Triangulation

The role each layer plays

Experiments (geo-lift, holdouts) produce causal answers with confidence statements, a few times a year, on the questions worth their cost. The MMM decomposes everything continuously and hosts the budget optimizer; it is the only layer that sees all channels in one frame, which is why allocation lives here.¹ Platform attribution updates hourly and remains genuinely useful for creative and bid decisions inside a platform, where its biases are roughly constant and comparisons are fair.

The stack is not three dashboards side by side; it is a hierarchy of trust. Experiments outrank the model; the model outranks the dashboards. Measured, a vendor built on exactly this premise, describes its offer in those terms, as do the calibration APIs in Meridian and PyMC-Marketing.²¹

Resolving disagreements

The layers will disagree; that is a feature. The procedure: first check units (platform ROAS counts touched revenue, the model counts incremental; a 3x gap can be definitional, not an error). If the model and an experiment disagree on a channel the experiment directly measured, the experiment wins and the model gets recalibrated. If the model and platform data disagree on direction over months, suspect the model's controls before its coefficients: an unlogged promotion or price change is the usual culprit.

A minimal stack at $5-30M spend

You do not need an enterprise contract to triangulate. Minimum viable version: platform dashboards you already have; one MMM (open-source if you have the data scientist, self-serve SaaS if not); and two to four geo experiments a year on your biggest or most argued-about channels. That combination, run honestly, beats any single instrument at any price, and it is the standard this site's decide track builds toward.