Google’s Attribution Models
Before we can dig a little deeper into this subject, we need to define what an attribution model is in the context of Google Ads.
Your account’s attribution model is the criteria that Google will use to accredit (or attribute) your conversions to your campaigns. If you’re tracking conversions (and you definitely should), some conversions will come from your Google Ads campaigns while others may come from social media, organic traffic, email marketing, or other sources. Google will use the attribution model that you select to chalk up your conversions to your campaigns. Different Attribution models may attribute conversions to the last ad that was clicked by a converting visitor, or the first ad, or an equal share of that conversion to all the ads that the converting visitor clicked on, etc. Historically, the best attribution model to use was determined by the scope and objective of your campaigns, the nature of your website, and the way visitors may interact with it. There wasn’t a one-size-fits-all attribution model. That’s what Google seems to be trying to change now, with the power of AI.
Data-driven attribution Vs. other models
According to Google:
Data-driven attribution gives credit for conversions based on how people engage with your various ads and decide to become your customers. It uses data from your account to determine which keywords, ads, and campaigns have the greatest impact on your business goals. Data-driven attribution looks at website, store visit, and Google Analytics conversions from Search (including Shopping), YouTube, and Display ads.
Basically, it uses reverse goal paths to determine how most users end up converting on your website and assigns a higher share of each conversion to the most impactful keywords, ads, and campaigns – allowing us to update our strategy based on what’s converting best.
All in all, this is not a radical change. Optimizing for our best-performing ads, keywords, and campaigns is something that we’ve always done. Data-driven attribution only lets us align our attribution model with our main objectives in an easier way, and that is a welcome change.