Let’s examine some fundamental metrics in MarketMuse that will help you create and optimize content better and faster. Specifically, we’re going to look at Distribution, Topic Depth, Topic Breadth, Content Score, and Word Count.
In the platform, you’ll notice we use the term Distribution or Suggested Distribution, along with a range of numbers 0, 1–2, 3–10, and 10+. Suggested Distribution refers to the number of mentions of a related topic, based on how experts use the term.
We use the term “mention” because it’s the simplest and most actionable way to describe topical depth. A lot of mentions (high distribution) suggests experts generally go deep when talking about that specific topic. Because it’s meant to be used as a guide and not to be taken literally, Distribution is shown as falling into one of the ranges described earlier.
An important point to note is that Mentions have nothing to do with keyword density. Once upon a time, the number of times a term was mentioned (keyword density) could affect the ranking of a page. Those days are long gone.
So use it for what it’s meant to be: a convenient way of gauging topical depth.
Is it bad to mention a topic more times than suggested? Not necessarily. If it leads to poor readability, excessive mentions will be flagged by tools like Grammarly or Writer. They’re very helpful and I suggest you incorporate them into your workflow.
What if you don’t mention a topic as many times as suggested? Again, it’s not necessarily a problem. The topic model provides 50 related topics and it’s unlikely that you will use them all. Of course, you’ll want to incorporate enough related topics into your content to have sufficient breadth of coverage.
Having said that, this provides a nice segue into talking about Content Score and how it relates to Distribution, Topic Depth, and Topic Breadth. First, I’ll explain how Content Score is calculated and then explain how it relates to those factors I just mentioned.
Content Score is calculated by adding the number of Mentions for each related topic. Because there’s a maximum of two points for each related topic and there are 50 related topics in the list, the theoretical maximum Content Score is 100. 50*2=100.
But don’t let that top score fool you. While your school teacher may have given you an A+ and made you feel good, in the real world, it’s all relative. There’s always room for improvement!
Aim to reach the Target Content Score and it will put you comfortably ahead of your Top 20 SERP competitors in comparison to content comprehensiveness. Content Score also provides a good estimation of topical breadth of coverage. In other words, how many different topics are covered in the article.
Let’s use a page with a Content Score of 16 as an example. That page could mention eight topics twice all the way up to 16 topics one time each to achieve that score. For a Content Score of 34, we could be looking at anywhere from 17 to 34 topics covered.
So to reach your target Content Score, you’ll generally need enough topical breadth combined with adequate depth of coverage. And you’ll want to achieve this within a certain number of words, known as the Target Word Count.
Target Word Count is derived by looking at the Top 20 SERP competitors, removing any extraneous word counts on both of the spectrum. Then we take the average and add a percentage to account for the increase in topical coverage. That’s because you’ll need a little extra room to cover those extra related topics.
Note that the relationship between the increase in Content Score and Word Count is not linear. So the Target Content Score may be double that of the Average Content Score, but the Target Word Count may only increase 15%.
That means content written to hit the Target Content Score within the Target Word Count will be more informationally dense and extensive than everyone else. You’re conveying significantly more information in the space of only a few more words. That means no filler words!
Remember to use these metrics as guides to creating rich content that both your audience and search engines will adore.
Written by Stephen Jeske