Answering the Top Questions Marketers Have About Artificial Intelligence
The term “artificial intelligence” is inescapable if you read the news, tech blogs or the latest business commentary. But what it actually means—and how marketers can make it work for them—is often unclear.
A new podcast episode from the Convince & Convert ContentPros podcast provides some much-needed guidance. In it, Marketing Artificial Intelligence Institute (@MktgAi) creator and PR 20/20 CEO Paul Roetzer (@paulroetzer) discusses what AI means and how marketers can begin leveraging it to boost visits, leads and sales.
Over more than 36 minutes, show hosts Jeffrey Cohen (@jeffreylcohen), Director of Strategy at Pace Communications, and Randy Frisch (@RandyFrisch), cofounder and COO at content marketing platform Uberflip, cover a ton of ground. This post pulls out relevant insights for marketers, entrepreneurs and executives in a hurry.
While these excerpts cover a lot, we highly encourage you to listen to the whole podcast here.
What Are AI, Machine Learning and Deep Learning?
Artificial intelligence—and related terms like machine learning and deep learning—are often used, but often misunderstood terms. Says Roetzer:
“The most simplistic way to think about it is: artificial intelligence is the umbrella of technologies and processes designed to make machines smarter.”
Machine learning and deep learning are related technologies nested under AI.
“AI is this big umbrella. Underneath that are things like machine learning, where a machine writes its own algorithms. It determines a path forward without humans telling it what to do.
Underneath that, you have deep learning. That’s actually trying to teach machines to think like humans through neural networks. If a machine looks at a dog and a cat, it inherently has no idea what those things are. As a human, you’re processing ‘it has legs, it has fur, it has ears, it has all these things [that make it a dog or cat].’ That’s how a human figures out what it is [...] in these layers. A machine is not built that way. Deep learning is this very advanced way of trying to get a machine to get intelligent almost to a human level.”
A key point to remember about artificial intelligence and related technologies Roetzer says is that they’re designed to enhance human knowledge and capabilities. They’re not necessarily designed to replace them (at least, in the near term).
Why Do Marketers Need to Pay Attention to AI Now?
Why should marketers care about AI and related technologies? Because, Roetzer says, they’re the future of personalized consumer experiences for increasingly complex content campaigns:
“Imagine if you have 10,000 ebook downloads from five different personas originating from five different channels (social, organic, paid, etc.).
The ability for a human to personalize the email chains and communication is almost impossible. And even if the human can think of how to do all that, there’s really no software available that makes it simple to visualize everything that’s happening within that campaign. That’s where AI excels: whenever there’s a ton of data that needs to be processed and you need to personalize at an extremely granular level.”
Artificial intelligence used by Amazon predicts products and information you’ll find useful based on everything you’ve looked at, bought or placed in your cart. It’s also predicting what you’ll buy in the future. Apply that to how we do content, Roetzer says, and imagine a world in which your experience on a website is completely personalized based on past behavior and predictions of what you’ll do next.
Right now, that is beyond humans, who are manually creating these experiences using automation software.
“Most of the automation we have today is ironically a very manual process. AI will in the not too distant future completely automate [typical marketing automation functions] in a very intelligent way.”
So, How Do Marketers Get Started with AI?
Roetzer points out that the natural entry point into AI for marketers is natural language generation (NLG), or using machines to automatically produce narratives from data.
“In 2015 at South-by-Southwest, I attended a panel discussion with the CEO of a company called Automated Insights and the managing editor of the Associated Press. They told the story of how the AP went from writing 300 earnings reports a quarter 100 percent by humans, to 3,000 a quarter 100 percent written by machines.
They took a dataset, in this case they extracted it from Zacks Investment Research, and they teach the machine how to write a data-driven story. You would not be able to tell the difference between a human-written and machine-written earnings report today. They do the same thing with fantasy sports updates and minor league baseball stories.”
The technology takes data and produces what appears to be natural language that sounds just like a human would, says Roetzer. And you can subscribe for a license to NLG software and teach a machine to write any story based on data.
Roetzer has the following advice for marketers looking to get started with AI and NLG:
“Start with the data you have. What stories are you already telling with it that you could tell more efficiently, and what stories aren’t you telling because the data just sits in spreadsheets and charts and no one ever really puts any narrative to it?”
Listen to the full podcast here.
About Mike Kaput
Mike Kaput is Chief Content Officer at Marketing AI Institute and a senior consultant at PR 20/20. He writes and speaks about how marketers can understand, adopt, and pilot artificial intelligence to increase revenue and reduce costs. Full bio.