LGJul 9, 2025

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

arXiv:2507.08860v11 citationsh-index: 4Has CodeInt J Data Sci Anal
Originality Incremental advance
AI Analysis

This addresses the problem for marketing and analytics teams in businesses by providing a profit-driven evaluation tool, though it is incremental as it builds on existing profit-based metrics with personalization.

The paper tackles the problem of evaluating churn prediction models in customer relationship management by introducing e-Profits, a business-aligned metric that quantifies performance based on customer-specific value, retention probability, and intervention costs, and demonstrates that it reshapes model rankings compared to traditional metrics like AUC and F1-score, revealing financial advantages in models previously overlooked.

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-specific 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 personalised retention rates and supports granular, per customer 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 is designed as an understandable, post hoc tool to support model evaluation in business contexts, particularly for marketing and analytics teams prioritising profit-driven decisions. All source code is available at: https://github.com/matifq/eprofits.

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