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unit-economics

unit-economics

github.com/anthropicsData & databases40.9k1.3k installsUpdated today
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About unit-economics

unit-economics is a financial analysis skill for assessing revenue quality, customer economics, retention, cohorts, and margins across business models. It turns these measures into a revenue quality assessment and diligence outputs.

Key features

  1. 01Tailor metrics to the revenue model
  2. 02Analyze ARR, revenue mix, and concentration
  3. 03Calculate customer acquisition and lifetime metrics
  4. 04Review retention, expansion, and cohorts
  5. 05Assess margins and benchmark metrics
  6. 06Produce a revenue quality score

Use cases

  1. 01Evaluating SaaS or subscription revenue quality
  2. 02Comparing customer economics across segments
  3. 03Reviewing cohort retention and expansion
  4. 04Preparing diligence findings and red flags

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/anthropics/financial-services --skill unit-economics

After installing, type/unit-economicsin Claude Code to confirm it loaded.

Details

From the project README

Unit Economics Analysis

Workflow

Step 1: Identify Business Model

Determine the revenue model to tailor the analysis:

  • SaaS / Subscription: ARR, net retention, cohorts
  • Recurring services: Contract value, renewal rates, upsell
  • Transaction / usage-based: Revenue per transaction, volume trends, take rate
  • Hybrid: Break down by revenue stream
Step 2: Core Metrics
ARR / Revenue Quality
  • ARR bridge: Beginning ARR → New → Expansion → Contraction → Churn → Ending ARR
  • ARR by cohort: Vintage analysis — how does each annual cohort retain and grow?
  • Revenue concentration: Top 10/20/50 customers as % of total
  • Revenue by type: Recurring vs. non-recurring vs. professional services
  • Contract structure: ACV distribution, multi-year %, auto-renewal %
Customer Economics
  • CAC (Customer Acquisition Cost): Total S&M spend / new customers acquired
  • LTV (Lifetime Value): (ARPU × Gross Margin) / Churn Rate
  • LTV:CAC ratio: Target >3x for healthy businesses
  • CAC payback period: Months to recover acquisition cost
  • Blended vs. segmented: Break down by customer segment (enterprise vs. SMB vs. mid-market)
Retention & Expansion
  • Gross retention: % of beginning ARR retained (excludes expansion)
  • Net retention (NDR): % of beginning ARR retained including expansion
  • Logo churn: % of customers lost
  • Dollar churn: % of revenue lost (often different from logo churn)
  • Expansion rate: Upsell + cross-sell as % of beginning ARR
Cohort Analysis

Build a cohort matrix showing:

CohortYear 0Year 1Year 2Year 3Year 4
2020$1.0M$1.1M$1.2M$1.1M
2021$1.5M$1.7M$1.8M
2022$2.0M$2.3M
2023$3.0M

Show both absolute $ and indexed (Year 0 = 100%) views.

Margin Waterfall
  • Revenue → Gross Profit → Contribution Margin → EBITDA
  • Fully loaded unit economics: what does it cost to acquire, serve, and retain a customer?
  • Gross margin by revenue stream (subscription vs. services vs. other)
Step 3: Benchmarking

Compare unit economics to relevant benchmarks:

  • SaaS Rule of 40: Growth rate + EBITDA margin > 40%
  • SaaS Magic Number: Net new ARR / prior period S&M spend > 0.75x
  • NDR benchmarks: Best-in-class >120%, good >110%, concerning <100%
  • LTV:CAC: Best-in-class >5x, good >3x, concerning <2x
  • Gross retention: Best-in-class >95%, good >90%, concerning <85%
  • CAC payback: Best-in-class <12mo, good <18mo, concerning >24mo
Step 4: Revenue Quality Score

Synthesize into a revenue quality assessment:

FactorScore (1-5)Notes
Recurring %
Net retention
Customer concentration
Cohort stability
Growth durability
Margin profile
Overall
Step 5: Output
  • Excel workbook with ARR bridge, cohort matrix, unit economics dashboard
  • Summary slide with key metrics and benchmarks
  • Red flags and areas for further diligence

Important Notes

  • Always ask for raw customer-level data if available — aggregate metrics can hide problems
  • NDR above 100% can mask high gross churn if expansion is strong enough — always show both
  • Cohort analysis is the single most important view for revenue quality — push for this data
  • Differentiate between contracted ARR and actual recognized revenue
  • For usage-based models, focus on consumption trends and expansion patterns rather than traditional ARR metrics
  • Professional services revenue should be evaluated separately — it's not recurring and margins are typically lower

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