Impact Sizing

/impact-sizing - Quantify Feature Value

Systematically estimate the impact of a feature using the 4-step framework.

Context Routing Logic (Internal - for Claude)

Automatic Context Checks: When this skill is invoked, immediately check:

Context Priority:

1. Feature definition and user impact FIRST
2. Business model and pricing SECOND
3. User base size and addressable segment THIRD
4. Historical precedent for similar features FOURTH

Cross-Skill Links:

• If sizing is unclear → Link to /impact-sizing (this skill)
• If comparing options → Use this to inform /experiment-decision
• If building business case → Reference in PRD and /strategy-check
• If identifying leading metrics → Connect to /feature-metrics and /metrics-framework

Step 0: Understanding What We're Sizing

Before we estimate impact, let me check what context exists...

Checking:

• brain/knowledge/product/features/ for the feature definition
• brain/ingestion/interviews/ for user research on this problem
• brain/knowledge/strategy.md for business model context
• brain/knowledge/product/ for comparable feature data

Based on what I find, I'll show you:

What We Know About This Feature

Feature Definition:

• [What problem does it solve?]
• [Who does it affect? Total addressable users: X]
• [User segment: SMB / Enterprise / Consumer / etc.]

User Impact:

• [Problem severity: from user research]
• [Expected behavior change: what users do differently]
• [Current workaround cost: time/money users waste today]

Business Context:

• [Revenue model: how does this make money?]
• [Existing similar features: what was their adoption?]
• [Resource constraints: time/team availability]

PM-Specific Diagnosis Questions

1. Addressability: Can you reach the entire user population, or only a segment?
2. Adoption Curve: Will this be immediate adoption or gradual ramp?
3. Monetization: Is this a direct revenue play or indirect (retention/expansion)?
4. Confidence: What data do you have vs what are you assuming?
5. Execution Risk: What could go wrong with adoption or implementation?

When to Use

• Prioritizing features in planning
• Justifying resource allocation
• Building business cases for executives
• Comparing multiple feature options

The 4-Step Framework

Step 1: Estimate Usage (Funnel)

Create a funnel from exposure to usage:

Total users who see feature: [number]
    ↓ (Drop-off: [reason])
Users eligible for feature: [number]
    ↓ (Drop-off: [reason])
Users who engage: [number]
    ↓ (Drop-off: [reason])
Users who complete action: [number]

Gotchas to consider:

• How many users are actually eligible?
• How often will users be exposed?
• What's the expected adoption curve?

Step 2: Calculate Impact

Progress through three levels:

Engagement Impact:

• DAU/MAU change
• Retention rate change
• Session frequency/duration

Top-Line Impact:

• Revenue change
• GMV change
• Conversion rate change

Bottom-Line Impact:

• Contribution margin
• Customer acquisition cost
• Lifetime value change

Step 3: Identify & De-Risk Assumptions

For each assumption, assess risk and plan mitigation:

Common de-risking actions:

• Old data → Work with analytics for fresh numbers
• Usability question → Test with prototype
• Similar to competitors → Benchmark research
• Industry standard → Collect benchmarks

Step 4: Define Takeaways

Three buckets:

1. Planning: Use for prioritization decisions
2. Experiment Execution: Determine experiment duration for stat sig
3. Feature Design: Identify levers to increase impact

Quick Start Prompt

When PM types /impact-sizing, respond:

Let's size the impact of your feature. I'll walk you through the 4-step framework.

**Step 1: Estimate Usage**
- What feature are we sizing?
- Who sees this feature? (total addressable users)
- What are the steps from seeing → using?

Once you share this, I'll help build the funnel and calculate impact.

Output Template

# Impact Sizing: [Feature Name]

## Usage Funnel
| Stage | Users | Drop-off Rate | Reason |
|-------|-------|---------------|--------|
| See feature | [X] | - | - |
| Eligible | [X] | [Y%] | [reason] |
| Engage | [X] | [Y%] | [reason] |
| Complete | [X] | [Y%] | [reason] |

## Impact Estimates

**Engagement Impact:**
- Metric: [metric]
- Current: [baseline]
- Expected change: [+/- X%]
- Confidence: [High/Med/Low]

**Top-Line Impact:**
- Metric: [revenue/GMV]
- Expected change: [$X / +Y%]
- Confidence: [High/Med/Low]

**Bottom-Line Impact:**
- Metric: [margin/LTV]
- Expected change: [$X / +Y%]
- Confidence: [High/Med/Low]

## Confidence Assessment
| Assumption | Confidence | De-risking Action |
|------------|------------|-------------------|
| [assumption] | [level] | [action] |

## Recommendation
[Proceed / De-risk first / Deprioritize]
Rationale: [why]

Driver Tree Example

Connect feature to business metrics:

Feature: [Name]
    ↓
[Engagement metric] +X%
    ↓
[Conversion metric] +Y%
    ↓
[Revenue metric] +$Z
    ↓
[Profit metric] +$W

Output Integration

Where Files Go

Impact sizing analysis:

• Active work: outputs/analyses/impact-sizing-[feature-name]-[date].md
• When finalized: Reference in PRD in Strategic Fit section
• Archive: Move to brain/knowledge/product/features/ as historical reference when feature ships

Link to Other Work

After sizing impact:

• Reference in PRD - "Users affected: X, revenue impact: $Y, confidence: [High/Med/Low]"
• Use in prioritization - Helps decide if this should be in Q# roadmap
• Support pitches - Share with executives when requesting resources
• Inform metrics - Use impact estimates to set success metric targets

Cross-Skill Integration

Feeds into:

• /prd-draft - Impact sizing goes into "Strategic Fit" section
• /strategy-check - Feature impact informs strategic pillar priorities
• /feature-metrics - Usage estimates inform what metrics can detect changes
• /experiment-decision - Impact size determines experiment duration/sample size

Pulls from:

• brain/ingestion/interviews/ - User pain and adoption patterns
• /user-research-synthesis - Qualitative insights about addressable users
• brain/knowledge/strategy.md - Business model and growth drivers
• brain/knowledge/product/ - Historical data on similar features

Tips

• Do the amount that fits your world - Few weeks? Address top assumption. More time? Go deeper.
• Never done - You can always upgrade the model as you learn more
• Connect to what matters - Executives care about revenue/profit, not engagement metrics alone
• Validate assumptions - The biggest unknowns are usually adoption rate and addressable market
• De-risking matters - Knowing what you don't know is worth more than precise wrong estimates

Output Quality Self-Check

Before presenting output to the PM, verify:

• File saved to correct location: Output saved to outputs/analyses/impact-sizing-[feature-name]-[date].md
• Context routing table was checked: Reviewed brain/knowledge/strategy.md, brain/knowledge/, and brain/knowledge/product/ for relevant context
• Driver tree has specific numbers: Every node in the driver tree contains actual estimates (not placeholders like "[X]" or "[number]")
• Confidence levels assigned: Each assumption in the confidence assessment table has a High/Med/Low rating with justification
• Revenue/user impact calculated with clear methodology: Impact estimates show the math (e.g., "10,000 eligible users x 30% adoption x $5 ARPU = $15,000/month"), not just final numbers
• De-risking actions identified: Every Low-confidence assumption has a specific, actionable de-risking step (not generic "do more research")
• Impact tied to strategic goal: The recommendation section explicitly references a strategic goal or OKR from brain/knowledge/
• Sensitivity analysis included: Output shows best-case, worst-case, and expected-case scenarios with the key variable that drives the range