When and How to Use Domain Authority, Page Authority, and Link Count Metrics – Whiteboard Friday
Posted by randfish
How can you effectively apply link metrics like Domain Authority and Page Authority alongside your other SEO metrics? Where and when does it make sense to take them into account, and what exactly do they mean? In today’s Whiteboard Friday, Rand answers these questions and more, arming you with the knowledge you need to better understand and execute your SEO work.
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So many of you have written to us at Moz over the years and certainly I go to lots of conferences and events and speak to folks who are like, “Well, I’ve been measuring my link building activity with DA,” or, “Hey, I got a high DA link,” and I want to confirm when is it the right time to be using something like DA or PA or a raw link count metric, like number of linking root domains or something like Spam Score or a traffic estimation, these types of metrics.
So I’m going to walk you through kind of these three — Page Authority, Domain Authority, and linking root domains — just to get a refresher course on what they are. Page Authority and Domain Authority are actually a little complicated. So I think that’s worthwhile. Then we’ll chat about when to use which metrics. So I’ve got sort of the three primary things that people use link metrics for in the SEO world, and we’ll walk through those.
So to start, Page Authority is basically — you can see I’ve written a ton of different little metrics in here — linking URLs, linking root domains, MozRank, MozTrust, linking subdomains, anchor text, linking pages, followed links, no followed links, 301s, 302s, new versus old links, TLD, domain name, branded domain mentions, Spam Score, and many, many other metrics.
Basically, what PA is, is it’s every metric that we could possibly come up with from our link index all taken together and then thrown into a model with some training data. So the training data in this case, quite obviously, is Google search results, because what we want the Page Authority score to ultimately be is a predictor of how well a given page is going to rank in Google search results assuming we know nothing else about it except link data. So this is using no on-page data, no content data, no engagement or visit data, none of the patterns or branding or entity matches, just link data.
So this is everything we possibly know about a page from its link profile and the domain that page is on, and then we insert that in as the input alongside the training data. We have a machine learning model that essentially learns against Google search results and builds the best possible model it can. That model, by the way, throws away some of this stuff, because it’s not useful, and it adds in a bunch of this stuff, like vectors or various attributes of each one. So it might say, “Oh, anchor text distribution, that’s actually not useful, but Domain Authority ordered by the root domains with more than 500 links to them.” I’m making stuff up, right? But you could have those sorts of filters on this data and thus come up with very complex models, which is what machine learning is designed to do.
All we have to worry about is that this is essentially the best predictive score we can come up with based on the links. So it’s useful for a bunch of things. If we’re trying to say how well do we think this page might rank independent of all non-link factors, PA, great model. Good data for that.
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You can read the full article at Moz Bloghttp://seopti.com/when-and-how-to-use-domain-authority-page-authority-and-link-count-metrics-whiteboard-friday/http://seopti.com/wp-content/uploads/2013/07/moz-logo.pnghttp://seopti.com/wp-content/uploads/2013/07/moz-logo.pngSEOMOZ