The Three E’s of Successfully Optimizing AI for Content

Jan 02, 2019


Article ImageEverything you seem to read now is talking about Artificial Intelligence (AI)--robots are taking over our jobs, another vendor is investing in AI, inherent biases in AI, governmental AI regulations. It’s everywhere!

But through this hype and bluster I—and I’m sure many other marketers—have wondered what practical applications AI and machine learning can really have for content marketers, and what of this information is just noise. What is real and what is hyperbole? What type of real impact can this emerging technology have on content production, content marketing, content management, and ultimately, customer experiences?

In a quest for more information on AI in content marketing, I’ve discovered that the most common definitions for AI in content marketing usually have been centered on applications for content intelligence, such as how emerging technologies can be used to “understand” the qualities inherent to a piece of content, such as an image, audio clip, or video.

When you step back and think, it’s really pretty amazing that a computer can do things like analyze an image or video, and understand not only the subject matter of the content, but also things like emotional quality of the image (e.g. is the person in the picture happy?), can transcribe the audio track into text (often within a matter of seconds or minutes too!), and maybe even recognize the actual face of the subject (e.g. recognize the person in the image or video is President John F. Kennedy). We’ve come a long way in not only developing these technologies, some of which have actually been around for years, but also in improving the accuracy of these AI technologies.

However, recognizing the subject or emotional quality in an image is only involved in one type of content process. As I continued my research I couldn’t help but think there’s got to be quite a few additional applications for this type of AI capability, as well as so much more that AI and machine learning could do for content marketers in general.

In what other ways can content marketers use AI to aid in other essential processes?

I believe that content marketers can take the application of AI and machine learning a step further, where it would be inclusive of recognizing the qualities inherent to content but also support other common needs of content marketers.

We call these the three E’s of AI and content marketing: Enrichment, Execution, and Experience

AI for Content Enrichment

Marketers can utilize intelligence technologies to automatically enrich content, which can save them time and money during the content creation process. For example, content tagging can be a monotonous and time-consuming task. But it’s also extremely useful to have tagged content, because it makes it much easier to search and find that content in the future.

AI can help with Content Enrichment by auto-tagging using pre-determined tags that further describe an image, such as by the person in it. But content marketers also can use AI speech-to-text capabilities to further identify and tag the person speaking in an audio or video clip.

AI also can “read” an image and perform sentiment analysis. This would enable the AI solution to perform a sentiment analysis and automatically add sentiment-oriented descriptive tags, such as “sad child” or “scared child” to an image of a child. This type of application would help content marketers more quickly find the image they want by searching using these emotion terms to ensure they choose the most accurate image for a project.

AI for Content Execution

Content intelligence technologies can do more though than just understand content qualities--they can also improve content execution processes. AI can be used to “understand” the type and amount of resources required to create various types of content, and then can add intelligence to the process by helping auto-route tasks to ensure content is created, approved, and pushed out on time. Intelligence can even be used to recommend alternate resources when preferred creatives, for example, are overbooked or there are upcoming, predicted bottlenecks. For example, if a graphic designer receives an assigned task, but is already bogged down with projects, AI can identify that risk, and to help reduce a potential bottleneck, automatically reassign that task to another designer on the team who has the same skill set but has more bandwidth. In another example, if a high-value graphic designer has the capacity now, but is predicted to have too many tasks in two weeks when the assigned project is going to kick into gear, AI can identify that risk and help reassign upcoming, less mission-critical design work to more junior graphic designers on the team.

Using AI for Better Content Experiences

Finally, marketers can use intelligence technologies to drive better content experiences that not only are engaging, but also impact the bottom line. This is critical for many content marketing teams today, as they can be seen as a cost center rather than as a revenue driver.

For example, as content marketing initiatives grow and mature, marketers can use analytics to gather and AI to automatically analyze and predict more holistic ROI metrics for them, such as:

  • How much money did they take to create?
  • How many and which resources were involved?
  • Were external agencies used and how much time did they spend?
  • What type of engagement did they produce across channels?
  • How many and what kind of leads did they produce?
  • What is the predicted ROI for future content marketing activities?

Content marketers can review such findings and even enable their AI applications to suggest content that will better resonate with their customers, project ROI for new content pieces, request different budget allocation scenarios to produce higher ROI, and even surface new content ideas based on past content’s performance.

Despite these potential applications for AI in content marketing, don’t believe any of the hype that AI ultimately will replace content marketers’ jobs in the future. AI and Machine Learning can never fully replace human creativity in the content creation and customer experience processes—after all, that’s the secret sauce of good content marketing. But utilizing these emerging technologies in their current state can help content marketers improve on the three main E’s—Enrichment, Execution, and Experience— of delivering great content at scale.


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