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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2507.08860 |
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| _version_ | 1866918288667181056 |
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| author | Manzoor, Awais Qureshi, M. Atif Kidney, Etain Longo, Luca |
| author_facet | Manzoor, Awais Qureshi, M. Atif Kidney, Etain Longo, Luca |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_08860 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | e-Profits: A Business-Aligned Evaluation Metric for Profit-Sensitive Customer Churn Prediction Manzoor, Awais Qureshi, M. Atif Kidney, Etain Longo, Luca Machine Learning 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. |
| title | e-Profits: A Business-Aligned Evaluation Metric for Profit-Sensitive Customer Churn Prediction |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2507.08860 |