Omnichannel (or what I would call consumer-centric retail) is a critical component of a successful retail operation. Recent research by Google indicates that omnichannel shoppers have a 30 percent higher lifetime value than single-channel shoppers. It’s easy to understand why retailers are growing their omnichannel activities. However, with this growth comes risk and opportunity.
One of the most effective tools for mitigating omnichannel risk is a prescriptive analytics solution. This robust technology’s increased visibility to the business helps identify and resolve sources of fraud, waste and risk in near real time, and before they can cause significant losses. Here are two ways retailers can use prescriptive analytics to protect their business and maximize omnichannel activities.
Identifying Organized Retail Crime
Organized retail crime (ORC) is a concern for retailers that offer multiple channels to connect with consumers. To commit fraud, ORC often leverages “gaps” such as lapses in oversight, disconnected chains of communication, poor accountability, and more. If these gaps aren't detected and addressed as part of an overall consumer-centric strategy, profits, margins and efficiency will suffer. A good prescriptive analytics solution will quickly identify gaps and send guided actions to the appropriate stakeholder in near real time.
For example, a fashion retailer adopted a prescriptive analytics solution to combat fraud. The solution quickly alerted asset protection (AP) to some suspicious behavior within the retailer’s call center. The retailer allowed its customer service representatives (CSRs) to “appease” customers by offering purchases for free to dissatisfied customers, along with a gift card. The solution identified several CSRs appeasing many more e-commerce customers than average. The solution further identified the suspect CSRs were shipping the appeased orders to the same five addresses, later linked to their friends and family. The prescriptive action to AP was to investigate the suspect CSRs for ORC activity.
AP found that these employees had formed an ORC group. Working together, they would legitimately buy a product online and, after receiving it, would call to complain they never got the product. Thus they ended up with a gift card and two products that they would later return in-store for cash. With this information, the retailer terminated all involved, solving a huge financial gap and improving its internal processes.
Maximizing Labor Productivity
Complex protocols can lead to training gaps. If undetected, the losses from these training gaps can increase over time, negatively impacting profits and the customer experience. Prescriptive analytics can empower employees at the edge to identify any training gaps so they can be corrected.
For example, an international retailer recently launched a new buy online, pick up in-store (BOPIS) program. To encourage customers to use the service, the retailer announced a guaranteed order turnaround time of just two hours.
The retailer invested in handheld devices to map out the most efficient picking route per order, and trained its associates on their use. For added assurance, it employed a prescriptive analytics solution to monitor BOPIS data for potential training opportunities. The solution soon detected a spike in orders that had not been fulfilled within the promised time period. The solution sent prescriptive actions to affected stores, informing their managers of each late fulfillment and directing the managers to verify picking compliance.
The investigation found that many pickers weren't using the routes dictated by the handheld devices. In fact, they weren’t using the devices at all. When interviewed, the pickers said they didn’t feel the devices were necessary since they already knew exactly where to find everything in the store. Instead, the pickers chose their own routes, resulting in much longer order fulfillment times. The retailer ordered retraining on the picking process, and fulfillment times overall decreased.
Prescriptive analytics is a key component of any retail strategy, especially for retailers focusing on consumer centricity. Its ability to provide new levels of visibility across the business makes it one of the best investments a retailer can make to optimize omnichannel efforts.
Guy Yehiav is general manager/vice president of Zebra Analytics, a retail analytics and data capture solution.
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As a leader in enterprise AI solutions at the edge, with 25+ years of experience driving profits with data and IoT in the retail and the supply chain industries, Guy oversees the corporate strategy, direction and success of Zebra Analytics at Zebra Technologies. He was previously the CEO of Profitect, where he guided the company through multiple years of significant growth, including 182% revenue growth and 137% headcount growth from 2018-2019, before being acquired by Zebra Technologies. This was his second exit for his shareholders, after growing the Demantra supply chain optimization software company and selling it to Oracle. He has lead companies that keep complexity at the back end and simplicity at the front end, cultivating machine learning and smart decision-making based on data to deliver stellar results for customers.