Shane asks, "What's the validity of domain authority in the current SEO landscape where Google is telling us to focus on EAT? Should I be concerned about measuring domain authority or is it more of a metric that does more to stroke ego like media impressions?"
The answer to this question requires some statistical knowledge. Fundamentally, the outcome we care about is traffic from organic search, so the question is, does domain authority have any relationship to organic search traffic? Let's see what we find from the SEO tool AHREFS and the R programming language; bear in mind every SEO tool has its own version of domain rating, so you'll want to perform this test using the data from the tool you use, with your industry-specific data.
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What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for watching the video.
In today's episode, Shane asks, What's the validity of Domain Authority and the current SEO landscape where Google is telling us to focus on EA t expertise, authority and trust? Should I be concerned about measuring domain authority? Or is it more of a metric that does more to stroke ego like media impressions? So this is a really good question because it requires a little bit of data homework, domain authority or domain rating or domain strength are arbitrary metrics put together by various SEO tool vendors, mas, RF, SpyFu, sem, rush, etc.
To give you some shorthand about how important that domain overall is.
So how do we do this? How do we go about turning an arbitrary metric into something that we can potentially get us out of? Let's go ahead and flip this around here.
To do this We're going to need a whole bunch of data.
And we're going to need to understand at a very basic level, is there any mathematical relationship between whatever your tools domain authority measures called, and an outcome we actually care about.
Now, for the most part, we're going to care about organic search traffic, right? That's the metric that is probably most meaningful to marketers because it means we actually got people to the front door, right? So let's, we're going to use the our programming language for this.
You can use this as a statistical tool of your choice, IBM Watson Studio, SPSS, our Python doesn't matter.
I'm going to go ahead and I'm going to, let's run all the prerequisites here so we can get all of our libraries loaded.
And then we're going to load in a whole bunch of exports from the RF tool and the exports.
I'm using our Essentially articles with the most common would have called stop words in the English language, the or, and a in as you etc.
These are going to give us a wide wide selection of content not just any one particular industry.
I'm looking also at articles this year published within calendar year 2020 at with working links.
Now what I've done is I've taken every metric they have and exported about 10,000 links per different metric because we want to see are there is there more than just domain rating or domain authority we want to look at.
Let's get rid of some row numbers here.
And then let's add in a days old just for fun to see if the age of the article matters.
Going to load our correlation plot library.
Remove numbers that are not remove variables that are not numbers you'll see in the original data set here.
There are things like the article title, the URL is Author of those are not necessarily things that, at least for this experiment we particularly care about.
So let's go ahead and then set any na which means not available to zero and run a correlation.
We're going to run a Spearman correlation tests.
There's Pearson spearmen.
And the other one, I can't remember the name it Pearson correlations are good for for normal distributions where there's a bell curve of results from marketing data most of the time, most marketing data looks like a power locker where you know, though the Pareto rule 8020 rule, and so you want to use a Spearman correlation most of the time, and let's remove the results of variables correlating themselves to that doesn't help anybody and will draw plot.
Okay, let's zoom in here.
Now what we're looking for is there's our domain rainville rating variable.
And we're looking for numbers in this section.
Really a point two, five or better that Kate's eight a week moderate correlation or, you know, minus point two, five week negative correlation basically looking for any correlation to major numbers that we care about.
And the one that we really care about, is this one, the traffic number, right? What is their relationship between domain rating? And that organic traffic? And the answer is no.
The other absolutely the point 008 Spearman correlation.
That pretty much says there's no relationship between domain rating in this tool and the outcome we care about.
question is What relationship is there between traffic that would be important? Number one here is referring domains, right referring domains.
point five nine is a very strong correlation between traffic which is the outcome we care about.
And referring domains.
What else we got here we've got point four, two, median number of social shares.
That's interesting most of the time, there isn't a strong correlation between social shares and SEO but in this particular set of 72,000 URLs, there is that I think bear some more investigation.
And then next on the list here is number of words.
That's another interesting word point three eight, Spearman correlation.
Again, we know that there is a bias in some types of algorithms in search towards longer form content.
And this for this data set seems to bear that out.
Now, bear in mind, let's go ahead and flip out of here.
Very mind a couple things with what it is we've just analyzed.
We took a very generic data set from a single SEO tool, the RF tool in this case, and we did a Spearman correlation to see with the outcome we care about, does domain reading matter for this example? The answer is no.
But There are some caveats on this.
Number one, this is not niche specific, right? This is not industry specific.
Instead of using the as the keyword term, you might want to use a piece of industry jargon from your industry and run this exact same analysis for your industry to see if there's something specific in your world that matters much more than the generic English language words.
Number two, caveat is that each SEO tool out there does its domain authority equivalent rating differently.
They have different behind the scenes algorithms, Maz has domain authority.
I think sem rush has doing strength RF says domain rating.
So you'll need to rerun these results with exactly the same technique, but export it from the tool that you use, because it may turn out that another vendor has a reasonably good domain authority.
This particular vendors domain rating doesn't really mean anything compared to the organic traffic outcome, and sidebar on that if your vendor doesn't provide you an organic traffic number, then you can't really do this analysis because you don't know what outcome it is you're going after.
So make sure whatever venue you use, there's a traffic outcome as well.
That way you're benchmarking against something meaningful.
Now, as we all know, correlation is not causation, right? Just because an article is longer does not mean that it will likely get you more organic traffic, it means there's a relationship between the two.
So as with all good data science, once you have some kind of answer here, in this case, we did that we found median shares was interesting.
We found a number of words was interesting.
Your next step is not to immediately change your strategy.
Your next step is to set up an experiment.
start testing it We produce no longer blog posts.
Let's say you do a blog twice a week on every other blog is a longer post by, say, by meaningful amount likes, if you normally blog 300 words or every other post this month should be 600 words.
And then at the end of the month, you check and see, did you see a relationship between longer posts and more organic search traffic? You may you may not, we don't know you won't know until you do the test.
So that's your next step is to test out and make sure that a correlation has a corresponding causation through your testing.
So keep that in mind as well.
In this case, my conclusion is for this data set, domain rating or domain authority isn't a meaningful metric here.
You have to test on your own.
Don't rely on just a generic test like we just did, to draw the conclusion for yourself, test for yourself, find out for yourself and then you can Go ahead and publish for your industry or your clients what it is that works best for your niche.
So really good question.
I wanted to show this as an example of the kind of work that you need to do to make that analysis so that you can get an answer that's customized for you rather than a generic because the generic answer may not be applicable to your industry.
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