When Google introduced RankBrain into its ranking algorithm, many wondered how that might impact SEO. Columnist Larry Kim theorizes that it has placed greater importance on user signals.
The way Google ranks pages in its search results today is much different from the way it ranked them two years ago.
Why? In early 2015, Google began its slow rollout of RankBrain, a machine-learning artificial intelligence system that helps process search results as part of Google’s ranking algorithm. As of June 2016, RankBrain is being used for all Google queries.
But how does machine learning impact rankings exactly? That’s the big question.
SEO used to be all about building links and using the right keywords. Links and keywords still matter, but machine learning has transformed the traditional SEO ranking model into something new.
Let me explain.
The new SEO ranking model
What is this new model? No one knows for sure, but here’s my theory:
A searcher enters their query. Google returns a set of relevant organic search results that are largely based on conventional ranking factors. Machine learning then becomes a “layer” on top of this. It becomes the final arbiter of rank — quality control, if you will.
It’s like Google is saying, “Great, I’ve successfully crawled and indexed this page. The page exists on a strong domain (it has a high level of expertise, authoritativeness and trust). The content is optimized, understandable, relevant and matches the searcher intent. BUT do any humans click on the result and engage with it?”
This last sentence is the key.
“Perfect SEO” is pretty imperfect if you’ve created content that ranks on search engines but doesn’t get any clicks.
It doesn’t matter how many links you have pointing at your page or if it’s optimized with all the right keywords — if the engagement is too low, then you’re out.