CAC and LTV Analysis
Overview
CAC (Customer Acquisition Cost) and LTV (Customer Lifetime Value) are the two fundamental unit economics metrics. Together they answer: "Does each customer generate more revenue than it costs to acquire them?" The LTV:CAC ratio is the single most important indicator of marketing efficiency and business model viability.
When to Use
Trigger conditions:
- User evaluating marketing spend efficiency
- User asks "what's each customer worth?" or "are we spending too much on marketing?"
- User assessing business model viability or fundraising metrics
- User needs to allocate budget across acquisition channels
When NOT to use:
- For product pricing decisions → use Pricing Strategy
- For customer segmentation → use STP or RFM
- For comprehensive financial analysis → use financial ratios
Framework
IRON LAW: LTV:CAC > 3 for Healthy Business
LTV:CAC ratio must be at least 3:1 for sustainable businesses.
- < 1:1 = You're LOSING money on every customer
- 1-3:1 = Unsustainable unless you can reduce CAC or increase LTV
- 3-5:1 = Healthy
- > 5:1 = Potentially underinvesting in growth (leaving market share on the table)
This ratio applies to the BLENDED average. Individual channels can be
below 3:1 if the overall blend exceeds it.
IRON LAW: CAC Must Include ALL Acquisition Costs
CAC = Total marketing & sales spend / Number of new customers acquired
"Total spend" includes: ad spend, marketing team salaries, sales team
salaries, tools, content production, events — EVERYTHING spent to acquire
customers in that period. Excluding salaries or tools understates true CAC.
Step 1: Calculate CAC
Basic formula:
CAC = Total acquisition spend in period / New customers acquired in period
By channel:
CAC (Channel X) = Spend on Channel X / Customers from Channel X
Include in total acquisition spend:
- Advertising (digital + offline)
- Marketing team compensation
- Sales team compensation (for B2B)
- Marketing tools and software
- Content production costs
- Events and sponsorships
- Agency fees
Step 2: Calculate LTV
Simple formula:
LTV = ARPU × Gross Margin % × Average Customer Lifespan
Where:
- ARPU = Average Revenue Per User per period (monthly or annual)
- Gross Margin % = (Revenue - COGS) / Revenue
- Average Customer Lifespan = 1 / Churn Rate
Cohort-based (more accurate):
Track actual revenue per customer cohort over time. Sum cumulative revenue per customer, apply gross margin.
Step 3: Calculate Key Ratios
| Metric | Formula | Healthy Benchmark |
|---|
| LTV:CAC | LTV / CAC | > 3:1 |
| Payback Period | CAC / (ARPU × Gross Margin) | < 12 months |
| CAC % of LTV | CAC / LTV × 100 | < 33% |
Step 4: Segment Analysis
Calculate CAC and LTV by:
- Channel: Which acquisition channels are most efficient?
- Customer segment: Which segments have highest LTV:CAC?
- Cohort: Is LTV improving or degrading over time?
Step 5: Optimization Strategies
To reduce CAC:
- Shift budget to lower-CAC channels
- Improve conversion rates (better landing pages, sales process)
- Increase organic/referral acquisition (content, word-of-mouth)
To increase LTV:
- Reduce churn (improve product, customer success)
- Increase ARPU (upsell, cross-sell, price increases)
- Extend customer lifespan (loyalty programs, switching costs)
Output Format
markdown
# CAC-LTV Analysis: {Company/Product}
## Unit Economics Summary
|--------|-------|-----------|--------|
| CAC (blended) | ${X} | — | — |
| LTV | ${X} | — | — |
| LTV:CAC | {X}:1 | > 3:1 | ✓/✗ |
| Payback Period | {X} months | < 12 months | ✓/✗ |
## CAC by Channel
|---------|-------|-----------|-----|-----------|
| {channel} | ${X} | {N} | ${X} | {X%} |
## LTV Calculation
- ARPU: ${X}/month
- Gross Margin: {X%}
- Avg Lifespan: {X} months (churn rate: {X%}/month)
- LTV = ${X}
## LTV:CAC by Segment
|---------|-----|-----|-------|--------|
| {seg A} | ${X} | ${X} | {X}:1 | Invest / Maintain / Cut |
## Optimization Recommendations
1. ...
2. ...
Examples
Correct Application
Scenario: CAC-LTV for a Taiwanese B2C subscription box (monthly NT$599)
CAC calculation:
| Item | Monthly Spend |
|---|
| Facebook/Instagram ads | NT$200,000 |
| Google Ads | NT$80,000 |
| KOL partnerships | NT$50,000 |
| Marketing team (2 people) | NT$120,000 |
| Total | NT$450,000 |
New customers in month: 300
CAC = NT$450,000 / 300 = NT$1,500
LTV calculation:
- ARPU: NT$599/month
- Gross Margin: 55%
- Monthly churn: 8% → Avg lifespan: 1/0.08 = 12.5 months
- LTV = NT$599 × 0.55 × 12.5 = NT$4,118
LTV:CAC = 4,118 / 1,500 = 2.75:1 — Below the 3:1 threshold. Need to either reduce CAC or improve retention.
Incorrect Application
What went wrong:
- CAC calculated as "ad spend / new customers" only, excluding NT$120K/month marketing team salary → True CAC is NT$1,500, not NT$1,100. Violates Iron Law: include ALL acquisition costs.
- LTV:CAC of 1.8:1 reported as "good because we're growing" → Growth at LTV:CAC < 3:1 means you're growing into larger losses. Violates Iron Law: ratio must be > 3:1.
Gotchas
- Attribution is messy: A customer who saw an Instagram ad, Googled your brand, then signed up via a referral link — which channel gets credit? Be consistent in attribution methodology (first-touch, last-touch, or multi-touch).
- Blended vs marginal CAC: Blended CAC includes all channels. Marginal CAC is the cost of acquiring ONE MORE customer. As you scale, marginal CAC typically rises (best channels saturate first).
- LTV is always an estimate: Future churn and spending behavior are uncertain. Use conservative assumptions and update with real cohort data as it accumulates.
- Payback period matters for cash flow: Even with LTV:CAC of 5:1, if payback takes 24 months, you need significant upfront capital. Fast-growing companies can die from long payback periods despite great unit economics.
- Negative churn is a superpower: If expansion revenue (upsells) exceeds lost revenue (churn), Net Revenue Retention > 100%. This means LTV grows over time — the best possible scenario.
Scripts
| Script | Description | Usage |
|---|
| Compute CAC, LTV, LTV/CAC ratio, and payback period | python scripts/cac_ltv.py --help
|
Run
python scripts/cac_ltv.py --verify
to execute built-in sanity tests.
References
- For cohort-based LTV calculation methods, see
- For channel attribution models, see
references/attribution-models.md