Prepare to rely more on machines for targeting your ads.
Today Google has announcement that it does Data-driven attribution is the default attribution model for all new conversion actions in Google Ads, as it moves away from last click attribution and other metrics.
As explained by Google:
âUnlike other models, data-driven attribution gives you more precise results by analyzing all the relevant data about the marketing moments that led to a conversion. Data-driven attribution in Google Ads takes into account several signals, including the ad format and the time between ad interaction and conversion. We also use the results of the hold experiments to make our models more accurate and calibrate them to better reflect the true incremental value of your ads. “
Essentially, Google says last click attribution isn’t accurate and is way out of date in terms of tracking true ad responses.
Last-click attribution gives credit for a conversion to the last item the user pressed or clicked, which is usually only part of the larger image. For example, if you saw an ad on Facebook, then you went to the website and then forgot about it, only to be called back later with another ad, say, Instagram, which then prompted you to search Google for reviews, which will then take you back to the website to make a purchase. In this scenario, the conversion would be attributed only to that end item, but there is a lot more to the purchase path to consider that last click attribution just doesn’t capture.
Of course, it’s difficult for any metric to measure this whole process, but Google’s data-driven attribution process aims to provide a more inclusive and indicative measure of advertising success.
Again, it cannot take into account all the elements of the discovery process, but providing more information about the performance of your Google ads – through Search, YouTube and Display – the system can better identify patterns among your advertising interactions that lead to conversion.
âThere may be certain steps along the way that have a higher likelihood of causing a customer to convert. The model then gives more credit to these valuable advertising interactions on the customer journey.“
The option offers another, machine-learning-based way to improve ad response, and as more platforms seek to limit access to data, amid broader changes. When it comes to data privacy, advertisers are increasingly being pushed towards improved system measures like this one to maximize ad performance.
Which in some ways makes it easier, but 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 not looking at the big picture, will eat away at your campaign’s potential and limit your performance results. But this will not be universal, and there will always be some who will be able to optimize, based on their own understanding, to improve their results.
Google notes that advertisers will still have the option to manually switch to one of the five rule-based attribution models, so it does not take full control away from you. 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, this is happening – while Google also notes that it is adding support for more conversion types, including in-app and offline conversions. It also removes the data requirements for campaigns so that you can use data-driven attribution for every conversion action.
Google is announcing that it will roll out data-driven attribution as the default model starting in October, with a view to enabling it in all Google Ads accounts by early next year.