Augmented attribution refers to a family of attribution algorithms that extend “classical” attribution (based on observed events and impressions) by incorporating modeled exposure signals and calibration mechanisms from performance studies.
Objective: to measure the post-view impact on a large scale , without depending on pixel instrumentation, while making the algorithm consistent with business evidence (MMM, incrementality, etc.).
Why “Augmented”?
1) Replace deterministic printing with modeled printing
In many contexts, impressions are not observable in a deterministic way (technical limitations, privacy, pixel-free environments, restrictions of certain platforms).
Augmented MTA addresses this problem by replacing "pixel-measured" impressions with modeled impressions , constructed from available signals (daily aggregated data).
What this changes:
we provide an exposure signal even when the print is not measured at the user level;
we can assign the post-viewwithout pixel deployment effort ;
We cover 100% of platforms , including walled gardens , with an approach compatible with their constraints.
2) Calibration: putting business truth back at the heart of the algorithm
An attribution model can be mathematically consistent but business-inconsistent if its estimates diverge from other measurement systems available on the Advertiser side (measured incrementality, MMM, lift studies…).
Augmented MTA therefore introduces a calibration capability, that is to say the possibility of anchoring (or “constraining”) post-print modeling on external references.
Incrementality tests (geo tests, conversion lift, holdout…): causal measures of lift, by channel / platform / campaign.
Media performance studies (panel, brand lift, proprietary studies…): signals of sensitivity, relative effectiveness, or thresholds.
What “reintegrating the scoring” means
Specifically, we customize a mitigation coefficient in the allocation algorithm for:
reconcile post-view attribution with observed incrementality,
to address structural biases (over/under-attribution of certain environments),
to stabilize the results over time and make comparisons more reliable.
Calibration does not replace attribution: it guides it . The algorithm retains multi-touch accuracy, while respecting a “ground truth” derived from causal or macro approaches.
Key Benefits
Pixel-free measurable post-view : drastic reduction in dependence on instrumentation.
Total platform coverage , including walled gardens.
More robust results thanks to exposure modeling.
Business alignment via calibration (MMM / incrementality / studies).
Improved actionability : more reliable budget trade-offs and cross-platform comparisons.