Consider one depressing fact: out of the millions of blog posts published on the internet every day, 50% of them get eight shares or fewer. That’s because even in today’s digital marketing age, with all the analytics at our disposal, many marketers still treat content marketing as mostly guesswork. We’re guessing whether our target audience will respond to a given piece of content based on often rough sketches of what that audience looks like.

Now imagine you not only have the ability to maximize the reach of your content but to make sure it also appeals to your specific audience on a personal level. That’s content intelligence—using software to reveal the full context of a piece of material as well as its context within your broader marketing strategy. It’s “technology that helps content understand itself,” according to Ryan Skinner of Forrester. And if your brand hasn’t given much thought to it up until this point, you should start doing so.

Here we address some questions regarding what content intelligence is, the pain points it addresses, why it’s an invaluable strategy to reach a wider audience, and how brands can implement it.

What is content intelligence?

Content intelligence relies on such strategies as data computation and algorithmic attribution. While data harvesting and big data are certainly one aspect of content intelligence, that’s just the tip of the iceberg. Beyond the acquisition of mass amounts of quality data needed to leverage content intelligence to its full potential requires first performing an audit of all your content marketing initiatives past and present.

Now, best practices state that you should be performing a content audit once a year anyway, so hopefully your marketing is relatively organized already. If not, simply roll up your sleeves and develop an organization, categorization, and tagging system for all content to date. And the reason you’ll want to include back content is that CI can help you refresh any of your evergreen content as well as update all files in your backlogs, allowing it to be viable and relevant now and into the future.

What pain points does content intelligence address?

The quick answer is that CI addresses most pain points in your content strategy. Take social media, for example. Many marketers are often burdened with one existential question regarding content strategy and social media: what do I share and via which channel? CI can analyze content from previous interactions and determine what new pieces of content you should share on which platform. It will also leverage consumer models in this endeavor.

For many marketers, another typical pain point involves not getting the right content to your audience segments. This is rarely a problem in the e-commerce world, for example, Netflix and Amazon tailor recommendations to customers based on past behavior. Wouldn’t it be wonderful if content marketing was this targeted and personalized?

With CI it can be. This software can leverage past search behavior to tailor content recommendations to your target audience. It can determine which pieces of content have the highest conversion rate and even tell you the best strategies for the succession of individual pieces of content (i.e., which piece of content should follow another piece to ensure the highest conversion rate).

Those are just a couple examples, and CI can factor in hundreds more to ensure your content reaches the right person at the right time. It all harkens back to that old adage: work smarter, not harder. In this case, you’re making your content work smarter.

How do I implement content intelligence in my organization?

Now that we know what CI does, it’s time to integrate a solid content intelligence platform into your marketing strategy. There are some essential features to look for when selecting your CI platform, and these include:

  • Quality of engagement scoring
  • Real-time analysis
  • Audience interests/insights
  • Author analysis

A CI platform that utilizes NativeAI, for example, offers publisher analytics that correlates with all engagement metrics—such as page views, social media shares, etc.—in one single metric called Engagement Quality. Ensuring your AI platform utilizes real-time analysis is also vital, as you want to be able to track all content no matter how quickly it may go viral. The same goes for audience interest insights, which is the key metric for connecting your content topics to an audience’s specific interest. Finally, the author analysis will segment data from the content creator to accurately compare audience engagement quality.

Conclusion

There are two great hurdles in the world of content marketing: producing engaging content, and ensuring that content gets in front of the right pair of eyes. Content intelligence excels at both of these fundamentals by supercharging content quality and delivery. It will give you more certainty than human decision making ever could in regards to what you need to write about today, and how quickly you can get it in front of those who will value it.