Context

StreamBox uses a binary classification model to predict which subscribers are likely to cancel in the next 30 days so the retention team can send discount offers. The team says the model's overall accuracy looks acceptable, but campaign ROI has been weaker than expected.

Current Performance

The model was evaluated on a holdout set of 10,000 subscribers.

The Problem

Leadership wants to know whether the model is actually useful for retention targeting. The confusion matrix shows the model correctly classifies many users overall, but it also misses a large share of churners.

Requirements

1. Explain what a confusion matrix is and how each cell (TP, FP, FN, TN) should be interpreted in this churn setting.
2. Use the confusion matrix to explain why accuracy alone is misleading here.
3. Assess whether the model is better suited for a precision-focused or recall-focused retention strategy.
4. Recommend 3-4 actions to improve model usefulness, including any threshold or evaluation changes.
5. Describe the business impact of false positives versus false negatives.

Constraints

• The retention team can contact at most 1,000 users per week.
• Each retention offer costs $8.
• Missing a true churner leads to an average expected revenue loss of $65.