The Three E’s of Successfully Optimizing AI for Content

September 6, 2018 Anjali Yakkundi

Everything I 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 I really wanted to know is what AI and machine learning could mean for content marketers, and what’s just overhyped.

In my quest for more information on AI in content marketing, I found that the most common definitions for AI in content marketing are centered on content intelligence: how emerging technologies can be used to “understand” the qualities inherent to a piece of content, such as an image, audio clip, or video.

It’s pretty amazing to think that a computer can do things like analyze an image and video, and understand not only the subject matter of the content, but also things like emotional quality (e.g. is the person in the picture happy?) and maybe even recognize the actual face of the subject (e.g. recognize the person in the image is President John F. Kennedy).

But as I continued my research, I couldn’t help but thinking there’s got to be so much more that AI and machine learning could do for content. And while delivering content at scale requires organizations to do these three major things, only one involves recognizing the content’s subject:

  • Enrich: Automatically capture inherent qualities for each piece of content

  • Execute: Automate the content creation process

  • Experience: Better understand content performance

And this is just the beginning. AI and Machine Learning can enable marketers to do so much more if they also follow these three E’s of for optimizing these technologies for content: Enrichment, Execution, and Experience.

Using 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 is monotonous and time consuming for me. But I also realize it’s extremely useful to have tagged content, because it will be much easier to search and find that content when I’m looking for it next time.

And that’s where AI can help. It can auto-tag using pre-determined tags that further describe an image, or use speech-to-text capabilities to identify and tag the person speaking in an audio clip.

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

Using AI for Content Execution

Content intelligence technologies can do more though then just understand content qualities- they can also improve content processes. For example, AI can “understand” the type and amount of resources required to create various types of content, so it can add intelligence and help auto-route tasks. For example, if I’m a graphic designer and I receive an assigned task, but I’m already bogged down with projects, or am out of the office, AI can automatically reassign that task to another designer on the team who has the same skillset but has more bandwidth.

Using AI for Better Content Experiences

Finally, marketers can use intelligence technologies to drive better content experiences.

For example, as my content marketing initiatives mature, I can use them to gather and analyze 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?

AI then can use its findings to start suggesting content that will better resonate with my customers, project ROI for new content pieces, suggest more budget allocations for higher ROI content programs, and even surface new content ideas based on past content’s ROI.

I’m not saying that AI will replace content marketers’ jobs anytime in the future: I realize that 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 the three E’s —Enrichment, Execution, and Experience— of optimizing AI in content can help organizations take their creative content ideas and deliver them faster and at scale.

If you’re interested in learning more about how organizations can utilize intelligence technologies in content and other marketing activities, visit Aprimo at booth 546 at Content Marketing World, or register for our upcoming webinar, “Optimizing AI for Marketing: What’s the future and what’s just hype.”


 

About the Author

Anjali Yakkundi

Anjali is a product marketing director at Aprimo, and looks after the strategy, go to market, positioning, and messaging for the Marketing Productivity, Plan and Spend, and Digital Asset Management products. Prior to joining Aprimo, she spent 8 years at Forrester Research where she covered the marketing technology, eCommerce, and digital agency spaces.

Follow on Twitter More Content by Anjali Yakkundi
Previous Article
Digital Marketers Struggle with Defining Value for Content, AI Solutions
Digital Marketers Struggle with Defining Value for Content, AI Solutions

This blog discusses trends that were talked about during DMEXCO 2018 conference, such as how to measure the...

Next Article
Determining a Business Value for Utilizing AI in Your Marketing
Determining a Business Value for Utilizing AI in Your Marketing

This blog describes several ways organizations can determine business values of utilizing Artificial Intell...