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Figaro Digital, December 2015
“From PageRank to RankBrain”,
an evolution of Googles algorithm
Who are Barracuda?
Dedicated search marketing agency (SEO and PPC)
Recognised by our peers for expertise and innovation within SEO
Blend of best in class and proprietary technology, including
our own Panguin SEO algorithm analysis tool
Collaborative working model
20 strong agency
Wide range of clients
It’s the next evolution of the algorithm…
Let’s go back a step or two, and grab
some context; what is Google trying to
achieve with its search engine?
the “perfect search engine” is something
that “understands exactly what you
mean and gives you back exactly what
If your website has something users want then Google
wants to return it…
"Even if you do brain-dead stupid things and shoot
yourself in the foot, but have good content, we still want
to return it,“
Matt Cutts, Former Head of Search Quality, Google
Even if you do everything right, it’s
complicated! And who’s doing it right all
That’s a tall task when programming an
effective search engine.
To achieve the ‘perfect search engine’
scenario, we need it to behave more like
a human, that is to say, behave with
human-like intelligence, particularly in
the area of decision making.
…because explicitly programming all
eventualities in search is a fools errand
and not scalable.
So how have Google done it up to now
and where’s it going?
We assume page A has pages T1...Tn which point to it (i.e., are citations). The
parameter d is a damping factor which can be set between 0 and 1. We usually set d to
0.85. There are more details about d in the next section. Also C(A) is defined as the
number of links going out of page A. The PageRank of a page A is given as follows:
PR(A) = (1-d) + d (PR(T1)/C(T1) + ... + PR(Tn)/C(Tn))
Note that the PageRanks form a probability distribution over web pages, so the sum of
all web pages' PageRanks will be one.
PageRank or PR(A) can be calculated using a simple iterative algorithm, and
corresponds to the principal eigenvector of the normalized link matrix of the web
3rd most important ranking signal!
RankBrain is the first system within Google’s
search algorithm to employ machine learning to
process huge volumes of search queries submitted
to Google, including voice search. It attempts to
understand what those queries mean and use that
understanding to provide better answers.
For example, the system helps with interpreting the
meaning of ambiguous or new search strings.
Google states that approximately 15% of search
terms have never been used before.
What is machine learning?
• STOP explicitly programming every eventuality.
• SPOT patterns and deal with new eventualities
based on what has been learnt.
• ASSESS decisions that have been made with
user feedback (click-through, bounce, dwell
• AMEND results for next time.
RankBrain is an application of machine
learning that is resulting in Google
behaving in a more ‘human’/intelligent
way (making decisions for itself based
on what it has learnt from patterns),
when serving search results to user
SO, WHAT DOES
FOR ME AS A
RankBrain is there to amplify the good
work being done in the areas of keyword
targeting, content and links.
It is not a factor that can be ‘directly’
influenced in the same way we can the
An example - Musement film map
An example - Musement film map
So, what are the lessons here ?
RankBrain changes nothing about the direction of travel
The underlying principles still apply
Underpin strategy with a strong technical foundation
And then move to content (E A T)
And ensure that its focused on users, not search engines
In summary, the advent of RankBrain in 2015
is symptomatic of Google’s push to make its
search products more ‘intelligent’.
This is a good thing for marketers who are
already engaging in best practice on-site SEO
and content marketing, because we’re already
doing what we need to do.
The rest need to get with the program!
RankBrain’s learning mechanisms are not in
continuous operation – rather, it employs ‘assisted
learning’ where new data sets and processing models
are periodically input by Google engineers, who then
assess the output and deploy the changes to the live
running system if they show improved results.