When to use

After an analytical finding surfaces a potential action, change, or opportunity. Use to produce a defensible numeric estimate that stakeholders can act on. Also use when prioritizing a backlog of initiatives — quantified impact is the primary ranking signal.

Process

1. Classify the impact type — revenue growth, cost reduction, risk reduction, or efficiency gain. Each type has a different formula family (see references/impact_quantification_framework.md).
2. Gather inputs — collect baseline metrics, affected population size, expected lift/reduction, time horizon, and confidence level.
3. Build the point estimate — use scripts/revenue_impact.py for revenue/growth scenarios or scripts/cost_savings.py for cost/efficiency scenarios.
4. Add uncertainty bounds — use scripts/confidence_interval.py to produce low/base/high estimates. Never deliver a single number without a range.
5. Document assumptions — fill in references/assumption_documentation.md for every input that is estimated rather than directly measured; note the sensitivity of the output to each.
6. Package the estimate — complete assets/impact_estimate_template.md with the range, assumptions, confidence, and recommended action; optionally build the full assets/business_case_template.md for larger decisions.

Inputs the skill needs

• Baseline metric value (current state)
• Affected population or volume
• Expected change (lift %, absolute, or rate change)
• Time horizon (monthly / annual)
• Confidence level in inputs (high / medium / low)

Output

• Impact estimate with low/base/high range
• Assumption log (source and sensitivity for each input)
• Completed impact_estimate_template.md or business_case_template.md