What is iROAS?
iROAS, or incremental return on ad spend, is the revenue your ads caused divided by the spend that caused it. It comes from a test with a control group, not from an attribution report, and it only pays once it clears your break-even ROAS.
By Joris van Huët, Founder & CEOUpdated 7 min read
Run the numbers for your store: the free break-even ROAS calculator.
iROAS, or incremental return on ad spend, is the revenue your ads caused divided by what you spent on them. Caused means sales that would not have happened without the ads, usually measured against a control group that saw none. If your iROAS sits below 1 divided by your margin, the extra sales lose money.
Ordinary ROAS and iROAS share a bottom line: spend. The top line is where they part ways. Google defines ROAS as attributed conversion value divided by total spend, and iROAS as incremental conversion value divided by ad spend. Attributed means credited by a rule. Incremental means the gap between people who could see the ads and a control group who could not. That gap is your incremental revenue.
In Google's geo lift example, an iROAS of 2 means every $1 invested brought $2 of conversion value that would not otherwise exist.
Meta measures the same idea under another name. Its holdout test reports show ROAS lift: a multiple of revenue generated for each dollar spent on Meta ads. Platform ROAS counts every sale its rules can pin on an ad. iROAS counts only the sales the ads added.
What one store's data shows
One store's anonymised GA4 export, 1 January 2024 to 21 August 2026. It holds shares of revenue only: no ad spend, no order counts. So it cannot produce an iROAS. It can show how much revenue an iROAS test has to look past.
| What the export shows | Share of revenue | Source cell |
|---|---|---|
| Direct, in last click, first click and touched views | 57.7% | Channels sheet, Direct row |
| Paid Social, in all three views | 0.0% | Channels sheet, Paid Social row |
| Journeys with 1 touch (0.5 days to buy) | 79.5% | Journeys sheet, 1 touch row |
Direct holds 57.7% of revenue in that store's Channels sheet, the same in every view. GA4's models keep direct visits out of the credit unless the path to key event consists entirely of direct visits. If the export follows that rule, more than half of the store's revenue arrived with no tracked ad, search or email in front of it.
That is the pile an iROAS test has to look past. Some of those buyers may have seen an ad, never clicked and typed the address later. Others may never have seen one. A view window can let a platform claim some of the first group, whether or not the ad changed their mind. A control group tells you how many would have bought anyway.
Paid Social shows 0.0% in all three views of the Channels sheet. The export holds no spend, so it cannot say whether the store ran paid social at all. If you run it and GA4 shows zero, any ROAS built on GA4 is zero too, while Meta's columns may show plenty. Neither number is an iROAS. A holdout is what tells you whether the ads moved sales.
One-touch journeys carry 79.5% of revenue in the Journeys sheet, at 0.5 days to buy. When one visit is the whole journey, whatever sent that visit takes the whole sale. If it was a brand search ad or a retargeting ad, its platform ROAS looks superb. iROAS asks whether that buyer needed the ad.
What the export cannot show is cause. With no spend and no control group, it shows where credit landed, not what the ads added.
Why does an iROAS above 1 still lose money?
Because iROAS counts revenue, and you pay for ads out of margin. Google's geo reports label a result Significant Positive iROAS when the net new revenue beat the money spent on the campaigns. That is good news about revenue. Your accountant will ask about profit.
The line that matters is break-even ROAS: 1 divided by your gross margin. One store's Break-even sheet shows the sum at a 40% margin: 1 divided by 0.40 is 2.5x. Below that line, the ads cost more than the gross profit on the sales they added.
Google's own example shows why the range matters too. Its geo report gives an iROAS of 2.2 as the point estimate, with a range from 1.3 to 3.5 in brackets. Hold that against the Break-even sheet's 2.5x for a 40% margin. The point estimate loses money, and so does most of the range. Only the top end pays. A result like that says the ads work. It does not say they pay.
What can an iROAS not tell you?
What the next euro earns. An iROAS averages over all the spend you tested. Google's open-source model, Meridian, tracks marginal ROI separately: the incremental outcome on your next dollar spent above the historical budget level. Meridian assumes diminishing marginal returns, so the next euro tends to earn less than the average. Google's geo beta runs only holdback or go-dark tests, which measure the spend you already have, not the extra you plan to add.
Which spend sits under the line. Google's two lift studies divide by different things. The geo version uses incremental cost: the difference of cost among treatment and control during the experiment period. The user-based version divides by total ad spend. Compare two iROAS numbers only when they share a denominator.
Whether it holds next quarter. Google calls incrementality experiments a way to see how effective ads are at a certain point in time. A new creative, a price change or peak season can move the answer.
Whether a test could see your line at all. Google's geo setup page describes a minimum detectable iROAS: the effect size a test needs for a high chance of detecting lift. If that minimum sits above your break-even, the test cannot reliably tell a profitable campaign from a useless one.
What to do this week
- Find your break-even line. In Shopify admin, open Products, click a best-seller and check Cost per item in the Price section. Then divide 1 by your gross margin. Pass: your best-sellers all have a cost, so you hold one break-even number. Fail: costs are blank, and Shopify reports profit only for products with a cost recorded when they sold.
- Put each platform's ROAS next to that line. In Google Ads, go to Summary in the Goals menu and read Conv. value / cost. In Meta Ads Manager, open Columns, then Customize columns, and add Purchase ROAS. Pass: the claim sits well above break-even, so a test can cut it and the campaign still pays. Fail: it barely clears break-even, so test before the next budget raise.
- Ask whether a test could see that line. In Google Ads, open Lift measurement in the Goals menu, select the plus button and choose Conversion Lift under Based on Geo. Pick the campaign and dates, then read the feasibility status in the right-hand column. Pass: High feasibility. Fail: no Conversion Lift option, since Google does not offer it in every account; try Meta's Experiments or a regional holdout.
Check the homework. Your GA4 Attribution paths export already holds the evidence. Causality Engine reads that one file and shows what each channel caused next to what last-click gave it, in 1 to 2 minutes, for €99 once (excluding VAT), refundable within 30 days. Check the homework
Sources, 1 October 2026: Understand your Conversion Lift based on users measurement data (Google Ads Help); Understand your Conversion Lift based on geography measurement data (Google Ads Help); View and understand holdout test results across Meta technologies (Meta Business Help Center); Get started with attribution (Analytics Help); Incremental Outcome, ROI, mROI & Response Curves (Google for Developers (Meridian)); Glossary (Google for Developers (Meridian)); Set up Conversion Lift based on geography (Google Ads Help); About Conversion Lift (Google Ads Help); Profit reports (Shopify Help Center); Understand your conversion tracking data (Google Ads Help); Customize columns in Meta Ads Manager (Meta Business Help Center); Purchase ROAS (return on ad spend) (Meta Business Help Center)
Related answers
Frequently asked questions
Is iROAS the same as ROAS lift in Meta?
Yes, in idea. Meta's holdout reports call it ROAS lift: a multiple of revenue for each dollar spent on Meta ads, measured against a holdout group. Google calls it Incremental ROAS. Check which spend each one divides by before you compare them.Can iROAS be higher than platform ROAS?
Yes. A lift study counts every sale in both groups, including people who saw an ad, never clicked and bought later. Click-based reports miss those buyers. If a lift beats the platform's own number, check the control group's setup before you celebrate.What is a good iROAS?
Any iROAS above your break-even ROAS, which is 1 divided by your gross margin. One store's Break-even sheet shows the sum at a 40% margin: 2.5x. Below that line, the ads add sales but lose money on them.
Go deeper: Causal attribution, explained.
Sixty-second versions of these ideas: Causality Engine on YouTube Shorts.
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Terms in this article
- AttributionAttribution identifies user actions that contribute to a desired outcome and assigns value to each. It reveals which marketing touchpoints drive conversions.
- Control GroupControl Group is a segment of an audience intentionally not exposed to a marketing campaign, used to measure the campaign's true causal impact.
- ExperimentsExperiments are scientific procedures that test hypotheses or demonstrate facts. In marketing, experiments like A/B tests determine the causal effect of campaign changes, enabling data-driven decisions.
- Holdout TestA holdout test is an experiment where a portion of the audience does not see a campaign. This measures the campaign's true incremental impact.
- IncrementalityIncrementality measures the true causal impact of a marketing campaign. It quantifies the additional conversions or revenue directly from that activity.
- Incrementality TestingIncrementality Testing measures the additional impact of a marketing campaign. It compares exposed and control groups to determine causal effect.
- Lift MeasurementLift Measurement: A method to determine the incremental impact of a marketing campaign by comparing exposed and control groups.
- RetargetingRetargeting is online advertising that targets users who have previously interacted with your website or content. Attribution analysis shows the causal role of retargeting in driving conversions and improving ad spend.