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A Comprehensive Analysis of the New Domain Authority

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A Comprehensive Analysis of the New Domain Authority

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Moz’s Domain Authority is requested over 1,000,000,000 times annually, it’s referenced an incredible number of times on line, and it has turned into a veritable household name among search engine optimizers for a variety of use cases, from determining the success of a link building campaign to qualifying domains for purchase. With the launch of Moz’s entirely new, improved, and much bigger link index, we recognized the ability to revisit Domain Authority with exactly the same rigor as we did keyword volume years ago (which ushered in the era of clickstream-modeled keyword data).

What follows is just a rigorous treatment of the new Domain Authority metric. What I won’t do in this piece is rehash the debate over whether Domain Authority matters or what its proper use cases are. I’ve and will address those at length in a later post. Rather, I intend to spend the next paragraphs addressing the new Domain Authority metric from multiple directions.

Correlations between DA and SERP rankings
The most important element of Domain Authority is how well it correlates with search results. But first, let’s have the correlation-versus-causation objection out of the way: Domain Authority doesn’t cause search rankings. It is not a ranking factor. Domain Authority predicts the likelihood that certain domain will outrank another. However, its usefulness as a metric is tied in large part to this value. The stronger the correlation, the more valuable Domain Authority is for predicting rankings.

Methodology
Determining the “correlation” between a metric and SERP rankings has been accomplished in lots of different ways over the years. Should we compare contrary to the “true first page,” top 10, top 20, top 50 or top 100? Exactly how many SERPs do we need to collect for our brings about be statistically significant? It’s critical that I outline the methodology for reproducibility and for any comments or concerns on the techniques used. For the purposes with this study, I chose to utilize the “true first page.” This means that the SERPs were collected using only the keyword with no additional parameters. I chose to utilize this specific data set for several reasons:

 

  • The true first page is what most users experience, thus the predictive power of Domain Authority is going to be focused on what users see.
  • By not using any special parameters, we’re likely to have Google’s typical results.
  • By not extending beyond the actual first page, we’re likely in order to avoid manually penalized sites (which can impact the correlations with links.)
  • We did NOT utilize the same training set or training set size as we did with this correlation study. That is to express, we trained at the top 10 but are reporting correlations on the actual first page. This prevents us from the potential of having a result overly biased towards our model.

I randomly selected 16,000 keywords from the United States keyword corpus for Keyword Explorer. I then collected the actual first page for most of these keywords (completely different from those found in working out set.) I extracted the URLs but I also chose to eliminate duplicate domains (ie: if exactly the same domain occurred, one after another.) For a length of time, Google used to cluster domains together in the SERPs under certain circumstances. It had been easy to spot these clusters, as the next and later listings were indented. No such indentations exist any longer, but we can’t be sure that Google never groups domains. If they do group domains, it would throw off the correlation because oahu is the grouping and not the original link-based algorithm doing the work.

I collected the Domain Authority (Moz), Citation Flow and Trust Flow (Majestic), and Domain Rank (Ahrefs) for each domain and calculated the mean Spearman correlation coefficient for each SERP. I then averaged the coefficients for each metric.

Outcome
Moz’s new Domain Authority has the strongest correlations with SERPs of the competing strength-of-domain link-based metrics in the industry. The sign (-/+) has been inverted in the graph for readability, although the specific coefficients are negative (and should be).

Moz’s Domain Authority scored a ~.12, or roughly 6% stronger than the following best competitor (Domain Rank by Ahrefs.) Domain Authority performed 35% better than CitationFlow and 18% better than TrustFlow. This isn’t surprising, for the reason that Domain Authority is trained to predict rankings while our competitor’s strength-of-domain metrics are not. It shouldn’t be taken as a poor our competitors strength-of-domain metrics don’t correlate as strongly as Moz’s Domain Authority — rather, it’s simply exemplary of the intrinsic differences involving the metrics. However, if you will want metric that best predicts rankings at the domain level, Domain Authority is that metric.

Note: In the beginning blush, Domain Authority’s improvements over your competition are, frankly, underwhelming. The stark reality is that we could very easily raise the correlation further, but doing so would risk over-fitting and compromising another goal of Domain Authority…

Handling link manipulation
Historically, Domain Authority has dedicated to only a single feature: maximizing the predictive capacity of the metric. All we wanted were the best correlations. However, Domain Authority is now, for better or worse, synonymous with “domain value” in lots of sectors, such as for instance among link buyers and domainers. Subsequently, as bizarre as it can sound, Domain Authority has itself been targeted for spam to be able to bolster the score and sell at an increased price. While these crude link manipulation techniques didn’t work so well in Google, these were sufficient to improve Domain Authority. We made a decision to rein that in.

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