Compare platform and causal ROAS in one hour: Three data pulls and one table turn a vague suspicion that reported ROAS is inflated into a per-channel number. Here is the hour, step by step.
Read the full article below for detailed insights and actionable strategies.
Channel comparison
Reported vs. true incremental ROAS
Data relevant to: Compare platform and causal ROAS in one hour
One hour, three pulls and a single table converts "I think our reported ROAS is inflated" into a per-channel number you can put on a slide. No modelling is required for the first two thirds of it.
The hour
| Minutes | What you pull | Where from |
|---|---|---|
| 0 to 10 | Actual revenue for the window | Your store admin |
| 10 to 30 | Claimed revenue and spend, per platform | Each ad platform, same window, same timezone |
| 30 to 40 | Build the table and compute the ratio | A spreadsheet |
| 40 to 60 | Causal estimate per channel | An analytics export and a read |
Watch the window and the timezone
The most common error is comparing platforms on different attribution windows or different timezones, which manufactures a gap that is not real. Set every platform to the same date range, note which attribution window each one is using, and write it down, because you will not remember next quarter.
Build the table
Five columns: channel, spend, platform-claimed revenue, reported ROAS, and share of total claimed. Then one line at the bottom: total claimed revenue against actual revenue, and the ratio between them.
A ratio above one is expected. What matters is the size, and which channels contribute most to the excess. That is usually visible immediately and it is often the most useful twenty minutes anyone on the team spends that quarter. The walkthrough is in the one-hour ROAS audit.
Add the causal column
Export the acquisition window from Google Analytics and run a causal read. Causality Engine returns a per-channel estimate with its confidence interval, the coverage share of your orders, and a design label, from a €99 one-time upload, refundable if it does not move a budget decision.
Put that column beside the reported one. The two are not the same number and are not meant to converge; the point is the pattern of the difference.
Reading the finished table
Look for three things. Which channels have the widest gap, which have almost none, and which have causal intervals so wide that the comparison is not meaningful yet. The third group is not a failure of the exercise, it is the list of channels where a proper test would be worth running.
What to do next
Do not reallocate on the strength of one hour. Use the table to choose the single channel where the gap is largest and the budget is biggest, and run a geo holdout on that one. The hour is triage; the holdout is the verdict.
The one habit worth keeping
Run the same table quarterly with the same definitions. The level is interesting once; the trend is useful forever, particularly when a platform changes its default attribution window and your gap moves without anything in your account changing.
The interactive demo shows what the causal column looks like before you export anything, and the attribution report that tells you which channels to cut covers how to present the finished version.
Related answers
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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.
Attribution Report
Attribution Report shows which touchpoints or channels receive credit for a conversion. It identifies which campaigns drive desired actions.
Attribution Window
Attribution Window is the defined period after a user interacts with a marketing touchpoint, during which a conversion can be credited to that ad. It sets the timeframe for assigning conversion credit.
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.
Google Analytics
Google Analytics is a web analytics service that tracks and reports website traffic.
Revenue
Revenue is the total income generated by the sale of goods or services related to a company's primary operations.
Related Articles
Sixty-second versions of these ideas: Causality Engine on YouTube Shorts.
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