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What Comes After Google RankBrain?

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articleimage1654 What Comes After Google RankBrain

Google’s latest update isn’t making waves because of how many ranking factors it changed, or how many queries it’s impacted, or even how detailed or effective it is. Though Google didn’t announce the update until the end of October, it had been running in the background for months. According to Google, it’s already helped to handle millions of queries, but if it’s so effective, why haven’t search marketers noticed it until now, and why is it so significant?

It all has to do with the type of update RankBrain is—a slowly building machine learning algorithm. In the SEO world, we’re used to large, manual pushes to the core Google ranking algorithm (and very rare algorithm replacements). Panda was the first example of this in 2011, followed by Penguin in 2012. After these twin heavy-hitters, search marketers buckled down, constantly on the lookout for the next major update to shake up rankings.

Two things have happened since then that have challenged our expectations: the first is that Google has broken up its “big” updates into much smaller, more manageable chunks. Part of this is to reduce the total impact of each update, and part of this is because they don’t have a lot to add. The second is the introduction of this machine learning algorithm, which changes the way Google’s algorithm will update in the future.

What Is Machine Learning?

articleimage1654 What Is Machine Learning

Machine learning is exactly what it sounds like; RankBrain is closely associated with the Hummingbird update Google released back in 2013, which brought a semantic understanding to Google’s analysis of user queries. RankBrain works by analyzing complex or ambiguous user queries, and finding ways to simplify those queries. However, it wasn’t pre-programmed for any specific courses of action; instead, it was programmed to experiment, learn, and essentially update itself.

So What Now?

articleimage1654 So What Now

If Google has a component of its algorithm that can update itself automatically, they can consider their job complete. Theoretically, if they could apply machine learning algorithms to every aspect of its search algorithm, Google’s search engine would be able to gradually update itself over time, always improving, without ever requiring human intervention. Combined with the knowledge that Google hasn’t pushed a big update to its algorithm since 2013 (ignoring the smaller, more gradual refreshes of Panda and Penguin), there’s a great deal of ambiguity in Google’s next move. Obviously, they’ll want to continue improving and refining their core algorithm, but how are they going to do it?

The Case for Big Manual Updates

articleimage1654 The Case for Big Manual Updates

Even though big manual updates have tapered off, there’s still a possibility that there are more to come. New technologies (on the order of mobile devices) could disrupt the current search format, and advances in semantic understanding or user patterns could force a manual push to become necessary. Plus, even though machine learning is great in theory, it’s untested and entirely unpredictable, necessitating a form of manual backup to serve as a complement.

If manual pushes remain, search marketers will need to remain vigilant, always watching for the next big change. Historically, these pushes have come without warning or instruction, and have shaken up what were considered “best practices” in SEO before their release.

The Case for Gradual Manual Updates

Though it’s possible that more big manual pushes wait on the horizon, it’s more likely that Google will stick with the slow, gradual manual updates that it’s been using for the past few years. These pushes happen manually, so they protect against the total reliance on machine learning for algorithm advancement, but they’re less intensive and more flexible than their larger, more significant counterparts. This retains some control for Google, and since its algorithm is in excellent shape currently, these gradual pushes spare it some effort.

If gradual pushes remain the mainstay, there’s almost nothing you need to change. Most current best practices will remain in their current form, demanding that you continue producing good content, building offsite relationships, and so on. The tweaks will come so slowly and imperceptibly that your bottom line will be barely affected.

The Case for More Machine Learning Updates

articleimage1654 The Case for More Machine Learning Updates

Gradual manual updates don’t take much time, but they do still take time. In Google’s ideal world, everything will be fully automated—just look at their efforts into self-driving cars. The likeliest scenario is that Google will strive for more updates like RankBrain, eventually turning its full algorithm into a giant, self-regulating, self-updating behemoth.

Even if Google decides to opt for an all-machine-learning version of its algorithm, it will be some time before it can handle such a task. In the meantime, we’re likely to see a gradually shifting hybrid of machine learning and gradual manual updates. This will give you time to prepare for unknowns, and gradually introduce you to the machine learning algorithm of tomorrow.

The Bottom Line

It’s likely that more machine learning algorithms will arise in Google after RankBrain, eventually shifting the algorithm from a monthly, gradually updating one to one that completely updates by itself. It’s likely that big manual updates are pretty much done for, and that gradual manual updates will serve as a complement in the interim. This means that your strategy won’t require much adjusting, especially in the short-term. Changes will happen so gradually, you’ll barely notice them, and by the time machine learning fully takes over Google, you’ll be well versed in its abilities (as long as you keep yourself in the loop).

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Kathrina Tiangco

Kathrina is AudienceBloom's project manager. She works closely with our writers, editors, and publishers to make sure client work is completed on time.

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