Feature Impact Assessment AI Prompt

Assess the business impact of this recently launched feature.

Feature: {{feature_name}}
Launch date: {{launch_date}}
Primary success metric: {{primary_metric}}
Data available: {{data}}

1. Pre/post comparison:
   - Define the pre-period (same length as post, ending at launch date)
   - Compare primary metric: pre-period average vs post-period average
   - Absolute change and % change
   - Account for trends: was the metric already trending up/down before launch?

2. Confound check:
   - What else changed during the post-period? (Seasonality, marketing campaigns, other feature launches)
   - How might these confounds explain the observed change?
   - Can any confounds be controlled for or isolated?

3. Adoption-outcome correlation:
   - Segment users by adoption level: non-adopters, light adopters, heavy adopters
   - Compare primary metric across adoption segments
   - Does heavier feature usage correlate with better outcomes?

4. Counterfactual estimation:
   - If possible: use a holdout group (users who did not have access to the feature) as a control
   - Difference-in-differences: compare the change in metric for treatment vs control groups
   - If no holdout: use synthetic control (similar product/market as proxy)

5. Secondary effects:
   - Did the feature have any unintended effects on other metrics?
   - Check: session length, support tickets, error rates, other feature usage

6. ROI estimate:
   - Translate the metric impact into business value: what is the estimated annual impact in revenue or cost?
   - How does this compare to the development cost of the feature?

Return: pre/post comparison, confound analysis, adoption-outcome correlation, counterfactual estimate, and ROI.