analyzing-unit-economics
Structures unit economic analysis with CAC, LTV, payback period, and cohort-based measurement. Use when analyzing unit economics, calculating LTV/CAC, or evaluating customer economics.
Best use case
analyzing-unit-economics is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Structures unit economic analysis with CAC, LTV, payback period, and cohort-based measurement. Use when analyzing unit economics, calculating LTV/CAC, or evaluating customer economics.
Teams using analyzing-unit-economics should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/analyzing-unit-economics/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How analyzing-unit-economics Compares
| Feature / Agent | analyzing-unit-economics | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Structures unit economic analysis with CAC, LTV, payback period, and cohort-based measurement. Use when analyzing unit economics, calculating LTV/CAC, or evaluating customer economics.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Analyzing Unit Economics Structures unit economic analysis with CAC, LTV, payback period, and cohort-based measurement for evaluating whether a business acquires and monetizes customers profitably. ## When To Use - Evaluating customer acquisition efficiency (CAC trending, channel-level CAC) - Calculating customer lifetime value (LTV) and LTV/CAC ratios - Determining payback period on acquisition spend - Comparing unit economics across customer segments, cohorts, or product lines - Stress-testing business model sustainability for board decks, fundraising, or strategic planning - Assessing whether to scale, hold, or cut spend on a specific acquisition channel ## Inputs To Gather - **Revenue data**: MRR/ARR per customer or ARPU by period; gross margin percentage or contribution margin per unit - **Acquisition cost data**: Total sales & marketing spend by channel; number of new customers acquired per period; breakdown of paid vs. organic vs. referral acquisition - **Retention/churn data**: Logo churn rate and revenue churn rate by cohort; expansion revenue (upsell/cross-sell) rates if applicable - **Cohort definitions**: Time-based cohorts (monthly/quarterly sign-up date), segment-based cohorts (plan tier, geography, company size) - **Time horizon**: Analysis window length and whether to use historical actuals, forward projections, or both - **Discount rate** (if computing discounted LTV): Weighted average cost of capital or hurdle rate [VERIFY — confirm with finance team which rate applies] ## Workflow 1. **Define the unit**: Clarify what constitutes a "unit" — individual customer, account, seat, contract, or transaction. This choice drives every downstream calculation. 2. **Calculate CAC**: - Fully loaded CAC = (Sales & marketing spend + attributed overhead) / New customers acquired - Segment by channel (paid search, outbound sales, partnerships, organic) where data permits - Flag whether CAC includes or excludes sales team base salaries — state the convention explicitly 3. **Calculate LTV**: - Simple LTV = ARPU × Gross Margin % × (1 / Churn Rate) - Cohort-based LTV: Track actual cumulative gross profit per cohort over time; fit a retention curve (exponential decay, shifted beta-geometric, or log-linear) to project remaining life - If expansion revenue is material, compute net revenue retention (NRR) and use it in place of simple churn: LTV = ARPU × Gross Margin % × (1 / (1 − NRR)) - For discounted LTV, apply the agreed discount rate period-by-period [VERIFY] 4. **Compute core ratios**: - **LTV/CAC ratio**: Target benchmark is ≥ 3× for SaaS; adjust expectations by industry [VERIFY — benchmark varies by business model] - **CAC payback period**: Months to recover CAC = CAC / (ARPU × Gross Margin %). For non-subscription models, use average order frequency × contribution margin per order - **Contribution margin per unit**: Revenue per unit minus all variable costs directly attributable to serving that unit 5. **Cohort analysis**: - Build a cohort retention table (rows = cohort, columns = period since acquisition) - Plot retention curves and identify whether newer cohorts retain better or worse than older ones - Calculate cumulative revenue and cumulative gross profit per cohort to see when each cohort "pays back" 6. **Sensitivity and scenario testing**: - Vary churn rate ±20% and show impact on LTV and LTV/CAC - Model the effect of a CAC increase (e.g., rising CPMs) on payback period - Test what gross margin improvement is needed to hit target LTV/CAC if current ratio is below threshold 7. **Interpret and contextualize**: - Compare current metrics to prior periods and to industry benchmarks [VERIFY — source benchmarks from credible surveys or databases] - Identify the primary lever (reduce churn, increase ARPU, lower CAC) with the largest marginal impact - Note any data gaps that limit confidence (e.g., insufficient cohort maturity, blended channel costs) ## Output Produce a structured **Unit Economics Analysis Report** containing: - **Executive summary**: One-paragraph verdict on unit economic health with headline LTV/CAC ratio and payback period - **Metric table**: CAC, LTV, LTV/CAC, payback period, contribution margin — shown overall and by segment/channel - **Cohort retention chart**: Heatmap or line chart of retention by cohort - **Cumulative gross profit curves**: Per-cohort view showing time to payback - **Sensitivity table**: Key metrics under bull/base/bear assumptions - **Recommendations**: Ranked list of actions (scale channel X, reduce churn via Y, raise price on segment Z) with expected metric impact - **Assumptions & limitations log**: Every assumption stated, every data gap flagged with [VERIFY] ## Quality Checks - Confirm CAC denominator counts only *new* customers, not reactivations, unless reactivation cost is separately tracked - Verify that LTV uses gross margin, not revenue — overstating LTV by ignoring COGS is the most common error - Ensure churn rate and ARPU are measured over the same time period (monthly churn with monthly ARPU, not mixing annual and monthly) - Check that cohort data has sufficient maturity (at least 6–12 months of history) before projecting long-tail retention - Cross-check total CAC spend × customer count against the P&L sales & marketing line to catch allocation errors - Validate that LTV/CAC ratio is not artificially inflated by excluding below-the-line costs from CAC (e.g., onboarding, customer success) - If using discounted LTV, confirm the discount rate is consistently applied and disclosed