/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:

Source	Files/Folders	Search Terms	What to Extract
Current PRD	`thoughts/shared/pm/prds/*.md`	feature name from chat	User impact, problem severity
User Research	`thoughts/shared/pm/*.md`	feature problem, user quotes	Addressable users, pain severity
Business Model	`thoughts/shared/pm/context/business-info-template.md`	pricing, revenue model, TAM	Revenue impact drivers
Historical Data	`thoughts/shared/pm/metrics/*.md`	similar features, baseline conversion	Reference adoption rates
Strategy	`thoughts/shared/pm/frameworks/*.md`	feature strategic fit	Resource availability, priority context

Context Priority:

Feature definition and user impact FIRST
Business model and pricing SECOND
User base size and addressable segment THIRD
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 /write-prod-strategy
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:

thoughts/shared/pm/prds/ for the feature definition
thoughts/shared/pm/ for user research on this problem
thoughts/shared/pm/context/business-info-template.md for business model context
thoughts/shared/pm/metrics/ 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

Addressability: Can you reach the entire user population, or only a segment?
Adoption Curve: Will this be immediate adoption or gradual ramp?
Monetization: Is this a direct revenue play or indirect (retention/expansion)?
Confidence: What data do you have vs what are you assuming?
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:

Assumption	Confidence	Risk	De-risking Action
[Assumption]	High/Med/Low	[Risk if wrong]	[Action]

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:

Planning: Use for prioritization decisions
Experiment Execution: Determine experiment duration for stat sig
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:

Save to: thoughts/shared/pm/analyses/impact-sizing-[feature-name]-[date].md
When finalized: Reference in PRD in Strategic Fit section

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
/write-prod-strategy - 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:

thoughts/shared/pm/ - User pain and adoption patterns
/user-research-synthesis - Qualitative insights about addressable users
[[business-info-template]] - Business model and growth drivers
thoughts/shared/pm/metrics/ - 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 thoughts/shared/pm/analyses/impact-sizing-[feature-name]-[date].md
Context routing table was checked: Reviewed thoughts/shared/pm/context/business-info-template.md, thoughts/shared/pm/frameworks/, and thoughts/shared/pm/metrics/ 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 thoughts/shared/pm/frameworks/
Sensitivity analysis included: Output shows best-case, worst-case, and expected-case scenarios with the key variable that drives the range