A diligence checklist for evaluating an attribution vendor

A diligence checklist for evaluating an attribution vendor

The question: a vendor promises to 'solve attribution.' How do you separate genuine measurement from a black box that launders last-click into a prettier dashboard?

The questions to ask, in order:

— What method underlies the credit — a fixed rule, an observational algorithm (Markov/Shapley/DDA), or actual experiments? If they won't say, that's your answer.
— How do you validate against incrementality? A serious vendor calibrates its model against geo or lift tests. 'Our algorithm is proprietary' is not validation.
— How do you handle the (direct) bucket, cross-device gaps, and view-through? Vague answers mean the hard parts are swept under the rug.
— Will outputs reconcile to a single source of truth, or just add a fourth conflicting number?
— Can I see the counterfactual logic, or only the final credit?

The red flags: guaranteed ROAS lift, no mention of confidence intervals, every channel improving at once (impossible — credit is zero-sum), and conflating their model's credit with proven causation.

The nuance: an observational tool can be genuinely useful as an allocation prior while still being unable to prove any channel is incremental. Good vendors say this plainly; weak ones blur the line.

Bottom line for practitioners: demand the method, the calibration approach, and the counterfactual logic. A vendor that distinguishes correlation from causation for you is worth more than one that hides it.