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Machine learning is changing the content marketing game. With the democratization of artificial intelligence tools — meaning you don’t need a data science degree to implement machine learning technology — you have at your fingertips a whole new world of content optimization, customization, personalization and relevance, at scale, whatever industry you’re in.
That means you’re able to deliver more engaging and meaningful content for your customers, with carefully crafted content style and beguiling calls to action at the right time and with the right cadence.
You’ve already got a vast volume of data about your customers and your market at hand; even more data is being produced and for sale every day. Machine learning is the tool that unlocks the underlying insights that data leads to, allowing you to map data and content to the right audiences.
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And once people engage with your content, machine learning can help you make sense of the data you gather, revealing what’s working, what’s not — and how to turn that into insight that optimizes your campaign and turn setbacks into leaps forward for not just your current campaigns, but future ones, allowing you to deliver truly personalized experiences at scale.
Unified theory of content marketing
Machine learning offers a huge leap forward in content marketing, which in most organizations tends to be strictly siloed — social marketers might be coordinating with the research team and the content marketing team, but marketers running each channel have a different — sometimes significantly different — view of who their customer is and what they want, across each of their individual campaigns. And that can lead to dissonance for your customers.
And then there’s machine learning, which gathers together all the data your marketing campaigns and research uncovers, in every channel, from email to social, search, discovery, promotion, and more. Better yet, it breaks down the silos between all these channels, giving you a unified view of your entire customer base. Machine learning can link the propensity and behaviors of your customers at all points of contact, allowing you to develop a truly comprehensive view of what your marketing target audience is.
Eliminating the guesswork
Getting ready to launch a new campaign? Machine learning offers you a perspective on and insight into customer engagement: a factual, data-driven view, in one place, of what worked in each and every one of these channels. From there, you’re not only able to tailor your campaigns based on your gut instincts, but work with the insights and feedback that your machine learning solution offers, from statistical significance to actual feedback and suggestions for future campaigns.
Companies like Marketo are now deeply invested in machine learning in the marketing space, working with customers to uncover everything that a machine learned algorithm can offer a marketer, says Arun Anantharaman, chief product officer at the company. Marketers are particularly eager to replace hunches with data. As Anantharaman explains, “One of our corporate marketing customers said AI is taking the guesswork out of determining what content will resonate for each person that interacts with their brand.”
Companies are now moving beyond A/B testing — up till now the primary way to understand the impact of content — to a place where data-fed algorithms are achieving significant results for something called Content AI.
“Just tell me what customer you’re going after, what the demographic and firmographic is, and then we’ll recommend, as a starting place, the 15 pieces that will perform the best of the thousand pieces of content in your repository,” Anantharaman says. “That eliminates two months of guesswork for marketers, testing what pieces from that huge repository of data would actually work.”
From there, machine learning adds an automation piece, enabling a campaign to cycle through those 15 content focus areas for those campaigns. One client saw a 75 percent direct lead conversion for things like form submissions, Anantharaman says.
Machine learning works when you have a lot of data and a lot of content, which means it’s not the perfect solution for every company. For smaller organizations just starting out, machine learning may not be the right solution. Their repository of data and content may simply not be enough.
And it’s also a tool that needs to be integrated fully into your marketing strategy. Machine learning insights aren’t one-and-done; they’re meant to continuously learn from ongoing data, offering smarter insights every time.
And that can often mean creating a whole new role within your company: putting the right people in place. It requires marketers who understand what needs to happen around personalization, around the notion of specific audience hyper-segmentation. They need to focus on mapping audiences to content to get outcomes you couldn’t have previously achieved.
But embedding machine learning and AI within your existing workflows doesn’t mean you need a data scientist on staff. Advances in machine learning technology mean that solutions can be tailored to specific companies, capabilities pre-filtered to capture what a marketer actually wants: data about open clicks, email activities, behavioral data, website actions, ad network engagement across the web, Facebook lead ads, LinkedIn lead generation ads, event engagement, and more. All of it presented to you in an easy to read format.
Then creating seamless integration with your own content, so that activating machine learning insights — a), what’s the right piece of content for my audience, and b), what’s the right audience? — can be literally as easy as clicking a checkbox.
Anantharaman says after Content AI, the next step is Audience AI — meaning that the marketer gains leverage to drive campaigns and identify audiences that have converted and had success in the past. The machine learning would scour your database for lookalike targets in real time, saving the marketer hours, days, and even months of time. With AI, that means producing a list of people that have a high chance of converting from a database in the hundreds of thousands, or maybe even the millions. With machine learning, you’ll be able to understand the success you had with previous campaigns, map that success to the attributes you want for a particular profile, and then generate the audiences that you need for the next campaign, in seconds. Something that could never be done manually.
For more on how Marketo is using Artificial Intelligence in Marketing, visit https://www.marketo.com/ai
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