Zurück zu Fähigkeiten

forecast-scenarios

guia-matthieu
Aktualisiert 2 days ago
1 Ansichten
111
20
111
Auf GitHub ansehen
Metaaidesign

Über

Diese Fähigkeit ermöglicht es Entwicklern, mit Sensitivitätsanalysen für die Finanzplanung optimistische, pessimistische und wahrscheinliche Umsatzszenarien zu modellieren. Sie ist ideal zum Erstellen von Prognosen, zur Präsentation von Vorstandsszenarien und zur Planung im Rahmen von Umsatzunsicherheiten. Das Tool integriert Methodiken wie Base/Bull/Bear-Case-Modellierung und Monte-Carlo-Simulationen für die strategische Risikobewertung.

Schnellinstallation

Claude Code

Empfohlen
Primär
npx skills add guia-matthieu/clawfu-skills -a claude-code
Plugin-BefehlAlternativ
/plugin add https://github.com/guia-matthieu/clawfu-skills
Git CloneAlternativ
git clone https://github.com/guia-matthieu/clawfu-skills.git ~/.claude/skills/forecast-scenarios

Kopieren Sie diesen Befehl und fügen Sie ihn in Claude Code ein, um diese Fähigkeit zu installieren

Dokumentation

Forecast Scenario Modeling

Create multiple revenue scenarios with variable assumptions to support strategic planning, board presentations, and risk management.

When to Use This Skill

  • Annual and quarterly planning
  • Board meeting preparations
  • Fundraising projections
  • Risk assessment and contingency planning
  • Evaluating strategic initiatives

Methodology Foundation

Based on McKinsey Scenario Planning and FP&A best practices, combining:

  • Base/Bull/Bear case modeling
  • Sensitivity analysis (variable impact)
  • Monte Carlo probability distributions
  • Driver-based forecasting

What Claude Does vs What You Decide

Claude DoesYou Decide
Structures scenario frameworkAssumption values
Calculates scenario outcomesWhich scenario to plan for
Identifies key sensitivitiesRisk tolerance levels
Models variable impactsStrategic responses
Presents range of outcomesFinal forecast commitment

What This Skill Does

  1. Scenario definition - Base, upside, downside cases
  2. Variable modeling - Test impact of changing assumptions
  3. Sensitivity analysis - Which variables matter most
  4. Probability weighting - Expected value calculations
  5. Action planning - What to do in each scenario

How to Use

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.

Instructions

Step 1: Define Scenario Framework

ScenarioDefinitionProbability
Best Case (Bull)Everything goes right15-20%
Likely Case (Base)Realistic expectations50-60%
Worst Case (Bear)Major headwinds20-25%

Step 2: Identify Key Drivers

Rank variables by revenue impact:

DriverImpactControllability
Win rateHighMedium
Pipeline volumeHighHigh
Deal sizeMediumLow
Sales cycleMediumMedium
Churn rateMediumMedium
PricingLowHigh

Step 3: Set Variable Ranges

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)

Step 4: Calculate Scenarios

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

Step 5: Sensitivity Analysis

Test: "What if X changes by 10%?"

Variable+10% Impact-10% ImpactSensitivity
Win Rate+$100K-$100KHigh
Pipeline+$90K-$90KHigh
Deal Size+$50K-$50KMedium
Churn-$30K+$30KMedium

Step 6: Calculate Expected Value

Expected Revenue =
  (Best × Probability) +
  (Likely × Probability) +
  (Worst × Probability)

= ($1.9M × 20%) + ($1.07M × 55%) + ($430K × 25%)
= $380K + $589K + $108K
= $1.077M

Examples

Example 1: Quarterly Forecast Scenarios

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

Example 2: Annual Planning Scenarios

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.

Skill Boundaries

What This Skill Does Well

  • Structuring scenario frameworks
  • Calculating outcomes from assumptions
  • Identifying key sensitivities
  • Presenting range of possibilities

What This Skill Cannot Do

  • Predict which scenario will occur
  • Know your specific business dynamics
  • Account for black swan events
  • Replace expert judgment on probabilities

When to Escalate to Human

  • Setting official targets
  • Board/investor commitments
  • Major strategic pivots
  • Assumptions requiring domain expertise

Iteration Guide

Follow-up Prompts

  • "What win rate do we need to hit Base case?"
  • "Show me monthly revenue trajectory for each scenario."
  • "Add a 'catastrophic' case if we lose our biggest customer."
  • "What's the probability-weighted forecast?"

Scenario Planning Cycle

  1. Set variables and ranges
  2. Calculate scenarios
  3. Identify early warning signals
  4. Define trigger points for action
  5. Review monthly against actuals

Checklists & Templates

Annual Planning Template

## [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 |
|------|-----------------|------------|

References

  • McKinsey Scenario Planning Guide
  • FP&A Forecasting Best Practices
  • SaaS Metrics and Financial Modeling
  • CFO.com Revenue Forecasting

Related Skills

  • pipeline-forecasting - Feed into scenario models
  • lead-scoring - Input for pipeline assumptions
  • account-health - NRR/churn inputs

Skill Metadata

  • Domain: RevOps
  • Complexity: Advanced
  • Mode: centaur
  • Time to Value: 60-90 min for full model
  • Prerequisites: Historical data, variable assumptions

GitHub Repository

guia-matthieu/clawfu-skills
Pfad: skills/revops/forecast-scenarios
0
ai-skillsanthropicclaude-codeclaude-skillsmarketingmcp-server

Verwandte Skills

content-collections

Meta

Diese Skill bietet eine produktionsgetestete Einrichtung für Content Collections – ein TypeScript-first-Tool, das Markdown/MDX-Dateien in typsichere Datensammlungen mit Zod-Validierung umwandelt. Verwenden Sie ihn beim Erstellen von Blogs, Dokumentationsseiten oder inhaltsstarken Vite + React-Anwendungen, um Typsicherheit und automatische Inhaltsvalidierung zu gewährleisten. Er behandelt alles von der Vite-Plugin-Konfiguration und MDX-Kompilierung bis hin zur Deployment-Optimierung und Schema-Validierung.

Skill ansehen

polymarket

Meta

Diese Fähigkeit ermöglicht es Entwicklern, Anwendungen mit der Polymarket-Prognosemärkte-Plattform zu erstellen, einschließlich API-Integration für Handel und Marktdaten. Sie bietet außerdem Echtzeit-Datenstreaming über WebSocket, um Live-Trades und Marktaktivitäten zu überwachen. Nutzen Sie sie zur Implementierung von Handelsstrategien oder zur Erstellung von Tools, die Live-Marktaktualisierungen verarbeiten.

Skill ansehen

creating-opencode-plugins

Meta

Diese Fähigkeit unterstützt Entwickler dabei, OpenCode-Plugins zu erstellen, die in über 25 Ereignistypen wie Befehle, Dateien und LSP-Operationen eingreifen. Sie bietet die Plugin-Struktur, Event-API-Spezifikationen und Implementierungsmuster für JavaScript/TypeScript-Module. Nutzen Sie sie, wenn Sie den Lebenszyklus des OpenCode KI-Assistenten mit benutzerdefinierter ereignisgesteuerter Logik abfangen, überwachen oder erweitern müssen.

Skill ansehen

sglang

Meta

SGLang ist ein hochperformantes LLM-Serving-Framework, das sich auf schnelle, strukturierte Generierung für JSON, Regex und agentenbasierte Workflows unter Verwendung seines RadixAttention-Prefix-Cachings spezialisiert. Es bietet deutlich schnellere Inferenz, insbesondere für Aufgaben mit wiederholten Präfixen, was es ideal für komplexe, strukturierte Ausgaben und Mehrfachdialoge macht. Wählen Sie SGLang gegenüber Alternativen wie vLLM, wenn Sie constrained decoding benötigen oder Anwendungen mit umfangreicher Präfix-Weitergabe entwickeln.

Skill ansehen