platform-attributed purchase value ÷ that platform's spendBoth numerator and denominator come from the selected advertising platform and inherit its attribution and reporting rules.
Platform ROAS divides revenue attributed by one ad platform by spend in that platform. Blended ROAS divides observed business revenue by total paid-media spend. The two ratios answer different questions and should be shown together, not forced to match.
Use platform ROAS to inspect the platform’s own attribution view. Use blended ROAS to monitor business-level paid-media efficiency. Compare trends only after aligning dates, currency, revenue scope, spend scope, and adjustments. Do not sum platform-attributed revenue across channels as if it were unduplicated store revenue.
Enter one currency and one reporting window. “All platform-attributed revenue” is the sum of revenue claimed by the ad platforms you are reviewing; it is used only as a diagnostic against observed store revenue.
platform-attributed purchase value ÷ that platform's spendBoth numerator and denominator come from the selected advertising platform and inherit its attribution and reporting rules.
observed store revenue ÷ all paid-media spendDefine store revenue and paid-media coverage explicitly. Include the same brands, countries, currencies, and dates in both.
total business revenue ÷ total marketing spendMarketing efficiency ratio can use a broader spend denominator than blended paid-media ROAS. Teams should not assume the labels are universal.
sum of platform-attributed revenue ÷ observed store revenueA result above 100% signals that attribution claims are not mutually exclusive or otherwise not comparable. It does not quantify causal overlap by itself.
| Dimension | Platform ROAS | Blended ROAS | Control |
|---|---|---|---|
| Revenue source | Platform-attributed purchase value. | Observed commerce or finance revenue under a written policy. | Label the numerator and source on every chart. |
| Spend scope | Spend reported by one platform or selected account set. | All paid-media spend in the chosen business scope. | Maintain an account-to-brand and market scope map. |
| Attribution | Uses the platform’s window, identity, and modeling rules. | Does not need channel attribution in the numerator. | Store the attribution setting with the result. |
| Overlap | Multiple platforms can claim the same conversion. | Each observed order appears under the commerce revenue policy. | Never create blended revenue by summing platform claims. |
| Organic demand | May receive credit if a platform interaction qualifies. | Includes revenue regardless of which paid platform claims it. | Interpret blended ROAS as total paid efficiency, not platform causality. |
| Adjustments | Refund and cancellation handling depends on event collection and platform processing. | Can be defined using observed order and refund facts. | Choose gross, net, or contribution basis consistently. |
| Date | Credit may follow platform reporting and attribution-date rules. | Revenue may follow order, payment, fulfillment, or refund date. | Compare cohorts and daily time series separately. |
| Currency | Account reporting currency and platform conversion policy. | Store, presentment, or finance reporting currency. | Convert numerator and denominator with one documented FX policy. |
ROAS is a ratio. To aggregate campaigns, dates, countries, or accounts, sum compatible revenue and spend first, then divide. An unweighted average gives a tiny campaign the same influence as a large one.
sum(revenue) ÷ sum(spend)Only combine rows with compatible revenue definition, attribution setting, currency policy, and scope.
average(row-level ROAS)This usually produces a different number because the rows have different spend weights.
The Semantic Contract Library exposes source report grains, metrics, and semantic capabilities. Check the Meta Ads connector and Google Ads connector, then use the metric aggregation and grain guide before combining channel results.
ROAS labels are not universal. Validate what each platform places in conversion value and cost, then validate the observed revenue basis independently before comparing or recomputing the ratios.
Metric Hive is built to keep source attribution metrics separate from canonical commerce facts and to recompute ratios from compatible base metrics. It can improve consistency and explainability; it cannot make platform attribution equivalent to incremental impact.