As you may very well know, we live in a time where two marketing dynamics are (seemingly) colliding: one is user privacy, and the second is the desire to gather data.
How do we gather data without breaching our users’ trust?
That’s a question that’s at the forefront of a lot of our activities, governing bodies, and developers.
Of course, Google understands this, and tries to make tools that respect privacy while advancing technologies.
And this is where one of Google’s latest blog posts, titled The Future of Attribution is Data-Driven, comes in.
Basically, privacy-related restrictions may limit the types of data we can gather, which results in gaps in our data gathering. (Basically, we can see that someone went from A to Z, but we don’t know what happened at B, C, and D.)
A key concept here is data-driven attribution, and to understand that, we have to know that it’s partially based on machine learning.
The best way I can describe data-driven attribution is to first understand what I mentioned earlier: there are limitations to the data we can gather.
We know that things are happening, but we might not know what’s happening where.
And, this is where machine learning comes in. As far as data gathering is concerned, Google’s machine learning capability basically fills in the blanks.
This allows Google Ads marketers to regain some of the insight that once lost to privacy restrictions, while still respecting those restrictions.
You can learn more at Google’s blog post.
Source: Google Analytics Twitter channel
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