How layered section tagging customizes data for deeper content analysis

Our publishers need to conduct content analysis in the most efficient way possible. That’s why we recommend implementing section variables, a customizable data input that enables better content attribution within Chartbeat across your site.

But did you know that section tags can be layered together for more targeted measurement of content performance?  With just a few quick adjustments to your section tagging, your organization can better compare content side-by-side.

Here, we’ll take you through layered section variables and the long-term value of a robust section tagging implementation for content creators.

(For more on optimizing content, see: How Recirculation builds engagement, supports reader acquisition efforts)

How section tagging works

Sections tags are defined at the page-level using the _sf_async_config variable. This variable can be assigned multiple values (Chartcorps recommends keeping it to 10 or less) based on your organization’s content attribution, enabling you to layer on increasing levels of specificity to stories.

What is section tagging?

Section tags allow publishers to filter content by — you guessed it — sections on your site. See a full refresher on integrating Chartbeat here.

Once these tags are in place, that page will appear in section pivots and queries across Chartbeat’s products. This way, you can group similar pieces of content to mirror your editorial or channel distribution strategy as well as day-to-day responsibilities.

Your content’s section tagging in action

Let’s look at an example. A media organization writing about the FIFA Women’s World Cup may want to assign a sports author to temporarily cover the U.S. Women’s National team. 

This author will write about a range of topics: the team as a whole, venue, or specific profiles of players, such as star Megan Rapinoe. Depending on how Chartbeat is implemented, each of these stories may automatically pull a section tag directly from the CMS, resulting in one or two tags being applied, as we show below:

_sf_async_config.sections=”sports,world cup”;

While this implementation is technically correct, it leaves out a lot of useful information, such as: 

  • Which topics specifically performed best? There may be a lot of interest in the World Cup, but within that topic, which content is most appealing to our loyal visitors? 
  • Engagement metrics. How does Engaged Time differ between recap stories (in which visitors may scroll through quickly for quick stats or insights) versus a longer feature piece?

That’s why it could make more sense to take our content attribution a step further. With layered section tagging, editorial teams can keep their normal views of the entire site content, while adding the capacity to dig deeper into specific topics. For example, a feature on Rapinoe could be tagged as:

_sf_async_config.sections=”feature,sports,world cup,USWNT,rapinoe”

As we show above, just by adding more section tags we can already pivot on our data to answer many more questions. 

(For further reading, see: Using the Engaged Time metric to grow audiences, unlock loyal readership)

Get an even deeper view of your content

Let’s say the sports editor is interested in learning how different World Cup features are performing in comparison to each other. The above section tagging scheme would ensure the Rapinoe piece appeared under both the “feature” and “world cup” sections, but these tags may also include unrelated features as well as non-feature pieces about the World Cup.

Therefore, it could be more helpful to filter exclusively on pieces that have both of these tags. Here’s how:

_sf_async_config.sections=”feature,sports,world cup,USWNT,rapinoe,feature – world cup”

By layering together “feature” and “world cup” into one section tag, we’ve created a more specific (and thus more useful) view of the data, cutting across format and content. We can also use this method to layer together content subject and content context as they change over time.

Lastly, if an editor wanted to measure performance of human-interest pieces about Rapinoe and teammate Alex Morgan’s advocacy for equal pay months after the World Cup ended? They could implement this layered tag:


_sf_async_config.sections=”feature,sports,human interest,equal pay,USWNT,rapinoe,morgan,equal pay – rapinoe,equal pay – morgan”


By using the tag above, the story will correctly appear under all of the broader section labels (e.g., “sports”, “USWNT”). On top of that, we’ve also now created a new view of analysis in “equal pay – rapinoe” and “equal pay – morgan.” This allows you to compare similar content and identify which pieces perform best as more stories develop across these topics.

(Related: Learn from previous coverage to help plan future stories)

The takeaway: Implementing layered section variables

Now that know the tools to go deeper into your content, here is how to begin implementing them:

  1. Get the team on board. Speak with the person in your organization responsible for implementing and/or maintaining your Chartbeat code. Found them? Ask about the process of adding section variables to an article’s page before it’s published.
  2. Work with your team to align section tags to content strategy. A good approach is first asking what questions or problems section variables can answer. That way, you’ll be better prepared to connect those solutions to content attribution.

The result? Data analysis to help you optimize for reader engagement faster.


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