Measure and optimize growth using the AARRR (Pirate Metrics) framework with stage-specific KPIs and funnel analysis
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Model best-case, worst-case, and likely revenue scenarios with sensitivity analysis for strategic planning. Use when: building financial forecasts; presenting board scenarios; planning headcount around revenue uncertainty; modeling pricing changes impact; preparing investor updates with upside/downside ranges
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Create multiple revenue scenarios with variable assumptions to support strategic planning, board presentations, and risk management.
Based on McKinsey Scenario Planning and FP&A best practices, combining:
| Claude Does | You Decide |
|---|---|
| Structures scenario framework | Assumption values |
| Calculates scenario outcomes | Which scenario to plan for |
| Identifies key sensitivities | Risk tolerance levels |
| Models variable impacts | Strategic responses |
| Presents range of outcomes | Final forecast commitment |
Model revenue scenarios for [Period]:
Current Status:
- YTD Revenue: $X
- Current Pipeline: $X
- Run Rate: $X/month
Key Variables to Model:
- Win rate: [Current: X%, Range: X-X%]
- Average deal size: [Current: $X, Range: $X-$X]
- Sales cycle: [Current: X days, Range: X-X]
- New pipeline creation: [Current: $X/month]
- Churn rate: [Current: X%]
Create best, likely, and worst case scenarios.
| Scenario | Definition | Probability |
|---|---|---|
| Best Case (Bull) | Everything goes right | 15-20% |
| Likely Case (Base) | Realistic expectations | 50-60% |
| Worst Case (Bear) | Major headwinds | 20-25% |
Rank variables by revenue impact:
| Driver | Impact | Controllability |
|---|---|---|
| Win rate | High | Medium |
| Pipeline volume | High | High |
| Deal size | Medium | Low |
| Sales cycle | Medium | Medium |
| Churn rate | Medium | Medium |
| Pricing | Low | High |
For each driver, define realistic bounds:
Win Rate:
- Best: 35% (team is hitting stride)
- Likely: 25% (current performance)
- Worst: 18% (market headwinds)
Pipeline:
- Best: $5M (strong marketing)
- Likely: $4M (normal cadence)
- Worst: $2.5M (budget cuts)
Revenue Formula (simplified):
Quarterly Revenue =
(Pipeline × Win Rate) +
(Expansion Revenue) -
(Churn)
Apply to each scenario:
Best Case:
$5M × 35% = $1.75M new + $200K expansion - $50K churn
= $1.9M
Likely Case:
$4M × 25% = $1M new + $150K expansion - $80K churn
= $1.07M
Worst Case:
$2.5M × 18% = $450K new + $100K expansion - $120K churn
= $430K
Test: "What if X changes by 10%?"
| Variable | +10% Impact | -10% Impact | Sensitivity |
|---|---|---|---|
| Win Rate | +$100K | -$100K | High |
| Pipeline | +$90K | -$90K | High |
| Deal Size | +$50K | -$50K | Medium |
| Churn | -$30K | +$30K | Medium |
Expected Revenue =
(Best × Probability) +
(Likely × Probability) +
(Worst × Probability)
= ($1.9M × 20%) + ($1.07M × 55%) + ($430K × 25%)
= $380K + $589K + $108K
= $1.077M
Input:
Model Q2 scenarios:
Current Status:
- Q1 Revenue: $850K
- Q2 Pipeline: $4.2M
- Monthly run rate: $280K
Variables:
- Win rate: Current 24%, Range 18-32%
- Avg deal: Current $45K, Range $38K-$55K
- New monthly pipeline: Current $800K, Range $500K-$1.2M
- Monthly churn: Current $25K, Range $15K-$40K
- Expansion: Current $50K/month, Range $30K-$80K
Output:
## Q2 Revenue Scenario Model
### Scenario Definitions
| Scenario | Probability | Description |
|----------|-------------|-------------|
| 🐂 Bull | 20% | Strong execution, market tailwinds |
| 📊 Base | 55% | Continue current trajectory |
| 🐻 Bear | 25% | Market slowdown, execution challenges |
### Variable Assumptions
| Variable | Bear | Base | Bull |
|----------|------|------|------|
| Starting Pipeline | $4.2M | $4.2M | $4.2M |
| Win Rate | 18% | 24% | 32% |
| Avg Deal Size | $38K | $45K | $55K |
| New Pipeline/mo | $500K | $800K | $1.2M |
| Monthly Churn | $40K | $25K | $15K |
| Expansion/mo | $30K | $50K | $80K |
### Q2 Revenue Calculations
#### 🐂 Bull Case: $1.42M
Starting Pipeline Revenue: $4.2M × 32% = $1.34M
Adjustment for deal size: $1.34M × ($55K/$45K) = $1.64M effective
New Pipeline Added (Q2): $1.2M × 3 months × 32% × 50% (partial close) = $576K
Expansion: $80K × 3 = $240K
Churn: -$15K × 3 = -$45K
Total Bull: $1.64M (existing) + $576K (new) + $240K (exp) - $45K (churn) Weighted at Q2 stage: $1.42M
#### 📊 Base Case: $980K
Starting Pipeline Revenue: $4.2M × 24% = $1.01M
New Pipeline (partial close): $800K × 3 × 24% × 50% = $288K
Expansion: $150K Churn: -$75K
Total Base: $1.01M × 0.9 (timing) + $150K - $75K = $980K
#### 🐻 Bear Case: $580K
Starting Pipeline Revenue: $4.2M × 18% = $756K × 0.85 (pushed deals) = $643K
New Pipeline: $500K × 3 × 18% × 40% = $108K Expansion: $90K Churn: -$120K
Total Bear: $580K
### Scenario Summary
| Scenario | Revenue | vs Q1 | Growth |
|----------|---------|-------|--------|
| 🐂 Bull | $1.42M | +$570K | +67% |
| 📊 Base | $980K | +$130K | +15% |
| 🐻 Bear | $580K | -$270K | -32% |
### Expected Value
E(Revenue) = ($1.42M × 0.20) + ($980K × 0.55) + ($580K × 0.25) = $284K + $539K + $145K = $968K
**Recommended Forecast: $950K - $1.0M**
### Sensitivity Analysis
| If This Changes... | Revenue Impact |
|--------------------|----------------|
| Win rate +5% | +$210K |
| Win rate -5% | -$210K |
| Pipeline +$500K | +$120K |
| Deal size +$5K | +$80K |
| Churn +$10K/mo | -$30K |
**Highest Sensitivity**: Win rate (focus on deal execution)
### Scenario Action Plans
**If Trending Bull:**
- Accelerate hiring plan
- Increase marketing spend
- Prepare for capacity constraints
**If Trending Base:**
- Stay the course
- Focus on execution
- Build pipeline for Q3
**If Trending Bear:**
- Tighten expense controls
- Extend runway calculations
- Double down on existing customers
- Consider discounting to close deals
Input:
Model 2026 annual scenarios for board:
2025 Results:
- Revenue: $4.2M
- Customers: 85
- ARR Growth: 45%
- NRR: 110%
2026 Planning Variables:
- New customer target: 40-80 new customers
- ACV range: $40K-$60K
- NRR range: 105%-120%
- Churn range: 5%-12%
Output:
## 2026 Annual Revenue Scenarios
### For Board Review
---
### Scenario Summary
| Metric | Bear | Base | Bull |
|--------|------|------|------|
| New Customers | 40 | 60 | 80 |
| New ACV | $40K | $50K | $60K |
| New ARR | $1.6M | $3.0M | $4.8M |
| NRR | 105% | 112% | 120% |
| Existing Base | $4.41M | $4.70M | $5.04M |
| **Total ARR** | **$6.0M** | **$7.7M** | **$9.8M** |
| **Growth** | **43%** | **83%** | **133%** |
### Detailed Calculations
#### 🐻 Bear Case: $6.0M ARR (+43%)
**Assumptions:**
- Conservative new sales (40 customers)
- Lower ACV ($40K avg)
- NRR dips (105%)
- Higher churn (10%)
Existing Customer Base: $4.2M × 105% NRR = $4.41M
New Customer Revenue: 40 customers × $40K = $1.6M
Total: $6.0M
**When This Happens:**
- Market downturn
- Sales execution issues
- Product-market fit challenges
- Key competitor gains ground
---
#### 📊 Base Case: $7.7M ARR (+83%)
**Assumptions:**
- Target new sales (60 customers)
- Target ACV ($50K)
- Maintain NRR (112%)
- Normal churn (7%)
Existing Customer Base: $4.2M × 112% NRR = $4.70M
New Customer Revenue: 60 customers × $50K = $3.0M
Total: $7.7M
**This Is Likely If:**
- Execute at current pace
- Market conditions stable
- Product roadmap delivers
- Team retention healthy
---
#### 🐂 Bull Case: $9.8M ARR (+133%)
**Assumptions:**
- Exceed targets (80 customers)
- Premium ACV ($60K)
- Strong NRR (120%)
- Low churn (5%)
Existing Customer Base: $4.2M × 120% NRR = $5.04M
New Customer Revenue: 80 customers × $60K = $4.8M
Total: $9.8M
**Required For This:**
- Strong product releases
- Successful enterprise push
- Favorable market timing
- Key hires perform
---
### Expected Value & Recommendation
E(ARR) = ($6.0M × 0.20) + ($7.7M × 0.55) + ($9.8M × 0.25) = $1.2M + $4.24M + $2.45M = $7.89M
### Board Recommendation
**Target: $7.5M ARR** (+79% growth)
| Metric | Target | Confidence |
|--------|--------|------------|
| New Customers | 55-60 | Medium-High |
| New ARR | $2.75M | Medium |
| NRR | 110%+ | High |
| Total ARR | $7.5M | Medium |
### Key Risks & Mitigations
| Risk | Impact | Mitigation |
|------|--------|------------|
| Sales hiring delays | -$1M | Recruit pipeline now |
| Enterprise deals push | -$800K | Parallel SMB motion |
| Key customer churn | -$500K | CSM investment |
| Competitor pricing | -$600K | Value selling training |
### Monthly Checkpoints
| Month | Bear | Base | Bull |
|-------|------|------|------|
| Q1 End | $4.8M | $5.2M | $5.8M |
| Q2 End | $5.3M | $6.2M | $7.4M |
| Q3 End | $5.6M | $7.0M | $8.6M |
| Q4 End | $6.0M | $7.7M | $9.8M |
Track monthly and adjust Q3 if trending to Bear.
## [Year] Revenue Scenarios
### Scenarios
| Case | Revenue | Growth | Probability |
|------|---------|--------|-------------|
| Bull | | | 20% |
| Base | | | 55% |
| Bear | | | 25% |
### Key Assumptions
| Variable | Bear | Base | Bull |
|----------|------|------|------|
### Sensitivity Analysis
| Variable | Impact per 10% |
|----------|----------------|
### Risk Register
| Risk | Scenario Impact | Mitigation |
|------|-----------------|------------|
pipeline-forecasting - Feed into scenario modelslead-scoring - Input for pipeline assumptionsaccount-health - NRR/churn inputs아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.
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Measure and optimize growth using the AARRR (Pirate Metrics) framework with stage-specific KPIs and funnel analysis
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Calculate A/B test statistical significance. Use when: determining if test results are significant; calculating required sample size; estimating test duration; analyzing conversion experiments; making data-driven decisions
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Assess customer account health using product usage, support sentiment, payment status, and relationship signals
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Analyze paid advertising performance across channels and recommend budget reallocation to maximize ROAS and minimize CAC. Use when: planning quarterly ad budget allocation, diagnosing underperforming ad channels, deciding whether to scale spend on a channel, calculating marginal ROI across Google Ads, Meta, LinkedIn, or TikTok, rebalancing media mix after performance shifts, or setting up a test-and-scale framework for new channels.
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Use when testing skills, commands, or agents for quality. Use after creating new skills, before deploying agents, or when debugging inconsistent agent behavior. Triggers on "evaluate", "test quality", "is this skill working", or QA of AI workflows.
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Use when analyzing server logs to understand how AI crawlers (GPTBot, ClaudeBot, PerplexityBot) interact with your site. Use when optimizing content placement for LLM retrieval, diagnosing why AI search isn't citing your content, or auditing crawl patterns to find optimization gaps.
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