That is the problem many growing companies run into. Ad platforms report strong performance, campaigns look “profitable,” and yet overall revenue or margin does not move the way the team expected. The issue is usually not one bad campaign. It is a measurement problem.
The short version: most teams are tracking attributed revenue, not incremental revenue.
Attributed revenue is the revenue a platform or analytics tool claims credit for. Incremental revenue is the revenue you would not have earned without the campaign.
That difference is where inflated ROAS happens.
What Inflated ROAS Actually Means
ROAS itself is a simple metric. Google describes target ROAS as the average conversion value (for example, revenue) you want per dollar spent, and even gives the basic math example of revenue divided by ad spend.
The issue is not the formula.
The issue is the revenue number used in the formula.
If the “revenue from ads” includes customers who would have purchased anyway, duplicate credit across channels, or conversions counted under generous windows, your ROAS can look stronger than reality. It is not always fake, but it can be over-credited.
Why ROAS Gets Inflated in Growing Companies
1) Attribution answers “who touched it,” not always “who caused it”
Attribution tools are useful. GA4 itself defines attribution as assigning credit across touchpoints on the path to a key action.
That is helpful for optimization.
But incrementality asks a different question: Would this conversion have happened without the ad?
Google’s own guidance makes this distinction clearly. In Think with Google’s measurement guidance, attribution and MMM are described as useful but limited for exact incremental return, and incrementality testing is presented as the way to quantify the added revenue caused by a campaign.
So when teams rely only on attribution reports, ROAS often looks better than true business impact.
2) Platform and channel windows can over-credit conversions
Google Ads defines a conversion window as the period after an ad interaction during which a conversion is recorded.
That sounds harmless, but this is where inflation often sneaks in:
- A user clicks an ad, leaves
- Comes back later through brand search, direct, or email
- Purchases within the conversion window
- The platform still claims the conversion
Nothing is “wrong” here. The system is doing what it was set to do.
But if your window is too wide for your buying cycle, or if different channels use different windows, your ROAS can become more of a crediting outcome than a causal outcome.
Google also notes that attribution settings affect how conversions are counted and can even affect automated bidding strategies like Target ROAS. That means inflated crediting can directly shape spending decisions.
3) Your remarketing and branded demand can look like “new growth”
This is one of the biggest reasons ROAS gets inflated.
Bottom-funnel campaigns often target people who already know your brand:
- repeat visitors
- cart abandoners
- branded searchers
- existing customers
These campaigns can produce very high reported ROAS because they sit close to purchase. But many of those users may have converted anyway.
That does not mean remarketing or branded search is bad. It means their reported ROAS often reflects conversion capture, not always incremental lift.
This is exactly why growing companies need a second lens beyond platform reporting.
4) Duplicate credit across platforms makes total ROAS look better than total reality
Google’s APAC Think with Google article calls this out directly: when businesses count each platform’s contribution as a full conversion, it can create duplication and over-counting across touchpoints.
This is common in multi-channel setups:
- Meta reports a conversion
- Google Ads reports a conversion
- GA4 shows a different path
- CRM records the sale later
Each system can be “right” inside its own logic, but the business can still be over-crediting total revenue if it adds those numbers together.
That is why teams often feel performance is strong everywhere, while finance sees a very different picture.
5) Missing or weak conversion data distorts value-based bidding
ROAS is only as good as the conversion value data going into the system.
Google recommends setting conversion values properly before using Target ROAS and notes you can use conversion value rules to better express value by user type, device, or location.
If your values are incomplete or too generic, ROAS can be misleading in a different way:
- low-value and high-value conversions treated the same
- offline sales are missing
- lead quality ignored
- refunds or cancellations not reflected in value logic
That does not always “inflate” ROAS, but it often creates a number that looks precise while being strategically weak.
6) Offline and delayed revenue is often missing from the picture
For many services, B2B, healthcare, and sales-assisted businesses, the real sale happens later:
- after a call
- after a demo
- after a sales rep follow-up
- after an in-person close
Google explicitly supports offline conversion imports for this reason, noting that ads may start a path that leads to an offline sale and that importing offline conversions helps measure what happens after the click or call.
If you do not import that data, your ROAS reporting can over-reward fast online events and under-represent true revenue outcomes.
How to Measure True Incremental Revenue Instead
The goal is not to stop using ROAS.
The goal is to use better inputs and add incrementality testing so ROAS becomes a more trustworthy decision metric.
Step 1) Clean up your attribution and conversion foundation first
Before you run any lift tests, fix the basics:
- set clear primary conversion actions
- pass accurate conversion values
- separate lead events from revenue events
- align naming across GA4, ad platforms, and CRM
In GA4, Google notes attribution reports support multiple models and are built to assign credit across touchpoints. In Google Ads, attribution model settings and conversion settings directly affect reporting and bidding.
This matters because if your foundation is messy, incrementality tests become harder to trust.
Step 2) Improve measurement match quality with first-party data
As privacy rules and browser limits reduce direct observability, match quality matters more.
Google states enhanced conversions can improve measurement accuracy by sending hashed first-party data (like email) in a privacy-safe way, and the same framework also supports offline workflows through enhanced conversions for leads.
This is one of the simplest upgrades growing companies can make before changing tools.
Step 3) Align attribution settings before comparing channels
Do not compare channel ROAS if each channel is using different assumptions.
At minimum, review:
- conversion windows
- attribution model
- what is included in the “Conversions” column
- whether the conversion is primary or secondary
Google confirms conversion windows are configurable and that attribution model settings affect conversion counting and bidding optimization.
This step alone often reduces “phantom winners” in a media mix.
Step 4) Run incrementality tests to find what actually drives lift
This is the most important step.
Google describes Conversion Lift as an incrementality tool and explains it works by separating treatment (people who saw ads) and control (people who did not) to measure incremental conversions.
Google’s Think with Google article also describes incrementality tests as randomized controlled experiments and explains that they can quantify the revenue you would have missed if the campaign had not run.
That is the answer to inflated ROAS.
Use attribution for daily optimization. Use incrementality for truth.
Where to start testing
Start with channels most likely to be over-credited:
- branded search
- remarketing
- high-frequency retargeting
- always-on paid social
- campaigns targeting existing customers
If a channel shows huge ROAS but weak lift in a holdout test, you found inflated credit.
Step 5) Calculate Incremental ROAS, not just Reported ROAS
Google’s measurement guidance explicitly defines the core formula:
Incremental ROAS = Incremental Revenue / Media Spend
This gives you a better budgeting metric than platform-reported ROAS because it reflects caused revenue, not just credited revenue.
A practical workflow looks like this:
- Daily: use platform and GA4 attribution reports for optimization
- Monthly/quarterly: run incrementality tests on key channels
- Budget planning: scale channels based on incremental ROAS, not reported ROAS alone
Step 6) Use data-driven attribution, but do not treat it as the final truth
Google’s GA4 and Google Ads documentation shows data-driven attribution is more advanced than last-click because it uses converting and non-converting paths, machine learning, and counterfactual methods to distribute credit.
That is a major improvement.
Still, data-driven attribution is attribution, not a replacement for controlled experiments. It helps you allocate credit more intelligently, but it does not remove the need for incrementality testing when real budget decisions are on the line.
The best setup for growing companies is:
- clean attribution
- accurate conversion values
- first-party data improvements
- offline conversion imports (if applicable)
- periodic incrementality tests
That combination gives you both speed and truth.
Final Takeaway
Your ROAS is usually inflated for one reason:
It measures credit better than causality.
That is normal. It is how most ad reporting works.
The fix is not to throw away ROAS. The fix is to stop treating reported ROAS as the only signal. Once you pair attribution reporting with incrementality testing and a stronger conversion value setup, you get a much clearer view of true growth.
That is when budget decisions get sharper, and scaling gets safer.
Book a Call With Y77.ai
If your dashboards show strong ROAS but revenue quality feels unclear, book a call with Y77.ai.
We help growing companies clean up attribution, improve conversion measurement, and build an incrementality-driven reporting setup so your team can scale based on real profit signals, not inflated credit.