Unit economics modeling

unit-economics-modeling

When to use

• A board ask: "is this business unit-economic?" — needs CAC / LTV / payback, not vibes.
• A new channel is scaling and the question is whether the CAC payback period is sustainable.
• A pricing or packaging change needs to be tested against contribution margin per cohort.
• A finance-partner needs to construct burn-multiple cognition before the next forecast or scenario pass.

Do NOT use for full-business intrinsic-value modeling, OKR setting, funnel-stage diagnosis, or backlog ranking (see Related Skills).

Cognition cluster

• Mental model 1 — First principles. Strip the unit to one paying customer and one fully-loaded acquisition dollar. Aggregate ratios ride on per-unit truth; if the unit is mis-defined (trial vs paid, household vs seat), every ratio downstream is decoration. See docs/contracts/mental-models.md § 1.
• Mental model 8 — Second-order thinking. A CAC drop driven by discounting lifts LTV/CAC on paper while shortening cohort retention — the second-order effect lands two quarters later in churn. Score the second-order cost of every lever, not just the first-order ratio. See mental-models.md § 8.
• Context-spine — product + fiscal-period. Read the product slot for what a "customer" actually is in this scope (seat vs household vs paid trial vs activated free), and the fiscal-period slot for the close-window the ratios must reconcile against (monthly close vs quarterly board pack vs annual plan). See context-spine.

Procedure

Step 0: Inspect

1. Confirm the business shape — SaaS / marketplace / transactional. The three canonical cases differ in revenue recognition and churn definition, not in arithmetic.
2. Confirm a fully-loaded CAC is computable: paid spend + sales comp + content/SEO allocation + tooling. Marketing-spend-only CAC is a vanity metric.

Step 1: Compute CAC per channel

1. CAC = (fully-loaded acquisition spend in window) / (new paying customers acquired in same window). Match window to sales-cycle length, not calendar quarter.
2. Compute by channel and blended. Blended-only hides the channel that is breaking the average.
3. Anti-pattern: counting trial signups as customers. Customer = first paid charge cleared.

Step 2: Compute gross margin

1. Gross margin = (revenue − COGS) / revenue. COGS includes hosting, payment fees, third-party APIs the customer's usage drives, and direct customer-success cost.
2. Gross margin must be per dollar of revenue, not per customer. Per-customer gross margin is contribution margin (Step 3).
3. SaaS healthy band: 70–85%. Marketplace: 15–40%. Transactional: 5–25%. Outside these — the business is mislabelled or the COGS allocation is wrong.

Step 3: Compute LTV

1. Pick the canonical formula for the case:
   • SaaS: LTV = ARPA × gross_margin / monthly_churn_rate. Use net-dollar churn for self-serve, gross logo churn for high-touch.
   • Marketplace: LTV = take_rate × GMV_per_user × retention_curve_AUC over 24 months. Steady-state extrapolation is dishonest below 24 months of cohort data.
   • Transactional: LTV = avg_order_value × gross_margin × purchases_per_year × avg_lifetime_years.
2. Cap implied lifetime at 5 years for any business with < 3 years of cohort history. Anything longer is a fairy tale.
3. State the formula used inline. Do not let the reader infer.

Step 4: Compute payback and ratio

1. CAC payback (months) = CAC / (ARPA × gross_margin) for SaaS; analogue for marketplace and transactional. Healthy SaaS: ≤ 12 months.
2. LTV / CAC ratio: target ≥ 3.0. Below 1.5 is acquisition-loss territory; above 5.0 means under-investment in growth (or bad LTV math).
3. Both numbers, not one. Payback drives capital efficiency; ratio drives long-run economics.

Step 5: Compute burn-multiple judgment

1. Burn multiple = net burn / net new ARR over the fiscal-period slot's reporting window (monthly close / quarterly / annual). It answers "how many dollars of cash do we burn to add one dollar of recurring revenue?" — a single ratio that condenses CAC, gross margin, and churn into capital efficiency.
2. Compute on net new ARR (gross new − churn − contraction). Burn-multiple on gross new ARR flatters the picture by exactly the churn rate; auditors and acquirers will recompute.
3. Read the ratio against the org-stage colour from the fiscal-period + product spine — do not hardcode a band here. The cognition is "smaller is better, and the direction across cohorts matters more than the point estimate." Bands belong in runway-cognition (O3) where stage context is the load-bearing input.

Step 6: Cohort the answer

1. Run Steps 1–5 by signup-quarter cohort. Trends matter more than the point estimate.
2. If LTV/CAC is improving but payback is lengthening, you are buying retention with discounting — flag.
3. If both deteriorate, the channel mix has shifted to a worse channel — segment by channel to find the leak.

Step 7: Validate

1. Sanity-check LTV against revenue retention. If implied LTV > 8× annual revenue per customer with monthly churn > 2%, the math is wrong.
2. Sanity-check CAC against fully-loaded P&L. If channel CACs sum to less than total acquisition spend, allocations are missing.

Gotcha

• Marketing-spend-only CAC is the most common deception. Sales comp, BDR salaries, content production, and tooling all belong in fully-loaded CAC.
• Net-dollar retention > 100% does not justify ignoring logo churn — they answer different questions.
• ARPA averaged across plan tiers hides churn concentrated in one tier. Compute per tier when tiers differ in price by more than 2×.
• Payback period using contribution margin (post variable-cost) is honest; payback using gross revenue is the kind of math VCs see in pitch decks and discount on sight.

Do NOT

• Do NOT extrapolate LTV beyond observable cohort data without saying so explicitly.
• Do NOT mix freemium activation rates with paid CAC; they live in different universes.
• Do NOT report a single LTV/CAC for a business with multiple distinct customer segments — segment first.

Related Skills

WHEN to use this

• The question is per-customer economics (CAC, LTV, payback, contribution margin).
• The decision is whether to scale a channel or pricing tier.

WHEN NOT to use this

• Whole-business intrinsic value with terminal value — route to dcf-modeling.
• Diagnosing where conversion drops — route to funnel-analysis.
• Ranking competing initiatives — route to rice-prioritization.
• Setting team objectives that move these metrics — route to okr-tree-modeling.
• Cash-runway shape, fundraise-trigger heuristics, or layoff-vs-cut-vs-grow framing — route to runway-cognition (O3).
• Multi-statement scenario construction over base / upside / downside — route to scenario-modeling (O4).
• Forecast-call construction (commit / best-case / pipeline) — route to forecasting (O2).

Wing-4 handoff: this skill ships the unit-economics-frame.md artifact that scenario-modeling (O4) reads as its money input (docs/guidelines/wing4-handoff.md § Chain 1).

When the agent should load this

• "What's our LTV / CAC?"
• "Is this channel paying back fast enough?"
• "Compute unit economics for this pricing tier."
• "Are we unit-economic at this CAC?"
• "Cohort our payback period."

Output

1. unit-econ-table.md — table per channel and blended: CAC · ARPA · gross margin · payback months · LTV · LTV/CAC · burn-multiple. With cohort columns (last 4 quarters).
2. assumptions.md — formula chosen (SaaS / marketplace / transactional), churn definition, COGS allocation method, lifetime cap. One bullet per choice.
3. cohort-trend.md — trend chart (ASCII or markdown table) of CAC, payback, LTV/CAC, burn-multiple over the last 4–8 cohorts. Annotate channel-mix shifts.
4. sanity-checks.md — explicit cross-checks (LTV vs annual revenue, channel CAC sum vs P&L). Flag any that fail with a one-line investigation pointer.
5. unit-economics-frame.md (Wing-4 handoff) — the typed artifact scenario-modeling (O4) reads: CAC / LTV ratio, contribution margin, payback band, burn-multiple verdict, segment scope, fiscal-period the frame reconciles against. Per docs/guidelines/wing4-handoff.md § Chain 1.