The Content Quality Metrics that Drive Engagement & Publishing Decisions

By NativeAI / April 26, 2018

In the era of "fake news" and with millions of small-time media properties vying for cheap attention, the public is yearning for quality content.

The desire for quality is apparent in the growth of paid subscribers at The New York Times, which is currently enjoying more than 3.5 million paid subscriptions. Audiences want quality content so much, they're signaling a willingness to pay for it.

But, "quality" is not merely measured by accuracy.

Quality is synonymous with content the audience cares about, and from viewpoints they can agree with.

The only way to crack this code of holistic "quality" is to analyze the data behind engagement. Data-driven publishing is the key. And, publishers are rushing to harness the power of big data to drive more engagement with their audiences and rise above the noise of poor quality content providers.

Understanding Quality Content Metrics

As a publisher, you need to consistently publish content that sparks interest in your audience. Understanding your audience interests beyond general topics, and digging down into deeper sub-interests, helps identify more intriguing topics and create more personalized content.

The best editorial teams rely on data to tell them which sub-topics are timely or interesting. National Geographic, for example, may choose to cover giant squid over sharks, and Teen Vogue may choose to spend more resources on stories about Harry Styles instead of Justin Bieber.

Knowing, not guessing, which topics your audience will engage with requires key content metrics, measuring publisher-centric KPI's, such as engagement, volume, interests, and channel performance.

Evaluating this data, helps you keep a finger on the pulse of your readers.

Engagement Quality

Engagement is tricky to measure without the right tool.

To effectively measure engagement, you have to combine, compare, contrast, and analyze many different metrics, like traffic volume and scroll depth. Depending on your content analytics tool of choice, you may have to reference different tools or data sets to find the relevant data. In most cases, there is too much data to manually analyze, which causes analysis paralysis and yields little-to-no value.

That's why we invented the Engagement Quality (EQ) metric. EQ is an exclusive NativeAI metric that uses engagement signals and aggregates all the data to formulate a single metric. This metric is found throughout our entire analytics platform, allowing you to easily measure engagement across entire sites, audience segments and channels, even down to individual stories or contributors.

Engagement Volume

Engagement volume is basic web metrics, and includes visits, views, page views, etc. Using engagement volume, publishers can see which topics, subtopics, types of content, and authors are driving the most traffic.

This data yields a lot of valuable information, such as new versus returning readers from a particular channel, the geographic locations that are driving the most traffic, and the device types that are driving the most engagement.

All of this data is necessary to make strategic decisions and to optimize content and editorial calendars over time.

Audience Interests

Gathering key insights into current audience interests is how news outlets like CNN, NPR, and The New York Times produce compelling and timely stories for their readers on a consistent, daily basis. With audience insights, publishers can easily adjust editorial calendars to reflect the subject matter and types of content that drive engagement.

NativeAI engineered an audience interest intelligence engine to coincide with our publisher analytics platform. The tool uses advanced algorithms to recognize more than one million distinct audience interests, and crawls each piece of content to identify unique entities and interests.

Engagement By Traffic Channel

You can uncover a lot of insights from monitoring referral channels. The engagement data from these channels helps to understand which topics, subtopics, and types of content that readers from specific channels are currently engaging with.

If a particular story goes viral, for instance, you can quickly identify that anomaly, and research the characteristics of that piece in regards to the channel. This data and analysis can yield golden opportunities to replicate over-performing stories and drive higher levels of engagement through each channel.

Engagement By Audience Segments

Audience segments is another way you can learn more about your readers. Segmenting readers by broad or niche interests, their devices, or the channels they came from can help you dive deeper into their preferences and the content that performs the best for that segment.

When your team utilizes the insights coming from audience segments, they can make improvements to social distribution strategies, gauge how readers interact on mobile, and develop stronger, more personalized stories and content.

Engagement Quality by Author

Tracking engagement by contributor allows you to break down anomalies by an author, or dive into the characteristics of their high-performance pieces. You can apply any of these insights to editorial standards, and provide feedback to other individual contributors.

As a result, you can deliver more content from favored authors to your readers. You can also determine if there's a certain tone of voice or style of writing that your audience prefers. All of these game-changing insights will ensure readers are getting better content that they'll actually engage with.

The Tools Needed To Measure Content Marketing Metrics

When it comes to these crucial content marketing metrics, publishers can't rely on Google Analytics or similar platforms designed for marketers and businesses. Instead, publishers should utilize tools specifically designed for publishers. Other tools will simply provide data that are too high level to make decisions about content and publishing.

Publishers can use an analytics tool such as NativeAI, to help report on the content marketing metrics that matter. Over time, these platforms will be crucial to improve their distribution strategies and editorial calendars. See how editorial teams are using audience insights to improve their performance with a demo of our a publisher analytics platform. How to Extract Actionable Publishing Insights From Deep Audience Data

Written by NativeAI / April 26, 2018