Unit Economics Agent Skill

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%.

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.

• 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.
• CAC, fully loaded. Include all sales + marketing cost; exclude customer-success/retention spend and returning customers.
• Contribution margin per customer. ARPA minus variable cost to serve — the dollars that actually pay back CAC.
• LTV, capped. Gross-margin LTV with a lifetime cap (see conservatism), not 1/churn run to infinity.
• Payback period. CAC ÷ monthly contribution margin.
• Ratios. LTV:CAC and NRR/GRR for context.
• 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.)

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:

# 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.