Explaining AI for Commerce

Omer Artun is founder and CEO of AgilOne, the B2C customer data platform. He also has a deep background in the academic foundations of AI: specifically, a doctorate in physics and computational neuroscience from Brown University. He’s also author of an alead-of-the-game look at predictive marketing, published by Wiley in 2015.

Who better to lay out for us the ways AI and machine learning can be applied in the worlds of marketing and commerce?

AI loves noisy data

“I have a PhD in machine learning from back in the old days,” Artun told me. “My whole passion, throughout my career, has been to bring AI into the business world. It’s become a trendy thing now, but when I was talking about it, six or seven years ago, people didn’t know what I was talking about.

“I felt I could make marketing more efficient if I introduced machine learning to it, because it’s a way to simplify how marketers can deal with data.  There are some natural properties of the AI and machine learning framework that fit really well with the problem set.

“Number one, you have to have lots of data. If you have small data-sets, machine learning can’t help. Second, a lot of problems have been approached with deterministic [rule-governed] algorithms; like if you’re doing your taxes, AI is no use; it’s very rigid formulas. Machine learning, however, is good where you don’t have explicit programming – you have implicit programming which learns from the data.

In fact: “It’s okay if data is noisy.  For example, say five people buy something. One person sees an ad, somebody searches it and buys it, somebody else browses for it, then goes away and buys it from a store. Their journeys are very different. That noise or variability is something that machine learning has an easier time dealing with.”

AI loves change

“Another type of problem AI really helps out with,” said Artun, “is where you have underlying data or patterns that change over time.” For example, someone changes their interests in buying patterns as they get older, or as their life changes. “The system needs to evolve with this. It needs to recognize the pattern of what’s happening today; but over time, as things change, it needs to adapt. Machine learning algorithms are good at adapting to changing data.”

Marketing starts with the basic heuristics of human behavior. In other words, how people typically figure out how to deal with their environment.  

“I go back to a 1940’s butcher shop, where one guy has a lot of customers,” Artun explained. “He knows them by name, he knows what they like to buy, and he can provide an ultimate experience to 50 or 100 people. Fast forward to today, and you have marketers with two million customers they need to deal with. You have to take all the heuristics that butcher had, seventy years ago, and try to mimic that in algorithms.” There are different ways to go about this with AI.

Unsupervised learning

Atun breaks AI down into unsupervised, semi-supervised, and supervised learning. The strategies have a wide range of applications, but here’s how Atun deploys them on customer data.

Unsupervised learning is what the machines (or algorithms) can do without human assistance, once they’re trained and set up with a steady diet of data. “For 2 million customers, I might have 10 or 15 personas, so that I can talk to people differently.  For example, some people buy joint medicine because they’re weightlifting; others buy it because they’re aging. Recognizing what else they buy, and how you group them, completely changes the conversation you have with those customers. If you buy heart medicine with joint medicine, you may be an older person trying to stay healthy.” 

Looking at the data, and grouping things together that are similar (“clustering”), reflects a very basic human heuristic. Importantly, it reduces complexity. That’s what the butcher did; but it’s what machine learning can do at scale.

Let’s block ads! (Why?)

All DMN Content

Add Comment