I won’t take much time to explain the BERT algorithm that Google recently implemented (October 2019). If you want a full, technical explanation, I recommend this article from George Nguyen. The short version is that BERT is probably the most significant algorithm since RankBrain, and it primarily impacts Google’s ability to understand the intent behind your search queries. Specifically, BERT is really good at Natural Language Processing (NLP), and the results are nothing short of impressive.
The lack of NLP in Google search algorithms is something that has been frustrating users since the beginning of search. The reason algorithm updates like BERT are such a big deal is that they are enabling search engines like Google to deliver the most relevant content to the user based on what the user is thinking about – not just the words they happened to use.
Intent has always been tricky to guess – especially for an algorithm – because words can mean so many different things in different contexts or adjacent to different words (river bank vs. blood bank). We all have that experience of trying to translate a complicated or obscure search intent into an easy-to-understand search query and being frustrated with the results we get.
A search algorithm that better understands how people combine words to mean different things is able to close that gap between intent and query.
In the past, the quality of your content mattered very little if your on-page SEO was technically off the mark. A page’s success or failure had more to do with how wisely you chose and optimized for a particular search phrase and less to do with the actual value of the content once a human started reading it. If the gap between intent and query disappears (as intended by BERT), we could reallocate the resources spent on those technical optimizations toward creating better quality human-optimized content without worrying about how it might affect our overall SEO.
The reality of any major algorithm update is that some pages will be affected positively and some will be affected negatively. Typically, if we find that a page has dropped in the Search Engine Results Pages (SERPs) for a target keyword or phrase after a major update, we make the technical changes to that page that we think the algorithm is looking for.
However, in the case of BERT, there’s really not a whole lot we can do if a page falls in the rankings. Since BERT was an update to Google’s NLP, pages that are affected negatively are probably pages that deserve to fall out of the rankings for those queries because they were irrelevant (or less relevant) to begin with. The fact that they were ranking well in the first place suggests that they were more heavily optimized for algorithms than for humans.
A great deal, actually. If algorithms like BERT continue to raise the bar on NLP, a user’s intent will become easier to determine. In addition, Google has been very open about their E-A-T principle (Expertise, Authority, and Trust) and how it affects search quality ratings. As this principle is implemented in more practical ways, and as search algorithms get better at processing language:
Even if we’re not quite to this point yet, a forward-thinking strategist is going to tell you that human-optimized content is 100% the way to go. If BERT has taught us anything, it’s that Google is actively working to reward valuable E-A-T content and throwing fewer bones to anyone relying solely on technical factors.