Wouldn’t getting your business website on the first page of Google be a whole lot easier if Google just told you what it was you needed to do to get it there? Of course it would.

Google doesn’t work that way, unfortunately. And tends to hide how search really works from us mere mortals to keep us guessing. But occasionally, out of the mouths of babes and innocents, comes mana from Heaven (if I may indulge in a tag-team of Biblical idioms, writing this as I am over the Easter long weekend).

A couple of days ago was just such an occasion.

Google’s Ranking Factors – Where Obfuscation is the State of the Nation

North of 200 ranking signals, and over 10,000 sub-ranking signals make getting your small businesses website on the first page of Google, insanely difficult. By their own admission, Google updates 500-600 algorithms per year. That’s nearly 1.6 algorithmic updates per day, folks. Which means Google are constantly changing the rules of search and moving the goal-posts. What worked in search engine optimisation yesterday, might not work tomorrow. And worse, what worked in search yesterday and got your business website so many Google first pages, may actually get your website penalised tomorrow.

What we do know, and have known for many years, is that the list of what Google uses to rank websites is changing all the time, and expanding exponentially. So much so that even those of us who work in search full-time are constantly getting whip-lash from trying to keep up with the speed of the changes flying past us.

Not only is what Google values in a website changing all the time, but the order and priority of the ranking or ‘trust’ factors (as we understand them) often back-flips when you least expect it, making a change in SEO strategy a matter of urgency, rather than something to do when you happen to get around to it.

Google’s Big 3 Ranking Signals – Cutting to the Chase

So back to that manna from Heaven I mentioned: in late October last year, Google dropped a bombshell (quite literally out of nowhere) that the third biggest ranking factor for getting on the first page of a Google search, was known as ‘RankBrain’.

To which the SEO industry as a whole went, ‘Huh? RankWhat?’

Google did this, as Google are want to do, in a totally off-the-cuff manner. Sergey Brin and Larry Page didn’t call a press conference. The WebSpam team didn’t thaw out Matt Cutts from his cryogenic slumber to tweet the news to the world. Nor did they so much as send out a press release. Rather, they introduced the world to RankBrain via a Bloomberg Business article titled: Google Turning Its Lucrative Web Search Over to AI Machines, published online on October 26, 2015.

RankBrain – What is it and Why Should I Care?

Before October 2015 there was no ‘RankBrain’. At least none that the SEO community knew about (though it’s believed to have been running for nearly a year behind the scenes). There was however, Hummingbird. And RankBrain, as it turns out, is actual a constituent component of the much larger, and all encompassing, Hummingbird algorithm.

Still Confused? Don’t Be. RankBrain (or RankBrian as I just accidentally typed – now there’s a parapraxis if ever there was one!) is a machine-learning artificial intelligence system used by Skynet (err, sorry, Google) to process and interpret complicated or previously misunderstood search queries into a mathematical form it can understand. These misunderstood or ‘new’ search queries apparently make up about 15% of all search, which is a staggering amount when you think about how many searches are done on Google every day, around the world.

Now, so far as we know, RankBrain is self-learning rather than self-aware. Which is probably for the best, given unleashing an A.I program on the internet can potentially have disastrous consequences…

You Said RankBrain Was Part of Hummingbird?

Yes. Hummingbird is a whole other discussion however, and covered in another SEO blog. But the long and the short of Hummingbird for those in the cheap-seats, is that it is an algorithm released in August 2013 that targets ‘Long tail or ‘Human’ search’. In other words, Hummingbird is an algorithm that largely ignores keywords and instead concentrates on the inherent logic driving the search question. Want an example? Okay. Say someone typed ‘Search Engine Optimisation Sydney‘ into Google (try it). That’s a string of keywords followed by a location. It’s easy for Google to work out what that particular searcher is looking for: they’re looking for an SEO company in Sydney. But what if someone types: ‘How do I get my small business website on the first page of Google?’ In this instance the searcher hasn’t typed the keywords ‘Search Engine Optimisation’ or ‘Sydney’. So how then does Google work out what the searcher is looking for?

This is where the Hummingbird algorithm comes into play. Hummingbird reads ‘between the lines’ of the search and answers the questions, ‘How do I get my small business website on the first page of Google’, by musing, ‘Okay, this searcher wants to rank their website on the first page of Google. Who provides that service? Oh, yes. Search Engine Optimisation companies do. So they’re looking for an SEO company.’  And because the searcher hasn’t specified a location, Hummingbird will assume the searcher is looking for an SEO company close to where they are. And Google works that out by pinging the device the search was made on (desktop, tablet or smartphone) and going, ‘Ah, you’re sitting in a Gloria Jeans cafe in North Sydney searching on your iPhone 6s Plus, so you must be looking for an SEO company close to North Sydney’.
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Hummingbird as it is understood by the SEO community, can now also be viewed as the over-arching name given to Google’s core search technology. For years there was no over-arching name; and if anything Google search as a whole was incorrectly thought to be called PageRank. But in mid-2013 Google released Hummingbird. And at time of writing, it is the understanding of the SEO community that Hummingbird is both ‘it’s own algorithm’ and the name given to Google search as a whole. For example, Panda, Penguin, Pigeon, Payday, EMD, Pirate, Mobile Friendly and Top Heavy are all uber important algorithms in their own right, but are all ultimately part of Hummingbird. RankBrain similarly falls into this category. It uses artificial intelligence (or A.I) to embed vast amounts of written language into mathematical entities know as ‘Vectors’. These vectors allow Google to interpret words or phrases it isn’t familiar with, and extrapolate data-sets. Or in layman’s terms, it allows Google to work out what the hell you’re asking, even if you’ve typed the question into Google like a drunk intern at a PR company who’s just returned home after attending Fashion Week for the first time and can’t spell for toffee because you’ve drunk 2 bottles of wine and 7 shots of Tequila.

​Clever, huh?

RankBrain – The Technical View

Of course, while RankBrain is primarily used by Google on the 15% of new-to-Google search queries, don’t be fooled into thinking that is all it does. Because it is (to quote Google), “Not limited to any particular set of queries”. In other words, the technology driving RankBrain also comes into play when trying to make sense of other long-tail searches. And post Hummingbird, long-tail Google searches make up over 40% (and growing) of all search queries. And as keyword driven search is dying (and it is) and question based and vocal search becomes more and more the norm, long-tail question driven search queries are more and more where the SEO gold is buried.

RankBrain is Not Self-Learning After All

Google’s Gary Illyes was asked on Twitter to clarify whether the RankBrain algorithm continually re-programs itself and learns as it goes along (as was originally believed by the SEO community). But it turns out it doesn’t. Rather, RankBrain is periodically ‘Re-trained” by  the boffins at Google, to do its job better, as and when new up-dates are released. In Illyes own words: “Its effects are expectable, not assumable”.

IIlyes went on to say: “We’ll keep experimenting with and testing new models, and we’ll make updates as we come up with models that do a better job than the existing one. That could be about refreshing the data or developing new neural net architectures.”

Thought Vectors and the Word2vec Connection

Another ingredient in RankBrain is believed to be Google’s Word2Vec Project, which is based on the twin architectural models of CBOW (Continuous Bag of Words) and Skip-Gram. Both of which are, in nerd-speak, considered “shallow neural models”. In essence, CBOW tries its level best to predict the current word, based on the words around it (because words make more send when viewed in context with the words nearby). Whereas Skip-Gram cleverly predicts meaning from the sense of the words it finds.

Or to simplify it even further:

• CBOW predicts the word given its context.
• Skip-Gram predicts the context given a word.

Whether RankBrain is actually using Word2vec (or a variation of the methodology) or not, is ultimately by-the-by. All we really need to know is that RankBrain is converting words and phrases into vectors, which it then uses to allow for a deeper understanding of the data it’s reviewing. The implications of all this being that if Google is able to convert a typed question into vectors to better understand the inherent meaning behind the question, then Google will be able to significantly improve the search results it shows. 

Distributional Hypothesis

Another potential element under-pinning RankBrain (and until Google opens their Kimono to reveal all, this is just more guess work on our part, remember) is thought to be ‘Distributional Hypothesis’.

In linguistics, Distributional Hypothesis states that words used together in a sentence tend to infer similar meanings. This is a theory born from Statistical Semantics. The distributional hypothesis for this infers that the more semantically similar two words are, the more distributionally alike they’ll be, and the higher probability that they occupy similar linguistic contexts.

Or to simplify it even further:
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• Distributional Hypothesis states that a word is more often than not characterised by the company it keeps.

Latent Semantic Indexing

Another element that could be at play in RankBrain, is known as ‘Latent Semantic Indexing’ (LSI). Which is a retrieval and indexing method that uses Singular Value Decomposition to identify patterns in the relationship between concepts and terms as viewed in written text.

Scalability in Vectors

Ultimately RankBrain will expand its remit from learning patterns within short strings of data, to learning patterns within entire documents. Because just as there is learning to be had in the former, there is surely deep learning to be had with the latter. And it’s not too far of a stretch to think that the RankBrain algorithm of a few years down the track will be able to read an entire book and distill its meaning into a few short paragraphs.

Cliff Notes look out!

Professor Geoff Hinton and Artifical Neural Networks

Okay, so let’s back track for a minute. RankBrain “Converts words and phrases into vectors,” right? But what exactly does that mean? To better understand that, we need to look to Professor Geoff Hinton of the University of Toronto’s work in ‘Word2vec Connections’. Mr. Hinton is a gentleman and scholar (as the old saying goes) with much wisdom in the field of artificial neural networks. And who happened to have owned a company called DNNresearch Inc, which was acquired by Google in June 2013.

(Ah…the plot thickens…)

Neural Net reporter, Jack Clark of Bloomberg (who wrote the original Bloomberg Business article) attempted to get clarification on the whole Word2ve Connection issue:

“They wouldn’t explicitly confirm that it (RankBrain) is Word2vec, but everything we discussed indicated it’s likely doing something roughly equivalent to Word2vec, and is also doing similar conversions for sequences which is likely connected to Sequence to Sequence learning (PDF: http://papers.nips.cc/paper/5346-sequence-to-sequence-learni…).

It also links to Geoff Hinton’s stuff on Thought Vectors which implicitly involves word2vec.“

When pushed on the subject, a Google spokesperson said “It’s related to Word2vec in that it uses ’embeddings’ — looking at phrases in high-dimensional space to learn how they’re related to one another.” Which is about as much clarification on the subject as we could rightfully expect to get.
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​For those with a mind to dig deeper into Rankbrain, there’s a Patent Application by Googe pending, which includes this:

Further RankBrain Reading

• Distilling the knowledge in a neural network, Hinton, G. E., Vinyals, O., and Dean, J. (PDF)
• Investigating Google RankBrain and Query Term Substituions, by the always erudite Bill Slawski. 
• Deep Learning, Nature, LeCun, Y., Bengio, Y. and Hinton, G. E. (PDF)

The 1st and 2nd Most Important Ranking Factors in Google Search

Which segues us rather nicely back to the question of, ‘Okay, so if RankBrain is the third most important factor in Google search, what are the first two?’.

This we didn’t need last week’s announcement to answer; because I, and many other SEO types, have known the answer to this question for years. The first most important thing (in the post Panda world) is QUALITY ORIGINAL CONTENT.  The second most important thing (in the post Penguin world) is QUALITY LINKS.

You’ll note I added the word ‘Quality’ to both those Google ranking factors? Good. Because if you think all you have to do to rank in Google is flood your website with cheap content (no doubt sourced out of Eastern Europe), and point thousands of low value links at it (no doubt sourced via an el-cheapo company in India), then you’re probably the proud owner of a time machine*, and are kickin’ it back in 2010 listening to Ke$ha’s mega-hit, ‘TiK ToK’, or else queuing up with the kids to see Toy Story 3 at the cinema for the umpteenth time. I say this as nobody with half a brain in their head would run that strategy today. Because if they did, their website would have the living crap kicked out of it by both Panda and Penguin, and find itself buried on page 35 of their particular Google search. And given that 92% of people don’t go past the first page of Google…good luck with getting the phone to ring from there.

*if you do own a time machine, might I suggest going back to 1997 and investing in Google when they were a start-up? As this is probably a safer bet than trying to con Google with low quality content and dodgy links in 2016…

So Who Announced the New Number #1 and #2 Ranking Factors?

The ‘official’ announcement on the first and second most important factors in ranking in Google search, was made on March 23, 2016, when Andrey Lipattsev, a ‘Search Quality Senior Strategist’ at Google, let the cat out of the bag in a video ‘Q&A with Google’. Given that Google had already announced that RankBrain was the third most important signal in search today, Lipattsev was pushed on what the first two were. And here is his answer:

“I can tell you what they are. It’s content. And it’s links pointing at your site’.

Andrey Lipattsev – The New Voice of Google Search?

Now whether Google gave Lipattsev the go-ahead to reveal this information to the world, or whether he simple forgot himself mid-interview and let it slip, we’ll never know. But when all’s said and done, what Lipattsev revealed is nothing smart SEO minds didn’t already know. If you want to rank your website on the first page of Google, you need to create original, engaging, brilliantly written content, that people will want to link to organically.
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And there you were thinking this Google business was complicated!