Three Things Content Marketers Don’t Know About Machine Learning — But Should!

We hear a lot about how machine learning will be a part of every content marketer’s future. But, in reality, machine learning is here and ready to work for you now. The best way to extrapolate the future is to look at what machine learning is doing today.

Marketers are under pressure to achieve faster content velocity. Demand for content is exploding, driven by the number of channels, the specificity with which marketers want to create targeted content for audiences, and how specific they want to be in messaging to individual audiences through those channels. The challenge is how to use, reuse, and remix content in new ways to reach those targeted audiences — motivating them to engage and respond — without recreating lots of content. There is no way to do this manually and at scale. Machine learning can help in three ways: by automating repetitive tasks, using content in targeted ways, and creating the right content.

Automate Repetitive Tasks — So You Can Focus on More Important Issues!
How much time are you spending on administrative tasks, such as asset tagging, versus content creation? Tagging assets with relevant keywords is essential to making them searchable, but it is also a tedious, time-consuming task that most marketers would rather avoid. Machine-learning technology can smartly include the most valuable or least expensive keywords in copy. It can associate similar or related assets, making it easier to combine relevant copy, images, and videos for target audiences. If your audience has consumed one bit of content, it can smartly recommend what to offer next for the highest conversions. It can help predict what content will lead to the behaviors — sharing or engaging with content, increased sales, improved customer loyalties — you’re trying to attain from customers. Adobe’s Smart Tag technology is available now to automate the insertion of metadata, so you can achieve better search results while slashing the amount of time you spend on this task.

Use and Reuse Your Content in Channel- and Audience-Specific Ways.
Each marketing channel presents a unique set of requirements for the size and resolution of marketing assets. When a new platform emerges — or if you decide to add a new channel — it could require the time and expense of redesigning existing assets. For example, if you have a piece of content delivered to a web channel or blog, machine learning can smartly crop that content for a mobile channel or reduce the copy in smart ways. Visual content and videos can be shortened to optimize experiences for different channels based on the patterns by which people are consuming them.

Machine learning will either provide recommendations — or actually provide a first draft of the new content — that can then help accelerate the pace by which you get those different pieces of copy or creative or even videos out onto the various channels and to the selected audiences.

Create the Right Content Without Creating a Lot of Content.
You don’t want to have to create massive volumes of content, hoping that just some of it will be effective. It’s more important to be able to create the right content that is effective in your channels, learn from that, and then create more content based on those insights and expand from there.

Machine learning can provide you with the intelligence needed to quickly determine what’s working as well as recommendations to point you toward amplifying things (or something similar) that also might work with that audience. The learning part of machine learning means that, over time, the machine becomes smarter. We are still in the early stages with this, but machines could potentially learn so quickly that you could remix, reuse, and adapt content almost instantaneously; test it; and learn whether it will be an improvement over your previous campaign or you need a different approach.

The Future Is at Your Fingertips!
The best way to think of machine learning is as an intelligent assistant that can quickly make conclusions or recommendations based on large amounts of varied data. Marketers can then learn and understand how content is consumed, how it impacts consumers’ behaviors, and how to create more relevant, interesting, and engaging experiences for consumers. The potential impact of machine learning on all aspects of the customer experience is vast — look for marketing-technology solutions that allow you and your team to experiment with deep learning to see how it can boost your productivity now.