Scope summary signal-steering. When three measurement methods assess the same Google Ads channel differently, that is a steering question, not a numbers question. Evidence-backed: Triangulated measurement and steering system developed as a prototype. Testable against known ground truth (whether the methods capture the value). Use cases: assembling methods, hypothesis tests and anomaly detection. Not evidenced: Production readiness. Functioning at scale on real data. Validation on real spend (deliberately out of scope).
signal-steering
When three measurement methods assess the same Google Ads channel differently, that is a steering question, not a numbers question.
- Objective
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Which figure do you trust for a Google Ads budget decision when three methods contradict each other?
The test case became a triangulated measurement and steering system: platform attribution, geo incrementality and marketing mix modelling applied to the same data. The objective was not a finished product, but a robust answer to a real budget-steering question.
- Approach
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Triangulating measurement on synthetic data with known, fixed ground truth.
A representative measurement and steering problem was reconstructed: platform attribution, geo incrementality and MMM on the same data; steering against incremental value rather than the platform figure; plus detection of faulty signals. The synthetic dataset is methodologically necessary. Only a known true value makes it possible to test whether the methods capture it. No real, third-party or live spend data.
- Why build a prototype
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A domain expert can now go much further than only a few years ago — as a means to an end, not an end in itself.
Developed with agentic AI: data generation, MMM (PyMC-Marketing), geo incrementality (DiD/GeoLift), allocation logic and interface were created within weeks, without a development team. Not a production-ready system, but far enough to test the steering question on a running model. The decision about which methods to triangulate and how to read the signal remained with the human.
Evidence-backed
- Triangulated measurement and steering system developed as a prototype
- Testable against known ground truth (whether the methods capture the value)
- Use cases: assembling methods, hypothesis tests and anomaly detection
Not evidenced · out of scope
- Production readiness
- Functioning at scale on real data
- Validation on real spend (deliberately out of scope)
Meta · Skai · Marin
Haus · INCRMNTAL
Recast · Mutinex
The entire system in one view. Below it, the decisive points step by step.
The most common figure lies
Every budget decision depends on one figure: what did this channel truly add? The platform's standard answer is Last-Click. For PMax, it reports an iROAS of 5.02. The true figure is 2.50. The platform claims revenue that would have arrived without it. Following that figure doubles down on a channel worth half as much.
How can 2.50 be “ground truth”?
In reality, no one knows the true value. What a channel added is a counterfactual that never took place. Measurement therefore always happens in the dark. The prototype creates a world in which the truth is fixed: synthetic data with a deliberately defined effect. This makes measurement testable for the first time.
This is not circular. The methods are not given the answer; they must infer it blindly from the data. The world is fixed, not the result. As in a flight simulator: the physics are fixed, but the pilot still has to fly. A method that misses 2.50 here will miss it in reality as well, only without anyone noticing.
A good system knows when it does not know
There was no experiment for calibrating Meta. The model then estimates 10.05, clearly wide of the mark. Rather than concealing this, it reports the blind spot itself through its own convergence diagnostic (R-hat ✗). The steering implication is concrete: the next geo test belongs on Meta.
The point is not the model. It is the decision behind it: which figure do you trust when three methods contradict each other, and where should the next budget go? The prototype makes this decision visible and testable before real money depends on it.
The corresponding steering case: sem-agentic — Google Ads SEM campaign steering →