<b>Google's Data-Driven Attribution gives you a number. Should you trust the mechanism behind it?</b>

<b>Google's Data-Driven Attribution gives you a number. Should you trust the mechanism behind it?</b>

Worth interrogating, because DDA is now the default in many accounts and most users couldn't describe what it computes.

<b>What it claims to do</b>

DDA uses a counterfactual approach loosely related to Shapley value: it compares paths that converted to paths that didn't, and assigns fractional credit based on each touchpoint's apparent contribution to conversion probability. Conceptually sound — far better than last-click.

<b>The nuance you don't see</b>

— It is still a <i>within-platform, observational</i> model. It can only weigh touchpoints it observes, and it sees a shrinking slice of the journey as cross-site signals erode.
— The training is opaque. You cannot inspect the feature weights, validate the counterfactual construction, or reproduce the numbers. For a model making budget decisions, that's a meaningful audit gap.
— It optimizes toward conversions the platform can <i>attribute to itself</i>, which structurally favors the platform's own surfaces.

<b>What the broader evidence says</b>

When DDA outputs are checked against geo holdouts, the directional read is often reasonable but the magnitudes drift — particularly inflating credit for branded and remarketing terms that capture existing demand.

<b>What to actually do</b>

— Use DDA for intra-platform optimization where it's strongest: relative bidding across keywords and audiences.
— Do not use it for cross-channel budget splits — that's outside its field of view.
— Sanity-check its biggest claims against an experiment at least twice a year.

Bottom line for practitioners: DDA is a good optimizer and a poor oracle. Trust it inside the platform's walls; verify it everywhere else.