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A formula for measuring content marketing success in engineering

A formula for measuring content marketing success in engineering

The algorithm we use at

As marketers to an engineering audience, we’re often called upon to be story-tellers.  There are lots of resources that can guide us on how to tell a better engineering story

But how do you know you’ve truly connected with your audience?  Should you measure Page Views?  Shares?  Likes?  Or some combination of all of the above? As an engineering publication, we have developed an algorithm to evaluate our stories for engineers.

My goal when I built this algorithm was to better understand the difference between good stories and great ones**:

Our algorithm for measuring what is great content

Using this will typically yield a score between 1 and 100 – where 100 is off the charts fantastic stories.

I’ll break down the relative weightings in the formula: 

Page Views are important.  Since stories get many thousands of page views on (but almost always fewer than 100,000), we can divide the absolute number of page views to get a useful comparison.

Comments are a measurement of engagement.  That said, some stories just get a ton of comments, and not always for the right reasons.  Accordingly, we apply the square root to keep the relative importance of comments under control. 

Another measure of engagement is social sharing.  Note that we rank Linkedin shares 5X more heavily (SQRT/2) than Facebook shares (Sqrt/10).  Linkedin shares are a measure of a reader’s willingness to professionally endorse a story to their colleagues. 

Say you are trying to reach a design engineer with your content marketing post.  It is likely that there are a lot more design engineers in the Linkedin network of any design engineer than in their Facebook network because Linkedin creates networks of people according to their job role.    And where is Twitter, you might ask?  Well, we aren’t seeing as many engineers sharing via Twitter as they do via Linkedin and Facebook, so I left it off the list.  (Plus I was getting tired of data entry.)

Bounce Rate and Exit % are figures from Google Analytics that are typically between .5 – 1.0.  In anaylzing the data, we didn’t find wild swings.  The reason for including these statistics was that lower bounce rates and exit rates (the number of visitors leaving the site after reading a story) are indicators that people expect to find more good stories after reading the one we are studying.  And that’s got to be a good thing.

Finally, we measured Time on Page divided by the average time on page for all stories.  In our case, the average time on page was three minutes, or 180 seconds.  So the factor we applied would increase or decrease the overall result depending on whether the story led a reader to spend more or less time reading it.

Can this algorithm help engineering marketers?

All of the stories on get thousands of page views.  Most get hundreds of social shares, many of those coming from Linkedin.  We have found that the best stories have strong headlines and images support, and they follow the best practices of good storytelling, including original research.  For all its flaws, this algorithm can help us identify when a story goes off the rails.  We can then track back to our storytelling guidelines to figure out why. 

Hopefully this algorithm helps can help out marketers too in evaluating the content that they themselves create for engineers.

Let me know what you think,



** You may be wondering why I took the square root of so many of the figures. It was to shrink them down in a relative way so that the sum was more likely to end up as a number less than 100.  The other thing that the square root does is discount any one factor that somehow got overblown, like a story getting virally shared on Facebook because a celebrity liked it.

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