Google is promoting data-driven attribution over traditional models

Google brings its new tool to understand the effect of switching to data-driven attribution. The advertisers are hesitating about possibilities for their possible losses if they switch to attribution. Google, addressing the issue, launched a new tool to analyze and simulate the growth using data-driven attribution.

Google is making it easier for advertisers to switch Google Ads campaign’s traditional attribute to data-driven attribution by letting them see how it will affect their campaign.

However, Google suggests advertisers who switched to attribution saw a 6% growth in their average conversion. The attribution identifies the undervalued touch points with the help of machine learning algorithms. It also helps you identify untapped opportunities. Smart advertisers react and gain performance from it.

Along with the statement, Google also announced the rollout of the tool that will help the advertiser understand the value of switching to data-driven attribution. The tool gives the demographics of how your campaign might have performed using data-driven attribution. Google aims to give transparency and confidence to advertisers to switch to the new attributes.

With new updates knowing what to expect is the key. That is why Google is launching a tool that will let you see the result of how the automated bidding would have performed with the attribution for the last seven days. Advertisers can use the tools to see and compare the results of their campaign data for clarification.

Furthermore, Google announced the expansion of the attribution model to the remaining types of ads as well. Previously, the data-driven attributions were only available for Shopping, Display, supported Search, and YouTube ads. Now they are expanding the support to app conversions and Discovery formats in the next year.

According to Google, machine learning and data-driven attribution can provide better campaign results than traditional methods. They are now making awareness efforts to make the user’s more comfortable with machine learning for better campaigning results in the future.