ATT opted-in rows versus modelled rows: keep them in separate columns
A blended iOS ROAS can hide a consented population inside a modelled one. A reproducible illustrative example with editable inputs and explicit limits.
A blended iOS ROAS can hide a consented population inside a modelled one. This lab forces the split.
Define the calculation before using it
Enter opted-in attributed revenue, modelled or probabilistic iOS revenue, and spend. Report ROAS for each series against the same spend only if that is the actual cost allocation; otherwise keep spend split too.
Work through the synthetic example
With 10,000 spend, 4,000 opted-in revenue and 9,000 modelled revenue, opted-in ROAS is 0.40 and modelled ROAS is 0.90. A blended 1.30 treats the methods as additive people. They may not be.
| Output | Worked-example result |
|---|---|
| Opted-in ROAS | 0.4000 |
| Modelled ROAS | 0.9000 |
| Naive blended ROAS | 1.3000 |
Use the artifact and preserve its assumptions
Open the editable calculator to change the inputs and inspect the sensitivity view. The CSV records synthetic inputs and expected outputs; the JSON fixture keeps the equations available for reproduction. These calculations have been checked against the stated example. No measured campaign data is included.
The sensitivity rows vary only modeled by 20% below and above the entered value. They are scenarios, not confidence limits or a forecast distribution. A row outside the model’s constraints is labelled rather than turned into a plausible-looking result.
Evidence and limits
Apple documents tracking-authorisation statuses. It does not define how an MMP should blend consented and modelled revenue. See App Tracking Transparency, especially “ATTrackingManager.AuthorizationStatus”.
Apple’s ATT documentation describes authorisation status, not a ROAS formula. Modelled totals are vendor-specific.
Background: ATT opt-in rates: what still moves them and Reading an MMP dashboard without fooling yourself. These existing articles provide context; the present calculation does not verify every archived claim.
No actual campaign outcome or recommended industry default is claimed.
