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Unit economics

Skill VRIL-LABS/skill-jam/skills/ai-ml/financial-services-plugins-main/financial-services-plugins-main/private-equity/skills/unit-economics

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npx -y skills add VRIL-LABS/skill-jam --skill unit-economics

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SKILL.md

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Unit Economics Analysis

description: Analyze unit economics for PE targets β€” ARR cohorts, LTV/CAC, net retention, payback periods, revenue quality, and margin waterfall. Essential for software/SaaS, recurring revenue, and subscription businesses. Use when evaluating revenue quality, building a cohort analysis, or assessing customer economics. Triggers on "unit economics", "cohort analysis", "ARR analysis", "LTV CAC", "net retention", "revenue quality", or "customer economics".

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 β†’ New β†’ Expansion β†’ Contraction β†’ Churn β†’ Ending ARR
  • ARR by cohort: Vintage analysis β€” 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 Γ— 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 β†’ Gross Profit β†’ Contribution Margin β†’ 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 β€” aggregate metrics can hide problems
  • NDR above 100% can mask high gross churn if expansion is strong enough β€” always show both
  • Cohort analysis is the single most important view for revenue quality β€” 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 β€” it's not recurring and margins are typically lower

Gives 0 of the 12 instructions most pricing monetisation skills give

Counted across 366 of the 366 authors here whose files we hold, read 2026-08-06

  • verify webhook signaturesin 23 of 366, across 19 files
  • differentiate tiers using features, limits, or supportin 15 of 366, across 4 files
  • read product marketing context before asking questionsin 14 of 366, across 6 files
  • base price on perceived value, not costin 14 of 366, across 3 files
  • use Van Westendorp to find acceptable price rangein 14 of 366, across 3 files
  • use MaxDiff to identify highly valued featuresin 14 of 366, across 3 files
  • choose a value metric that scales with customer valuein 14 of 366, across 9 files
  • handle webhook events idempotentlyin 12 of 366, across 6 files
  • understand the upgrade context before recommendingin 11 of 366, across 4 files
  • align the pricing metric with delivered valuein 10 of 366, across 4 files
  • install stripe packagein 10 of 366, across 5 files
  • calculate unit economics metricsin 10 of 366, across 5 files

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once.

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