Ltv Cac

LTV / CAC

Compute LTV, CAC payback, and LTV/CAC ratio with the four canonical formulas side by side. Anchor the verdict on Skok's 3:1 rule, layer on a16z NDR, Sequoia contribution-margin, and Tunguz AI-inference adjustments. Output a styled Excel workbook with a sensitivity heatmap and a 36-month synthetic cohort projection.

Step 1: Ask the user up to 6 inputs

Ask in order. The first 3 are required; the rest have defaults.

1. ARPU (monthly revenue per customer, $), e.g. 200
2. Customer churn rate (monthly, decimal), e.g. 0.03 for 3%/mo
3. CAC (customer acquisition cost, $), e.g. 1500
4. Net revenue expansion (monthly, decimal, optional), e.g. 0.005 for 0.5%/mo. Default 0. If they have NDR > 100%, this is positive.
5. Gross margin (decimal, optional), e.g. 0.78. Default 0.78 (78%, typical SaaS).
6. Inference / variable cost per customer per month ($, optional), only relevant for AI products. Default 0.

If the user is hesitant on inputs, suggest they start with the example fixture (examples/inputs.json).

Step 2: Run

python skills/ltv-cac/run.py \
  --arpu 200 \
  --churn 0.03 \
  --cac 1500 \
  --expansion 0.005 \
  --gross-margin 0.78 \
  --inference-cost 0 \
  --out-dir /tmp/ltv-cac--

Or load all inputs from a JSON file:

python skills/ltv-cac/run.py --inputs path/to/inputs.json --out-dir /tmp/ltv-cac

Step 3: KPIs the user gets

Verdict sheet (the headline)

• Skok basic LTV: ARPU × GM / churn (the canonical 3:1 reference)
• NDR-adjusted LTV: ARPU × GM / (churn − expansion) (a16z framework)
• AI-adjusted LTV: (ARPU × GM − inference) / churn (Tunguz inference erosion)
• Sequoia contribution-margin LTV: combines the above two: (ARPU × GM − inference) / (churn − expansion)
• CAC Payback: months to recover CAC from gross profit. Basic and AI-adjusted.
• LTV/CAC ratio: color-coded: red 5
• Verdict statement: plain-English read with Skok 3:1 anchor + AI flag if inference erodes LTV >20%
• NDR: monthly and annual-compounded
• Citations: every formula tagged with its source

Sensitivity sheet

• LTV/CAC heatmap across monthly churn (1% to 10%) × gross margin (50% to 90%)
• Same color coding as the verdict
• Lets the founder see "if I cut churn from 5% to 3% at the same GM, where does my LTV/CAC land?"

Cohort Projection sheet

• Synthetic 100-customer cohort projected forward 36 months with the user's churn + expansion + inference inputs
• Columns: customers retained, MRR, cumulative gross profit, vs cohort CAC
• Line chart of cumulative GP vs CAC
• Payback callout: "Cohort breaks even at lifetime month N" or "Doesn't break even within 36mo"

Step 4: Output

Default output (--output all) writes to /tmp/ltv-cac--/:

• ltv_cac_workbook.xlsx: the 3-sheet styled Excel file
• summary.md, markdown digest with the verdict + tables, paste-able into a doc
• inputs.json, the inputs you used (re-runnable: pass with --inputs)
• ltv_summary.csv, every formula's LTV + ratio
• cac_payback.csv, basic and AI-adjusted payback months
• ndr.csv, monthly + annual NDR
• sensitivity.csv, full heatmap data
• cohort_projection.csv, month-by-month projection

Step 5: After running

Show the user 4-5 lines:

1. The headline verdict (one of: Underwater 🔴 / Tight 🟡 / Healthy 🟢 / Possibly under-investing 🟦)
2. Skok basic LTV/CAC ratio
3. CAC payback months
4. NDR (annual)
5. Path to the workbook

If the AI-adjusted LTV diverges from the basic LTV by >20%, flag inference cost as a key sensitivity.

When to use

• A founder asks "is my SaaS healthy?" / "should I scale paid acquisition?"
• An investor wants LTV, CAC payback, NDR for a deck
• Modeling unit economics for a new pricing tier
• Comparing scenarios: "what if I raise prices 10%?" / "what if I cut churn from 5% to 3%?"

When NOT to use

• The user has actual cohort data and wants observed retention curves → use /cohort-analysis instead. This skill is for forward-looking modeling from assumptions.
• Pre-revenue product with no churn data → there's nothing to compute. Suggest gathering 3-6 months of data first, then running both /cohort-analysis (observed) and /ltv-cac (modeled) side by side.

Try it without thinking

The examples/inputs.json ships with a typical mid-stage B2B-SaaS profile (ARPU $200, 3% churn, $1,500 CAC, 78% GM, no inference cost). Run:

python skills/ltv-cac/run.py --inputs skills/ltv-cac/examples/inputs.json --out-dir /tmp/ltv-cac-demo
open /tmp/ltv-cac-demo/ltv_cac_workbook.xlsx

Frameworks referenced

• David Skok / Matrix Partners: "SaaS Metrics 2.0", origin of the 3:1 LTV/CAC rule and the canonical LTV = ARPU × GM / churn formula.
• a16z: "The 16 Startup Metrics", introduced NDR (Net Dollar Retention) as a first-class LTV input.
• Sequoia Capital: argues for contribution-margin LTV (deduct variable costs from GM) over headline gross-margin LTV.
• Tomasz Tunguz / Theory: "Unit Economics of LLMs", showing how variable inference costs erode AI-product LTV in ways the traditional formulas miss.

The skill computes all four side by side so the user sees where they agree (and where AI economics break the textbook).

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: NachoLafuente
• Source: NachoLafuente/5050-gtm
• License: MIT

Install and usage instructions live in the source repository linked above.