Financial Unit Economics
SkillMonitoring & opsAnalyzes profitability per customer, product, or transaction to determine business model viability and scalability. Covers CAC, LTV, contribution margin, cohort analysis, and growth-readiness assessment. Use when evaluating business model viability, validating startup metrics (CAC, LTV, payback period), making pricing decisions, comparing business models, or when user mentions unit economics, CAC/LTV ratio, contribution margin, customer profitability, or break-even analysis.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the Financial Unit Economics skill
What this skill tells your AI
The instructions your AI receives, as published by lyndonkl/claude in skills/financial-unit-economics/SKILL.md and read by ahel’s review.
Table of Contents
- Workflow
- Common Patterns
- Guardrails
- Quick Reference
Example
Scenario: SaaS startup, $100/month subscription
- CAC: $20k spend / 100 customers = $200
- Gross margin: ($100 - $20 variable) / $100 = 80%
- Monthly churn: 5% -> Average lifetime = 20 months
- LTV: $100 x 20 months x 80% = $1,600
- LTV/CAC: 8:1 (healthy, >3:1), Payback: 2.5 months (good, <12 months)
- Interpretation: Strong unit economics. Can profitably scale marketing spend.
Workflow
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.
Common Patterns
Pattern 1: SaaS Subscription Model
- Key metrics: MRR, ARR, churn rate, LTV/CAC, payback period, CAC payback
- Calculation: LTV = ARPU × Gross Margin % ÷ Churn Rate
- Benchmarks: LTV/CAC ≥3:1, Payback <12 months, Churn <5% monthly (B2C) or <2% (B2B)
- Levers: Reduce churn (increase LTV), upsell/cross-sell (increase ARPU), optimize channels (reduce CAC)
- When: Subscription business, recurring revenue, retention critical
Pattern 2: E-commerce / Transactional
- Key metrics: AOV (Average Order Value), repeat purchase rate, contribution margin per order, CAC
- Calculation: LTV = AOV × Purchase Frequency × Gross Margin % × Customer Lifetime (years)
- Benchmarks: Contribution margin ≥40%, Repeat purchase rate ≥25%, LTV/CAC ≥2:1
- Levers: Increase AOV (bundling, upsells), drive repeat purchases (loyalty programs), reduce variable costs
- When: Transactional business, e-commerce, retail
Pattern 3: Marketplace / Platform
- Key metrics: Take rate, GMV (Gross Merchandise Value), supply/demand CAC, liquidity
- Calculation: LTV = GMV per user × Take Rate × Gross Margin % ÷ Churn Rate
- Benchmarks: Take rate 10-30%, LTV/CAC ≥3:1 for both sides, network effects kicking in
- Levers: Increase take rate (value-added services), improve matching (increase GMV), balance supply/demand
- When: Two-sided marketplace, platform business
Pattern 4: Freemium / PLG (Product-Led Growth)
- Key metrics: Free-to-paid conversion rate, time to convert, paid user LTV, blended CAC
- Calculation: Blended LTV = (Free users × Conversion % × Paid LTV) - (Free user costs)
- Benchmarks: Conversion ≥2%, Time to convert <90 days, Paid LTV/CAC ≥4:1
- Levers: Increase conversion rate (improve product, optimize paywall), reduce time to value, lower CAC via virality
- When: Product-led growth, freemium model, viral product
Pattern 5: Enterprise / High-Touch Sales
- Key metrics: CAC (including sales team costs), sales cycle length, NRR (Net Revenue Retention), LTV
- Calculation: LTV = ACV (Annual Contract Value) × Gross Margin % × Average Customer Lifetime (years)
- Benchmarks: LTV/CAC ≥3:1, Sales efficiency (ARR added ÷ S&M spend) ≥1.0, NRR ≥110%
- Levers: Shorten sales cycle, increase ACV (upsell, premium tiers), improve retention (NRR)
- When: Enterprise sales, high ACV, long sales cycles
Guardrails
-
Fully-loaded CAC: Include all acquisition costs (sales salaries, marketing spend, tools, overhead allocation). Excluding sales team salaries is a common miss that inflates perceived economics.
-
True variable costs: Only include costs that scale with each unit (COGS, hosting per user, transaction fees). Exclude fixed costs (rent, core engineering). Accurate margins are essential for LTV.
-
Cohort-based LTV: Early cohorts are not the same as recent cohorts. Track retention curves by cohort. Base LTV on observed retention, not assumptions.
-
Use conservative time horizons: LTV is a prediction. For new products with limited data, weight recent cohorts more heavily and avoid projecting far beyond observed behavior.
-
Optimize both payback and LTV/CAC: High LTV/CAC but long payback (>18 months) strains cash. Fast payback (<6 months) allows rapid reinvestment.
-
Analyze at channel level: Blended metrics hide the truth. CAC and LTV vary by channel (paid search vs. referral vs. content). Break down separately to optimize spend.
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Retention drives LTV exponentially: Improving monthly churn from 5% to 4% increases LTV by 25%. Retention improvements typically matter more than acquisition improvements.
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Gross margin floor: SaaS needs >=60% gross margin, e-commerce >=40%, to be viable. Low margin means even high LTV/CAC ratios yield poor cash flow.
Common pitfalls:
- ❌ Ignoring churn: Assuming customers stay forever. Reality: churn compounds. Use cohort retention curves.
- ❌ Vanity LTV: Using unrealistic retention (e.g., 5 year LTV with 1 month of data). Stick to observed behavior.
- ❌ Blended CAC: Mixing profitable and unprofitable channels. Break down by channel, segment, cohort.
- ❌ Not updating: Unit economics change as product, market, competition evolve. Re-calculate quarterly.
- ❌ Missing costs: Forgetting support costs, payment processing fees, fraud losses, refunds. Track everything.
- ❌ Premature scaling: Growing before unit economics work (LTV/CAC <2:1). "We'll make it up in volume" rarely works.
Quick Reference
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:
- Revenue data: Pricing, ARPU, AOV, transaction frequency
- Cost data: Sales/marketing spend, COGS, variable costs per customer
- Retention data: Churn rate, cohort retention curves, repeat purchase behavior
- Channel data: CAC by acquisition channel, LTV by segment
- Time period: Cohort definition (monthly, quarterly), historical data range
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 metrics
Signals
- GitHub stars
- 158
- Forks
- 23
- Last commit
- Sep 2026
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- github.com/lyndonkl/claude