Google is Reducing Their Choice of Attribution Models.
Paid Search, Data Solutions
Google has recently announced that they will be removing any non last-click rule based attribution models from both Google Ads and GA4.
This new announcement means that the only attribution models left to choose from are Last-Click and Data Driven attribution.
Starting in June 2023, the ability to choose these position/rule based models will disappear. From September 2023, Google will begin switching any conversion actions currently set up with these models over to data driven attribution. Last-click will still be available if you do not wish to use DDA for your campaigns from the summer onwards.
Why has Google chosen to reduce your choices?
Google have stated that over the past few years, Data Driven attribution has been the most adopted model within both Google Ads and Analytics and the use of the rule based models has significantly reduced. Less that 3% of Google Ad web conversions were attributed using first-click, linear, time decay or position-based models (Global Google Data, Feb-March 2023). Therefore it seemed sensible to sunset these lesser used models in order to improve the ease of use of both platforms.
There has also been a huge push over the past year by Google to improve automation, machine learning and their own algorithms. This steer towards data driven features is not new and with attribution being a pain point for a lot of brands, we assume that Google are trying and take the lion's share of the harder work.
What is Google's Data Driven Attribution Model?
How Data Driven Attribution works.
Using Google AI, data driven attribution gives credit for conversions based on how users are engaging with a variety of your ads before making a purchase or completing another valuable conversion action.
The aim is to highlight which ads, campaigns and channels have the greatest impact on your conversion goals, rather than just showing which tipped the user over the conversion edge. DDA can look at interactions across your website visits, store visits (if applicable) and Google Analytics conversions from Search, Shopping, Youtube, Display and Discovery Ads.
Each model is unique and specific to each advertiser as it users your own data to map out interactions and consumer journeys. The AI compares the path of those users to convert to ones who don't and identifies patterns within the interactions which have ultimately lead to a conversion. It calculates the probabilities of certain steps leading a user to purchase and then credits those interactions with more weight. Theoretically, it allows advertisers to see which ads or campaigns are having the greatest influence on decision making, even if that is further up your marketing funnel.
If you are also using an automated bid strategy within Google Ads, this information will also be used to help bolster conversions.
What do you need to run Data Driven Attribution?
The most important thing you need in order for DDA to run effectively shouldn't be a surprise. It's data.
Although most conversion actions are eligible for DDA no matter the conversion or interaction volume, some will need at least 300 conversions and 3000 ad interaction within 30 days in order to be eligible to begin. If you then begin to slip below this volume over time, you will be alerted by Google. And if it continues to remain below the threshold for a further 30 days, your attribution will automatically transition to a last-click model.
Pros and Cons for data driven attribution.
The most obvious positive to come out this change is that historically, choosing a relevant attribution models for your campaigns has often been difficult. It's difficult to choose which rule-based model, if any, are best to reflect the journey of your customers. DDA seemingly eliminates that choice and for those retailers who have an absolute abundance of traffic, conversion and data in general, would seem the most sensible route to take.
However, for scaling or medium sized brands, having to potentially rely on a last-click models only for periods of time is not useful.
Also with the removing of 3rd party cookies across browsers, the iOS14 update and and increasing knowledge around privacy, the amount of data that any brands are able to accurately collect and keep is dwindling. Without a robust 1st party data collection solution, brands will struggle.
Revenue Growth Agency would recommend continuing to use Analytics to report on certain metrics but would highly suggest implementing 1st party server side tracking and then researching separate attribution platforms which can give you an incredibly accurate view of how all of your channels are working together.
What does this mean for marketers?
The most important thing for marketers to do right away is dive into which models you are currently using for campaigns within Google Ads and within Analytics; in particular your GA4 set up.
Then the choice is yours as to whether you wish to transition all across to DDA or mix and match with the last-click model. There is no right or wrong answer and for some brands who do not meet the DDA requirements, last-click may be your only option for certain conversion goals for now.
It's important to remember that DDA is not gospel. It's using data modelling, probabilities and AI to estimate which interactions are having the greatest impact on your conversion goals. So whilst its no doubt a lot more helpful (and in someways truer) than models such as Linear and Position Based, be careful not to bet all your winnings on it.
Alternatives to Google for attribution.
If you've decided that it might be the time to begin looking for an alternative to Google Analytics, there are various options for you to choose from. Depending on your needs, take a look through the below list and see which one might work for you.