Prescriptive analytics is recognized as a fast, effective, and simple solution empowering retail and CPG companies to understand and act on their data. Its ability to interpret massive amounts of data, extract revenue-impacting insights, and distribute them with corrective actions to the appropriate stakeholders make it an invaluable revenue-growing tool for profit-minded retailers.
Some may not realize the benefits of prescriptive analytics are more than just financial. Many times I have seen an organization deploy a prescriptive analytics solution and achieve improved interdepartmental communication and collaboration. Here are three ways this robust solution can bridge communication gaps and silos and create more unity:
Linking e-commerce and brick-and-mortar operations
There is often a communication gap between e-commerce and brick-and-mortar operations. Depending on the retailer, these two channels don’t necessarily interact or cross paths regularly. E-commerce and brick-and-mortar have a lot to learn from each other about increasing sales, improving fulfillment, and more. A good prescriptive analytics solution can break through their silos and facilitate information-sharing.
For example, a retailer used its prescriptive analytics solution to alert both its in-store and e-commerce channels to each other’s major sales trends, as a way of facilitating collaboration. The solution quickly sent the retailer’s in-store merchandisers a list of items commonly purchased together online, but rarely bought together in stores, such as playing cards and cocktail mixers. This opportunity included prescriptive actions directing the merchandisers to place these like items closer together in stores to encourage upsells and impulse buys. This simple tactic worked, and was the first step to a now-improved line of communication between the retailer’s in-store and e-commerce functions.
Eliminating bias
Different retail functions don’t always see eye-to-eye on certain matters. This bias can create major headaches throughout the organization when trying to problem-solve, especially when the debate turns into finger-pointing. The best prescriptive analytics solutions leverage machine learning and AI to perform root cause analysis that leads directly to the source of the problem. By delivering the exact problem and its fix to the appropriate stakeholders, prescriptive analytics simply eliminates any opening for bias.
For example, a footwear retailer had a long-standing problem with shipping-manifest discrepancies. Many delivery trucks would arrive at stores missing several pairs of expensive shoes that the warehouse had listed as packed. The warehouse insisted the manifests were accurate, and accused the delivery drivers of stealing product. This didn’t seem likely, as the security seals on the affected trucks showed no evidence of tampering. The Logistics team stood by its drivers, and in turn accused the warehouse of mishandling product. Without a clear root cause, the two departments could only point fingers, and losses increased.
When the retailer adopted a new prescriptive analytics solution, the root cause became apparent. The solution analyzed warehouse packing practices, delivery times, and GPS data for delivery trucks looking for anomalies, and quickly found that numerous trucks with missing products had made unauthorized stops en route to their destinations. The retailer’s Asset Protection (AP) team received a prescriptive action directing them to interview the drivers of these shipments for an explanation.
AP learned that the drivers of these trucks had simply stopped en route to their stores, broken their trucks’ seals, and helped themselves to merchandise. The drivers would then replace the broken seals with new ones before finishing their routes. Upon delivery, the store-level receivers would see an intact seal, with no evidence of theft.
It was clear that the delivery drivers were the culprits, not the warehouse. The retailer took disciplinary action against the drivers who had stolen merchandise. Since implementing prescriptive analytics, the retailer has reduced shrink by more than 27 percent.
Enabling cross-training
Organizations recognize that collaboration is a key component to success. A great example of collaboration in retail is peer-peer cross-training at the store level. Prescriptive analytics can identify cross-training opportunities and direct managers on how to adjust their schedules to enable them.
As an example, a fashion retailer noticed a drop in sales revenue at several of its stores. Its prescriptive analytics solution identified the root cause as a higher-than-normal rate of single-item transactions, compared to the district’s benchmark. This indicated that the stores’ associates were not upselling or cross-selling their customers to expand their baskets. The prescriptive analytics solution sent an opportunity and prescriptive action to these stores, directing their Store Operations Managers to adjust employee schedules to pair the associates with lower numbers, with those who had a higher average number of items per basket. This enabled the latter group to observe and learn from the former group about upselling and cross-selling. The improved collaboration resulted in a 6.9 percent lift in sales for the retailer.
With prescriptive analytics, financial benefits are only the beginning. This easy-to-deploy solution can break down silos and facilitate once-poor communication across the business, helping all functions develop a new appreciation for how much they influence each other’s successes.