Retail investment in Machine Learning AI algorithms falls into two general buckets: the customer experience (a combination of an E-Commerce site’s search engine and customer service bots) and operations functions (stocking, fraud detection, and other so-called back office functions) that in both the physical and virtual worlds have begun to rely increasingly on machines to augment if not entirely replace humans. Understanding the relationships contained in customer data generates hugely powerful – and lucrative – insights that can be applied to everything from merchandising programs, seasonal inventory decisions, product placement, loyalty programs, and other programs that speak directly to cost of customer acquisition and retention on one side, and lifetime customer value on the other.
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- Understand the applications of predictive analytics in E-Commerce and retail
- Read about the cross-disciplinary expertise required across visual and text applications to build a modern retail product
- Explore the human factor in building top-of-the-line customer experiences driven by data, both online and offline
- See how spotting suspicious user behavior and potential fraudulence is tracked
First 300 words
Shopping at a mass market retail chain such as Walmart, Target, or Amazon, comes with the often-tacit assumption that the retailer is over time learning quite a bit about you, your likes, dislikes, and behavior. We are (most of us) creatures of habit, and those habits form patterns that savvy businesses exploit through timely – and targeted – offers. At its simplest? Buy a razor, for example, and you can expect to see a coupon with your receipt, for razor blades – or shaving cream, or even skin moisturizers. We know intuitively that it wasn’t a human being generating the offer – at least not directly. No matter. Most shoppers don’t understand the mechanics of how a store discount coupon is generated, how a machine learning algorithm ties a specific purchase to a logically adjacent marketing offer. Most just shrug and go on with their day – after setting aside the coupon for the next time they shop.
But if a shopper searches an E-Commerce site for work pants and is taken to a product page for plants and shrubbery? Shrugs instantly give way to annoyance (at best) over the inability of the search engine to provide at least a relevant result. Most people are reasonably forgiving if the result is at least in the ballpark, if not a strong match to their query. But fail the relevancy test and alarm bells go off. Some shoppers might pick up the phone and demand a human worker who can get them what they want. But others will just leave the site in frustration and look elsewhere. And therein lies a multi-billion-dollar risk that leading traditional and E-Commerce retailers are loath to accept.
Roughly a quarter century after Amazon started selling books online, and 20 years after Walmart launched Walmart.com, even…
