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.

翻译：客户关系管理中的保留活动通常依赖使用AUC和F1分数等传统指标评估的流失预测模型。然而，这些指标无法反映财务结果，并可能误导战略决策。我们提出e-Profits这一新颖的业务对齐评估指标，该指标基于客户生命周期价值、保留概率和干预成本来量化模型性能。与假设固定群体级参数（如预期最大利润）的现有利润指标不同，e-Profits采用Kaplan-Meier生存分析来估计基于任期条件（客户级）的单周期保留概率，并支持细粒度的单客户利润评估。我们在两个电信数据集（IBM Telco和Maven Telecom）上对六种分类器进行基准测试，结果表明与传统指标相比，e-Profits重塑了模型排名，揭示了先前被AUC或F1分数忽视的模型所具有的财务优势。该指标还能在细分层面揭示哪些模型能为高价值客户实现投资回报最大化。e-Profits提供了一个透明的客户级评估框架，在运营流失管理中架起了预测建模与利润驱动决策之间的桥梁。所有源代码均发布于：https://github.com/Awaismanzoor/eprofits。