After publishing over 750 data-driven research briefs, we’ve started to take a long hard look at the research content we create and the data underlying it. Data-driven content has been a powerful way for us to build our brand, generate leads, highlight our product and get press. And so it’s become increasingly important to us that we understand how to improve our research using data.
Our goal is to take an algorithmic and technology-based approach (akin to what we do in our product) to determine what type of research content we should do more of, less of, or start doing.
We’re going to share some insights we’ve gathered in a series of posts called “The Science of Content”.
In this first post, we are going to dig into the Content Marketing Power Law.
The Power Law is a phenomena in statistics that defines a relationships between two quantities where one quantity varies as a power of another. A power law graph looks like the below:
At its most basic, the Power Law implies that a handful of data points dominate in terms of value. In the graph above, the few data points to the left are where most of the value is. When folks say something follows the 80-20 rule, i.e. 80% of our revenue comes from 20% of our customers, they are describing a power law. The power law phenomenon occurs in many areas including venture capital as just one example.
Why is this important to us?
We’re a data and predictive analytics company, but given the role data-driven content has played in our growth, we can be thought of as a Mediata company (a term coined by Rafat Ali, CEO of Skift). Rafat defines a mediata firm as one “built from the ground up to take advantage of the organic fusion of media and data.”
Candidly, we were not organically built this way, but necessity being the mother of invention forced us to embrace content. We didn’t have the sales budgets, sales teams, brands or history of Capital IQ, Dow Jones, or Thomson-Reuters, and as a result, we had to find a way to establish ourselves and our credibility scalably and efficiently.
With this as our goal, we began writing data-driven research briefs and reports using our private company financing and exit data. As we saw results, we doubled down on these efforts and now consistently publish 8-12 data-based research briefs per week.
And that brings us to the content marketing power law
We took all of our historical research briefs and broke them down into unique pageview buckets of 1000 each. As you can see below, the vast majority of our content generates less than 1000 pageviews.
When we put the articles into 1000-pageview buckets as above, you quickly see that almost 73% of our research briefs have received fewer than 1000 page views (73%). Almost 88% of articles are below 2000 page views. Just 3% are more than 5000 unique page views.
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If you plot each article and its individual page views, the Power Law really becomes apparent. In our case 10% of our research briefs account for 53% of pageviews and the top 20% of briefs account for 69% of pageviews.
In other words, just a few of the research briefs account for the lion’s share of the “value” (pageviews in this analysis) followed by a long tail of research briefs that generate a lot fewer pageviews. This is the Content Marketing Power Law.
The pageview data does tell us several things about our audience:
What topics are of interest
What content formats they prefer
What visualizations/graphs resonate
What promotional tactics work to increase research brief exposure
However, as a SaaS company, pageviews, while useful, are not our final measure of success. And so the other part of our algorithm examines the role of content in:
Generating new leads
- Understanding lead quality (because not all leads are created equal)
Nurturing existing leads to move them through the sales funnel
If a piece of data-driven content with < 1000 pageviews generates high quality leads, it is still worth publishing. If it builds affinity for our product with a prospective customer and hence makes them more likely to become a client, it is also worth publishing.
We’ve spent a lot of time on lead and customer attribution and how it ties back to content. Doing this lets us properly analyze each piece of research and holistically understand its value to our business.
We’ll dive into other aspects of how we use data to algorithmically optimize our content and sales process in a future ‘Science of Content’ post.
If you’ve used data in a novel way to optimize your content marketing efforts, we’d love to hear what you’ve done and the results you’ve acheived. Of course, if any questions or comments on the content marketing power law or anything else, feel free to leave a note or comment below as well.