ORCID

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.

Keywords

Churn prediction, Customer relationship management, Machine learning & Artificial intelligence, Profit maximising churn prediction

Publication Date

2026-01-01

Publication Title

International Journal of Data Science and Analytics

Volume

22

Issue

1

ISSN

2364-415X

Deposit Date

2026-03-10

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

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