Fractl has produced thousands of content marketing campaigns across every topic, and for the past seven years, we’ve been keeping track of each and every campaign in order to refine and improve the content we produce on behalf of our clients.
In my last post for Moz, I explained how to set realistic digital PR expectations for your content based on your niche. In this topic, I want to dive a little bit deeper into the data and share insights about how the source of your content can be just as important in determining how your content will perform.
In this analysis, I looked at 1,474 client content campaigns across six different data source categories:
Publicly available data
It’s important to note that there are countless other data sources that we use for content campaigns every day at Fractl that are not mentioned in this article. In this analysis, each category has at least 20 campaigns, while some categories have several hundred campaigns.
It’s also important to note that averages were collected by excluding upper outliers. For campaigns that went “viral” and performed well above the norm, we excluded them in the calculation so as not to skew the averages higher.
In addition to sharing link and press averages, I will also be walking through how to produce pressworthy, sharable content from each data source and providing examples.
Across the entire sample of 1,474 campaigns, a project on average received 24 dofollow links and 89 press mentions in total.
A press mention is defined as any time the content campaign was mentioned on a publisher’s website.
There were some individual data source category averages that were on par with the sample average, while other categories deviated greatly from the sample average.
Publicly available data
For almost any niche out there, you can bet there is a publicly available data set available for use. Some examples include data from the CDC, the U.S. Census, colleges and universities, the WHO, and the TSA. The opportunities really are endless when it comes to using publicly available data as a methodology for your content.
While free data sets can be a treasure trove of information for your content, keep in mind that they’re not always the simplest to work with. They do require a lot of analysis to make sense of the massive amount of information in them, and to make the insights digestible for your audience.
Take for example a campaign we produced for a client called Neighborhood Names. The data was free from the US Census, but in order to make any sense of it, our researchers had to use QGIS, Python, text-mining, and phrasemachine (a text analysis API) just to narrow it down to what we were looking for.
And what were we looking for? Looking at neighborhood names across America seems boring at first, until you realize that certain words correspond to wealth.
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