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:2606. 07582v1 Announce Type: cross Abstract: Customer churn prediction is essential across data-driven industries such as insurance, digital banking, eCommerce, and subscription platforms, where retaining existing customers is typically more cost-effective than acquiring new ones.
By Joyjit Roy, Samaresh Kumar Singh, Laxmi Shaw
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
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
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:2609.09766v1 Announce Type: new
Abstract: Churn models typically identify high-risk customers but do not specify which feasible retention action should be considered or why that action is appro...
By MinJoo Kim, SanJin Park, SeungHwan Cho
arXiv:2608.30364v1 Announce Type: new
Abstract: Retail banking attrition is usually represented as a terminal binary event, even though client relationships often weaken earlier through partial movem...
By Ananyaa Chopra, Brandon Xu, Brendan Yuen, Lauren Zung, Sarabroop Aulakh
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
Transaction propensity prediction in B2B e commerce presents unique challenges distinct from B2C contexts, primarily due to the heterogeneous procurement behaviors of organizational entities, which violate SMOTE's implicit assumption of within class feature homogeneity. Specifically, B2B buyers exhibit multi modal procurement cycles that render linear interpolation between minority class samples structurally invalid, producing synthetic data that does not represent real purchasing behavior.
arXiv:2606. 17931v1 Announce Type: new Abstract: In recent years, electronic (E) commerce services have rapidly increased in the daily lives of people, which helpsthem to purchase products online.
By Degala Pushpa Sri, Mayank Atreya, Lakshmi. H, Navin Chhibber, Mukesh Soni
The study introduces Smooth Net Benefit (σNB), a differentiable approximation of Net Benefit, as a training objective aimed at aligning predictive models with threshold‑specific clinical decisions. Experiments on the Framingham cardiovascular risk dataset and 44 TabZilla datasets show that σNB training yields modest improvements for logistic regression but little to no benefit for more flexible models such as GAMs and XGBoost. The authors conclude that σNB is not a universal replacement for negative log‑likelihood training, though it may be worth exploring in contexts where model flexibility is limited.
By Koen M. F. Gorgels, Lasai Barre\~nada, Maarten van Smeden, Ben Van Calster, Ewout W. Steyerberg, Wouter A. C. van Amsterdam
arXiv:2608. 12007v1 Announce Type: cross Abstract: Consumer reviews play an important role in shaping brand perception and business strategies, particularly in service-driven industries such as retail coffee.
By Muntasir Hasan Kanchan, Md. Alamgir Hossain, Md. Samiul Islam, Muhammad Masud Tarek