You could say that many if not most of the important retail technology innovations introduced over the last couple of decades have been around “stuff.” The supply chain has been focused on building new capabilities that can quickly and efficiently move goods to stores, run lean inventories and cut operating costs. This has helped retailers become more cost-effective in serving their customers. The collection and analysis of data has been critical to these initiatives, as retailers have migrated from old paper-based planning and execution systems to more accurate and efficient digital systems. But it was still all about stuff.
Retailing has now shifted its focus to people, to the consumers themselves or, as we at JDA like to say, “the New Boss.” And with this new focus on people, data science and machine learning are poised to help take retail to new levels of efficiency, profitability and customer satisfaction. JDA Labs has been leading the development of these innovations and I’d like to share with you a little bit about what we’ve been working on.
User-Centric Retail Planning
We’re now well into the new age of “me commerce.” Consumers can shop anywhere and anytime, in a store, online at home or when mobile. They have near-instant access to product reviews and comparative pricing information and can be easily swayed by social media in their buying decisions. At no previous time has retail faced such incredibly powerful and extremely demanding customers.
So how do we satisfy these demanding customers? Give them what they want. Well, that’s only part of it. Not only do we have to give them what they want, we also have to do that profitably. A retailer could easily get financially bent out of shape while trying to cater to a very diverse and demanding customer base. To give them what they want in a way that doesn’t drive up our operating costs, we have to get to know them and their buying behavior better.
Big data intelligence helps solve this problem. For example, early last year JDA introduced a new retail planning solution, called Retail.me, which leverages big data to help retailers visually plan based on consumers’ actual buying behaviors. That second part is critical, “actual buying behaviors.” This requires the analysis of vast amounts of structured and unstructured data.
The solution is very intuitive because it was built with a user-centric design as its core paradigm, but what makes it powerful is that it includes a lot of state-of-the-art data science and machine learning to support the analytics that it provides. Retailers can now give consumers what they want, when they want it, and still protect their margins.
The New Store Experience
So now that we can give consumers what they want, when they want it, they should be satisfied, right? Not yet. Because deep down, what modern consumers really want is an experience. They want a quality shopping experience that caters to their uniqueness, and they want that experience to be consistent across the omnichannel.
The store is no longer a brick-and-mortar establishment that enables sales. Increasingly, we see stores as being fulfillment centers or mini distribution centers. To give consumers the experience they demand, stores have to optimize labor and tasks in a more complex shopping environment, and enable a whole host of new processes that stores simply haven’t had to do in the past.
Optimization also requires good data and visibility across store operations. Here our work with Intel’s RFID technology to better understand product placement and inventory visibility within the store helps us optimize key processes. Advanced RFID tagging can be implemented at the item level so data on consumer preferences can be gathered and analyzed in near-real time as they shop. This analysis guides store processes as well as associates to deliver a great experience to the customer. RFID is just one part of the Internet of Things (IoT) that provides the power of generating data that was simply not possible before.
Meet The New Store Associate
Cognitive robotics can do even more. This is one of the most advanced technologies for retail that we’re pursuing at JDA Labs but it’s part of the continuum of everything we’ve been discussing around the new retail focus on the customer.
We’ve partnered with Softbank Robotics to program their “Pepper” robot to research the concept of using robots as associates of the future. These robotic associates will be able to pull together and synthesize many pieces of information about a consumer in ways that a human can’t.
Let’s look at a scenario that illustrates the concept. When someone in the store is looking for a pair of shoes and can’t find them, they can walk up to Pepper and say, “Pepper, I’m looking for size 8 of these shoes and I only find size 9.”
Pepper would respond with, “Let me check.” It would then synthesize all the current inventory and supply chain information and be able to tell the customer, “You’re right, we don’t have any size 8s at the store but we have three pairs at a store five miles down the road, and here’s how you can get there. Or just let me take an order from you here and have it shipped to your house. We also have many other popular shoe designs in that size. Here, let me show you those.”
Always Learning And Remembering
In the long run, we believe that cognitive robotics will be a very powerful differentiator for retailers that choose to go with that capability. Because as the robot is responding to your needs for more information around a product or help in finding a product, it knows if you’ve asked that question when you’re happy, or when you’re upset, or when you’re frustrated. It knows if you’re male or female, it roughly knows your age group, and it knows a host of other information, and it remembers it. Human associates help customers but once the help is done they don’t necessarily track all of that information. Whereas a robot is not only able to synthesize a whole set of information and be able to provide that to the customer, but in the process it is actively collecting information in order to learn from it.
So it’s quite possible that when someone is approaching from five or six feet away, Pepper knows to a certain probability what questions this person might ask. Over time, it will know what kind of products are always missing, what are people always asking for, and bringing all of that information to not just automate a certain business process, but really to optimize and finally to learn from it to provide better customer service, and also to understand what products to carry and where. All of that starts to become more of a process that is data-driven and less of an art form that’s based on a gut feeling. With cognitive robotics, we feel there is a stronger place for data science to supplement the art as well.
Suresh Acharya, Head of JDA Labs, leads a team of professionals dedicated to the innovation and advancement of Data Science, Cloud Technology and User Experience. During his tenure at JDA, Acharya has worked in a number of areas spanning Demand Planning, Supply Chain, Transportation, and Pricing and Revenue Management. He has worked closely with a number of Fortune 500 customers in designing algorithmic capabilities to optimize their supply chain, transportation and retail planning needs. Prior to joining JDA, Acharya worked in the Operations Research group of US Airways where he built the first generation of fleet assignment models. Suresh holds a patent in Demand Decomposition and was part of the JDA team that was named a finalist in the INFORMS Edelman competition of 2012.