Posted by randfish
Do more tweets of a URL lead to higher search rankings on Google? Do longer articles get more shares on Facebook? Do emails that contain images have lower open rates?
These, and hundreds of other questions marketers are constantly asking, can be answered mathematically through correlation data. Yet, it seems there's an unfortunate bias against correlations, specifically in the SEO community. Part of this has to do with the well-known maxim "correlation is not causation." This is eminently true.
However, I LOVE to know correlation, even when it's wholly disconnected from causation, and I'm surprised more marketers rail against the acquisition of this knowledge. After all, we constantly use correlation-based observations in our everyday lives, scientists use it frequently to discover potential hypotheses and put forward experiments to test them.
For example, I personally care less about what Google actually uses as ranking elements in their massive algorithm than on what kinds of sites and pages tend to perform well. To my mind, it's much more fascinating to learn, that, for example, stories that appear in the Google News results are much more likely to have images originally sourced by the news publisher than it would be to find out that the algorithm uses an exponential decay factor on freshness based on inputs from a certain set of trusted account usage. The former is actionable; the latter much less so.
We can apply this to email outreach, public relations, talks at conferences, conversion rate optimization (a practice based almost entirely on correlation), and virtually any other quantifiable practice in our work.
Here are just a few examples of great work in the field of marketing that leverage correlation data:
- Dan Zarrela's series on the Science of Social Media, Science of Retweets, Science of Timing and Science of Facebook Marketing
- The Open Algorithm Project from Mark Collier
- Correlation data in SEOmoz's own Ranking Factors Study
- What Mike King learned analyzing 300K outreach emails w/ Buzzstream
I fail to understand why this work is criticized as being "just correlation; doesn't mean anything" rather than embraced as "awesome; new correlation data on which to form testable hypotheses." Yes - correlation does not imply causation. But it does show a relationship, and those relationships can form the basis of guesses and tests. I find it challenging to argue why this work should not be done and shared, yet the bias is clearly out there.
Of course, there's always the danger of presenting correlation research which is then misinterpreted or misused, as the folks from PHDComics brilliantly illustrated below:
But, I'd rather risk some misunderstanding and have the data available than not investigate the connections between things in the marketing world out of fear.
Here's just a few ideas for correlation-based research that I'd love to see someone put together:
- Correlation between a topic/phrase/brand trending on Twitter and search volume spiking on Google
- Correlation between Facebook shares, Tweets and Google+ shares for URLs across various industries (where are some networks potentially stronger/weaker, what are the outliers, etc)
- Correlation between amount of funding and revenue/growth/success across industries (think this would be fascinating to entrepreneurs)
- Correlation between types of share buttons used on a website and quantity of shares received
- Correlation between # of email subscribers to an RSS feed and the rankings / social shares of that feed's content
- Correlation between search rankings and RSS feed inclusion overall (do URLs that are included in feeds tend to perform better than those that aren't?)
- Correlation between sentiment (positive, negative, neutral) of content on various sites and their success in social media
- Correlation between social shares and traffic
- Correlation between Klout score and traffic driven to URLs shared (to see if Klout lines up with how much traffic that person's tweets/shares drive)
If you or your team feel confident, capable, and excited about potentially doing this work but need some funding or publishing support, we'd love to talk. Just drop me an email (rand followed by the @ and seomoz dot org).
p.s. Check out Dr. Pete's excellent "Mathographic" on correlation vs. causation to learn more about the difference and the nuances.
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