Intro from rand – this post comes from dr. Matt peters, seomoz’s data scientist. He came on board in february of this year after stints at harvard (working on climate science models), washington mutual, jp morgan and fannie mae (analyzing mortgage securities) and more (including some research into google places rankings last november). Matt’s particularly passionate about bringing the best practices of scientific and quantitative analysis to the world of inbound marketing, and I’m very excited to welcome him to the moz community.
One of the most interesting findings from our 2011 ranking factors analysis was the high correlation between facebook shares and google us search position.
Facebook and other social media correlations with google search position
This blog post presents some New Zealand Business Fax List modeling results that look at whether google may be using facebook shares directly in it’s relevance calculation, or whether the correlation between facebook shares and search position is coincidental, aka the byproduct of other causal factors.
Correlation and causation
As we have said time and time again on this blog, in our presentations and when speaking, correlation is not causation. However, this post will discuss issues of both correlation and causation, so for the purposes of this discussion it’s important to Email Lists understand the relationship between them on a deeper level. Correlation does not, in general, imply causation.
Before we start our work on the 2011 Ranking Factors, we had some reasons to believe that Facebook data may be us by Google. There was an interview with Google/Bing in December 2010 where they disclosed that they were using social media signals in to rank search results. We also began seeing Facebook share information in our search results, so we knew that Google had access to at least some Facebook data.
Even having this public comment from Google and seeing the Facebook data in search results (you can also observe them in Google realtime, e.g. here), we were still surprised at the size of the correlation in our ranking factor result and we wonder whether it was causal or the result of other factors like links. As a simple check.