AI-Driven Analytics: Taming The Wild World of Grocery Data

AI-Driven Analytics: Taming The Wild World of Grocery Data

When it comes to gathering data, few retail categories handle a greater volume than the grocery business. With potentially hundreds of thousands of SKUs and millions of transactions, it’s no surprise that supermarkets have been at the forefront of many important data-driven innovations over the decades, from barcode scanners and panelist data to loyalty programs and even the humble coupon. But this is also a crowded marketplace with fierce competition, including brick-and-mortar specialty food retailers and the expansion of grocery sections in big box stores. Then, of course, there is the loud march of e-Commerce into the grocery space providing a myriad of pickup and delivery options, while the biggest online behemoth, Amazon, is shaking up the entire space with its recent acquisition of Whole Foods. Grocers are well aware that in order to keep up, they need to leverage their hard-earned data and glean the best insights from consumer behavior and shopping preferences. The largest retailers, such as Walmart, have gotten a big head start in this regard — for years, they have used predictive analytics to forecast demand and set prices. Midsize and smaller retailers have been relegated to the limits of traditional business intelligence and reporting in an attempt to derive insights. However, these older, legacy business intelligence and predictive technologies are simply not able to handle the vast amounts of data grocers now have — the millions of variables in complex environments are well beyond traditional tools — and are certainly beyond human capacity. Over the past several years, with the additional computing power of GPUs, it has become possible to analyze all of a...