Innovative Retail Technologies

MAR-APR 2017

Innovative Retail Technologies (formerly Integrated Solutions For Retailers) is the premier source for innovative yet pragmatic technology solutions in the retail industry.

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we can't do them as fast as computers. Structured tax calculation programs come to mind," says Artun. " Then, there are tasks that humans can do really well that computers struggle with, such as processing cues from 'noisy' environments to draw conclusions." It's his intent to develop the machine's ability to more closely mimic how humans learn and retain information and apply that ability to marketing. "Retail marketing is the perfect use case for artificial intelligence," says Artun. "No two humans exhibit the exact same shopping habits, and picking up on those habits is challenging in a noisy environment like retail where shopper data sets are typically incomplete." Humans have a set of heuristics, defined as that which enables a person to discover or learn something for themselves. In computer science, a heuristic is loosely defined as an approach to problem solving — or in this case, artificial learning — that's not guaranteed to be optimal or perfect, but sufficient for practical intentions. So, while it may be impossible for a computer to predict what will trigger a buying response from every single one of a million customers, artificial intelligence makes it entirely possible for that computer to determine what will increase the odds of a purchase among a segment of those customers who share common attributes. The more granularly those segments are articulated, the more valuable the sales and promotion impact. While the fact-based establishment of dozens, hundreds, or even thousands of these buyer persona segments would be arduous work for humans, it's merely an algorithmic exercise for computers. Machine learning makes it possible for computers to gain artificial intelligence by using structured calculations to chart what's anticipated versus what actually happens, then draw a conclusion about that scenario moving forward. That, says Artun, lends itself to retail marketing, where the "what's anticipated" is what you might think, hope, or attempt, while the "what actually happens" is the consumer's end behavior (i.e., purchase, no purchase, abandoned cart, etc.). Applied to marketing, artificial intelligence can make customer-specific determinations, such as whether a customer is likely to buy or engage if they're sent a promotional email, how that likelihood is affected if the email contains a discount, or what a specific customer's lifetime value might be. " The ability to make predictions about customers and drive them to certain actions is a powerful and valuable thing," says Artun. "If, for instance, you know who needs a discount to buy and who doesn't, you can get surgical about your offers and maximize your revenue." Artun explains that this "likely to buy" scenario is an example of supervised learning where the machine is trying to predict an outcome. Equally important to the success of artificial intelligence in retail, he says, is unsupervised learning, which is a bit more arbitrary. By "listening" to what people say and "observing" what they do on e-commerce and social media sites, machines can learn from the reinforcement of consumers' actions. " The more reinforcement we can gather, the more likely we'll be able to accurately and proactively recommend products shoppers are likely to purchase, or offer next-best alternatives for products that might be out of stock," says Artun. Who Needs Artificial Intelligence? Which retailers is AI right for? While it's somewhat obvious that the aforementioned image recognition/ artificial intelligence technology duo promises particular benefits to the likes of grocers and convenience stores, customer segmentation enabled by machine learning has broad appeal to any retailer that sees frequent and fast-paced SKU changes, price changes, and inventory turn. "Anywhere you need to more clearly understand the relationship between the customer and the product and apply analytics at the point of execution makes artificial intelligence a good fit," says Artun. The question, then, becomes not who needs it, but who doesn't? 27 Mar-Apr 2017 Artificial intelligence is … part of the process of identifying all the individual products in the picture and determining what 's there on the shelf, what 's not, and what needs to be done. Doug Benson, director of America's marketing, Trax Image Recognition If, for instance, you know who needs a discount to buy and who doesn't, you can get surgical about your offers and maximize your revenue. Omer Artun, CEO and founder, AgilOne

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