Attribution Model Review
States which direction your model is wrong in, since every attribution model is wrong in a known and predictable way.
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Running it, start to finish
- State which direction your current model is biased in.
- Add a self-reported question at signup.
- Run a holdout on the channel most in dispute.
What you get back
The output this produces, every time.
- Names the specific direction your model is wrong in rather than debating which model is right.
- Identifies the self-fulfilling loop where attribution bias becomes budget bias that confirms itself.
- Recommends self-reported attribution, the cheapest correction for channels the model scores as zero.
Getting better results
Where this usually goes wrong, and how to avoid it.
- Add the how-did-you-hear question. It is imperfect and it consistently surfaces channels your model reports as contributing nothing.
- Treat untracked channels as underfunded. Word of mouth and podcasts score zero structurally, not because they do nothing. That bias compounds every budget cycle.
- Run one incrementality test. A single holdout on one channel tells you more than any amount of model refinement.
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Written for The AI University. Every prompt in this library is original work — authored, tested and revised here, not collected from elsewhere. 365 of them, free with an account.