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skill Business

Financial Model

financial-model

Builds financial models for startups with revenue drivers, cost assumptions, and sensitivity analysis. Use when creating detailed financial models for fundraising or planning.

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When to Use This Skill

Use this skill when you need to:

  • Build a bottoms-up financial model for a startup or small business
  • Create investor-ready financial projections with detailed assumptions
  • Model revenue drivers, unit economics, and cash flow
  • Run sensitivity analysis on key business assumptions

DO NOT use this skill for simple budgets (use budget-planner) or basic revenue forecasts (use revenue-forecast). This is for comprehensive financial models with multiple interconnected assumptions.


Core Principle

A FINANCIAL MODEL IS ONLY AS GOOD AS ITS ASSUMPTIONS — EVERY NUMBER MUST TRACE BACK TO A STATED ASSUMPTION THAT CAN BE CHALLENGED, TESTED, AND UPDATED.


Phase 1: Model Inputs

Required Inputs

Input What to Ask Default
Business model "How do you make money? (SaaS, e-commerce, services, marketplace)" No default — must be provided
Revenue streams "What are your revenue sources and pricing?" No default — must be provided
Current metrics "Current MRR, users, conversion rates, churn?" Pre-revenue if not provided
Cost structure "Fixed costs, variable costs, planned hires?" Will build from scratch
Model timeframe "12, 24, or 36 months?" 24 months
Model purpose "Who is this for? (internal planning, seed raise, Series A)" Internal planning

GATE: Do not proceed without business model and revenue stream details.


Phase 2: Assumptions Sheet

Every model starts with a documented assumptions sheet.

## Key Assumptions

### Revenue Assumptions
| Assumption | Value | Source/Rationale |
|-----------|-------|-----------------|
| Starting MRR | $[X] | Current |
| Monthly user growth rate | [X]% | [Based on: historical, benchmark, or target] |
| Conversion rate (trial to paid) | [X]% | [Source] |
| Average revenue per user (ARPU) | $[X] | [Current pricing] |
| Monthly churn rate | [X]% | [Historical or industry benchmark] |
| Expansion revenue rate | [X]% | [Upsell/cross-sell estimate] |

### Cost Assumptions
| Assumption | Value | Source/Rationale |
|-----------|-------|-----------------|
| Customer acquisition cost (CAC) | $[X] | [Current or estimated] |
| Gross margin | [X]% | [Based on COGS breakdown] |
| Monthly fixed costs (current) | $[X] | [Actual] |
| Planned hires | [X] people by month [X] | [Hiring plan] |
| Average fully-loaded salary | $[X]/month | [Market rate] |
| Marketing spend (% of revenue) | [X]% | [Target] |

GATE: Present assumptions to the user for validation before building the model.


Phase 3: Model Build

Revenue Model (Bottoms-Up)

## Revenue Model

### Monthly Cohort Model
| Month | New Users | Churned | Active Users | MRR | Expansion | Total MRR |
|-------|----------|---------|-------------|-----|-----------|-----------|
| M1 | [X] | [X] | [X] | $[X] | $[X] | $[X] |
| M2 | [X] | [X] | [X] | $[X] | $[X] | $[X] |
| ... | | | | | | |
| M24 | [X] | [X] | [X] | $[X] | $[X] | $[X] |

### Annual Revenue Summary
| | Year 1 | Year 2 |
|--|--------|--------|
| ARR (ending) | $[X] | $[X] |
| Total Revenue | $[X] | $[X] |
| YoY Growth | — | [X]% |

Expense Model

## Expense Model

### Monthly Expense Breakdown
| Category | M1 | M6 | M12 | M18 | M24 |
|----------|----|----|-----|-----|-----|
| COGS | $[X] | $[X] | $[X] | $[X] | $[X] |
| Personnel | $[X] | $[X] | $[X] | $[X] | $[X] |
| Marketing | $[X] | $[X] | $[X] | $[X] | $[X] |
| G&A | $[X] | $[X] | $[X] | $[X] | $[X] |
| **Total Opex** | **$[X]** | **$[X]** | **$[X]** | **$[X]** | **$[X]** |

### Headcount Plan
| Role | Start Month | Monthly Cost | Purpose |
|------|------------|-------------|---------|
| [Role] | M[X] | $[X] | [Why needed] |

Cash Flow and Runway

## Cash Flow

| | M1 | M6 | M12 | M18 | M24 |
|--|----|----|-----|-----|-----|
| Revenue | $[X] | $[X] | $[X] | $[X] | $[X] |
| Expenses | $[X] | $[X] | $[X] | $[X] | $[X] |
| Net Cash Flow | $[X] | $[X] | $[X] | $[X] | $[X] |
| Cash Balance | $[X] | $[X] | $[X] | $[X] | $[X] |
| Runway (months) | [X] | [X] | [X] | [X] | [X] |

**Break-even month:** M[X]
**Cash needed to reach break-even:** $[X]

Phase 4: Sensitivity Analysis

Key Variable Sensitivity

## Sensitivity Analysis

### Impact of Growth Rate Changes on M24 ARR
| Growth Rate | M24 ARR | M24 Runway |
|------------|---------|-----------|
| [Base - 3%] | $[X] | [X] months |
| [Base rate] | $[X] | [X] months |
| [Base + 3%] | $[X] | [X] months |

### Impact of Churn Rate on M24 ARR
| Churn Rate | M24 ARR | LTV |
|-----------|---------|-----|
| [Base - 1%] | $[X] | $[X] |
| [Base rate] | $[X] | $[X] |
| [Base + 1%] | $[X] | $[X] |

### Unit Economics Summary
| Metric | Value |
|--------|-------|
| CAC | $[X] |
| LTV | $[X] |
| LTV:CAC | [X]:1 |
| Payback period | [X] months |
| Gross margin | [X]% |

Example: SaaS Startup Seed Stage

Inputs: $5K MRR, $29/mo ARPU, 8% monthly user growth, 3.5% monthly churn, $120 CAC, $8K/mo fixed costs.

M12 projection: $14.2K MRR, 490 active users, $10.5K monthly expenses, break-even at month 9. M24 projection: $38.6K MRR, 1,330 active users, $18K monthly expenses (added 2 hires), $12K net monthly cash flow.

Sensitivity: If churn increases to 5%, M24 ARR drops 32%. If growth decreases to 5%, M24 ARR drops 41%. Growth rate is the most sensitive variable.


Anti-Patterns

  • Top-down models — "we will capture 1% of a $10B market" is not a financial model. Build bottoms-up from unit economics.
  • Static assumptions — costs and growth rates change over time. Model step changes in hiring, pricing, and growth.
  • No assumptions documentation — every number must have a rationale. Undocumented assumptions cannot be validated.
  • Ignoring churn — for subscription businesses, churn is the most important variable. A 1% difference in monthly churn dramatically changes 24-month outcomes.
  • Perfect hockey sticks — real growth is lumpy. Include realistic ramp-up periods for new channels and hires.

Recovery

  • Pre-revenue business: Model from first customer acquisition. State assumptions clearly and emphasize the sensitivity analysis — investors know the numbers are uncertain.
  • No historical data for assumptions: Use industry benchmarks and clearly label them. Update the model monthly as real data replaces assumptions.
  • Model too complex: If the user is overwhelmed, simplify to revenue, expenses, and cash flow. Add complexity as they get comfortable.
  • Numbers do not work: Show which assumptions need to change for the model to work — higher ARPU, lower churn, faster growth, or lower costs. Let the user decide which lever to pull.

View source on GitHub →