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name: financial-unit-economics description: 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.

Financial Unit Economics

Table of Contents

• Purpose
• When to Use
• What Is It?
• Workflow
• Common Patterns
• Guardrails
• Quick Reference

Purpose

Financial 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.

When to Use

Use this skill when:

• Business model validation: Determine if startup/new product can be profitable at scale
• Pricing decisions: Set prices based on target margins and customer economics
• Marketing spend: Assess ROI of acquisition channels, optimize CAC
• Growth strategy: Decide when to scale (raise funding, increase spend) based on unit economics
• Product roadmap: Prioritize features that improve retention or reduce churn (increase LTV)
• Investor pitch: Demonstrate business model viability with CAC, LTV, payback metrics
• Channel optimization: Compare profitability across customer segments or acquisition channels
• Subscription models: Analyze recurring revenue, churn, cohort retention curves
• Marketplace economics: Model take rate, supply/demand side economics, liquidity
• Financial planning: Forecast cash flow, runway, burn rate based on unit economics

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?"

What Is It?

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:

• CAC (Customer Acquisition Cost): Total sales/marketing spend ÷ new customers acquired
• LTV (Lifetime Value): Revenue from customer over their lifetime minus variable costs
• Contribution Margin: (Revenue - Variable Costs) ÷ Revenue (as %)
• LTV/CAC Ratio: Measures return on acquisition investment (target: 3:1 or higher)
• Payback Period: Months to recover CAC from customer revenue
• Cohort Analysis: Track metrics over time for customer groups (by acquisition month/channel)

Quick example:

Scenario: SaaS startup, subscription model ($100/month), analyzing unit economics.

Metrics:

• CAC: $20k marketing spend, 100 new customers → CAC = $200
• Monthly revenue per customer: $100
• Variable costs: $20/customer/month (hosting, support)
• Gross margin: ($100 - $20) / $100 = 80%
• Monthly churn: 5% → Average lifetime = 1 / 0.05 = 20 months
• LTV: $100 revenue × 20 months × 80% margin = $1,600
• LTV/CAC: $1,600 / $200 = 8:1 ✓ (healthy, >3:1)
• Payback period: $200 CAC ÷ ($100 × 80% margin) = 2.5 months ✓ (good, <12 months)

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:

• Early warning system: Detect unsustainable business models before scaling losses
• Data-driven growth: Know when unit economics justify increasing spend
• Channel optimization: Identify which acquisition channels are profitable
• Pricing power: Quantify impact of price changes on profitability
• Investor confidence: Demonstrate path to profitability with clear metrics

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

Critical requirements:

1. 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.

2. 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.

3. 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.

4. Time horizon matters: LTV is a prediction. Use conservative assumptions. For new products, LTV estimates are unreliable (insufficient data). Weight recent cohorts more heavily.

5. 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.

6. Channel-level analysis: Blended metrics hide truth. CAC and LTV vary by channel (paid search vs. referral vs. content). Analyze separately to optimize spend.

7. 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.

8. 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:

• ❌ 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 breakdowns
• cohort-retention-table.csv: Retention curves by cohort
• channel-profitability.csv: CAC and LTV by acquisition channel
• recommendations.md: Pricing, channel, growth recommendations based on metrics