For quite some time I have theorized that a Google +1 was much more valuable than a Facebook Like or a Twitter Retweet. Today is the start of that theory becoming fact. Google Plus announced the social product is going to start suggesting posts that have been +1′d by your friends. Unlike Facebook and Twitter, users have the option to turn this off. I would guess that most people will not turn it off as they want to find the best content on the Internet. This means those with a large following will be able to influence a larger group of people. Take this trip with me. Having a Google Plus Following of 100,000 vs 5,000 I hate to be the numbers guy but this is a scenario where the numbers do matter.
If a Google Plus user has 5,000 followers they can influence up to 5,000 with a +1. If I share a post and one of my followers with a following of 5,000 decides to give it a +1 there is an opportunity to have a reach of 5,000. On the flip side of that equation let us say that I have 100,000 followers. If my friend that has 5,000 shares a post and I decide to +1 that content there is an opportunity for up to 100,000 people to see that post. Vic Gundotra has always said that the early adopters are going to benefit greatly from the updates we see with the Google social layer. I can honestly say that I did not see this one coming. I never even thought about the influence of a single +1. I am extremely happy that I cleaned out my circles in November of 2012 and followed only the best of the best on Google Plus. I am very selective with my +1s and that will remain true moving forward. Rather than just going down a stream and +1ing everything you see it is important to recognize that your following will see what you have +1′d. Most of us want to share the best content on the web. There is no reason to share something that would not be of interest to your following. Take the time to clean up your circles and follow only those that create amazing content for the web. It is not hard to do this as most people know who shares good stuff and who shares fluff. The Google +1 Gets Even More Powerful When building relationships on Google Plus I would never suggest only moving forward with those that have a large following. In fact, some of my strongest relationships have been built before that individual “got big”. That said, I think you can make an honest assessment about a user within a few interactions with said individual. I enjoy nothing more than seeing a new Google Plus user gain a large following because they share amazing content. I have taken a few under my wing and helped them grow at an accelerated pace. The new Google +1 recommendation means that your social network just became exponentially bigger. The more +1s you get per post the more viral it can be. If you get +1s from influencers and amazing content creators you could see true virality. This is something to keep in mind when deciding to share to Google Plus. I think we are going to see another wave of writers and content creators join Google Plus because they have a better opportunity to build readership. Those who have been on Google Plus for quite some time will enjoy the benefits that are being baked into this social layer. Well done Google, well done.
- See more at: http://www.wojdylofinance.com/why-a-google-1-from-an-influencer-is-so-valuable/#sthash.2ccY76Ug.dpuf
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.
Overview
Every two years, Moz runs a Ranking Factors study to determine which attributes of pages and sites have thestrongest 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.
Survey
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.
Correlations
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
Page Authority is a machine learning model inside our Mozscape index that predicts ranking ability from links and it is the highest correlated factor in our study. As in 2011, metrics that capture the diversity of link sources (C-blocks, IPs, domains) also have high correlations. At the domain/sub-domain level, sub-domain correlations are larger then domain correlations.
In the survey, SEOs also thought links were very important:
Survey: Links 
Anchor text
Over the past two years, we've seen Google crack down on over-optimized anchor text. Despite this, anchor text correlations for both partial and exact match were also quite large in our data set:
Interestingly, the surveyed SEOs thought that an organic anchor text distribution (a good mix of branded and non-branded) is more important then the number of links:
The anchor text correlations are one of the most significant differences between our results and the Searchmetrics study. We aren't sure exactly why this is the case, but suspect it is because we included navigational queries while Searchmetrics removed them from its data. Many navigational queries are branded, and will organically have a lot of anchor text matching branded search terms, so this may account for the difference.
On-page
Are keywords still important on-page?
We measured the relationship between the keyword and the document both with the TF-IDF score and the language model score and found that the title tag, the body of the HTML, the meta description and the H1 tags all had relatively high correlation:
Correlations: On-page 
See my blog post on relevance vs. ranking for a deep dive into these numbers (but note that this earlier post uses a older version of the data, so the correlation numbers are slightly different).
SEOs also agreed that the keyword in the title and on the page were important factors:
Survey: On-page
We also computed some additional on-page correlations to check whether structured markup (schema.org or Google+ author/publisher) had any relationship to rankings. All of these correlations are close to zero, so we conclude that they are not used as ranking signals (yet!).
Exact/partial match domain
The ranking ability of exact and partial match domains (EMD/PMD) has been heavily debated by SEOs recently, and it appears Google is still adjusting their ranking ability (e.g. this recent post by Dr. Pete). In our data collected in early June (before the June 25 update), we found EMD correlations to be relatively high at 0.17 (0.20 if the EMD is also a dot-com), just about on par with the value from our 2011 study:
This was surprising, given the MozCast data that shows EMD percentage is decreasing, so we decided to dig in. Indeed, we do see that the EMD percent has decreased over the last year or so (blue line):
However, we see a see-saw pattern in the EMD correlations (red line) where they decreased last fall, then rose back again in the last few months. We attribute the decrease last fall to Google's EMD update (as announced by Matt Cutts). The increase in correlations between March and June says that the EMDs that are still present are ranking higher overall in the SERPs, even though they are less prevalent. Could this be Google removing lower quality EMDs?
Netmark recently calculated a correlation of 0.43 for EMD, and it was the highest overall correlation in their data set. This is a major difference from our value of 0.17. However, they used the rank-biserial correlation instead of theSpearman correlation for EMD, arguing that it is more appropriate to use for binary values (if they use the Spearman correlation they get 0.15 for the EMD correlation). They are right, the rank-biserial correlation is preferred over Spearman in this case. However, since the rank-biserial is just the Pearson correlation between the variables, we feel it's a bit of an apples-to-oranges comparison to present both Spearman and rank-biserial side by side. Instead, we use Spearman for all factors.
Social
As in 2011, social signals were some of our highest correlated factors, with Google+ edging out Facebook and Twitter:
SEOs, on the other hand, do not think that social signals are very important in the overall algorithm:
This is one of those places where the correlation may be explainable by other factors such as links, and there may not be direct causation.
Back in 2011, after we released our initial social results, I showed how Facebook correlations could be explained mostly by links. We expect Google to crawl their own Google+ content, and links on Google+ are followed so they pass link juice. Google also crawls and indexes the public pages on Facebook and Twitter.
Takeaways and the future of search
According to our survey respondents, here is how Google's overall algorithm breaks down:
We see:
- Links are still believed to be the most important part of the algorithm (approximately 40%).
- Keyword usage on the page is still fundamental, and other than links is thought to be the most important type of factor.
- SEOs do not think social factors are important in the 2013 algorithm (only 7%), in contrast to the high correlations.
Finally, my MozCon slides contain some more details and data:
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