Get ready to put more reliance on machines for your ad targeting.

Today, Google has announced that it’s making data-driven attribution the default attribution model for all new conversion actions in Google Ads, as it moves away from last-click attribution and other measurements.

As explained by Google:

“Unlike other models, data-driven attribution gives you more accurate results by analyzing all of the relevant data about the marketing moments that led up to a conversion. Data-driven attribution in Google Ads takes multiple signals into account, including the ad format and the time between an ad interaction and the conversion. We also use results from holdback experiments to make our models more accurate and calibrate them to better reflect the true incremental value of your ads. “

Essentially, Google’s saying that last-click attribution is not accurate, and is largely outdated in terms of tracking true ad response.

Last-click attribution assigns the credit for a conversion to the last element the user tapped on or clicked, which is generally only one part of the broader picture. For example, if you saw an ad on Facebook, then went to the website, then forgot about it, only to be reminded later with another ad in, say, Instagram, which then prompted you to do a Google search for reviews, which then lead you back to the website to make a purchase. In this scenario, the conversion would be attributed to only that final element, but there’s a lot more to consider in the path to purchase that last-click attribution just doesn’t capture.   

Of course, it’s difficult for any measurement to measure this whole process, but Google’s data-driven attribution process aims to provide a more inclusive, indicative measure of advertising success.

Google Makes Data-Driven Attribution the Default for All Conversion Actions in Google Ads

Again, it can’t account for every element in the discovery process, but by providing more insight into your Google ad performance – across Search, YouTube, and Display – the system can better identify patterns among your ad interactions which lead to conversion.

“There may be certain steps along the way that have a higher probability of leading a customer to complete a conversion. The model then gives more credit to those valuable ad interactions on the customer’s path.”

The option provides another, machine learning-driven way to improve ad response, and as more platforms look to limit data access, amid broader data privacy shifts, advertisers are increasingly being driven towards improved system measurements like this to maximize ad performance.

Which, in some respects, makes things easier, but it also reduces control, and limits your potential for manual optimization. For some, that’s probably a good thing – pulling the trigger too early on a change, or failing to consider the bigger picture, will eat away at your campaign’s potential, and limit your performance results. But that won’t be universal, and there will always be some who are able to optimize, based on their own understanding, to improve their results.

Google does note that advertisers will still have the option to manually switch to one of the five rule-based attribution models, so it’s not taking your control away entirely. But as more platforms encourage more reliance on data-driven models, it will take some time, and experimentation, to assess the best ways to maximize your ad results.

Either way, it’s happening – while Google also notes that it’s adding support for more conversion types, including in-app and offline conversions. It’s also removing the data requirements for campaigns so that you can use data-driven attribution for every conversion action.

Google says that it will roll out data-driven attribution as the default model starting in October, with a view to having it active in all Google Ads accounts by early next year.



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