How to Leverage AI for Your Content Marketing Game
In this, the “new” era of personalized advertising, content marketers looking to help their brands reach the right audiences are being told to use hyperlinks, create internal content networks, adopt machine-mediated conversational marketing tools, and fly over the moon!
Well, no, maybe not the last one. But trying to identify what these tips mean for a small business (or even a large one) can be difficult if you don’t know where to begin. And for those of you just learning that your competitors already use machine learning technologies in their strategy, well, this might be a good place to start.
A Link to the Past
As many of today’s top content marketers have shared, current brand-building strategies are structurally the same as they were pre-digital media. In short, they’ve generally been understood to have three main pillars: Build, Grow, and Convert. As you’ll see throughout this article, these pillars are largely cyclical; the success of one is dependent on the success of the others.
With the help of machine learning technologies and applications, marketers have gotten creative in rethinking these principles to better suit their development, promotion, and deployment strategies. Are you ready to do the same?
In a market that follows the Power Law, where 5–10% of content is responsible for 90% of an organization’s traffic, it is critical that content creators, marketers, and strategists have a sense of what is trending; in other words, what will be most interesting to their prospective and existing audiences. During the Build Phase, these experts must work together to generate the kind of high-quality content their audience desires.
In the past, this process might have involved reading other headlines, checking sales statistics, and creating more articles that suited the masses. In the age of digital media, where trends change by the minute and businesses risk erasure among a long list of search terms, content creators and marketing teams alike have been adopting technologies that assist them in creating personalized, meaningful content (at the right time), not only boosting engagement, but helping businesses take better care of their customers.
One of the first places AI technologies have been introduced is in the audience research, or audience targeting stages of content creation. You know it well. It’s a room of harried interns shouting, “Who wants to love us?!”
What, no one does that anymore?
Okay, then everybody already knows: To better build high quality content, those involved should have a quality understanding of their audience: where they hang out, what kinds of questions they’re asking, what knowledge they have, and what knowledge are they looking for.
For larger companies, this means dragging through screens and screens of clicks and likes to nail down one lead or trend that might relate to the customers who might want to buy a given product. But, as Jeffrey Kranz — CEO and co-founder of content marketing and strategy agency Overthink Group — says here, inventorying this type of information is a tiring, labor-intensive process.
Thankfully, his and other companies have come up with AI solutions that utilize machine learning for that very purpose. These technologies quickly amalgamate varying inputs and generate individualized client profiles of those most likely to purchase a given product or service. Some go as far as to anticipate gaps in existing content, helping creators stand out from their competitors.
One company disrupting the content strategy market and helping marketing teams optimize their search engine visibility is MonkeyLearn. To expand, a way that MonkeyLearn applies machine learning to client goals is to automatically tag relevant conversations based on content. Basically, their technologies use this information, and turn unstructured text data (emails, texts, comments) into structured data, ready for analysis and purchase forecasting.
Still feeling clueless?
Take InMobi as another example. They’re the leading global provider of cloud-based intelligent mobile platforms. With a wide range of products to help with data management which measurably improves customer satisfaction, InMobi has in fact been helping reveal and organize big-brand customer insights since 2007, including Netflix, Amazon, and Samsung. From a consumer perspective, it’s easy to visualize how these technologies are put to work to help people like us find the content we want.
Machine learning also supports content marketers who are responsible for sifting through similarly large amounts of data, matching customers to campaigns and optimizing demand. Companies like Salesforce do this every day, helping clients build content strategies that are both client-centric and, as is always nice, environmentally friendly!
You may be saying, so what? That’s not worth it. But let’s pause here and really visualize the sheer amount of data we’re talking about. Google Search, in 2016. How many searches do you think that little white search bar got per minute?
Who knows how many of those searches could have brought your business page to the front if you had have been on the “up-and-up” on what your clients were doing? These systems not only help you keep an eye on your competition, but they give you an idea of where your audience is going for the information they’re looking for.
The point is, these technologies are getting rid of the hours and hours of data-point analysis work it takes to forecast your next best content move, helping you move quicker in a market where speed is critical.
Before we move onto the Grow phase of content strategy evolution, it’s also important to note that although many marketing teams are becoming more strategy-oriented in their content creation, it is nevertheless important to make sure your content is meaningful, authentic, and if you can manage — emotionally driven. That means hiring talented writers, designers, and artists to give you a unique edge, generating content that really stands out.
Think of it like a 50–50 relationship. Creators and strategists, working together with AI technologies, side by side, making sure the other succeeds in establishing active engagement. Taking inventory, analyzing, utilizing, and ultimately gaining client-centric focus.
Pretty symbiotic, if you ask me!
Next, during the Grow Phase, where past practices dictated answering to physical sales metrics — a.k.a, to what service or product sold best — today’s methods involve coming together to build promotional strategies that increase the impact of your content, broaden your following, and which move toward creating an internal content network that helps keep clients on your site, longer. We’ve seen some of this technology manifest in our social media atmosphere, where specific algorithms help customize the content we see on our dashboards.
A good promotional strategy should thus make use of these algorithms to promote content via direct, social, and referral traffic sources. This step is equally critical in terms of content success; as good as your content may be, it won’t do much good if it barely reaches an audience whose listening.
To break this down a bit further, when we talk about “growing” our content strategy, we can visualize our content as having a network of subtopics that someone reading our initial article might be curious about as well.
For instance, say I sell athletic apparel and my web presence is centered around health, marathons, and travel. Someone has come to my site because an article titled: “Six Ways to Improve Your Endurance” caught their attention and aligned with their interests. As it happens, my lovely content creators have also created six other articles that have to do with endurance running techniques.
Not only can I choose to apply product placement hyperlinks within that content, but I can have the article itself (or a list of recommended articles like the ones at the bottom of this page) act as a home base for the other related content I’ve hosted on my platform. This process results in what is sometimes referred to as an internal content network, in effect giving you more “screen time” with which to interact with your audience.
What the example above illustrates is the compounding effect that is now being incorporated into many content marketing strategies. For instance, when all blog posts are in the same topic cluster, it is likely that consumers will click through multiple page links on the same subject to learn as much as they can. This added time leads to greater information growth, and better supports the goals of marketing: to appeal to the client in a way that will have them looking for ways to buy the service or product you offer.
In short, each post will support the growth of each one in its cluster, via backlinks as well as their main directory, and should lead to verifiable ROI’s.
Machine learning technologies thus come into play to help content teams understand resultant analytic data on another level, extending their capacity to identify customer trends. Another such company helping brands find the right audience, and vice versa, is Alliai. They allow their clients access to structured Artificial Intelligence technologies that build backlink streams and optimize their personal consumer outreach strategies. In this way, machine learning is helping even small brands get noticed, fill in the empty spaces, and better compete with other providers on a global scale.
Yes, blah blah blah, no one’s forgotten the real goal of any marketing enterprise. But after driving enough (hopefully prolonged) traffic to the page, content marketers are still responsible for converting this traffic into a specific funnel that meets their objectives. Remember, likes and shares don’t equal visits. You want engagement: comments, clicks, and eventually…clients. Learning what customers like also resets the Build, Grow, Convert process. (If you’re “totally diehard”…feel free to read this article twice!)
At this point, you might be able to guess where machine learning tools come in handy converting strategy into profit for brands big and small. As consumers, we’re already familiar with one popular example: the “may I help you” chatbot.
A recent study notes that 89% of consumers want to use messaging to communicate with businesses. Lucky for us, machine learning is getting better at “understanding” human voices (whether verbal or spoken) to offer up quick and efficient solutions without the help of a service agent. Take Growthbot, for instance, a technology that recognizes written questions regarding good marketing practice, and responds with some pretty useful advice. They’ve found an incredible smart way of fusing their marketing tools with the very services they offer.
The Chatbot is of course just one of the many “conversion events” that web marketing teams can utilize to increase profits and engagement. There are many paths that lead to great conversion, and many of them will seem familiar; anything from newsletter signups to contact forms, from free trial offers to “Add Product to Cart” action buttons, machine learning continues to be of service to these efforts, organizing the resultant data for the benefit of future campaigns.
A few tips, while we’re on the subject:
· Prospective consumers are more likely to buy on a repeat visit, as opposed to their first interaction.
· Conversion rates increase when something is offered in return, most effective being either an EBook or How-To Guide.
· Coordinating a free product giveaway will increase conversion, and can boost your existing campaign network.
Another important aspect of this relationship between machine learning and conversion is the way these technologies support marketers in determining verifiable opportunities for improvement, and do so objectively and efficiently.
Although a small business might want to get by on doing this kind of work on their own at first (determining what works and what doesn’t), larger media or content sites should consider optimizing the bulk of their data automatically using available machine learning technologies.
Of course, we can all afford to take a leaf out of Amazon’s book, who obviously thrives so much in the content marketing niche that they now offer their own machine learning services under the title AWS Machine Learning. Here, machine learning systems help business pages provide consumers with an individualized client experience, and even find customers that are at a high risk of attrition.
In action, these technologies offer major benefits toward upping your content “game”, helping you build meaningful relationships, grow alongside your clients, and convert them to your cause.
It’s like if Moneyball met Content Marketing! Same old game, with better results on the scoreboard.
(Now…how do I get machine learning to help me meet Brad Pitt…?)