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Why Google removed the Average Position metric

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Why Google removed the Average Position metric

On August 12, a bit unexpectedly, Google announced that the average position would have been officially removed.

The chosen date was September 30, when all rules, custom columns and reports using an average position were disabled. The average position – the search metric by excellence – would have ended up in the basement, Google Ads would have changed its face.

Whenever you are competing in the Google Ads auction, you are awarded an Ad Rank, based on the ad’s bid and its Quality Score. The Ad Rank, in turn, determines where the ad ranks in paid search results. As the word itself says, the average position was, therefore, the parameter that indicated the position of an ad against those of its competitors on a Google search.

The position of the ad referred to the order of the results of the paid search: it did not show, concretely, what was the history in terms of classification of the ad, compared to the others that appeared in that list. And for this reason, starting to think about switching methods, Google decided to change.

Why the average position has disappeared

In short, because Google thought it was no longer a really useful and consistent metric for what advertisers really needed. And in November, to continue on a new parameterisation that would have replaced the average position, it launched a suite of new metrics related to the performance and visibility of the SERP:

Absolute Top Impression

  • What percentage of ads appears in the top of the page (the first paid positions of SERP)

Top Impression

  • What percentage of ads appears in the absolute top of the page (the first position ever)

According to Google, these metrics will provide much more relevant information to understand the progress of a campaign. In the margin of the announcement, it was confirmed that the new metrics had a relevance that would be confirmed in the long run. Having a clear idea of your relevance to SERP is essential for making informed bidding decisions: an essential step, therefore, for strategies that succeed in combining the forecasting factor with the one that always looks with a focused eye to sudden and fast changes.

What does it really change?

Google’s message was clear: we shouldn’t focus on immediately-approachable, sometimes not really accurate metrics. But on conversions ones.

On the other hand, the more the platform leverages Machine Learning to optimise the delivery of ads, the more we have to trust that they are the most concrete criterion to analyse. And in fact, we notice more and more features that are now automated and “take away control” from the advertiser.

Although automation is useful, it is not a substitute, but a supplement. And in a landscape where automation is becoming more and more indispensable, putting aside the average location to encourage a strategy that reflects this change was inevitable.