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Google Removes 4 Attribution Models in Ads and Analytics: What it Means for Marketers

Mike Rancourt

Group Product Manager

In early April, Google announced that Google Ads and Google Analytics 4 (GA4) will deprecate multiple attribution models including time decay, position-based, linear, and first-click, and will make their Data Driven Attribution (DDA) model the default. This means a variety of tried-and-true models will no longer be available and that existing actions with these models will be converted. 

If you’re currently relying on the soon-to-be-deprecated attribution models, don’t worry. Here’s how and when your campaigns will be impacted, what to do, and how to move forward.

How and When Will My Conversion Actions Be Affected?

Time decay, position-based, linear, and first-click attribution models are no longer available as  new conversion actions in GA4 starting in May 2023. Google Ads will follow suit in June 2023.

In September 2023, any conversion actions still using the deprecated models will be switched over. Although they will be getting rid of most rules-based attribution models, they will be maintaining last click as an option. 

What is Data Driven Attribution (DDA)?

Generally, Google’s DDA model can be explained as a custom attribution model based on machine learning. It is unique to each business that uses it because it is based on data from the actual customer journey. Data-driven attribution models consider both converting and non-converting paths, which can increase your understanding of each individual customer touchpoint.

Google describes their DDA as: “giving 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, Display, and Discovery ads.” 

What Should Marketers Do to Keep Their Attribution Accurate?

While Data Driven Attribution (DDA) is a more technically advanced model, it doesn’t mean marketers should abandon their other attribution models and switch to DDA entirely. 

Although DDA can be a valuable attribution model because it allows for deeper understanding of every step in your customers’ journey to conversion, it’s hard to know how the model is built or how it works. By removing other models, Google eliminates well-known attribution practices, each of which provides unique insights about the customer journey. DDA within Google tools is limited because it only considers Google properties in its modeling. It can feel like a black box.

Analytics isn’t a one lane highway. To be smart about your analytics practices, you need to look at the data through different lenses in order to gain a better understanding of the truth without anchoring yourself to a one-sided view.

With AdRoll, you can get to know your most important customers and personalize their journeys. We offer a suite of free, powerful cross-channel attribution tools that you need to effectively monitor and optimize all your marketing activities in one place.

In the end, keeping your data accurate has less to do with which model you’re using across the board and more about how you’re using models to understand your data and the goals you’re trying to achieve. 

Better Understand Your Campaign Performance With AdRoll

In the attribution world, true campaign success relies on looking at the results of different attribution models in order to see performance from multiple angles. While some of the models Google is deprecating may not be the best “one size fits all” solution, they allow for different views of the same dataset, providing deeper insight into what is working and what is not.

AdRoll’s goal is to put the right tools in front of our customers in order to make it as easy as possible for them to succeed. AdRoll’s Cross-Channel Performance Dashboard gives customers easy-to-use attribution options that provide different perspectives on their conversion and revenue data. It will continue to support the attribution models that our customers know, trust, and derive insights from; these models include: first touch, last touch, last click, equal weight, positional, and time decay. 

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