Turning Customer Data into Actionable Insights in Retail
When it comes to data in retail, getting the right answers requires asking the right questions.
As data analytics have taken off in retail, I’ve seen companies racing to make investments in the space — implementing robust analytics platforms and onboarding teams of data scientists in a rush to stay ahead of their competition. According to a Gartner study of retail technology, 45 percent of retailers either have up-to-date AI technology in place or have plans to start an upgrade within two years. And another study estimates that the market for AI in the supply chain will grow to $10 billion by 2025, up from less than $1 billion in 2018.
In my experience, however, the companies that have the most success with their data analytics programs aren’t necessarily those that invest the most in software or hire the most people. Rather, the retailers creating the greatest value through analytics tend to be the ones that approach their initiatives strategically and have the ability to make their data actionable.
The truth is that nearly all retailers have the data they need to improve efficiency, boost sales and delight their customers. In fact, stores are practically drowning in data from point-of-sale systems, customer mobile devices and inventory management systems. What makes a leader in retail is the real challenge of wading through what can seem an overwhelming amount of information and assessing the data that can help drive relevant and insightful decisions.
Once analytics platforms and data scientists are in place, the trick is to ask questions that will lead to actionable decisions and plans. In particular, retailers have an opportunity to create value from analytics in three areas.
Order Fulfillment and Shipping
Buy online, ship from store (BOSFS) is an emerging area where retailers are showing a competitive advantage. Retailers today have an area of tremendous opportunity for improvement and new value, if they have the strategy in place to fulfill successfully. This new trend enables retailers with brick-and-mortar stores to treat their physical locations as warehouses of sorts — shipping online orders straight from the store to help bring down costs and fulfill orders more quickly. When executed well, this strategy can actually give traditional retailers a leg up on their online-only competitors while meeting the demand from consumers to have their orders fulfilled in a day or two.
By strategically using data analytics, retailers can improve this process. Say, for instance, that a customer in Iowa orders four items from a retailer’s website, and the company has three physical locations within 100 miles of the customer’s house. The retailer needs to figure out not only which items are already present at which of the stores, but also how to bundle the shipments in a way that will limit costs and prevent stockouts at the physical locations. It’s a complex problem, but one that can be solved with analytics — if retailers ask the right questions of their data.
Effective Inventory Management
Along with customer service, the ability of shoppers to walk into a physical store and immediately get what they need is one of the single greatest advantages that traditional retailers have over their online competitors. However, inventory issues can erase this advantage and quickly lead to customer frustration. Data analytics can help retailers to accurately forecast demand and ensure that they’re stocked up on the items their customers expect to be on hand.
This isn’t only an analytics problem. Radio-frequency ID tracking and smart shelf technologies can also be incorporated to help stores keep better tabs on their inventory, so staff members can check availability. These tools can help a retailer to not only see that an item is in stock, but also locate it in the store, even if it’s not where it should be. They also help to reduce theft by enabling retailers to determine when an item left the store. Data analytics are a crucial piece to helping retailers optimize their inventory management processes.
Customer Satisfaction
When I talk to retailers about their data analytics objectives, nearly all of them say they want to improve the customer experience. But this is a broad goal, and stores are likely to create greater and more measurable value by breaking down the overarching category of customer satisfaction and how best to achieve this.
For example, personalization is a powerful objective for many retailers. A study by Forrester found that 77 percent of customers are more likely to choose a brand that offers a personalized experience. Asking the right questions and using data effectively can help retailers deliver this personalized experience and keep their customers coming back.
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