e-Profits: A Business-Aligned Evaluation Metric for Profit-Sensitive Customer Churn Prediction

e-Profits: A Business-Aligned Evaluation Metric for Profit-Sensitive Customer Churn Prediction | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article e-Profits: A Business-Aligned Evaluation Metric for Profit-Sensitive Customer Churn Prediction Awais Manzoor, Atif Qureshi, Etain Kidney, Luca Longo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7614644/v2 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 25 Feb, 2026 Read the published version in International Journal of Data Science and Analytics → Version 2 posted 13 You are reading this latest preprint version Show more versions 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 \emph{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, \emph{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 \emph{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. \emph{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: \url{ https://github.com/Awaismanzoor/eprofits} . Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 25 Feb, 2026 Read the published version in International Journal of Data Science and Analytics → Version 2 posted Editorial decision: Accepted 26 Jan, 2026 Reviews received at journal 20 Jan, 2026 Reviews received at journal 19 Jan, 2026 Reviews received at journal 11 Jan, 2026 Reviewers agreed at journal 11 Jan, 2026 Reviews received at journal 11 Jan, 2026 Reviewers agreed at journal 07 Jan, 2026 Reviewers agreed at journal 07 Jan, 2026 Reviewers agreed at journal 07 Jan, 2026 Reviewers agreed at journal 07 Jan, 2026 Reviewers invited by journal 07 Jan, 2026 Submission checks completed at journal 07 Jan, 2026 First submitted to journal 06 Jan, 2026 You are reading this latest preprint version Show more versions Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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