Proving it worked, answered in full.

Because the alternative is to describe a three-verb model and let the fourth appear later as a launch. The four verbs are the measurement model; brand lift is the one that answers whether anything changed in people's heads, and leaving it out would make the model look complete when it is not. The API returns "remembered" with the status placeholder and a note that the study has not been commissioned, and the Cint adapter is non-transacting pending a contract. That is what this page says too.

That if your spend, geography and conversion volume cannot detect a lift of a plausible size at 80% power, the engine returns an underpowered verdict and the number of days the design would need — and no lift figure. Most incrementality tools will happily return a number in that situation. The number is noise, and the harm is that it is indistinguishable from a real one once it is in a slide.

At most 90 days. After that the hashed id is persistently stripped from the exposure event, which re-grades the event to a coarser key rather than deleting the record. The rule lives in the key-translation service, which is the only module we have that performs identity joins at all.

You can see a sequence, and that is a different claim. All seven channels write into one ledger, so the mechanism can show that a postcode sector exposed to a DOOH panel was later exposed to a CTV ad and later produced a conversion. Be exact about what feeds it: the ledger is fed only by the deterministic sandbox today, there is no inbound ingest route for viewshed panels, CTV logs or IP logs, and sandbox rows are labelled modelled rather than observed. So it is a working mechanism awaiting live input rather than a sequence anyone has yet seen in real delivery. Turning a sequence into a causal statement takes a powered geo-lift test in any case, and out-of-home is a good fit for one because the buy is bought by place.

No. Nothing has run live, and a geo-lift is graded causal only when every geo outcome is observed, every delivery row maps to a live line and control exposure is verified — conditions sandbox data cannot meet by construction. Any lift figure you see in a demonstration is injected sandbox data labelled as a modelled demonstration. The engine, the power gate and the refusal are real and tested; the result is not yet evidence about a real campaign, and we do not print one as if it were.

There is a modelled layer, and it is labelled modelled wherever it appears — deduplicated reach and frequency being the main case. What the product will not do is present a model output as evidence of causation. Modelled estimates and powered causal results are two of the four provenance labels precisely so that they cannot be read as the same thing.