Artificial intelligence—you’ve heard the term, you know it’s a trend, you know you’re “supposed” to be using it, but if you were asked to explain what it means for marketers in a few short sentences, could you do it? If not, then you’ve come to the right place.
A quick Google search of “marketing AI” returns 950,000,000 results. And although Google does its best to surface the most relevant content to the first page (using AI to inventory, categorize and label, and recommend content, might I add), I doubt you have time to sift through each mention until you find a piece that actually gets down to brass tacks.
As a marketer, the things you likely want to know are these:
AI sometimes feels like “the man behind the curtain”—elusive, complex, and a little scary. Instead of skimming by with surface-level knowledge, marketers should learn more. So here are a few questions, definitions, and tactics for evaluating marketing technology solutions that claim to be “powered by AI.”
Breaking Down the Buzzwords
Artificial intelligence (AKA intelligent automation)
My all-time favorite definition of AI for marketers (and there are many definitions) comes from Paul Roetzer of the Marketing AI Institute: “AI is technology that automates a task previously done by a person.” Pretty simple, right?
Every time you see AI in the context of martech, just substitute out the term “artificial intelligence” for “intelligent automation,” which can mean one of two things in marketing:
As we collect more and more data, and the capabilities of marketing tech improve, the tasks we’re able to automate within marketing will certainly increase.
Should you be worried that all functions of marketing will be completely automated? That’s simply not the case yet. The likely evolution will be that the more functions are automated the more opportunity for marketing strategy and creativity.
Data science (AKA the tech that enables AI)
When you think about science, the first few things that likely come to mind are test tubes, beakers, and your 9th grade biology class—not data. But if you ask anyone who actually builds marketing AI for a living, you’ll get a different answer.
According to the Berkeley School of Information, data science is the practice of “organizing and analyzing massive amounts of data,” and to be an effective data scientist, one must be “able to identify relevant questions, collect data from a multitude of different data sources, organize the information, translate results into solutions, and communicate their findings in a way that positively affects business decisions.”
The three most important pieces of data science to understand are as follows:
Questions to Ask When Considering AI-Powered Marketing Tools
Next time you come across a too-good-to-be-true piece of marketing tech that promises to exceed your KPIs and make your boss smile, ask yourself the following:
When considering new technology, it’s all about knowing the right questions to ask. So, hopefully, those questions will help make AI a little more approachable for your marketing team.
Artificial intelligence, Big Data, and machine-learning aren’t going anywhere, so the quicker you are able to determine the need-to-know info about the systems you are considering implementing, the faster you will be on your way to staying ahead of marketing’s biggest disruption.