---
name: "unit_economics"
description: "Work out whether a business makes money per customer: CAC, LTV, the LTV to CAC ratio, payback period, and contribution margin, with a self-contained calculator. Triggers on: unit economics, CAC vs LTV, customer acquisition cost, payback period, is this business viable, contribution margin, LTV CAC ratio."
metadata: { "includeInPrompt": true }
---

# Unit Economics

## Purpose

Answer one question with numbers: does this business make money on each customer it acquires? Covers customer acquisition cost (CAC), lifetime value (LTV), the LTV:CAC ratio, the CAC payback period, and contribution margin per customer. It does not cover full P&L accounting, fundraising, or valuation; and it does not set prices (see the catalog's `pricing-basics`, which picks the number to charge; this skill checks whether the business works at that number). Built for founders and operators sizing a business model, not accountants.

## Workflow

1. **Get the inputs.** Ask for: average revenue per customer per month (or per order times orders per year, divided by 12), gross margin as a decimal, monthly churn rate (or average customer lifespan in months), and fully-loaded CAC: ad spend plus sales labor plus onboarding cost, divided by customers acquired in the same period. Mark every number they cannot source as a guess.

2. **Compute LTV.** LTV = ARPA x gross margin x average customer lifespan in months. If churn is known, lifespan = 1 / monthly churn rate. A 5% monthly churn means a 20-month average lifespan. Run the self-contained script below with their numbers.

3. **Compute the LTV:CAC ratio.** Divide LTV by fully-loaded CAC. 3:1 or better is the common healthy benchmark for subscription businesses; below 1:1 the business loses money on every customer it wins. Treat 3:1 as a rule of thumb, not a law: capital-efficient businesses can work leaner, and early-stage numbers are noisy.

4. **Compute the CAC payback period.** Months to recover CAC = CAC / (ARPA x gross margin). Under 12 months is the common target for SMB SaaS; enterprise sales can run longer. If payback exceeds the average customer lifespan, the model is broken no matter what the ratio says.

5. **Break down contribution margin per customer.** Revenue minus the variable costs tied to serving one more customer: COGS, payment processing fees, support cost per ticket times tickets per customer, hosting per user. Fixed costs (salaries, rent) stay out of unit economics; they decide whether the company as a whole survives, not whether one customer is worth acquiring.

6. **Stress-test with churn.** Recompute LTV at double the stated churn. If the ratio collapses below 2:1, the business is fragile to retention misses. Name the single assumption the whole verdict hinges on, in one sentence.

### Calculator (self-contained)

Save as `unit_econ.py` and run with `python3 unit_econ.py`. Edit the INPUTS dict with the business's numbers. Standard library only.

```python
#!/usr/bin/env python3
# Save as unit_econ.py and run: python3 unit_econ.py
# Edit the INPUTS dict with the business's numbers. Mark guesses as guesses.

INPUTS = {
    "arpa_monthly": 49.0,     # avg revenue per account per month
    "gross_margin": 0.80,      # 0-1
    "monthly_churn": 0.05,     # 0-1; set lifespan_months instead to override
    "lifespan_months": None,   # overrides churn when set
    "cac": 180.0,              # fully-loaded customer acquisition cost
}

def main():
    i = INPUTS
    lifespan = i["lifespan_months"] if i["lifespan_months"] else 1.0 / i["monthly_churn"]
    monthly_contrib = i["arpa_monthly"] * i["gross_margin"]
    ltv = monthly_contrib * lifespan
    ratio = ltv / i["cac"]
    payback = i["cac"] / monthly_contrib
    verdict = "healthy" if ratio >= 3 else "watch" if ratio >= 1 else "losing money per customer"
    print(f"LTV: ${ltv:,.2f}  (ARPA ${i['arpa_monthly']:.2f} x margin {i['gross_margin']:.0%} x {lifespan:.1f} mo)")
    print(f"CAC: ${i['cac']:,.2f}")
    print(f"LTV:CAC = {ratio:.2f}:1  ({verdict})")
    print(f"Payback: {payback:.1f} months")
    if i["monthly_churn"]:
        stress_life = 1.0 / (2 * i["monthly_churn"])
        stress_ltv = monthly_contrib * stress_life
        print(f"2x-churn stress: LTV ${stress_ltv:,.2f}, ratio {stress_ltv / i['cac']:.2f}:1")

if __name__ == "__main__":
    main()
```

## Output Contract

Deliver: (a) the inputs used, with every guess labeled as a guess; (b) LTV with the math shown; (c) fully-loaded CAC and what went into it; (d) the LTV:CAC ratio with the benchmark read; (e) the payback period in months with the read; (f) contribution margin per customer and its variable-cost parts; (g) the 2x-churn stress test; (h) the one assumption the verdict hinges on.

## Operating Rules

1. Fully-loaded CAC or nothing. Ad spend alone understates it; include sales labor and onboarding cost.
2. Lifespan comes from churn, not from hope. 1 / churn is the default; a founder's "customers stay forever" gets replaced by the churn math.
3. Ratio and payback together. A good ratio with a payback longer than the customer lifespan is still a broken model.
4. Benchmarks are rules of thumb. 3:1 and 12 months are common targets, not guarantees; say so out loud.
5. Mark guesses. Every estimated input gets labeled, and the verdict carries that uncertainty.
6. Fixed costs stay out. Unit economics is the per-customer question; company-level burn is a different analysis.