Your boss: “So will this [insert content marketing tactic here] conquer the world, get hundreds of thousands of shares and rival Gangnam Style?”
You: [string of excuses, qualifications, wild guesses and tenuous supporting arguments]
Your boss (thinking): “He has no idea, does he?”
Admit it. When producing content, we just don’t know what’s going to take off.
Or even what’s going to do better than the last thing.
Fortunately, we can assess what has already done well, and progress from there.
Before you think “not another damn data post”, stop right there! I’m going to show you what posts did well, and what posts didn’t do as well on top marketing sites (Econsultancy.com, Hubspot’s blog and the SEOMoz blog). And give you some idea why. That’s stuff you can use. Back to our story…(don’t worry, I’ll get to the findings real quick)
I’ve argued before for using social share data as the content marketing guide rail (conversions and sales are more important obviously, but they’re strategic measures that won’t impact tactics like “what do I write about tomorrow? Or next week? Or which format – infographic, micrographic, list post, resource, controversial opinion piece, etc.?”).
Social share data shows how the content rubber hit the real world road. Sometimes people share content that they think is terrible, but – generally – a share is proxy for “I appreciated this, and I think you might too”, which is a pretty good baseline measure for what you’re doing in content marketing. (For what it’s worth, MarketingProfs’ Ann Handley and Hubspot’s Dan Zarrella seem to concur.)
Another advantage of social share data is that it’s public, meaning you can examine other sites’ data. Competitors’ data. Media sites’ data. Influencers’ data. Fantazballs!
So that’s just what I did:
Welcome to the big marketing blog popularity study!
I pulled social share data (specifically, the number of tweets – direct, not RTs) for three big marketing blogs/sites – econsultancy.com, the Hubspot blog and the SEOMoz blog. I created a baseline from forty randomly selected recent posts, and then pulled data along a series of dimensions that I assumed may impact popularity, such as:
- Title characteristics
- Etc. etc. etc.
What does this tell you? Explicitly it tells you what kinds of content got the most shares on those sites, which implicitly tells you what kind of content might get the most shares on your site. But, implicitly, it tells you some things about how people relate to content sites, how they relate to content, how they relate to writers, to topics and to ideas.
[Be advised that findings are not drawn on thousands or hundreds of thousands of data points, but rather on dozens. Outliers can skew results - something I'll try to make explicit when I see it.]
If you found the study useful or interesting, please do share. For the next study