financial-unit-economics

$npx mdskill add lyndonkl/claude/financial-unit-economics

Calculate CAC, LTV, and margins to validate business viability.

  • Validates startup metrics like payback period and pricing decisions.
  • Depends on financial databases and customer transaction records.
  • Recommends scaling strategies based on LTV/CAC ratio thresholds.
  • Delivers clear profitability scores and growth-readiness assessments.

SKILL.md

.github/skills/financial-unit-economicsView on GitHub ↗
---
name: financial-unit-economics
description: Analyzes 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.
---
# Financial Unit Economics

## Table of Contents
- [Workflow](#workflow)
- [Common Patterns](#common-patterns)
- [Guardrails](#guardrails)
- [Quick Reference](#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](resources/template.md#unit-definition-template).

**Step 2: Calculate CAC**

Total acquisition costs (sales + marketing) ÷ new units acquired. Break down by channel if applicable. See [resources/template.md](resources/template.md#cac-calculation-template) and [resources/methodology.md](resources/methodology.md#1-customer-acquisition-cost-cac).

**Step 3: Calculate LTV**

Revenue over unit lifetime minus variable costs. Use cohort data for retention/churn. See [resources/template.md](resources/template.md#ltv-calculation-template) and [resources/methodology.md](resources/methodology.md#2-lifetime-value-ltv).

**Step 4: Assess contribution margin**

(Revenue - Variable Costs) ÷ Revenue. Identify levers to improve margin. See [resources/template.md](resources/template.md#contribution-margin-template) and [resources/methodology.md](resources/methodology.md#3-contribution-margin-analysis).

**Step 5: Analyze cohorts**

Track retention, LTV, payback by customer cohort (acquisition month/channel/segment). See [resources/template.md](resources/template.md#cohort-analysis-template) and [resources/methodology.md](resources/methodology.md#4-cohort-analysis).

**Step 6: Interpret and recommend**

Assess LTV/CAC ratio, payback period, cash efficiency. Make recommendations (pricing, channels, growth). See [resources/template.md](resources/template.md#interpretation-template) and [resources/methodology.md](resources/methodology.md#5-interpreting-unit-economics).

Validate using [resources/evaluators/rubric_financial_unit_economics.json](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

1. **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.

2. **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.

3. **Cohort-based LTV**: Early cohorts are not the same as recent cohorts. Track retention curves by cohort. Base LTV on observed retention, not assumptions.

4. **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.

5. **Optimize both payback and LTV/CAC**: High LTV/CAC but long payback (>18 months) strains cash. Fast payback (<6 months) allows rapid reinvestment.

6. **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.

7. **Retention drives LTV exponentially**: Improving monthly churn from 5% to 4% increases LTV by 25%. Retention improvements typically matter more than acquisition improvements.

8. **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 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

More from lyndonkl/claude

SkillDescription
abstraction-concrete-examplesBuilds structured abstraction ladders that translate high-level principles into concrete, actionable examples across 3-5 levels. Bridges communication gaps, reveals hidden assumptions, and tests whether abstract ideas work in practice. Use when explaining concepts at different expertise levels, moving between abstract principles and concrete implementation, identifying edge cases by testing ideas against scenarios, designing layered documentation, decomposing complex problems into actionable steps, or bridging strategy-execution gaps.
academic-letter-architectGuides the creation of evidence-based academic recommendation letters, reference letters, and award nominations that combine concrete examples, meaningful comparisons, and genuine enthusiasm. Use when writing recommendation letters for students, postdocs, or colleagues, or when user mentions recommendation letter, reference, nomination, letter of support, endorsement, or needs help with strong advocacy and comparative statements.
adr-architectureDocuments significant architectural and technical decisions with full context, alternatives considered, trade-offs analyzed, and consequences understood. Creates a decision trail that helps teams understand why decisions were made. Use when choosing between technology options, making infrastructure decisions, establishing standards, migrating systems, or when user mentions ADR, architecture decision, technical decision record, or decision documentation.
adverse-selection-priorProduces a Bayesian prior probability that an offered transaction is +EV for the recipient, given that the counterparty chose to propose it. Applies Akerlof market-for-lemons logic -- if they offered it, they believe it is +EV for them, so the prior that it is +EV for us is materially below 50%. Reusable across trade evaluation, waiver drops (another team dropping a player is also adverse selection), job-offer analysis, M&A, and any "someone offered me this" situation. Use when you receive an unsolicited trade/offer/proposal, analyzing incoming trade prior, evaluating why a counterparty proposed a deal, or when user mentions adverse selection, market for lemons, why did they offer this, incoming trade prior, they proposed it, Bayesian adjustment on received offer.
alignment-values-north-starCreates actionable alignment frameworks that give teams a shared North Star (direction), values (guardrails), and decision tenets (behavioral standards). Enables autonomous decision-making while maintaining organizational coherence. Use when starting new teams, scaling organizations, defining culture, establishing product vision, resolving misalignment, creating strategic clarity, or when user mentions North Star, team values, mission, principles, guardrails, decision framework, or cultural alignment.
analogy-weight-checkFor every analogy in a substacker draft, verifies it carries mechanical weight — the analogy does real work explaining the mechanism, not merely decorates it. Cross-references analogy-catalog.md for novelty (is this analogy reused from a prior post?) and domain fit (biology > organizational > sports preferred; physics/military disfavored). Use whenever an analogy appears in the draft. Trigger keywords: analogy weight, decorative, mechanical weight, reused analogy, catalog check, metaphor check.
answer-uncomfortable-questionTakes one strategic question about substacker ("should we launch paid?", "is this section dead?", "are we writing for the wrong audience?") and produces the mandatory evidence + reasoning + downside triad plus a recommendation. Used 3 times per Growth Strategist review. Trigger keywords: uncomfortable question, strategic question, evidence reasoning downside, triad.
attribute-performanceFor each substacker post that materially over- or under-performs the rolling baseline (|z| ≥ 1.0), produces a plain-English attribution paragraph with calibrated confidence (high / medium / low / unexplained). Considers subject-line effect, topic zeitgeist, external share, day-of-week, length effect, and audience-notes signals. Labels unexplained outliers explicitly rather than fabricating a story. Use after compute-baseline when outlier posts exist. Trigger keywords: attribution, why did this post work, outlier explanation, performance analysis.
auction-first-price-shadingComputes the optimal shaded bid for a first-price sealed-bid auction given a true private value, an estimate of the number of competing bidders N, and a value-distribution assumption. Implements the `(N-1)/N` equilibrium shading rule for uniform private values, adjusts for log-normal or empirical value distributions, layers a risk-aversion adjustment, and caps output against the bidder's remaining budget. Domain-neutral auction theory reusable across fantasy sports (baseball FAAB, NBA/NHL waiver auctions), prediction-market limit sizing, sealed procurement bids, and any blind-bid context. Use when user mentions "first-price auction bid", "sealed bid shading", "(N-1)/N", "FAAB bid amount", "auction shading", "optimal bid first-price", "bid for sealed-bid", "blind bid sizing", or when downstream logic needs a principled shade factor rather than an ad-hoc heuristic.
auction-winners-curse-haircutApplies a Bayesian haircut to a bid valuation for common-value auctions where winning is itself evidence the bidder over-estimated. Takes a raw valuation, a value-type classification (common_value / private_value / mixed), the number of informed bidders N, and a signal-dispersion estimate, and returns an adjusted valuation. Domain-neutral and reusable across fantasy FAAB, prediction markets, M&A bids, ad-auction budgets, and any generic bidding context. Use when user mentions "winner's curse", "common value auction", "valuation haircut", "adverse valuation", "Bayesian bid adjustment", or "over-paying in auction".