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The leader in Applied Big Data for brick-and-mortar stores
The technology industry is in the midst of a critical trend called Big Data, which is the use of previously unavailable quantities of storage and computing power to handle vast quantities of data to solve problems that previously would have been unsolvable. One particular subset of the Big Data megatrend is Applied Big Data for the physical world—using this unprecedented computing power to understand, map, and react to the real world in more empowered ways than we could have before. Such applications tend to be task-specific and aimed at concrete and highly valuable goals.
RetailNext is the leader in Applied Big Data for brick-and-mortar stores, in which we seek to draw actionable inferences from customers’ behavior in brick-and-mortar retail environments. To do so, we must account for diverse factors like the physical location of shoppers and employees in the store (and be able to differentiate between the two); the layout, fixtures, and planogram of the store; staffing schedules; complete detail on actual sales, and even the weather. Input sources can include video cameras, Wi-Fi tracking tags, RFID, and other in-store systems like those for Point-of-Sale (POS), staffing, and task management.
Via these data sources, retailers presently using our system are collecting about 10,000 data points per store visitor. Across our full customer set of more than sixty retail chains the RetailNext system collects over 75 petabytes (75,000,000 GB) of raw data across more than 400 million shopping trips per year. This information comes in from more than 30,000 sensors across thousands of stores in more than 50 retail chains and 20 countries. We process this flood of raw data into the trillions of analytical data points (one level more abstracted than the raw data) that then drive the measurement, analysis, and direct management for which retailers use our system.
The potential for retailers is as large as the data sets. Retailers can gain a precise, factual understanding of how shoppers move around their stores—where they go, in what order, how long they stay, when they come to the store, and how all of these questions map to actual sales. Retailers have optimized store layouts, fixtures, staffing, and even product offerings based on what they learned. CPG manufacturers also use this category to more thoroughly understand how their packaging, merchandizing, and marketing decisions affect the full path to purchase.