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:
| Cohort | Year 0 | Year 1 | Year 2 | Year 3 | Year 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:
| Factor | Score (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.