM—on the IBM Telco Customer Churn dataset, finding comparable performance with Logistic Regression achieving the highest AUC-ROC and LightGBM the highest accuracy. Explanations are provided via SHAP and LIME at both global and instance levels, and a four‑layer CRM integration architecture is proposed to translate risk scores and attribution vectors into actionable retention strategies, projecting a 3.3–5.3 percentage point reduction in churn and $199K–$319K savings per campaign cycle.
By Sandeep Gaddamwar
arXiv:2607. 10260v1 Announce Type: new Abstract: Customer churn is a major challenge for telecommunication companies, directly eroding revenue and long term customer relationships.
By Nada Ali, Lina Ahmed, Tahani Abdalla Attia Gasmalla
arXiv:2606. 06776v1 Announce Type: new Abstract: Customer churn prediction is a central task in customer analytics, particularly in non-contractual, pay-per-use service environments where disengagement is not explicitly observed and must be inferred from behavioral inactivity.
By Muhammad Jawad Mufti, Omar Hammad, Haitham Saleh, Muqaddas Gull
arXiv:2606. 00169v1 Announce Type: cross Abstract: Increased competition and the growing similarity of products and services offered by retailers have lowered the barriers for customers to switch to competitors.
By Syed Saad Saif, Giulio Maggiore, Paolo Russo, Damiano Distante
arXiv:2608.20343v1 Announce Type: new
Abstract: This study develops and evaluates a bankruptcy prediction framework that integrates consensus-based feature selection, hybrid resampling, stacking ense...
By Obu-Amoah Ampomah, Edmund Fosu Agyemang, Kofi Acheampong, Louis Agyekum, Enock Adu Bonsu, Eric Nyarko
The paper audits the IBM Telco Customer Churn benchmark, revealing that common practices inflate performance metrics. It shows that pre‑split SMOTE boosts churn‑class F1 by 13.1 points, that isotonic regression is the best calibration method while temperature scaling fails on tree ensembles, and that the cost‑optimal decision threshold is 5–10 times lower than the F1‑optimal one, saving about $77,000 per 1,000 customers. The authors also test generalisation on Iranian Telecom and Bank churn datasets, and propose a four‑component reporting checklist with reproducible code.
By Soumyadeep Roy