Financial Model: Building financial projections...
Builds monthly revenue projections, expense forecasts, unit economics (CAC, LTV, payback), break-even analysis, cash flow tracking, and scenario modeling (best/base/worst).
Activation
When this skill activates, output:
Financial Model: Building financial projections...
Then execute the protocol below.
Context Guard
| Context | Status |
|---|---|
| User says "financial model", "projections", "revenue forecast" | ACTIVE |
| User mentions MRR, churn, CAC, LTV, runway, or break-even | ACTIVE |
| User wants to forecast revenue, expenses, or cash flow | ACTIVE |
| User wants to set pricing tiers | DORMANT: use Pricing Strategy |
| User wants to generate an invoice | DORMANT: use Invoice Generator |
Common Mistakes
| Mistake | Why It's Wrong |
|---|---|
| "Hockey stick revenue" | Realistic projections beat optimistic fantasies. Start conservative, model scenarios. |
| "Forget to model churn" | SaaS without churn modeling is fiction. Even 3% monthly churn compounds fast. |
| "Revenue only, no expenses" | Revenue without expenses is a dream. Model all costs to see actual profitability. |
| "One scenario only" | A single forecast is a guess. Model best/base/worst to understand the range. |
| "Skip unit economics" | If CAC > LTV, growth loses money. Unit economics tell you if the business model works. |
Protocol
Step 1: Gather Business Data
If the user hasn't provided details, ask:
- Business model: SaaS, e-commerce, service, marketplace, or other?
- Revenue streams: subscriptions, one-time sales, services, ads?
- Current numbers: existing revenue, customers, growth rate?
- Pricing: price points, tiers, average revenue per user?
- Costs: known fixed and variable costs?
- Funding: bootstrapped or funded? Current cash balance?
Step 2: Revenue Model
SaaS / Subscription revenue:
Month N Revenue = (Previous customers - Churned + New) × ARPU Where: - Previous customers: end of prior month - Churned: Previous × monthly churn rate - New: Acquired through marketing/sales - ARPU: Average Revenue Per User (monthly)
| Month | Starting | New | Churned | Ending | MRR | ARR |
|---|---|---|---|---|---|---|
| 1 | 0 | [X] | 0 | [X] | $[X] | none |
| 2 | [X] | [X] | [X] | [X] | $[X] | none |
| 3 | [X] | [X] | [X] | [X] | $[X] | none |
| ... | ||||||
| 12 | [X] | [X] | [X] | [X] | $[X] | $[X] |
E-commerce / Transaction revenue:
Monthly Revenue = Visitors × Conversion Rate × Average Order Value Where: - Visitors: Monthly unique visitors (organic + paid) - Conversion Rate: % of visitors who purchase (target: 1-3%) - AOV: Average Order Value
Service revenue:
Monthly Revenue = Active Clients × Average Monthly Retainer + Project Revenue (one-time)
Step 3: Unit Economics
Key SaaS metrics:
CAC (Customer Acquisition Cost): = Total Sales & Marketing Spend ÷ New Customers Acquired Target: recover within 12 months LTV (Customer Lifetime Value): = ARPU × Gross Margin% × (1 ÷ Monthly Churn Rate) Example: $50 × 80% × (1 ÷ 0.05) = $800 LTV:CAC Ratio: = LTV ÷ CAC Target: > 3:1 (every $1 spent acquires $3+ in lifetime value) Payback Period: = CAC ÷ (ARPU × Gross Margin%) Example: $200 ÷ ($50 × 80%) = 5 months Target: < 12 months
Unit economics table:
| Metric | Value | Target | Status |
|---|---|---|---|
| ARPU (monthly) | $[X] | none | none |
| Monthly churn rate | [X]% | <5% | [OK / At Risk] |
| CAC | $[X] | none | none |
| LTV | $[X] | >3× CAC | [OK / At Risk] |
| LTV:CAC ratio | [X]:1 | >3:1 | [OK / At Risk] |
| Payback period | [X] months | <12 months | [OK / At Risk] |
| Gross margin | [X]% | >70% (SaaS) | [OK / At Risk] |
Step 4: Expense Forecast
Fixed costs (monthly):
| Category | Monthly Cost | Annual Cost | Notes |
|---|---|---|---|
| Salaries & wages | $[X] | $[X] | [Headcount × avg salary ÷ 12] |
| Office / co-working | $[X] | $[X] | |
| Software & tools | $[X] | $[X] | [List: hosting, SaaS tools, etc.] |
| Insurance | $[X] | $[X] | |
| Legal & accounting | $[X] | $[X] | |
| Total fixed | $[X] | $[X] |
Variable costs (scales with revenue):
| Category | Cost Basis | Monthly Estimate | Notes |
|---|---|---|---|
| Hosting / infrastructure | [X]% of revenue | $[X] | Scales with users |
| Payment processing | 2.9% + $0.30/txn | $[X] | Stripe standard rate |
| Customer support | $[X] per 100 customers | $[X] | |
| Sales commissions | [X]% of new revenue | $[X] | |
| Marketing spend | $[X] fixed + [X]% of revenue | $[X] | |
| Total variable | $[X] |
Total monthly burn:
Burn Rate = Fixed Costs + Variable Costs - Revenue Runway = Cash Balance ÷ Monthly Burn Rate
Step 5: Break-Even Analysis
Break-Even Point (customers): = Fixed Costs ÷ (ARPU - Variable Cost per Customer) Break-Even Point (revenue): = Fixed Costs ÷ Gross Margin% Example: Fixed costs: $10,000/month ARPU: $50/month Variable cost per customer: $10/month Break-even: $10,000 ÷ ($50 - $10) = 250 customers
Monthly P&L projection:
| Mo 1 | Mo 3 | Mo 6 | Mo 12 | |
|---|---|---|---|---|
| Revenue | $[X] | $[X] | $[X] | $[X] |
| COGS / variable costs | ($[X]) | ($[X]) | ($[X]) | ($[X]) |
| Gross profit | $[X] | $[X] | $[X] | $[X] |
| Gross margin % | [X]% | [X]% | [X]% | [X]% |
| Operating expenses | ($[X]) | ($[X]) | ($[X]) | ($[X]) |
| Net income | ($[X]) | ($[X]) | $[X] | $[X] |
| Cumulative cash | $[X] | $[X] | $[X] | $[X] |
Step 6: Scenario Modeling
Three scenarios:
| Assumption | Worst Case | Base Case | Best Case |
|---|---|---|---|
| Monthly new customers | [X] | [X] | [X] |
| Monthly churn rate | [X]% | [X]% | [X]% |
| ARPU | $[X] | $[X] | $[X] |
| Marketing spend | $[X] | $[X] | $[X] |
| Hiring timeline | Delayed | On time | Accelerated |
12-month outcome by scenario:
| Metric | Worst | Base | Best |
|---|---|---|---|
| Customers (Mo 12) | [X] | [X] | [X] |
| MRR (Mo 12) | $[X] | $[X] | $[X] |
| ARR (Mo 12) | $[X] | $[X] | $[X] |
| Monthly burn (avg) | $[X] | $[X] | $[X] |
| Break-even month | Mo [X] | Mo [X] | Mo [X] |
| Runway remaining | [X] months | [X] months | [X] months |
| Cash needed | $[X] | $[X] | $0 |
Step 7: Cash Flow Summary
Monthly cash flow:
| Month | Revenue | Expenses | Net | Cumulative |
|---|---|---|---|---|
| 1 | $[X] | $[X] | ($[X]) | $[X] |
| 2 | $[X] | $[X] | ($[X]) | $[X] |
| 3 | $[X] | $[X] | ($[X]) | $[X] |
| ... | ||||
| 12 | $[X] | $[X] | $[X] | $[X] |
Key dates:
- Cash-flow positive: Month [X] (when monthly net turns positive)
- Break-even (cumulative): Month [X] (when cumulative losses are recovered)
- Runway exhausted: Month [X] at current burn (worst case)
Output Format
markdown# Financial Model: [Business Name] ## Revenue Model [From Step 2: monthly revenue projections] ## Unit Economics [From Step 3: CAC, LTV, payback, margins] ## Expense Forecast [From Step 4: fixed + variable costs] ## Break-Even Analysis [From Step 5, break-even point + P&L] ## Scenario Analysis [From Step 6: worst/base/best] ## Cash Flow [From Step 7, monthly cash flow + key dates] ## Key Assumptions [List every assumption with the value used]
Completion
Financial Model: Complete! Business model: [Type] 12-month ARR (base case): $[X] Break-even: Month [X] LTV:CAC ratio: [X]:1 Runway: [X] months Scenarios modeled: 3 (worst/base/best) Next steps: 1. Validate assumptions with real data (update monthly) 2. Track actual vs projected monthly 3. If LTV:CAC < 3:1, reduce CAC or increase ARPU before scaling 4. If runway < 6 months, raise capital or cut burn 5. Update the model quarterly with actuals
Level History
- Lv.1: Base: Revenue models (SaaS, e-commerce, service), unit economics (CAC, LTV, payback, LTV:CAC, gross margin), expense forecast (fixed + variable), break-even analysis with P&L projection, 3-scenario modeling (worst/base/best), cash flow timeline with key dates (cash-positive, break-even, runway). (Origin: MemStack Pro v3.2, Mar 2026)
