“Because of how we’ve been improving and training our data-driven attribution models, we’ve eliminated [that] previously existing requirement,” she said.
The change to data-driven attribution default also consolidates more ad spend – and thus more data – into one channel. Srinivasan said Google’s modeling has improved to the point that it can run data-driven attribution for any campaign type. But Google wants more and more advertisers to use data-driven attribution because the quality of Google’s data modeling is tied to the quantity of impressions and conversions it sees.
Modeled data will be even more important when third-party cookies are phased away, Srinivasan said. Machine learning can “compensate for gaps in data” if advertisers can’t effectively track customers or conversions, she said.
Data-driven attribution may not be more privacy-compliant than last-click, in and of itself. But in a privacy-forward environment where connecting ad impressions to online user activity is often prohibited, it will be the reliable methodology.
“To create the most effective and durable privacy-centric solutions, we need to continue to think about how we’re leveraging the data we do have as intelligently as possible,” Srinivasan said.
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