Use when evaluating business model viability, analyzing profitability per customer/product/transaction, validating startup metrics (CAC, LTV, payback period), making pricing decisions, assessing scalability, comparing business models, or when user mentions unit economics, CAC/LTV ratio, contribution margin, customer profitability, break-even analysis, or needs to determine if a business can be profitable at scale.
Analyzes unit economics (CAC, LTV, payback) to validate business model viability and profitability at scale. Triggers when evaluating startup metrics, making pricing decisions, or assessing scalability.
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resources/evaluators/rubric_financial_unit_economics.jsonresources/methodology.mdresources/template.mdFinancial Unit Economics analyzes the profitability of individual units (customers, products, transactions) to determine if a business model is viable and scalable. This skill guides you through calculating key metrics (CAC, LTV, contribution margin), interpreting ratios, conducting cohort analysis, and making data-driven decisions about pricing, marketing spend, and growth strategy.
Use this skill when:
Trigger phrases: "unit economics", "CAC/LTV", "customer acquisition cost", "lifetime value", "contribution margin", "payback period", "customer profitability", "break-even", "cohort analysis", "is this business viable?"
Financial Unit Economics is the practice of measuring profitability at the most granular level (per customer, product, or transaction) to understand if revenue from a single unit exceeds the cost to acquire and serve it.
Core components:
Quick example:
Scenario: SaaS startup, subscription model ($100/month), analyzing unit economics.
Metrics:
Interpretation: Strong unit economics. Each customer generates 8× their acquisition cost. Can profitably scale marketing spend. Payback in 2.5 months means fast capital recovery.
Core benefits:
Copy this checklist and track your progress:
Unit Economics Analysis Progress:
- [ ] Step 1: Define the unit
- [ ] Step 2: Calculate CAC
- [ ] Step 3: Calculate LTV
- [ ] Step 4: Assess contribution margin
- [ ] Step 5: Analyze cohorts
- [ ] Step 6: Interpret and recommend
Step 1: Define the unit
What is your unit of analysis? (Customer, product SKU, transaction, subscription). See resources/template.md.
Step 2: Calculate CAC
Total acquisition costs (sales + marketing) ÷ new units acquired. Break down by channel if applicable. See resources/template.md and resources/methodology.md.
Step 3: Calculate LTV
Revenue over unit lifetime minus variable costs. Use cohort data for retention/churn. See resources/template.md and resources/methodology.md.
Step 4: Assess contribution margin
(Revenue - Variable Costs) ÷ Revenue. Identify levers to improve margin. See resources/template.md and resources/methodology.md.
Step 5: Analyze cohorts
Track retention, LTV, payback by customer cohort (acquisition month/channel/segment). See resources/template.md and resources/methodology.md.
Step 6: Interpret and recommend
Assess LTV/CAC ratio, payback period, cash efficiency. Make recommendations (pricing, channels, growth). See resources/template.md and resources/methodology.md.
Validate using resources/evaluators/rubric_financial_unit_economics.json. Minimum standard: Average score ≥ 3.5.
Pattern 1: SaaS Subscription Model
Pattern 2: E-commerce / Transactional
Pattern 3: Marketplace / Platform
Pattern 4: Freemium / PLG (Product-Led Growth)
Pattern 5: Enterprise / High-Touch Sales
Critical requirements:
Fully-loaded CAC: Include all acquisition costs (sales salaries, marketing spend, tools, overhead allocation). Underestimating CAC makes unit economics look better than reality. Common miss: excluding sales team salaries.
True variable costs: Only include costs that scale with each unit (COGS, hosting per user, transaction fees). Don't include fixed costs (rent, core engineering). LTV calculation requires accurate margin.
Cohort-based LTV: Don't average across all customers. Early cohorts ≠ recent cohorts. Track retention curves by cohort (acquisition month/channel). LTV should be based on observed retention, not assumptions.
Time horizon matters: LTV is a prediction. Use conservative assumptions. For new products, LTV estimates are unreliable (insufficient data). Weight recent cohorts more heavily.
Payback period vs. LTV/CAC: Both matter. High LTV/CAC but long payback (>18 months) strains cash. Fast payback (<6 months) allows rapid reinvestment. Optimize for both.
Channel-level analysis: Blended metrics hide truth. CAC and LTV vary by channel (paid search vs. referral vs. content). Analyze separately to optimize spend.
Retention is king: Small changes in churn have exponential impact on LTV. Improving monthly churn from 5% to 4% increases LTV by 25%. Retention improvements > acquisition improvements.
Gross margin floor: Need ≥60% gross margin for SaaS, ≥40% for e-commerce to be viable. Low margin means high LTV/CAC ratio still yields poor cash flow.
Common pitfalls:
Key formulas:
CAC = (Sales + Marketing Costs) ÷ New Customers Acquired
LTV (subscription) = ARPU × Gross Margin % ÷ Monthly Churn Rate
LTV (transactional) = AOV × Purchase Frequency × Gross Margin % × Lifetime (years)
Contribution Margin % = (Revenue - Variable Costs) ÷ Revenue
LTV/CAC Ratio = Lifetime Value ÷ Customer Acquisition Cost
Payback Period (months) = CAC ÷ (Monthly Revenue × Gross Margin %)
CAC Payback (months) = S&M Spend ÷ (New ARR × Gross Margin %)
Gross Margin % = (Revenue - COGS) ÷ Revenue
Customer Lifetime (months) = 1 ÷ Monthly Churn Rate
MRR (Monthly Recurring Revenue) = Sum of all monthly subscriptions
ARR (Annual Recurring Revenue) = MRR × 12
ARPU (Average Revenue Per User) = Total Revenue ÷ Total Users
NRR (Net Revenue Retention) = (Starting ARR + Expansion - Contraction - Churn) ÷ Starting ARR
Benchmarks (varies by stage and industry):
| Metric | Good | Acceptable | Poor |
|---|---|---|---|
| LTV/CAC Ratio | ≥5:1 | 3:1 - 5:1 | <3:1 |
| Payback Period | <6 months | 6-12 months | >18 months |
| Gross Margin (SaaS) | ≥80% | 60-80% | <60% |
| Gross Margin (E-commerce) | ≥50% | 40-50% | <40% |
| Monthly Churn (B2C SaaS) | <3% | 3-7% | >7% |
| Monthly Churn (B2B SaaS) | <1% | 1-3% | >3% |
| CAC Payback (SaaS) | <12 months | 12-18 months | >18 months |
| NRR (SaaS) | ≥120% | 100-120% | <100% |
Decision framework:
| LTV/CAC | Payback | Recommendation |
|---|---|---|
| <1:1 | Any | Stop: Losing money on every customer. Fix model or pivot. |
| 1:1 - 2:1 | >12 months | Caution: Marginal economics. Don't scale yet. Improve retention or reduce CAC. |
| 2:1 - 3:1 | 6-12 months | Optimize: Unit economics acceptable. Focus on improving before scaling. |
| 3:1 - 5:1 | <12 months | Scale: Good economics. Can profitably invest in growth. |
| >5:1 | <6 months | Aggressive scale: Excellent economics. Raise capital, increase spend rapidly. |
Inputs required:
Outputs produced:
unit-economics-analysis.md: Full analysis with CAC, LTV, ratios, cohort breakdownscohort-retention-table.csv: Retention curves by cohortchannel-profitability.csv: CAC and LTV by acquisition channelrecommendations.md: Pricing, channel, growth recommendations based on metricsCreating algorithmic art using p5.js with seeded randomness and interactive parameter exploration. Use this when users request creating art using code, generative art, algorithmic art, flow fields, or particle systems. Create original algorithmic art rather than copying existing artists' work to avoid copyright violations.
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