One of the factors in the roaring comeback of retail is the increasing use of big data in retail. Despite articles about the impending death of brick-and-mortar at the hands of e-commerce, retail employment continues to rise and sales figures are promising. Now more than ever, retailers are using big data to understand their customers’ habits and preferences. This enables them to employ dynamic marketing tactics targeted to specific audiences. But some of the most lucrative uses of big data in retail are operational is nature, driving efficiency, reducing waste, and boosting the bottom line. Read on to learn how 3 giants have harnessed big data in retail to great success.
1) Home Depot Invests in Customer Order Management
Although it complicates the customer experience, Home Depot prides itself on allowing customers to special order home improvement items that are temporarily out-of-stock or not included in the retailer’s regular product line. It’s just one way the brand competes with Amazon. In fact, Carol Tomé, Home Depot’s CFO and EVP of Corporate Services, tells RIS, “Each store is like a brand and these customer orders are essential to the store,” indicating that Home Depot’s special orders are particularly important in supporting the bottom line at store level.
But management could see that the special order protocol was negatively impacting customer experience. While sales associates could place orders for a customer’s desired product, they had little to no visibility into the order’s status or where the item was in the manufacturing process. Enter a new Customer Order Management System (COMS), which Home Depot rolled out to all its U.S. stores in 2014. The system empowers associates by giving them visibility into when an item will arrive, and creates a friction-less experience for customers, who can ask associates questions and obtain information about their order status.
The COMS uses algorithms that collect information across the supply chain—from manufacturers, suppliers, and logistics teams—and then synthesizes the data. While the task of obtaining such information from vendors sounds lofty, Home Depot says that their largest suppliers already have the technical capability to work with COMS. Home Depot is even committed to working with smaller suppliers to get them up and running with the system. COMS is poised to do more than improve the in-store special order process. Customers will eventually be able to get information on the status of their orders independently in real time using their personal devices. And, Home Depot is tweaking COMS algorithms to eventually drive sales and balance store and online inventory.
In a larger sense, COMS is linked to Home Depot’s “click-and-collect” effort (also known as BOPUS for “buy online, pick up in store”) to get products into consumers’ hands faster by letting them place an online order and pick up in store. According to CNBC, click-and-collect shopping is gaining popularity with customers and is lucrative for retailers. When customers come in to pick up the item(s) they purchased online, most add on impulse purchases that drive sales even higher. Advanced inventory data systems that provide accuracy and are updated in real time are one of the most powerful examples of big data in retail. Retailers who leverage this power are able to execute click-and-collect in a way that tangibly improves the lives of both their customers and operations teams.
2) Walmart Reduces Supply Chain Inefficiencies
Execs at Walmart observed issues in their supply chain that they’d need to correct if they ever hoped to compete with Amazon. Amazon Prime’s vast assortment and 2-day shipping window is the new normal. Walmart customers expected them to offer an omnichannel, Amazon-like experience. Struggling with late shipments from suppliers and lag times between receiving, processing, and stocking goods, Walmart needed to rethink supply chain management. So that’s why, earlier this year, Walmart asked suppliers to deliver more goods to warehouses on time, or face fines. This concept, called on-time, in-full (OTIF) is just the tip of the iceberg when it comes to Walmart’s plan to conquer supply chain issues.
Although the burden is ultimately on the supplier to comply with Walmart’s strict OTIF requirements, Walmart had to harness big data to make the case for supplier adoption. Kathryn McLay, Walmart’s senior vice president of logistics for U.S. flow, tells Supply Chain Dive that Walmart spent time with suppliers going through and clarifying data to make sure that there was one version of the truth they could agree on. Likewise, to ensure suppliers are complying, Walmart is crunching more big data on whether vendors are meeting expectations, and then proceeding as needed by imposing the appropriate fines.
And Walmart is doing more than address supply chain problems caused by suppliers. They’re using machine learning to derive critical information from big data that allows them to eradicate internal supply chain issues, too. According to a Walmart TODAY, Walmart is using “simulations to track the number of steps from the dock to the store,” ensuring that once shipments are received, they are getting onto the floor or into backstock as quickly as possible. This means that the retailer is optimizing routes to and from the dock and tracking how many times a product is touched on its way into customers’ hands.
At a larger scale, Walmart is using big data to analyze transportation lanes and routes for the company’s trucks. In this way, Walmart is reducing transportation costs and making more efficient scheduling times for its drivers. Big data in retail can target supply chain inefficiencies, Walmart is using big data to make the case for demanding and ensuring supplier compliance, while correcting a range of internal issues related to their supply chain.
3) Macy’s Rethinks Inventory Management
Macy’s recently announced plans to address replenishment obstacles by installing radio-frequency identification (RFID) technology on all merchandise. As someone who once managed a 3,000 square-foot shop for a major brand in Macy’s NYC flagship, I can speak firsthand to how difficult it was to identify replenishment issues. Even in that shop, which employed at least half a dozen full time sales and stock associates, it was nearly impossible to pinpoint display issues. I could ask our overnight stock team to pay special attention to polo shirts or cable knit sweaters, but I couldn’t say whether we were missing a specific SKU without checking it myself. Even when I did know we were missing size medium in our light wash jean jacket, for example, I couldn’t say for sure whether our stock room, sprawling two floors at the other end of the store, had any in back-stock to replenish the missing goods without walking back there myself.
This is just one of the structural challenges Macy’s faces. Foot traffic may be down, and those customers who do enter Macy’s doors may be prone to leave unsatisfied if they’re unable to quickly find what they’re looking for. In response to this and other challenges, Macy’s recently revealed plans to overhaul their replenishment process by installing radio-frequency identification (RFID) devices on 100% of their merchandise. RFIDs allow retailers to track merchandise throughout the retail supply chain. Macy’s hopes that data collected from RFID devices will result in inventory accuracy and replenishment success.
While Macy’s RFID initiative is still in its nascent stages, the department store is ready seeing promising results. For instance, Macy’s has reduced its inventory accuracy variance by 2%-4.5% with regular RFID cycle counts. The retailer also saw a decrease in inventory markdowns, boosting the bottom line. Beyond that, display compliance improved and full price sales increased, specifically within the women’s shoe department. Furthermore, Forbes reports that Macy’s sales volume may have surged more than 200% since the technology was implemented. Bill Connell, Macy’s senior vice president of transportation, store operations and process improvement, tells Forbes that RFID devices have improved profitability by reducing out-of-stocks, and that when an item’s availability increases, it can lead to substantial, measurable sales increased.
These examples of big data in retail show just 3 ways that discounters, department stores, and hardware stores are using big data to drive sales. Machine learning tools allow retailers to target a range of critical, persistent challenges to their business. For the retailers mentioned here, these tools are one of many in their arsenal that allow them to compete in the age of Amazon. Other retailers should know that machine learning tools exist that can help them overcome the most seemingly insurmountable operational issues. The best solutions not only partner with leadership, giving you insight into larger challenges and opportunities as you seek to improve operations at a high level, but also put power in the hands of floor sales and stock teams to make changes at store level.
To read more about the use of big data in retail, check out our blog here. To learn how retailers are attacking ticketing issues, out of stocks and other operational issues using CB4’s operations tools click here.