Context

ShopEase uses a binary classification model to predict which customer support tickets should be escalated to a senior retention team because they are likely to result in subscription cancellation within 30 days. The model performs reasonably well offline, but business leaders are unsure how to interpret the metrics in terms of staffing, saved revenue, and customer experience.

Current Performance

The Problem

The VP of Customer Success wants to know whether this model is good enough to expand nationwide. They do not want a metric-by-metric definition; they want a business interpretation of what the current performance means, what tradeoffs exist, and whether threshold changes would improve outcomes.

Requirements

1. Interpret the current metrics in business terms using the provided numbers.
2. Explain the practical impact of false positives and false negatives.
3. Assess whether the model is better than the previous rules system and why.
4. Recommend whether ShopEase should tune the decision threshold for higher precision or higher recall.
5. Describe how you would present this performance to a non-technical executive audience.

Constraints

• Senior retention team can handle at most 13,500 escalated tickets per month.
• Each unnecessary escalation costs about $8 in agent time.
• Each missed churn-risk customer represents about $120 in monthly recurring revenue at risk.
• Leadership prefers simple, decision-oriented communication over technical detail.