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- [Brad] At this stage,
it's clear that search engines are rapidly evolving
in their quest to process information
in the same way that we humans do.
And while they continue to evolve,
we'll continue to try and reverse engineer how they work.
Despite incredibly complex machine learning processes,
search engines are still machines
and will for the foreseeable future use models
that we can loosely decipher to improve our ranking odds.
Early on, we gamed the system
and identified the concept of keyword density.
It was easy to manipulate and it didn't take long
for search engines to move away
from measuring the repetition of a single word
to factor in rank.
Most recently, it's believed that Google uses
term frequency-inverse document frequency
to figure out the information on a page
and how relevant it is to a search query.
Now, we can use the acronym TF-IDF to talk about this.
So TF-IDF is a numerical statistic
that reflects how important a word is
to a document within a collection.
TF-IDF reveals a value,
and this value increases proportionally
to the number of times a word appears on your site
while also being offset by the frequency of the word's
appearance in general across any piece of content.
Let me break this down a little so it's clear.
Let's say we want to determine which webpage on the internet
is most relevant to the query The Quick Brown Fox.
Well, we could start by removing any document
from the search index that doesn't contain the phrase
The Quick Brown Fox, but that's still a ton of results.
So next, we could count the number of times
we see the words on all the remaining websites
and the number of times we see each word
becomes the term frequency.
But in our phrase, The is a really common word,
so term frequency would rank websites that use The a lot.
And we know as humans that the important terms
in this phrase are quick, brown, and fox.
So what we need is to reduce the importance of The
and increase the importance of quick, brown, and fox.
And this is done
through an inverse document frequency factor.
We look at all the websites on the web
and diminish the importance of terms
that occur very frequently regardless of the content
and increase the importance of terms that occur rarely.
It's through this model
that Google has a much better understanding of your content.
It can see what terms you emphasize most
and what terms define the content on the page.
We can take this analysis and apply it to competing pages
currently ranking in the top 10.
And what we'll see is what terms provide the most value
for a particular search query.
Because it's not just the search phrase,
The Quick Brown Fox,
it's all the additional words that support this topic.
In this example, are we referring to the pangram
The Quick Brown Fox Jumps Over The Lazy Dog,
or are we looking at what kind of fox is quick and brown,
or maybe there's a new viral video going around
on what did the quick brown fox say.
All the words you use to surround your core topic
help provide additional signals
so Google knows what your content is about
and it can shift the search results accordingly.
Let's take a look at a tool that helps with this
and run through an example to further reinforce this idea.
Now, there's a tool provided by Ryte
and you'll get to it at ryte.com and it is a TF-IDF tool.
And I've already signed up for a free trial
and I've gone into the dashboard
and selected Content Success.
It's here that we can input a keyword
that we're trying to rank for,
and we can select the language
and the country that we're trying to do so in.
So let's say we want to do quick brown fox.
We'll select Start Content Analysis
and the tool starts to crawl.
It's here that we see our content report
and we can identify what terms are most important
to be included with the topic quick brown fox,
how many documents contain them,
as well as the average value.
And as we scroll down,
we can see these terms as they descend in popularity.
At the top, you can filter in more concepts
or you can reduce the number of concepts that you see.
So for all of the pages that are ranking in the top 10,
we can see that they're likely including
these additional terms alongside the quick brown fox.
At the top, you can also choose if you'd like to bring in
a two-word combination report,
which is going to get rid of the single words
and bring in two words.
And this will also allow you to look at
what is being discussed within these pieces of content.
Along the left-hand side,
I can choose the Competition Report
and this will show you where this content is coming from.
So Wikipedia, the Quick Brown Fox Letterpress,
threadless.com, and you can see what terms
and at what frequency each site is using
alongside this content.
Now, this can be a little challenging to try to evaluate
in a couple of minutes,
and it really takes getting in and practicing
with a TF-IDF tool to really begin to unlock
how valuable it is to see this in your content optimization.
So I encourage you to read more up on the TF-IDF concept
and check out sites like ryte.com to be able to pull reports
and run this type of analysis.
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