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.
Read the full article below for detailed insights and actionable strategies.
The numbers behind the problem
iOS tracking loss
Google Brand cannibalization
Klaviyo overstatement
TikTok attribution lag
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, with a confidence interval, the coverage share of your orders, and an observational design label. €99 for a first read, refundable if it does not move a budget decision. Unlimited uploads, direct integrations, developer API keys and the MCP server are on Pro at €299 a month.
The interactive demo runs it on a sample store first, with no signup and no account.
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.
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Key Terms in This Article
Analytics
Analytics is the systematic computational analysis of data. It reveals customer behavior and measures campaign performance.
Attribution
Attribution identifies user actions that contribute to a desired outcome and assigns value to each. It reveals which marketing touchpoints drive conversions.
Causal Inference
Causal Inference determines the independent, actual effect of a phenomenon within a system, identifying true cause-and-effect relationships.
Causality
Causality is the relationship where one event directly causes another, essential for identifying specific actions that drive desired outcomes in marketing.
Confidence Interval
Confidence 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.
Conversion
Conversion is a specific, desired action a user takes in response to a marketing message, such as a purchase or a sign-up.
Dashboards
Dashboards are graphical user interfaces that provide at-a-glance views of key performance indicators (KPIs). They monitor campaign performance and visualize attribution insights.
Google Analytics
Google Analytics is a web analytics service that tracks and reports website traffic.
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Sixty-second versions of these ideas: Causality Engine on YouTube Shorts.
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