Your own numbers settle the reported ROAS argument
Two numbers you already own end most arguments about reported ROAS. Neither requires a vendor, a model or a login you do not already have.
Three platforms, three answers, one budget to defend. Your GA4 export already holds the answer, and the first finding is free and never leaves your browser. Get your first finding free
By Joris van Huët, Founder & CEOUpdated 3 min read
Run the numbers for your store: the free break-even ROAS calculator.
Two numbers you already hold end most arguments about reported ROAS: what your store actually took, and what the platforms collectively say they caused. Neither requires a vendor, a model or a login you do not have.
The reason this is powerful is that it does not depend on anyone accepting a methodology. It is arithmetic on two totals.
The comparison
| Number | Source | Property |
|---|---|---|
| Actual revenue | Your store admin | Transactional, cannot be revised by a third party |
| Claimed revenue | Sum across ad platforms | Each computed under rules its own author set |
If the second exceeds the first, the excess is overlap: conversions claimed by more than one party. That is expected under any set of platform rules, and quantifying it is the point.
Why this ends the argument
Because there is nothing to dispute. Nobody has to accept that causal inference is superior, or that a particular vendor's model is well specified. The store total is the store total, and the platform claims are what the platforms published. The gap follows.
What people do with the gap varies, but the existence of it stops being contested, and that is usually the blocker. The full method is in the claim ratio.
The second thing you own
Your analytics export. Whatever else changes, a Google Analytics export of a past window is yours, reproducible, and readable by any method you choose now or later. That property is what makes it a good foundation for measurement, more than any feature of a particular tool.
It also means a measurement relationship does not have to be a data relationship. You can run a read, keep the output, and owe nobody anything afterwards. We made the general argument in you do not own your attribution data.
What the platforms own
Their own rules, their own windows, and the definition of what counts as a touch. All of those can change without notice, and when they do your reported numbers move without anything in your business changing. That is the strongest practical argument for anchoring on your own totals: they are stable when the definitions around them are not.
Adding the third number
The causal estimate is the third number and the only one that requires a method. Causality Engine produces it from your export: per channel, what it caused next to what last-click gave it, with a data-health score rather than a confidence interval. €99 for a first read, refundable within 30 days, no questions asked.
What to do with the finished picture
Anchor the total on your store, use the causal read for the split, and treat platform claims as an input to neither. Then keep the platform dashboards for what they are genuinely good at, which is optimising delivery within a channel rather than deciding how much the channel deserves.
The line worth remembering
You cannot audit a number whose definition belongs to someone else. You can always audit your own bank.
Related answers
Frequently asked questions
How do I check whether my ad platforms are overclaiming?
Total your store revenue for a window, sum what every ad platform claims for the same window, and compare. Any excess is conversions claimed by more than one party. It needs no tooling and nobody has to agree on a methodology first.Why anchor reporting on store revenue rather than analytics?
Because store revenue is transactional and cannot be revised by a third party, while analytics depends on collection and platform figures depend on rules their authors can change without notice. The store total stays stable when definitions around it move.Are ad platform dashboards useless then?
No. They are good at optimising delivery within a channel, which is what they were built for. They are a poor basis for deciding how much a channel deserves, because the rules defining their numbers are set by a party selling the media.
Go deeper: Causal attribution, explained.
Sixty-second versions of these ideas: Causality Engine on YouTube Shorts.
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Terms in this article
- AnalyticsAnalytics is the systematic computational analysis of data. It reveals customer behavior and measures campaign performance.
- AttributionAttribution identifies user actions that contribute to a desired outcome and assigns value to each. It reveals which marketing touchpoints drive conversions.
- Causal InferenceCausal inference is the set of methods for estimating how much an outcome changes because of a cause, rather than alongside it. It works by comparing what happened with an estimate of what would have happened without the cause.
- CausalityCausality is the relationship where one event directly causes another, essential for identifying specific actions that drive desired outcomes in marketing.
- Confidence IntervalConfidence Interval is a statistical range of values that likely contains the true value of a metric. In marketing analytics, it quantifies uncertainty around estimates, indicating the precision of an outcome or causal effect.
- ConversionConversion is a specific, desired action a user takes in response to a marketing message, such as a purchase or a sign-up.
- DashboardsDashboards are graphical user interfaces that provide at-a-glance views of key performance indicators (KPIs). They monitor campaign performance and visualize attribution insights.
- Google AnalyticsGoogle Analytics is a web analytics service that tracks and reports website traffic.