unit-economics

name	unit-economics
description	Use when analyzing SaaS unit economics, cohort retention (NDR/GRR), LTV/CAC, CAC payback, magic number, or rule of 40. Computes deterministic metrics from customer-month transaction data and flags anomalies vs. the seller's stated narrative.

Unit Economics & Cohort Analysis

What this is (and isn't)

This is deterministic computation, not prose. The cohort triangle, retention metrics, and unit-economics ratios come from numpy/pandas in services/financial/cohort_analyzer.py. The LLM's job is interpretation, not calculation.

The killer feature is reconciliation: when the CIM claims 115% NDR but the cohort data shows 92%, that 23-point spread is the kind of finding that closes IC memos. The analyze_unit_economics MCP tool detects these automatically when you pass stated_* parameters.

When to use

• Reviewing a SaaS or recurring-revenue deal
• Validating CIM claims about retention, churn, or sales efficiency
• Building cohort triangles for a quality of revenue analysis
• Stress-testing the LBO base case (does the model assume retention the cohorts don't support?)

How to use

Step 1 — Get the stated narrative

Read what the seller claims. Look in:

• The CIM's "key metrics" or "unit economics" section
• Management presentations
• Data room metrics dashboards

Capture:

• NDR (e.g. "115%")
• GRR (e.g. "94%")
• CAC payback months
• LTV/CAC ratio

If the seller doesn't state these, that's itself a finding — note it.

Step 2 — Load transactions

Format: [{customer_id, period: 'YYYY-MM-DD', revenue}, ...] — one row per customer per month.

Sources, in order of preference:

1. Customer-level revenue export from the seller (cleanest)
2. Subscription/billing system export (Stripe, Chargebee, Recurly)
3. Reconstructed from CRM + invoice data
4. Aggregated cohort data (less ideal — you lose granularity)

If you only have aggregated cohort data, the raw cohort triangle CAN'T be recomputed; you'll have to trust the seller's triangle and just compare the headline metrics.

Step 3 — Estimate LTV/CAC inputs

Pull from financials:

• sales_marketing_spend — last 12 months
• new_customers_in_period — count of new logos in the same period
• gross_margin — as decimal (0.75, not 75)
• new_arr_in_period — for magic number (uses quarter-annualized formula)
• ebitda_margin and revenue_growth_yoy — for rule of 40

If any are missing, the tool returns None for that metric — that's fine, partial analysis is still valuable.

Step 4 — Call the tool

mcp__ololand__analyze_unit_economics(deal_id, transactions, ...) with all the inputs.

Step 5 — Interpret

On retention spreads:

• 0-5pp: probably timing/methodology difference, not a finding
• 5-10pp: medium severity, ask the seller about it

• 10pp: high severity, this is a flag for the IC memo

On payback/LTV ratios:

• Within ±20% of stated: reconciles
• ±20-50%: medium
• Beyond ±50%: high — the unit economics narrative is broken

On decay fit:

• R² > 0.85: clean exponential decay, the cohort behavior is predictable
• R² < 0.7: cohorts behave differently — could be product-market fit changes, segment shifts, pricing changes. Worth investigating.

On rule of 40:

• 40: strong

• 20-40: typical
• <20: weak — model the LBO with conservative assumptions

Anti-patterns

• Don't compute cohorts in your head from monthly summaries. Use the engine.
• Don't ignore anomalies because they're "small." Even a 5pp NDR spread on a $50M ARR business is $2.5M of phantom revenue in the model.
• Don't assume LTV-CAC is meaningful for sub-12-month-old cohorts. The decay fit needs history.
• Don't blend B2B and B2C cohorts. Run them separately if the deal has both.