Answer
Algorithms tend to optimize overall accuracy by favoring the majority class, potentially achieving high accuracy while failing to identify churners, necessitating alternative measures like precision, recall, F1-score, and AUC-ROC that better capture minority-class performance, with the cost of false negatives typically exceeding that of false positives from a business perspective.
A model achieved exceptionally high precision of 98.1%, meaning nearly all customers flagged as high-risk were true churners, though recall was 67.9%, indicating the model failed to identify approximately 32% of actual churners, suggesting the model is optimally calibrated for efficient resource allocation in retention campaigns, minimizing the cost of false alarms while capturing the majority of at-risk customers.
Start with conservative risk thresholds and adjust based on 30 days of results, as too many false positives will overwhelm your team. Keep a human-in-the-loop where CSMs should validate or override predictions when needed.