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What is Unit Economics?

Unit economics is the analysis of revenue and cost on a per-customer (or per-unit) basis. It answers the most fundamental question about any business: does the company make more money from each customer than it spends to acquire and serve that customer? If the answer is yes, and by a healthy margin, the business can scale profitably. If not, growth actually destroys value. The concept originated in subscription and SaaS businesses, where the math is cleanest: you spend money to acquire a customer (CAC — Customer Acquisition Cost), that customer pays you a recurring fee, and the total revenue over their lifetime (LTV — Lifetime Value) must significantly exceed the acquisition cost. A healthy SaaS business has an LTV:CAC ratio of 3x or higher, meaning each dollar spent on sales and marketing returns at least three dollars over the customer relationship. But unit economics applies to any business. A restaurant’s unit is a meal or a seat. An industrial services company’s unit might be a service contract or a truck route. The principle is universal: understand the economics at the atomic level, and you understand whether the aggregate business is healthy.

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Why It Matters

For PE investors, unit economics is the best tool for assessing revenue quality — the durability, predictability, and profitability of a company’s revenue. A company with strong unit economics (high retention, low CAC payback, expanding customer value) commands a premium valuation. A company with weak unit economics (high churn, expensive customer acquisition, declining customer value) is risky regardless of its top-line growth. Unit economics also reveal the truth behind aggregate metrics. A company reporting 120% net dollar retention might look fantastic — but if that number is driven entirely by a few large upsells masking 30% logo churn, the picture is very different. Cohort analysis (tracking each year’s customer group over time) is the gold standard for cutting through aggregate numbers.

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Key Concepts

TermDefinition
ARR (Annual Recurring Revenue)The annualized value of all active recurring contracts; the core metric for subscription businesses
CAC (Customer Acquisition Cost)Total sales and marketing spend divided by new customers acquired in a period
LTV (Lifetime Value)The total revenue (or gross profit) a customer generates over their entire relationship
LTV:CAC RatioLTV divided by CAC; measures the return on customer acquisition investment (target: >3x)
CAC Payback PeriodThe number of months to recover the cost of acquiring a customer from their revenue
NDR (Net Dollar Retention)The percentage of beginning-of-period ARR retained from existing customers, including expansion and contraction (target: >110%)
Gross RetentionThe percentage of ARR retained excluding expansion — measures pure customer stickiness (target: >90%)
Cohort AnalysisTracking each year’s (or quarter’s) customer group over time to see how revenue from that group evolves
Rule of 40Revenue growth rate + EBITDA margin should exceed 40% for healthy SaaS businesses

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How It Works

1

Identify Business Model

Determine the revenue model: SaaS/subscription, recurring services, transaction/usage-based, or hybrid. Tailor the analysis framework accordingly.
2

Analyze ARR and Revenue Quality

Build the ARR bridge (Beginning ARR to New to Expansion to Contraction to Churn to Ending ARR), analyze cohort vintages, assess revenue concentration (top 10/20/50 customers), and evaluate contract structure (ACV distribution, multi-year percentage, auto-renewal percentage).
3

Calculate Customer Economics

Compute CAC (total S&M spend / new customers), LTV ((ARPU x Gross Margin) / Churn Rate), LTV:CAC ratio, and CAC payback period. Break down by segment (enterprise vs. SMB vs. mid-market).
4

Assess Retention and Expansion

Calculate gross retention, net retention (NDR), logo churn, dollar churn, and expansion rate. Build a cohort matrix showing how each vintage retains and grows over time.
5

Benchmark and Score

Compare metrics to benchmarks and produce a revenue quality score (1-5) across: recurring percentage, net retention, customer concentration, cohort stability, growth durability, and margin profile.

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Cohort Analysis Template

CohortYear 0Year 1Year 2Year 3Year 4
2020$1.0M$1.1M (110%)$1.2M (120%)$1.1M (110%)
2021$1.5M$1.7M (113%)$1.8M (120%)
2022$2.0M$2.3M (115%)
2023$3.0M
Show both absolute dollars and indexed (Year 0 = 100%) views.

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How to Add to Your Local Context

claude plugin install private-equity@financial-services-plugins
Customize the analysis: 
## Benchmarks by Sector
### B2B SaaS
- NDR: >120% best-in-class, >110% good, <100% concerning
- LTV:CAC: >5x best-in-class, >3x good, <2x concerning
### Recurring Services
- Gross retention: >95% best-in-class, >90% good, <85% concerning
- Revenue per customer growth: >5% annually
Connect to your CRM (Salesforce, HubSpot) or data warehouse for automatic cohort data extraction.

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Best Practices

NDR above 100% can mask high gross churn if expansion is strong enough — always show both metrics. Cohort analysis is the single most important view for revenue quality — push for this data.
  • Always ask for raw customer-level data if available — aggregate metrics can hide problems
  • 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 is not recurring and margins are typically lower
  • Be skeptical of “adjusted” metrics — understand every adjustment