The short answer: A healthy LTV:CAC ratio for a venture-backed SaaS company is 3:1 or better, with CAC payback under 12 months for SMB motions and under 18 months for enterprise. But the ratio alone lies. Without a payback period and a cohort-level view of retention, LTV:CAC is a vanity metric that has funded more failed companies than it has saved.
This page gives you the full model: the formulas, a working calculator you can rebuild in a spreadsheet in ten minutes, benchmark tables by ARR stage, and the cohort projection layer most founders skip.
The Calculator: Inputs, Formulas, and Outputs
Everything below is designed to be reproduced in Google Sheets or Excel. We at Growaton build a version of this for nearly every new pod engagement during the Diagnostics phase — usually in the first week, because you cannot prioritize growth work without knowing whether acquisition is actually profitable.
Step 1 — Calculate blended and paid CAC
Blended CAC = (Total S&M spend in period) / (New customers acquired in period)
Paid CAC = (Paid media spend + paid-attributed headcount) / (Paid-sourced new customers)
Inputs you need:
| Input | Where it lives | Common mistake |
|---|---|---|
| Total sales & marketing spend | Finance / P&L | Excluding salaries, benefits, and tooling |
| Paid media spend | Ad platforms | Using platform-reported spend instead of billed spend |
| New customers acquired | CRM / billing system | Counting trials or free signups as customers |
| Attribution split | Analytics | Self-reported vs. last-touch mismatch |
Track both. Blended CAC tells you what the business actually costs to grow. Paid CAC tells you whether a specific channel is scalable. Founders who only track blended CAC drown when organic plateaus and they discover paid CAC is 4x higher than they assumed.
Growaton's rule: fully loaded CAC includes salaries, contractor fees, agency retainers, martech, and content production. If your CAC calculation only includes ad spend, you're understating it by 40–70% in most seed-to-Series-A companies.
Step 2 — Calculate LTV (three ways, in order of rigor)
Simple LTV (fast, imprecise):
LTV = (ARPA × Gross Margin %) / Monthly Revenue Churn Rate
Expansion-adjusted LTV (better):
LTV = (ARPA × Gross Margin %) / (Monthly Gross Churn % − Monthly Expansion %)
This is net revenue retention in disguise. If NRR > 100%, the denominator goes negative and LTV goes infinite — which is mathematically true and practically absurd. Cap the projection at 36 or 60 months.
Cohort-based LTV (correct):
LTV = Σ (Cohort revenue in month n × Gross Margin %) for n = 1 to N
Sum actual observed revenue per cohort, then project the tail using the retention curve. This is the only method that survives due diligence.
Step 3 — The three outputs that matter
LTV:CAC Ratio = LTV / CAC
CAC Payback (mo) = CAC / (ARPA × Gross Margin %)
Burn Multiple = Net Burn / Net New ARR
Burn multiple isn't strictly a unit economics metric, but David Sacks' framework makes it the fastest sanity check on whether your LTV:CAC math is translating into actual capital efficiency. A 4:1 LTV:CAC with a burn multiple of 3.5x means your model is wrong somewhere.
Worked Example: A Seed-Stage B2B SaaS
Let's run real numbers. Company: horizontal B2B SaaS, $1.4M ARR, self-serve + light sales assist.
| Metric | Value |
|---|---|
| Monthly S&M spend (fully loaded) | $95,000 |
| New customers per month | 38 |
| Blended CAC | $2,500 |
| ARPA (monthly) | $340 |
| Gross margin | 78% |
| Monthly gross logo churn | 3.2% |
| Monthly expansion revenue | 1.1% |
Simple LTV: ($340 × 0.78) / 0.032 = $8,288 LTV:CAC: 8,288 / 2,500 = 3.3:1 ✅
Expansion-adjusted LTV: ($340 × 0.78) / (0.032 − 0.011) = $12,629 LTV:CAC: 5.1:1 ✅✅
CAC Payback: $2,500 / ($340 × 0.78) = 9.4 months ✅
Looks excellent. Now the cohort layer.
Where the model breaks
When we pulled this company's actual cohort data, month-1 churn was 11% — not 3.2%. The 3.2% figure was an average across all customers, dominated by a stable base of long-tenured accounts acquired through founder-led sales. New cohorts from paid acquisition looked nothing like the aggregate.
| Cohort month | % of original cohort retained (paid-acquired) |
|---|---|
| Month 1 | 89% |
| Month 3 | 71% |
| Month 6 | 58% |
| Month 12 | 44% |
| Month 24 | 33% |
| Month 36 | 29% |
Summing actual gross-margin-adjusted revenue across 36 months and projecting a flattening tail: cohort LTV = $5,940. Real LTV:CAC = 2.4:1. Real payback, accounting for early churn, stretched to 13.8 months.
That's the difference between "raise the Series A on these numbers" and "fix activation before you spend another dollar on paid." Same company, same month, two answers. The cohort view was the right one.
This is the core reason we run cohort analysis before recommending any channel scaling. It's also the pattern behind how we 3x'd activation with 14 experiments in 90 days — the acquisition math only worked after the retention curve changed shape.
Cohort Projections: The Layer Most Calculators Skip
Aggregate churn is a weighted average that hides your worst customers behind your best ones. Cohort projection fixes this in three steps.
Build the retention curve
Group customers by acquisition month. For each cohort, plot the percentage of original revenue retained in each subsequent month. You need at least 6–9 cohorts to see a pattern, and at least 12 months of data to trust the tail.
Fit the curve, don't extrapolate the line
SaaS retention curves are almost never linear. They decay steeply, then flatten as your product-market-fit core stabilizes. A power-law or exponential-decay fit is more accurate than a straight-line projection. Practically:
- Steep-then-flat (retention stabilizes above ~30% by month 12): healthy. Project the flat portion forward with mild decay.
- Steady linear decline: dangerous. You have no retained core. LTV is much lower than aggregate churn suggests.
- Steep-then-steeper: you're acquiring the wrong customers. Fix ICP targeting before scaling spend.
Segment by channel, plan, and ICP
The single highest-leverage move in unit economics analysis: split cohorts by acquisition channel.
| Channel | CAC | 12-mo retention | Cohort LTV | LTV:CAC | Payback |
|---|---|---|---|---|---|
| Organic / SEO | $780 | 61% | $8,100 | 10.4:1 | 3.1 mo |
| Referral | $410 | 68% | $9,400 | 22.9:1 | 1.7 mo |
| Paid search | $3,100 | 44% | $5,900 | 1.9:1 | 14.2 mo |
| Paid social | $4,400 | 31% | $3,700 | 0.8:1 | 22.6 mo |
| Outbound SDR | $6,200 | 72% | $19,800 | 3.2:1 | 11.8 mo |
Blended, this company sits at a respectable 3.3:1. Channel-by-channel, paid social is destroying capital and outbound is quietly the best scalable motion. You cannot see that from a single blended number — and the decision it drives (kill paid social, double outbound, protect referral) is worth more than any A/B test you'll run this quarter.
Benchmarks: What "Good" Actually Looks Like in 2026
Targets shift by stage, motion, and ACV. These reflect what we see across seed-to-Series-C SaaS, fintech, marketplace, and e-commerce engagements, cross-checked against public benchmark data from OpenView's SaaS Benchmarks and ChartMogul's SaaS retention research.
| Stage / ACV | Target LTV:CAC | Target CAC payback | Acceptable NRR | Gross margin |
|---|---|---|---|---|
| Pre-seed / Seed (<$1M ARR) | Directional only | <12 mo | >85% | >65% |
| Self-serve SMB (ACV <$5K) | 3:1+ | 6–12 mo | 90–100% | 75–85% |
| Mid-market (ACV $5K–50K) | 3:1+ | 12–18 mo | 100–115% | 72–82% |
| Enterprise (ACV >$50K) | 3:1+ | 18–24 mo | 110–130% | 70–80% |
| Marketplace (take-rate model) | 3:1+ | 9–15 mo | Depends on GMV retention | 50–70% |
| E-commerce / DTC | 2.5:1+ (contribution margin basis) | 1–3 mo (first order or 2nd) | Repeat rate >30% | 40–60% |
How to read your own number
| Your LTV:CAC | What it means | What to do |
|---|---|---|
| Below 1:1 | You lose money on every customer | Stop scaling spend. Fix pricing, retention, or channel mix. |
| 1:1 – 2:1 | Marginal; growth is capital-destructive | Diagnose before investing. Usually a retention or ICP problem. |
| 3:1 – 5:1 | Healthy and fundable | Scale the channels that produce it. Monitor by cohort. |
| Above 5:1 | Often underinvestment, not excellence | You're likely leaving growth on the table. Test spend increases. |
That last row surprises people. A 9:1 blended LTV:CAC at $3M ARR usually means you're 90% organic and haven't found a paid motion — which is a ceiling, not a moat. We've told several founders their unit economics were too good and they needed to spend more aggressively to find the true edge of efficient acquisition.
Six Ways Founders Get This Wrong
1. Using revenue LTV instead of gross-margin LTV. If your gross margin is 70%, you're overstating LTV by 43%. Always multiply by gross margin. For infrastructure-heavy or fintech products with interchange/processing costs, the gap is larger.
2. Excluding salaries from CAC. Your growth marketer's $140K salary is customer acquisition cost. So is the SDR team, the agency retainer, and the $3K/month martech stack.
3. Averaging churn across all customers. Covered above. This is the single most common source of a wrong answer.
4. Ignoring payback period entirely. A 5:1 LTV:CAC with a 30-month payback will kill a company with 14 months of runway. Payback is a cash-flow metric; LTV:CAC is a profitability metric. You need both. Bessemer's Efficiency Score work frames this well — capital efficiency is about when the money comes back, not just whether it does.
5. Using a lifetime assumption longer than your company has existed. If you're 18 months old, you have no idea what month-48 retention looks like. Cap LTV projections at 24–36 months for early-stage companies. Investors will discount anything longer anyway.
6. Broken attribution feeding the whole model. If you can't reliably attribute customers to channels, every channel-level number above is fiction. This is why our engagements often start with attribution infrastructure — we cut a Series A SaaS company's CAC by 47% primarily by rebuilding their attribution model, which revealed that two channels they were defunding were actually their best.
From Numbers to Decisions: A Practical Sequence
Once your calculator is populated, the diagnosis usually points to one of four workstreams:
If payback is too long → attack CAC or pricing
Reduce cost per acquisition (conversion rate optimization, channel reallocation, better targeting) or increase early revenue capture (annual prepay incentives, higher entry-tier pricing, expansion at onboarding). Annual contracts alone can cut payback by 60%+ because you collect 12 months of cash on day one.
If LTV is too low → attack retention and expansion
Activation rate, time-to-value, onboarding, and expansion motion. This is almost always higher leverage than acquisition work at seed and Series A. A 5-point improvement in month-3 retention compounds through the entire cohort curve.
If the ratio is fine but growth is slow → attack volume
You have efficient acquisition and aren't spending enough. Increase budget in the channels with sub-12-month payback until efficiency degrades. Find the edge empirically.
If you can't trust the numbers → attack instrumentation
Event tracking, CRM hygiene, revenue recognition, attribution. Unsexy, and the prerequisite for everything else. Our 4-Phase Growth Framework starts with Diagnostics and Measurement for exactly this reason: you can't run a Conversion or Scale phase on data you don't believe.
Rebuild This Calculator in Your Own Spreadsheet
Minimum viable version — six tabs:
- Inputs — S&M spend, customers acquired, ARPA, gross margin, churn, expansion, all by month
- CAC — blended and by channel, fully loaded
- Cohort table — customers or revenue retained by cohort × month (the classic triangle)
- Retention curve — fitted decay curve with 36-month projection
- Unit economics — LTV, LTV:CAC, payback, burn multiple, by channel and segment
- Scenarios — what happens if churn drops 1pt, CAC rises 20%, or ARPA increases 15%
The scenario tab is the one founders use most. Modeling "what if month-3 retention improves 8 points" is how you decide whether to hire another AE or another product engineer.
If you want the numbers pressure-tested against benchmarks and turned into a prioritized 90-day plan, that's exactly what happens in a free growth diagnostic conversation — we'll look at your actual cohort data and tell you which of the four workstreams above deserves your next dollar. No pitch deck required.
