Mastering Content Marketing Analytics: 3 Things Publishers Should Be Doing Right Now

By NativeAI / May 1, 2018

Why would anyone want to write about the Kardashians?

Since 2007, publishers have churned out thousands of articles about the reality television family. In any given week, publications such as The Washington Post, Forbes, and even Fortune distribute articles that range from the business and financial tips from the sisters to pregnancy rumors.


What's so special about the Kardashians that require so much frequent coverage and detail about their lives?

It's simple: Readers are actively engaging with stories about the Kardashians.

That’s why publications like Rolling Stone, who produce content about Nina Simone, or the state of the Oklahoma school system, also write articles such as “The Kardashians: The Egos That Ate America.”

It’s not the preference or the desire of Rolling Stone to cover reality television stars; it's to meet the demand of their audience. It’s a subject that their readers want to engage with, and the analytics prove it.

In this article, we'll explore how publishers can harness the power of content analytics, and how to extract the most value without getting bogged down in data analysis.

Mastering Content Marketing Analytics for Publishers

As a publisher, you can’t expect to write endless amounts of articles about the Kardashian family, for example, as a whole and expect that content to perform. Audiences aren't interested in just anything about the family, they are interested in specific things, like a new makeup of fashion relationship, or a juicy new romance rumor.

With content marketing analytics, editors can find these areas of opportunity. It’s why we see pieces such as “Kardashian-Jenner Baby Timeline” in Billboard Magazine, or “Is Kim Kardashian West’s Halloween Costume Controversy Really That Fair?” in Vogue. These pieces were never crafted by chance. The topics come from data that indicated readers would be interested in that particular subject, at that exact time.

The most successful digital publishing properties use this data to frequently write pieces that generate the highest interest. So, when it comes to the content that drives engagement, it all goes back to the data. As that Rolling Stone article states, “you might loathe the Kardashians, and that's more than understandable,” but until readers stop being hooked by the content, content regarding the family isn't going anywhere.

Using the following crucial content marketing metrics, publishers can learn how to use data to enhance their content strategies.

1. Studying Volume vs. Engagement Quality


It's important to track the volume of traffic. However, understanding the quality of each visit is just as important, if not more so. While you can't expect to analyze every single visit, you still need to have an understanding of how well content is performing beyond engagement metrics.

Your content marketing analytics platform should provide a way to aggregate engagement metrics into a single data set, that can be easily tracked over time.

NativeAI, for example, developed a custom Engagement Quality (EQ) metric. Using the EQ metric, publishers can reference a single metric that aggregates and measures all the engagement-specific metrics, like the time spent on an article, a reader's scroll depth, user actions, story length, and more. NativeAI combines those metrics into an engagement score, which publishers can monitor and breakdown engagement by segment, acquisition channel, location, and even user loyalty.

2. Tracking Audience Interests


A lot of publishers understand that audience interests go beyond topic categories. Topics can go down to a granular level with sub-interests on top of sub-interests. For instance, a publisher can break down a general topic such as Politics into World Politics, then into European Politics, Economic Policies, and find opportunity in content about the latest Brexit news.

In the Kardashian example again, in addition to all of the potential topics and sub-topics, it also might be valuable information to know that 80% of an audience prefers news on Khloe over Kim.

All of these audience interests are crucial for publishers to track.

Over time, interests can shift from one aspect to another. Publishers that want long-term success need to invest in ongoing analysis and research regarding their readers wants, needs, and desires. Only having a periodic focus group or conducting one-time analysis won't be sufficient for substantial insights. Editorial teams must stay abreast of fluctuations in interests over time, and in real-time.

3. Tracking Engagement By Audience Segments


Tracking volume by segment is one thing, but tracking engagement by segment is another. Volume is great for showing channels that are generating a lot of readers, but the engagement by segment will help show which specific topics, subtopics, authors, and more are resonating the most with that particular audience segment.

Publishers can track engagement quality and interests by each audience segment to identify valuable behavioral patterns. Editors can reveal quick insights, and make timely updates to content and editorial strategies. As a result, teams can optimize their publishing strategies right away.

Tools To Measure Content Marketing Analytics

In order to get the most from tracking content marketing analytics, publishers need to put these insights into action. Editorial teams must understand why their results are what they are, and make timely changes to their strategies. Publishers can track and measure these results, then compile them within a content performance report to monitor ongoing improvements over time.

Related Reading: How to use content analytics for digital publishing

The key is presenting all the analytics data in an easy-to-understand format. Rather than spending time deciphering data, publishers can focus on putting those insights into action. To learn how your team can compile and visualize performance data, see what the NativeAI Analytics Tool does in this free content analytics demoHow to Extract Actionable Publishing Insights From Deep Audience Data

Written by NativeAI / May 1, 2018