This article is the sixth in a series on DOOH Audience Impressions which explores what the factors are that can accelerate DOOH towards the “holy grail” of cross-platform media compatibility. In it, we will illustrate a way of comparing audience measurement providers and methods in a cross-platform (online and offline) way. This checklist will later be used to compare Quividi to other alternatives. This article is also an excerpt from a larger work, Quividi’s DOOH Audience Impressions White Paper which can be downloaded here.
Much emphasis is put by the standards on the qualifications of measurement systems themselves are not all equal with regards to transparency and accountability. For the historical reasons mentioned earlier, legacy out-of-home measurement has some catching up to do to be at the level of data fidelity delivered digitally.
For example, for much of OOH today, only audience modeling is used and there is no verification of actual audience delivery. It is assumed that if the media was showing, that the audience from the plan was delivered as-is. This has not been acceptable for online ads since viewability was introduced in 2014.
So how can we rate measurement frameworks in a cross-platform way?
Enter the Accountability Checklist!
We can compare OOH measurement methods against an ideal set of requirements to deliver advertiser accountability which we have grouped into 3 dimensions of assessment. In total, the checklist is 10 points long as described below. The following section will look at this checklist in detail.
An independent measurement provider is clear of biases from either the buyer or the seller of the media it is measuring.
An auditable measurement provider keeps a paper trail of the underlying data used to estimate audience impressions for verification purposes.
The model and methodology used by the provider is disclosed for scrutiny by the stakeholders in the transaction, or by a 3rd party auditor.
The model has sufficient detection data volume compared to the final audience impression volume that any adjustments or extrapolations are statistically significant.
The model is based on cross-platform human-valid audience impressions instead of a raw reporting of the detection metric. Data fidelity is explained in more detail.
More On Audience Modeling: Recency, Density and Fidelity
What is Data Recency?
On the right, we have data based on what is collected locally, and in the present.
What Is Data Density?
What is Data Fidelity?
For mobile data, are device access logs being used as audience or is a reasonable effort being made to transform them into a qualified audience? For example, is geofencing being used to ensure the detected mobile devices are within the viewability zone of the display?
How close is the measured metric to the human-valid cross-platform audience impression?
In conclusion, in this article we presented a 10-point checklist to compare audience measurement providers both online and offline and we defined certain key concepts for performing this comparison such as granularity, density and fidelity.