Attribution
Data-Driven Attribution
An algorithmic attribution model offered by platforms like Google that uses machine learning to assign conversion credit based on observed path patterns. More accurate than rules-based models but still limited to the platform's own data and biased toward the platform's channels. Google's DDA will naturally favor Google touchpoints. Best used as one input among many rather than as a single source of truth for cross-channel budget decisions.
Why it matters
It is presented as the objective option but it is trained on the platform's own observed conversions, so it inherits whatever that platform can and cannot see.
In practice
Compare its output against a holdout at least once. If DDA and an incrementality test disagree materially, the test is the one to trust.
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