By definition, machine learning is simply a form of Artificial Intelligence (AI) that gives computers the ability to learn and adapt based on incoming data or signals. That is, the ability to develop knowledge, information and skills it was not specifically taught by its programmer.
What is Google RankBrain?
RankBrain is a machine learning (AI) algorithm that Google uses to sort the search results. It helps Google process, understand search queries and provide more relevant search results for users.
A great way to think about artificial intelligence in the context of search is by looking at the semantic search functionality of Google’s Hummingbird update. Using artificial intelligence, Google can not only handle the increasing volume of search traffic that comes as a byproduct of increasing Internet access, but also offer a more intuitive, responsive experience that allows users to find the information they need faster and in a more fluid way.
Google was making huge investments in machine learning and artificial intelligence to filter search results. They announced and unleashed RankBrain, their machine learning ranking program, in 2015 — and digital world has been watching carefully ever since. RankBrain was referred to as a “top ranking factor,” which led many to ask how to optimize for RankBrain. However, it’s not really a factor like links or mobile-friendliness.
RankBrain is not a standalone technology, it is a part of Google’s overall search “algorithm,” a computer program that’s used to sort through the billions of pages it knows about and find the ones deemed most relevant for particular queries.
What is Google trying to accomplish?
There are a variety of ways Google can increase its revenue. Here are some of the more obvious:
Increase their users
Increase the number of times each user returns
Increase the revenue generated per user
Reduce the need for users to leave their sites to complete an action
Increase the number of ways a user can be reached
So, Google needs to be present in as many places as possible; they need users to rely on them consistently and frequently; they need to hold their users in their sphere of influence so as to increase their ability to advertise to them; and they need to find ways to increase their revenue from the users they have performing the tasks they’re already doing.
Before RankBrain and since then:
Before RankBrain, Google would scan pages to see if they contained the exact keyword someone searched for. But because these keywords were brand new, Google had no clue what the searcher actually wanted. So they guessed.
RankBrain tries to actually figure out what you mean. You know, like a human would. How? By matching never-before-seen keywords to keywords that Google HAS seen before. In short: Google RankBrain goes beyond simple keyword-matching. It turns your search term into concepts and tries to find pages that cover that concept. Google doesn’t just read your content and rank your website for keywords. They look at context now, which means that they can tell by your search terminology what you really want to see.
RankBrain has two main jobs:
Understanding search queries (keywords)
Measuring how people interact with the results (user satisfaction)
In short how RankBrain works:
When someone searches for something unique on Google, the algorithm depends on RankBrain to find the searcher’s intent.
Google’s algorithm can often determine great results for common searches, but it’s not so great at doing that for uncommon searches.
So instead of playing alone, Google’s algorithm asks the AI, RankBrain, what it thinks the intention of the searcher is.
RankBrain then makes its best guess based on past precedents, the location of the searcher, and even the habits of the searcher.
Then, Google spits out the results to the searcher.
According to Google, RankBrain is now processing every search query Google receives, and is now the third most-important ranking signal in the Google algorithm. Interestingly, although every search query is being processed by RankBrain, that doesn't necessarily mean that it's influencing every query. Google uses it only when it needs a bit of extra help determining what the searcher is most likely looking for. We’ve all typed something into Google and received completely irrelevant results. That’s what happens when RankBrain fails, proving that the system is imperfect. And yet, it’s far better than the days of Google’s basic algorithm. It’s a step in the right direction of search optimization.
To clearly conceptualize RankBrain, it can help to put yourself in Google’s shoes, trying to understand the intent of a search engine query like “Olympics location.” What is the true intent of this search? Does the searcher want to know about the Summer or Winter Olympic Games? Are they referring to an Olympics that just concluded, or one that will take place four years from now? Is the searcher attending the Olympics right now, sitting in a hotel and looking for directions to the venue for the opening ceremonies? Could they even be looking for historic information about the location of the very first Olympics in ancient Greece? Now, imagine that in trying to answer this query, all you have is simplistic algorithm signals like the quality of content or the number of links a piece of content has earned to rank results for this searcher. Imagine that the Winter Games in Sochi, Russia just concluded last month and the official Sochi Olympics website has earned millions of links for its content about this past event. If your algorithm is simplistic, it may only show results about the Sochi Games, because they have earned the most links… even if the searcher was actually hoping to learn the location of the next Winter Olympics in Pyeongchang, South Korea.
It’s within this complicated but common situation that the capacity of RankBrain emerges as essential. It’s only by being able to mathematically calculate results based on patterns the machine learning algorithm has “noticed” in searcher behavior that Google can determine that, for example, the majority of people looking up “Olympics location” want to know where the very next Games (be they Summer or Winter) will be held. So, in this case, a Google answer box with the upcoming Games’ location in it will serve the majority of searchers’ needs. While that answer box may address the intent behind most “Olympics location” searches, there are notable exceptions Google must address. For instance, if the search is being performed by a user within an Olympic city (like Pyeongchang) the week of the games, Google might instead provide driving directions to the pavilion where the opening ceremonies will be held. In other words, signals like user location and content freshness must be taken into account to interpret intent and deliver the results most likely to satisfy searchers.
Google's mission: to terminate any web pages from its results even if they don't provide the highest-quality content and to find the most relevant answers for users. Now digital experts who want to gain precious visibility on always-shrinking organic SERPs must prepare to fight a new war: The war against the machines.
How to rank tomorrow by reading RankBrain’s mind today
We are heading to a world where machine learning will facilitate rapid adjustments to algorithms and such customization of individual results and understanding of context that the only requirement of a result is that it meet the user’s needs. Not that it’s organic, not that it has links — just that it meets a need.
When asked Google, if it has additional recommendations around RankBrain, its advice has not changed: Simply “create useful, high quality content. Optimizing for RankBrain is actually super easy, and it is something we’ve probably been saying for 15 years now, – and the recommendation is to – write in natural language. Try to write content that sounds human. If you try to write like a machine then RankBrain will just get confused and probably just push you back. If you have a content site, try to read out some of your articles or whatever you wrote, and ask people whether it sounds natural. If it sounds conversational, if it sounds like natural language that we would use in your day-to-day life, then sure, you are optimized for RankBrain. If it doesn’t, then you are ‘un-optimized.’” But what does writing naturally look like? It means writing like you talk.
And that is what we need to do:
Create genuinely helpful content
Pay attention to semantic search
Focus on long-tail keywords
Optimize anchor text
Write in natural language
We cannot keep up with the algorithms at this point, the only thing we can keep up with is the technology and understanding how users interact with it (with the understanding that Google needs to make money and that there are only so many ways that can be accomplished). You can’t win by trying to game an algorithm that’s increasingly based on machine learning, but what you can do is understand the goal and build your quality content internet presence toward that. Start using these strategies today. After all, you will only be able to rank tomorrow if you prepare today.
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