Reported ROAS vs the ROAS you actually earned
Platform-reported return on ad spend (ROAS) is attributed revenue divided by ad spend, and the platform picks which revenue counts. True incremental ROAS (iROAS) is what you would lose if you switched the channel off, measured by holding a group out and comparing. The first number is a marketing report. The second is the one your finance team cares about.
The honest formula is iROAS = (test revenue - control revenue) / test spend. You cannot get it from a dashboard, only from a geo or audience holdout. The calculator below lets you take a platform-reported figure and discount it by an incrementality factor so you can see the phantom revenue you have been counting. Edit every field. The defaults are an example, not a leapbuzz benchmark.
▸ Calculator 1 · Reported ROAS vs incremental ROAS
CAC payback, and the gross-margin trap
Customer acquisition cost (CAC) payback period is the number of months to earn back what you spent acquiring a customer. The trap is running it on revenue. You recover CAC out of gross profit, not top-line revenue, so the correct formula is CAC / (monthly ARPA × gross margin) where ARPA is average revenue per account.
The benchmark everyone quotes, under 12 months, is now the top of the class. The 2024 SaaS Benchmarks from High Alpha, run with OpenView and Paddle across more than 800 companies, put the median CAC payback around 18 months, up from 14 the year before. KeyBanc's 2024 survey put its median near 20 months. So a 14-month payback is healthy today, not alarming.
▸ Calculator 2 · CAC payback period (gross-margin aware)
| Payback | Read | What to do |
|---|---|---|
| Under 12 months | Best-in-class | Consider spending faster while the maths holds. |
| 12 to 18 months | Healthy, at the 2024 median | Hold the line, watch margin and churn. |
| 18 to 24 months | Worth watching | Audit channel mix and gross margin before scaling. |
| Over 24 months | Cash-flow risk | Fix unit economics before adding budget. |
LTV:CAC done on profit, not revenue
The 3:1 lifetime-value-to-CAC rule comes from David Skok's SaaS metrics writing on the For Entrepreneurs blog: below 3:1 your unit economics are too weak to fund growth, well above it you may be under-investing. The catch is that the L in LTV must be gross profit. Gross-margin LTV is (ARPA × gross margin) / monthly churn. Run it on revenue and you inflate the ratio by one over your margin, so a 50 percent-margin business bragging about 3:1 is really sitting at 1.5:1.
KeyBanc's 2024 SaaS survey confirms companies overwhelmingly compute LTV on gross profit for exactly this reason. The calculator shows both numbers side by side so the inflation is visible. Skok himself warns the rule was never meant for pre-product-market-fit companies, and it says nothing about cash timing, so read it next to your payback period, never alone.
▸ Calculator 3 · LTV:CAC (gross-margin adjusted)
The lead-to-close chain a CFO will believe
For pipeline businesses, ROI lives in the funnel, not the click. Closed-won deals are leads × (lead to MQL) × (MQL to SQL) × (SQL to close), where MQL is a marketing-qualified lead and SQL is a sales-qualified lead. Multiply the stage rates, divide your paid spend by the deals that result, and you get paid CAC, the number that matters for budget decisions.
Do not confuse paid CAC with blended CAC. Blended CAC mixes in organic, referral and word-of-mouth customers who cost no media, so it is always lower and it always flatters paid channels. Quote blended CAC to justify ad budget and you will over-invest in your weakest channel. This calculator runs the paid chain on gross profit so the ROI multiple is the one finance will sign off.
▸ Calculator 4 · Lead-to-close unit economics
What platform offers AI-driven ROI calculators for ad campaigns?
No major ad platform ships a literal profit ROI calculator, because platforms measure revenue, not profit. What they ship is AI-driven forecasting of conversions and conversion value, and several of them are genuinely useful inside their own walls. The catch is universal: every one of them forecasts against the conversions you already track, so the output inherits your attribution model and never subtracts the baseline of what would have happened anyway.
| Tool | Where it genuinely helps | Where it misleads |
|---|---|---|
| Google Ads Performance Planner | Forecasts clicks, conversions, conversion value and ROAS from your account history and simulated auctions, refreshed about every 24 hours. Good for spotting diminishing returns inside Google. | Forecasts against your tracked conversions, so the projection carries your attribution, and it cannot see cannibalised organic or cross-platform overlap. |
| Meta Ads Manager estimates | Estimated results during setup give a relative read on reach and likely cost per result inside Meta. | Estimates reflect Meta's default 7-day click plus 1-day view attribution, which runs hot on retargeting and low-funnel campaigns. |
| Microsoft Advertising planner | Mirrors Google's planner for the Microsoft and LinkedIn-fed search network, useful for B2B budget planning. | Same attribution dependence as Google; not an incrementality measure. |
| HubSpot and CRM analytics | Ties ad spend to closed-won deals, which is closer to real pipeline ROI than front-end ROAS. | Only as honest as the attribution and stage definitions you feed it; still no holdout baseline. |
| Lift and experiment tools | Meta Conversion Lift and geo experiments are the only native features that measure true incrementality. | Need real volume and correct randomisation, and most advertisers skip the design work. |
So the honest answer to the query is: use Performance Planner and its peers for intra-platform budget allocation, where they are strong, and never read their output as your business ROI. For the money question, the only tools that count are incrementality experiments and marketing mix modelling. If you want the AI to do real ROI work rather than flattering forecasts, that is where an AI marketing strategy engagement earns its keep, and where the right Google and Meta setup, plus a credible search advertising structure, stops the platforms from grading their own homework.
How to sanity-check any ROI calculator
Whether it is a platform tool, a spreadsheet, or one of the four above, three questions tell you whether to trust a calculator's output as ROI. Ask them before you move budget.
- What attribution feeds it? If the answer is last-click or platform-attributed, the number is descriptive, not causal. It tells you what got credit, not what caused the sale.
- Does it use gross margin or revenue? Revenue-based ROI, LTV and payback are inflated by one over your margin. A calculator that never asks for your margin is quietly optimistic.
- Does it subtract a baseline? An honest tool nets out the conversions that would have happened anyway. View-through conversions and branded search are where this matters most, and where most tools simply count everything.
One more structural point. The same conversion often gets claimed twice. Meta counts a 7-day click plus 1-day view, Google leans on last-click, and the two do not share an identity graph, so a buyer who clicks a Meta ad on Monday and searches your brand on Wednesday is counted by both. Sum the dashboards and your reported conversions exceed reality. Deduplicate against one source of truth, then validate channel lift with holdouts. Getting that source of truth right, and the website and tracking under it, is often a build job rather than a media job; we treat website development as part of the same engagement and you can talk to us about it. The proof that this discipline pays out is on our results page, and the reporting cadence that keeps it honest is in GA4 actionable insights.