2013 Search Engine Ranking Factors
Posted by Matt Peters
Yesterday at MozCon, I presented the results from Moz’s Ranking Factors 2013 study. In this post I will highlight the key takeaways, and we will follow it up with a full report and data set sometime later this summer.
Every two years, Moz runs a Ranking Factors study to determine which attributes of pages and sites have the strongest association with ranking highly in Google. The study consists of two parts: a survey of professional SEOs and a large correlation study.
We’ll dive into the data in a minute, but some of the key findings include:
- Page Authority correlates higher than any other metric we measured.
- Social signals, especially Google +1s and Facebook shares are highly correlated.
- Despite Penguin, anchor text correlations remain as strong as ever.
- New correlations were measured for schema.org and structured data usage.
- More data was collected on external links, keywords, and exact match domains.
Cyrus Shepard and Matt Brown organized this year’s survey of 120 SEOs. In a few weeks, we’ll release the full survey data. For now, thank you to everyone who participated! This wouldn’t have been possible without your help, and we appreciate the time and effort you put in to answering the questions.
The survey asked respondents to rate many different factors on a scale of 1-10 according to how important they thought they were in Google’s ranking algorithm. We present the average score across all responses. The highest-rated factors in our survey had average scores of 7-8 with less-important factors generally ranging from 4-6.
To compute the correlations, we followed the same process as in 2011. We started with a large set of keywords from Google AdWords (14,000+ this year) that spanned a wide range of search volumes across all topic categories. Then, we collected the top 50 organic search results from Google-US in a depersonalized way. All SERPs were collected in early June, after the Panda 2.0 update.
For each search result, we extracted all the factors we wanted to analyze and finally computed the mean Spearman correlation across the entire data set. Except for some of the details that I will discuss below, this is the same general process that both Searchmetrics and Netmark recently used in their excellent studies. Jerry Feng and Mike O’Leary on the Data Science team at Moz worked hard to extract many of these features (thank you!):
When interpreting the correlation results, it is important to remember that correlation does not prove causation.
Rand has a nice blog post explaining the importance of this type of analysis and how to interpret these studies. As we review the results below, I will call out the places with a high correlation that may not indicate causation.
Enough of the boring methodology, I want the data!
Here’s the first set, Mozscape link correlations:
Correlations: Page level
Correlations: Domain level
You can read the full article at Moz Bloghttp://seopti.com/2013-search-engine-ranking-factors/SEOMOZ