{"ID":2896533,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.08860","arxiv_id":"2507.08860","title":"e-Profits: A Business-Aligned Evaluation Metric for Profit-Sensitive Customer Churn Prediction","abstract":"Retention campaigns in customer relationship management often rely on churn prediction models evaluated using traditional metrics such as AUC and F1-score. However, these metrics fail to reflect financial outcomes and may mislead strategic decisions. We introduce e-Profits, a novel business-aligned evaluation metric that quantifies model performance based on customer lifetime value, retention probability, and intervention costs. Unlike existing profit-based metrics such as Expected Maximum Profit, which assume fixed population-level parameters, e-Profits uses Kaplan-Meier survival analysis to estimate tenure-conditioned (customer-level) one-period retention probabilities and supports granular, per-customer profit evaluation. We benchmark six classifiers across two telecom datasets (IBM Telco and Maven Telecom) and demonstrate that e-Profits reshapes model rankings compared to traditional metrics, revealing financial advantages in models previously overlooked by AUC or F1-score. The metric also enables segment-level insight into which models maximise return on investment for high-value customers. e-Profits provides a transparent, customer-level evaluation framework that bridges predictive modelling and profit-driven decision-making in operational churn management. All source code is available at: https://github.com/Awaismanzoor/eprofits.","short_abstract":"Retention campaigns in customer relationship management often rely on churn prediction models evaluated using traditional metrics such as AUC and F1-score. However, these metrics fail to reflect financial outcomes and may mislead strategic decisions. We introduce e-Profits, a novel business-aligned evaluation metric th...","url_abs":"https://arxiv.org/abs/2507.08860","url_pdf":"https://arxiv.org/pdf/2507.08860v2","authors":"[\"Awais Manzoor\",\"M. Atif Qureshi\",\"Etain Kidney\",\"Luca Longo\"]","published":"2025-07-09T11:22:24Z","proceeding":"cs.LG","tasks":"[\"cs.LG\"]","methods":"[]","has_code":false,"code_links":[{"ID":612287,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2896533,"paper_url":"https://arxiv.org/abs/2507.08860","paper_title":"e-Profits: A Business-Aligned Evaluation Metric for Profit-Sensitive Customer Churn Prediction","repo_url":"https://github.com/Awaismanzoor/eprofits","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}