What is ROAS (return on ad spend)
Also known as: Return on Ad Spend
ROAS (return on ad spend) is how much revenue your campaigns generated for each dollar spent on media. A ROAS of 4 means every $1 in ads brought in $4 in sales. It does not subtract product costs, so it only tells you whether a campaign is profitable when compared with the business's margin.
ROAS = revenue attributed to ads ÷ ad spend
It is shown as a multiplier (4x) or a percentage (400%). Both mean the same thing.
A cosmetics store spent $8,000 on ads in a month, and sales attributed to them totaled $32,000. ROAS was $32,000 ÷ $8,000 = 4.
The store's margin, after product cost, shipping and fees, is 30%. The minimum ROAS to break even is 1 ÷ 0.30 = 3.33. At a ROAS of 4, the campaign is profitable. With a 20% margin, break-even ROAS would be 5, and the same ROAS of 4 would be losing money.
Break-even ROAS: the number to know first
Whether a ROAS is good or bad depends on margin. The math that separates profit from loss is simple: break-even ROAS = 1 ÷ margin. With a 25% margin, an ad needs to bring at least $4 in sales for every $1 spent just to break even.
Without that number, a ROAS target is a guess. Some businesses celebrate a ROAS of 3 while losing money on every sale, and others cut a campaign with a ROAS of 2 that, thanks to a high margin, was profitable.
| Minimum ROAS to break even | |
|---|---|
| 50% margin | 2 |
| 40% margin | 2.5 |
| 30% margin | 3.33 |
| 20% margin | 5 |
| 10% margin | 10 |
ROAS and ROI: which one to use
ROAS looks at revenue; ROI looks at profit. ROAS is the number the ad platform sees and optimizes for, so it is the daily dashboard. ROI is what tells you whether money was left at the end of the month.
The two work well together once break-even ROAS is defined: the ROAS target becomes the translation of the business's profit goal into the platform's language.
Target ROAS bidding in Google Ads
Google Ads has an automated bid strategy that uses ROAS as its target. According to Google Ads Help, it uses Google's AI to predict the value of each potential conversion on every search and adjusts bids to maximize return: higher bids where a sale is likely to be worth more, lower where it is not.
Since June 2026, Google calls this strategy simply "Target ROAS" (previously "Maximize conversion value with a Target ROAS"). The behavior did not change, only the name.
To work well, it needs accurate conversion values. If the account records the same value for every sale, or none at all, the algorithm optimizes in the dark.
Common ROAS mistakes
- Setting a target without knowing your margin. The target has to start from break-even ROAS.
- Counting sales twice. If Google and Meta both claim the same sale, adding their ROAS together inflates the result.
- Looking only at the account average. Campaigns for products with different margins need different targets.
- Setting the target too tight. A very high ROAS usually comes with very low volume: the campaign only sells to people who were already going to buy.
Frequently asked questions
How do you calculate ROAS?
Divide the revenue attributed to your ads by what you spent on them in the same period. $20,000 in sales from $5,000 in ads is a ROAS of 4.
What is a good ROAS?
Anything above your break-even ROAS, which is 1 divided by your margin. With a 30% margin, any ROAS above 3.33 is profitable.
What is the difference between ROAS and ROI?
ROAS divides revenue by ad spend. ROI uses profit and can include other costs. ROAS is for optimizing campaigns; ROI shows whether money was left over.
Is a ROAS of 4 good?
It depends on margin. With a 30% margin, yes: break-even is 3.33. With a 20% margin, no: break-even is 5, and a ROAS of 4 loses money.
Is ROAS a percentage or a multiplier?
Both. A ROAS of 4, 4x and 400% all say the same thing: each dollar spent brought in four in revenue.
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Rodrigo Fávaro
Founder of ROO3, a marketing and technology agency in São José do Rio Preto, Brazil. Builds AI products running in production (Tobia, gerar.app, Pense Mercado) and maintains the AI Benchmark, a public ranking of AI models.
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