LP Magazine

JAN-FEB 2019

LP magazine publishes articles for loss prevention, asset protection, and retail professionals covering shrinkage, investigations, shoplifting, internal theft, fraud, technology, best practices, and career development.

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ACADEMIC VIEWPOINT Interview with Guy Yehiav Yehiav is CEO of Profitect, a well-known data-analytics solution provider in the retail loss prevention industry. LPM asked the 25-plus-year retail supply chain industry veteran to explain how advanced analytics is contributing to positive changes in omni-channel retailing. Using Analytics to Maximize On-Shelf Availability and Supply Chain Processes D ata analytics has been one of the major technology applications driving the evolution of retailing. While many of the analytics platforms are focused on in-store operations, the same techniques can help retailers fine-tune their logistics and supply chain operations to maximize merchandise on-shelf availability and support omni-channel retailing. LPM: In layman's terms, what is predictive versus prescriptive analytics? YEHIAV: Predictive and prescriptive analytics differ in their end purposes and user types. Predictive analytics uses many techniques from machine learning, pragmatic artificial intelligence (AI), modeling, and more to process data, find trends within it, and use those trends to make predictions about future business performance and generate reports to be interpreted by data analysts. Prescriptive analytics does the same, except that it uses those trends to tell the right person the right action to take for an optimal outcome. A prescriptive analytics solution might have thousands of users versus just a few with a predictive analytics solution. LPM: How is analytics used differently in supply chain versus store operations? YEHIAV: Supply chain is using analytics to maximize on-shelf availability and optimize their processes through just-in-time delivery, full truckloads, on-time shipment complete, forecast accuracy, efficiency at the distribution centers (DCs), and other factors. So in a nutshell, predictive analytics makes predictions, while prescriptive analytics makes predictions and tells you what to do about them to drive optimal ROI, revenue, and margin improvements. Store operations is using analytics to maximize customer satisfaction, optimize P&L, optimize labor, minimize shrink, execute promotions, optimize merchandise displays and promotions, and other examples. As both areas are leveraging a significant amount of data, the common solution has been predictive analytics and/or exception-based reports. However, these methods are not ideal, with the following six common points of failure: ■ Did anyone open the email or report? ■ Do they understand the reports, fields, and columns? ■ Do they see the insights? Do they understand them? ■ Do they know what is expected of them in responding to this insight? ■ Did they follow through and execute the task(s)? ■ What is the value of doing a given task compared to others? Prescriptive analytics has turned this upside down. It provides those who receive the reports with clear, actionable directives in plain text that anyone can understand and follow through on. By tracking both the opportunity and the response to it, prescriptive analytics provides a level of comprehension and visibility that predictive analytics and exception-based reporting can't match. Combine that with a user-feedback loop, and you have a systematic process with built-in continuous improvement. The solution cuts through any biases that may arise during the interpretation of the reports. LPM: What is the "Amazon model, " and how is it different from the normal retail model? YEHIAV: Amazon's flexible technology stack allows it to offer consumers a broader product assortment (endless aisles, the long tail of merchandising), greater convenience, highly competitive pricing, and other services like movie streaming and cloud management through Amazon Web Services—all of which make Amazon a formidable competitor for traditional multichannel retailers. But when compared to the normal retail model, retailers have something that Amazon doesn't—actual stores (although it seems to be on their radar, given recent deals with brick-and-mortar stores like acquiring Whole Foods, partnering with Kohl's, and leveraging the physical supply chain with companies like Best Buy to sell its own, private-label merchandise like TVs). Despite Amazon's convenience, consumers have proven that they still enjoy a fully immersive shopping experience—like at Apple. According to the latest US Census data released in August 2017, nearly 90 percent of all retail purchases in So in a nutshell, predictive analytics makes predictions, while prescriptive analytics makes predictions and tells you what to do about them to drive optimal ROI, revenue, and margin improvements. STRATEGIES 58 JANUARY–FEBRUARY 2019 | LOSSPREVENTIONMEDIA.COM

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