Google Analytics 4 upgrades to Data-driven Attribution Model

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Google Analytics 4 upgrades to Data-driven Attribution Model
The tech giant is introducing several changes to the new Google Analytics platform, including making data-driven attribution the default model.

Earlier this month, we discussed how Google is updating the default attribution model on Google Ads. Now, they officially announced that they’re applying the same attribution model to Google Analytics 4

In the spirit of efficiency, here’s our 30-second rundown on this update:

What? Google is changing the default attribution model for Google Analytics 4 from last-click to data-driven.

Why? Data-driven is a more reliable model to show the true impact of each channel, so marketers can easily recognize which channel is having the most impact.

Will this affect me? Depends on you. Your default model will change for GA4, but you can always change it back to another model if you prefer.

If that’s all you needed, then great, we’re glad we could help. Stay in touch with us and get twice monthly updates, so you don’t miss a beat, by joining our mailing list below.

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If you want a bit more information on the changes, keep reading.

Why is data-driven attribution better?

We won’t get into all the different types of attribution models here, as we’ve covered that in previous articles. We will, however, explain a bit more about why Google decided to change the default and why that’s good for you.

Previously, Google defaulted to the last-click attribution model, which meant 100% of the credit for a conversion went to the last thing someone clicked on. We all know how complex buyer journeys are and just how many retargeting ads someone needs to see before they buy, so it seems very simplistic to credit everything to just one ad.

Google’s own recent study showed it took one person 375 touchpoints before they completed a purchase for a pair of headphones!

With the data-driven attribution model, you get a clearer picture of how all your marketing activities come together overall. This means you won’t over or under-credit any channel, since you’ll have more visibility into the different touchpoints that all played a part.

Conversion_paths_report_DDA
Conversions by channel grouping using data-driven attribution (Google)

By using the power of machine learning, data-driven attribution modeling can help marketers improve overall ROAS (return on ad spend), make smarter decisions on where to double down, and overall help them scale their business more effectively.

Why change Google Analytics’ attribution model now?

Now that we’ve covered off why data-driven attribution is better than the previous model, last-click, let’s touch on why now.

Firstly, technology has simply gotten better. AI and machine learning have come leaps and bounds. It’s not as cutting-edge, as well, so it’s perfectly reasonable, and almost expected, that the technology will be available for free. Of course, Google Analytics being free is our (and probably everyone’s) favorite thing about it!

Secondly, online privacy is getting stronger and tracking is getting weaker as time goes on. Once cookies and pixels are a thing of the past, we’ll need a more robust way to gain accurate data about our marketing performance.

Gain the upper hand in ad optimization

Attribution models like this can help marketers get a better understanding of what’s working, but the reality is that data analytics still takes a long time. From our customers, we know the average marketer spends 6-8 hours a week optimizing their ads across multiple channels.

This is exactly why we created Optily in the first place. We want marketers to go back to marketing and let the software make recommendations on the optimal budget split needed for maximum impact. Try it free for two weeks.!

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