Pages on Priceonomics sorted by most views. See more here.
Even if you read Priceonomics regularly, you may not know how we make money. Since we don’t have traditional display ads, it’s not exactly obvious. A click to our site doesn’t make us an additional penny, like it does at ad-supported content sites.
Instead, we make money by selling things. And our articles help promote those things. Namely, organizations pay to use Content Tracker (our content optimization software), and our Data Studio, which helps companies turn information into content that spreads (also known as a viral article).
We’re even throwing a conference about the topic of using data to create content marketing, and it's on its way to selling out! You should come.
Figuring out how to tell people and companies about these things that make money (and fund our writing) is a bit tricky. Only some of our readers work for businesses interested in content services and software. How can we promote our "business" in every post without being overly promotional?
So, we thought, why not let everyone see all the information revealed by our Content Tracker software about the popularity of each of our articles, as well as how we use the software to plan our publishing schedule and how we A/B test our article titles? Our readers might like that, and potential customers might as well.
So that’s what we did. You can now see under the hood of Priceonomics. At the top and bottom of every article, there are high level stats and a link to more details. And if you click through, you can see a lot more details about our stats and internal operations.
At the top of Priceonomics articles, you can click "More stats." More stats are displayed at the end of the article, plus a link to further information about the article.
What are some interesting things to check out on the Priceonomics public dashboard? You can see data on how many people have read and shared each article. You can sort the data to view articles by most popular or most shared.
If you click through an article, you can see who has shared it on Twitter or who has linked to it.
We’ve also included our editorial calendar, so you can see ideas we’re working on and when we’re planning on publishing them.
Perhaps the neatest thing is you can see which titles we’ve considered for most of our articles. If you go to the tab A/B Test, you can see the click-through rates on some of the titles we considered.
If you’re trying to conduct scientific research (about how content spreads or whatever), you should know that this data set is not perfect. A couple years back, for example, we had an issue with our view counting on Google Analytics, and it double counted the number of pageviews for articles for a couple of months. That's not a big deal for internal use, but it's an example of the random issues that pop up over the years.
Another thorn is that the “mentions” count finds every inbound link to an article. Something like a single mention on Reddit can spawn dozens of unique inbound links from various subdomains on Reddit or URLs that are only slightly different from each other.
Lastly, we experiment a lot on “conversions.” Sometimes we define it narrowly (how many users sign up), and sometimes it’s very broad (how many users viewed a marketing page). So its meaning is not consistent across articles or over time.
There are a lot of wrinkles in the data, so if you base your PhD dissertation on it, you’ll probably make an error and get kicked out of the program. But for benchmarking or for analysis that's not life and death, this data should be fine.
So, check it out. This is data is powered by Priceonomics Content Tracker, which tracks the views, social sharing, links, and conversions of every piece of content you publish. The basic dashboard is free.
We’re in the process of adding an API to Content Tracker so that companies can do whatever they want with their content stats. If you want early access to the API, send us a message here.