unit-economics

# Unit economics Answer one question honestly: **does one customer pay back more than it cost to win and serve them, and how fast?** You compute four load-bearing numbers — CAC, contribution margin, LTV, CAC payback period — plus the two ratios operators and investors actually argue about (LTV:CAC, NRR). Then you diagnose *why* a number is where it is and name the single lever that moves it. This is a measurement-and-diagnosis skill, not a projection skill. You do not build the multi-year model here (that is the `financial-model` sibling); you build the per-customer truth that the model's growth assumptions have to rest on. ## What this skill produces A **unit-economics worksheet** (`unit-economics.{yaml,csv,md}`) where: - a block of **named inputs** — period S&M spend, new customers, monthly ARPA, gross margin %, monthly churn, optional segment rows — is stated explicitly; - every **derived figure** (CAC, contribution margin, LTV, payback, LTV:CAC) is recomputed *from those inputs* so the relationships are self-consistent; - the worst number is diagnosed and one concrete lever is prescribed. `scripts/verify.sh` re-derives the figures and fails if the arithmetic lies (see the last section). If you only hand back prose, you have not finished — emit the worksheet. ## The four numbers and the input people get wrong Each formula has one input that, done sloppily, silently invalidates everything downstream. Tag the input, not just the result. | Number | Formula | The input people get wrong | |---|---|---| | **CAC** | fully-loaded S&M spend ÷ new customers (same period) | the *numerator* — ad-spend-only CAC understates true CAC ~3.5× | | **Contribution margin / customer** | ARPA − variable cost to serve (COGS + variable support + payment fees) | confusing it with company-wide gross margin % | | **LTV** | (ARPA × Gross Margin %) ÷ churn rate | using *revenue* instead of *gross-margin* dollars | | **CAC payback (months)** | CAC ÷ (monthly ARPA × Gross Margin %) | leaving gross margin out of the denominator | **Worked Bad → Good — the revenue-LTV inflation.** Same inputs: ARPA $400/mo, gross margin 75%, monthly churn 3%. ```text Bad (revenue LTV): 400 / 0.03 = $13,333 <- overstates by 33% Good (gross-margin LTV): (400 * 0.75) / 0.03 = $10,000 <- what a customer is actually worth ``` LTV must use gross-margin dollars because not all revenue is profit — hosting, support, and payment fees come out first. The revenue version flatters the LTV:CAC ratio and is the single most common dishonesty in a deck. (Beancount.io "2026 SaaS Metrics Stack" 2026-05-10; ChartMogul LTV guide; both accessed 2026-06-02.) Full derivations, cohort-LTV vs formula-LTV, the Skok discounted model, and segment roll-up math live in `references/formulas.md`. Read it before you defend a number to an investor. ## Order of operations (the spine) Run these in order — each step is an input to the next, so skipping one makes everything after it fiction. 1. **Gross margin first.** Every downstream metric multiplies by it. If gross margin is unknown, the COGS per unit must be tracked first (route to `cost-tracking`) — you cannot compute LTV or payback on a guessed margin. 2. **CAC, fully loaded.** Include all sales + marketing cost; exclude customer-success/retention spend and returning customers. 3. **Contribution margin per customer.** ARPA minus variable cost to serve — the dollars that actually pay back CAC. 4. **LTV, capped.** Gross-margin LTV with a lifetime cap (see conservatism), not 1/churn run to infinity. 5. **Payback period.** CAC ÷ monthly contribution margin. 6. **Ratios.** LTV:CAC and NRR/GRR for context. 7. **Diagnose & prescribe.** Find the worst number, name its cause, name the lever. ## Get the inputs honest CAC is fully loaded or it is a lie. Decide what goes in the numerator before you divide. | In the CAC numerator | Out of the CAC numerator | |---|---| | Sales + marketing salaries & benefits | Customer-success / retention spend (that protects LTV, it doesn't acquire) | | Sales commissions & SDR/AE comp | R&D / product engineering | | Ad spend, content, events, agencies | Overhead/G&A not tied to acquisition | | Marketing & sales tooling | (Denominator) returning / reactivated customers — only count *new* logos | | Founder selling time (impute a salary) | | Three rules that catch most errors: - **Gross margin, not opex margin.** Gross margin reflects COGS (hosting, support, payment fees), not salaries/rent. Mixing in opex understates margin and quietly tanks LTV. (Beancount.io 2026-05-10, accessed 2026-06-02.) - **Match time units.** Monthly ARPA pairs with monthly churn; annual with annual. Mixing them silently 12×'s or ÷12's the LTV — the most common arithmetic error in the whole exercise. (ChartMogul; metrickit LTV guide; accessed 2026-06-02.) - **Right period, right denominator.** Spend in period P ÷ customers acquired in period P. Don't divide this quarter's spend by all-time customers. ## Segment before you optimize A single blended number hides the only insight that matters. Reporting one blended CAC of $1,200 when self-serve is $80 and field sales is $40,000 tells you nothing actionable — the channels have wildly different CAC, ARPA, and churn. (lucid.now LTV/CAC errors; andrewchen; accessed 2026-06-02.) ```text Blended CAC $1,200 = self-serve $80 + inside sales $900 + field $40,000 (lever: scale) (lever: AE ramp) (lever: ACV / cycle) ``` Two more separations to keep clean: - **Paid CAC vs blended CAC.** Blended includes free/organic; paid isolates the channels you can actually scale with money. Optimize paid; report both. - **Self-serve / inside sales / field sales.** Split on go-to-motion, because the lever for each is different. If one segment is bleeding while blended "looks fine," that's exactly the non-obvious failure this skill exists to surface. ## Benchmarks (2025-2026) A ratio is a conversation-starter, not a pass/fail grade. Read the whole row before you celebrate or panic. | Metric | Elite / top quartile | Healthy / median | Concern | |---|---|---|---| | CAC payback | < 12 months | B2B SaaS median 12-18 mo | 24+ months = sustainability concern | | Payback by ACV | ~9 mo at ACV ≤ $5K | — | ~24 mo at ACV > $100K (expected, not bad) | | LTV:CAC | 3:1 to 5:1 | median B2B ≈ 3.2:1 | < 3:1 overspending; **> 5:1 under-investing in growth** | | NRR | top quartile 115-125% | healthy 105-115% | < 100% net contraction | | GRR | — | — | high NRR + low GRR = expansion masking churn | (Beancount.io 2026-05-10; First Page Sage CAC Payback Benchmarks 2025; Optifai B2B LTV benchmark, 939 companies; cast.app NRR; all accessed 2026-06-02.) The honest framing: a **2.5:1 with 9-month payback and 120% NRR beats a 4:1 with 36-month payback and 95% NRR.** Payback and NRR decide whether you can survive the gap between spend and return; the ratio alone can't. **Rule of 40** (ARR growth % + profit margin % ≥ 40%) is the company-level destination these feed — cite it, but compute it in `financial-model`, not here. ## Diagnose & prescribe (this is where the flow branches) Find the worst number, then route to the lever — and to the sibling skill that owns that lever. | Bad number | Likely cause | Lever (and owner) | |---|---|---| | CAC too high | wrong channel mix / paid-heavy | shift to founder-led & organic; cut the bleeding channel — diagnose by segment here, then `pricing` if the fix is monetization | | Payback too long | low margin-dollar capture per month | move to annual prepay; shorten free trial; lift gross margin (`cost-tracking` for COGS) | | LTV too low | dominant churn term | attack the biggest churn driver → `retention`; lift expansion to push NRR > 100% | | Margin too low | COGS per unit too high | instrument per-unit COGS / inference cost → `cost-tracking` | | LTV:CAC > 5:1 | under-investing in growth | spend *more* on acquisition — you're leaving money on the table, not winning | | NRR ≫ GRR | expansion masking a churn problem | fix gross retention first → `retention`; don't let expansion hide the leak | The skill stops at "here is the worst number and the lever." Executing the lever (set the price, design the save-play, project the new curve) belongs to the sibling, not here. ## Conservatism rules (so LTV isn't fantasy) Small churn errors explode LTV because of the 1/churn term — at 1% monthly churn the formula implies a ~100-month (8+ year) lifetime, which no early-stage company has data to claim. - **Cap assumed lifetime at 3-4 years** for early-stage (≤ 48 months), or apply a **×0.7 conservatism multiplier** to formula LTV. - **Cohort beats formula.** Once you have multi-year retention data, compute cohort LTV from observed retention — the formula is a placeholder until then. - **Discount future value** in the advanced (Skok) model: ~20-25% discount rate pre-scale, ~10% at scale, because a dollar of contribution margin three years out is worth less than one today. - **NRR > 100% means the simple churn-only LTV understates** the cohort — note it rather than silently leaving value on the table. (Beancount.io 2026-05-10; metrickit LTV guide; forEntrepreneurs SaaS Metrics 2.0; all accessed 2026-06-02.) Worked discounted and cohort examples are in `references/formulas.md`. ## Anti-patterns | Anti-pattern | Why it's wrong | Do instead | |---|---|---| | Revenue LTV (ARPA ÷ churn) | overstates value; not all revenue is profit | gross-margin LTV: (ARPA × GM%) ÷ churn | | Ad-spend-only CAC | understates true CAC ~3.5× | fully-loaded S&M ÷ new customers | | Monthly ARPA with annual churn | silently 12×'s or ÷12's the LTV | match time units before dividing | | Near-zero churn → 30-year lifetime | fantasy LTV no one can back with data | cap lifetime ≤ 48 mo or ×0.7 multiplier | | One blended CAC/LTV | hides self-serve vs field; nothing actionable | segment by go-to-motion first | | Counting CS/retention spend in CAC | inflates CAC; that spend protects LTV, doesn't acquire | keep CS out of the numerator | | Reading only the LTV:CAC ratio | ignores payback & NRR that decide survivability | read payback + NRR alongside the ratio | | Quoting NRR while GRR is weak | expansion masks a churn leak | report GRR floor too; fix retention first | ## The worksheet format Emit this so the numbers are checkable, not just asserted: ```yaml # unit-economics.yaml inputs: period_sm_spend: 120000 # fully-loaded S&M, this period new_customers: 80 # new logos only, same period monthly_arpa: 400 gross_margin_pct: 0.75 monthly_churn: 0.03 # monthly, to match monthly_arpa # lifetime_cap_override: 60 # only if you justify > 48 months outputs: cac: 1500 # 120000 / 80 contribution_margin: 300 # 400 * 0.75 ltv: 10000 # (400 * 0.75) / 0.03 (NOT 13333) payback_months: 5 # 1500 / (400 * 0.75) ltv_cac: 6.67 # 10000 / 1500 -> >5: under-investing segments: # split when blended hides the truth - name: self-serve cac: 80 - name: field-sales cac: 40000 ``` `scripts/verify.sh` parses this file and **fails if**: CAC ≠ spend ÷ new_customers; contribution_margin ≠ ARPA × GM%; payback ≠ CAC ÷ (ARPA × GM%); the stated LTV matches the *revenue* form instead of the gross-margin form; ltv_cac ≠ LTV ÷ CAC within 0.05; or implied lifetime (1/churn) > 48 months with no `lifetime_cap_override`. It is read-only and exits 0 on a clean worksheet (and on no worksheet at all). ## Where this hands off - Multi-year P&L / scenario / cap-table model → `financial-model`. - Setting the actual price, tiers, discount floor → `pricing`. - Churn-prevention program (health scores, save-plays, win-back) → `retention`. - Per-unit COGS / infra / AI inference cost so gross margin is even knowable → `cost-tracking`. - Forecasting future MRR / cohort projection → `forecasting`. - Writing the investor unit-economics narrative / data-room exhibit → `investor-materials`. - Generic KPI chart / dashboard surface → `dashboard`.