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name: unit-economics description: "Model the unit economics of a business — CAC, LTV, payback, contribution margin — from real inputs. Use when asked to calculate unit economics, work out LTV:CAC, find the payback period, or check whether a business model is viable per customer. Produces a computed unit-economics summary (LTV, CAC, ratio, payback, contribution margin) with a verdict and the levers that move it most."

Unit Economics Skill

A business is only viable if each customer is worth more than it costs to acquire and serve. This skill computes the core unit economics — CAC, LTV, the LTV:CAC ratio, payback period, and contribution margin — from real numbers (not vibes), states a clear verdict against the rule-of-thumb benchmarks, and shows which lever moves the model most.

Required Inputs

Ask for these only if they aren't already provided:

• ARPA — average revenue per account, per month (or per period).
• Gross margin % — the share of revenue left after cost-to-serve.
• Churn % — monthly customer (or revenue) churn — drives LTV.
• CAC — fully-loaded cost to acquire a customer (sales + marketing ÷ new customers).

Output Format

Unit Economics: [business]

1. The numbers — computed, with the formula shown (use the helper script so they're consistent):

Metric	Value	Benchmark
Lifetime (1/churn)
LTV (ARPA × margin ÷ churn)
CAC
LTV : CAC		≥ 3:1 healthy
Payback (months)		< 12 healthy
Contribution margin

2. Verdict — healthy / borderline / underwater, in one line, against the benchmarks (LTV:CAC ≥ 3, payback < 12 months).

3. Biggest levers — which input, improved realistically, moves the model most (usually churn or CAC), with the rough effect.

4. Caveats — where the inputs are assumptions vs. measured, and what to validate before betting on this.

Programmatic Helper

scripts/unit_econ.py (stdlib only) computes the model so the numbers are calculated, not estimated:

# in.json: {"arpa": 50, "gross_margin": 0.8, "monthly_churn": 0.03, "cac": 400}
python3 scripts/unit_econ.py in.json
python3 scripts/unit_econ.py in.json --json

Quality Checks

• LTV uses gross margin, not raw revenue (a common, model-breaking error)
• The numbers are computed by the helper, not eyeballed
• Verdict is stated against the standard benchmarks (LTV:CAC ≥ 3, payback < 12mo)
• The biggest lever is identified with its rough effect
• Assumed inputs are flagged separately from measured ones

Anti-Patterns

• Do not compute LTV on revenue instead of gross margin — it inflates LTV and hides an unviable model
• Do not ignore payback — a great LTV:CAC with a 30-month payback can still starve a business of cash
• Do not treat blended CAC as paid CAC — separate organic from paid or the model lies
• Do not present assumptions as facts — label estimated churn/CAC and validate them
• Do not optimise the smallest lever — model which input actually moves the outcome

Based On

SaaS unit-economics practice (David Skok / for Entrepreneurs) — margin-based LTV, LTV:CAC ≥ 3, payback < 12 months.

Example Trigger Phrases

• "Calculate unit economics."
• "Work out LTV:CAC."
• "Find the payback period."
• "Check whether a business model is viable per customer."