You’ve heard it before — probably on this very website: content is king. I’m not disputing that. In fact, I agree. In the digital age, brands live and die by the content they produce — from their website to their emails to their social media collateral. Content is now a cornerstone of marketing and commerce, so naturally many companies are expanding their content teams.
But the truth is more manpower isn’t what they need at all. What you produce and how much you produce is only as good as the way you use it. Not to mention, consumers are beginning to experience content overload, making them both less likely to ever see a brand’s content and more likely to ignore it if they do. There’s simply too much for customers to see.
This means your team can produce the best content in the world, but if it doesn’t get in front of the right people, what difference does it make?
Less Really is More
Trust me when I say consumers don’t want to see more content — they want to see the right content.
While many brands are choosing to push out more content with the hopes that something sticks, the smart approach is to customize content based on interactions, purchasing behavior and demographic data. Instead of increasing manpower to produce more content, brands should supplement their content team with technologies that gets their content in front of the right people at the right time.
Fortunately, customizing content is becoming much easier as technology evolves.
Machine learning, for example, can be applied to big data to learn and predict which content is most likely to interest individual customers. A WCM platform supported by machine learning algorithms can automatically match website visitors with personalized content and landing pages based on their past and real-time behavior – all without the need to be programmed.
Half Man, Half Machine
With technologies for managing and delivering content becoming more advanced than ever, brands must be comfortable relying less on man and more on machine – or at least strive for an equal balance.
While companies like Facebook and Google are starting to use machine learning to improve site search functionality and customer service, it is time for companies of all sizes to reconsider their content marketing strategy and begin investing in rules-based and machine learning for content optimization.
Personalizing content takes a lot of time and manpower. To customize content to individual customer preferences and behaviors, companies have to collect and analyze a colossal amount of data. This is a difficult, time-consuming and expensive undertaking.
And acting on this data in real time is nearly impossible without machine learning algorithms. Even if companies are able to bankroll this process, there’s a good chance they’ll still mistarget their content because, after all, we’re only human.
But artificial intelligence eliminates the risk of human error and eases the unthinkable workload this degree of personalization calls for. By applying machine learning algorithms to big data, marketers can deliver personalization at a depth and scale we never would have thought possible 10 years ago.
The Balancing Act
Don’t take this to mean you should go out and fire your content team — in fact, please don’t! Every modern brand needs a content team, but they also need a strategy that balances volume and precision.
This means that modern brands need to invest in enough employees on their content team to generate quality content, but also to invest in the right technologies that will help them cut through the noise, and deliver that quality content to the right consumer, in the right place, at the right time.
And even as brands begin balancing their content strategy between humans and technology, these platforms shouldn’t be considered a threat to content teams. If anything, they should be liberating. Since WCM platforms with machine learning capabilities can do all of the legwork of personalization, it frees up bandwidth and budget to create even more dynamic content.
So as marketers plan for 2017, they will want to consider their current practices and decide whom or what they want to invest more in — man or machine.
Justin Anovick is VP, product at Episerver
