Podcast: How AI future proofs merchandise management

Retailers face many challenges in today’s turbulent marketplace – unpredictable consumer behavior, disconnected systems and overwhelming amounts of data are only a few. Joe Skorupa, RIS News Editorial Director, and Kevin Sterneckert, Symphony RetailAI Chief Marketing Officer explores how AI is driving a stake through the heart of time consuming linear merchandising processes and their traditional approach of managing by averages.

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    [Joe Skorupa]

    Well, it’s a pleasure talking to you Kevin and as you know, I recently conducted some research on the topic of AI driven merchandising and honestly, it was clear from the data that AI will drive a stake through the heart of time consuming linear merchandising processes and their traditional approach of managing by averages.

    Honestly Kevin, we’ve had conversations about this topic before, and I got some amazing ideas from you as to how retailers are falling behind and using legacy systems and old methods of merchandising when others are not. I think that’s a great topic for us to begin with.

    [Kevin Sterneckert]

    Yeah, I agree. It’s really interesting when we as an industry did not have some of the tools that are available today, when we did not have the data sources that were available today, category management and processes were built around the idea of category reviews that happened once or twice a year, where the business working with suppliers would spend a concentrated amount of effort for a point-in-time analysis of the customer, and the opportunities, and products, and objectives, et cetera, and then make adjustments to in essence course correct for how the customer was behaving and how the business was performing.

    Fast forward to where we’re at today and going forward, this idea of reviewing the category once or twice a year and course correcting is not fast enough. With the customer changing on a daily basis, by a moment basis, so much transparency with information, and influencers, and opportunities, and what’s available on the market, retailers need to respond far quicker. But the systems and the processes that have been used and built for the past decades are not conducive to that. And that presents a real problem if you’re only changing your representation to the customers a couple of times a year, when your customers life’s likely changing three or four hundred times a year.

    [Joe Skorupa]

    The speed that we’re talking about here is really caused by the omni channel consumer, the consumer that clicks between websites and mobile apps often times while standing in the isle of a brick and mortar store. That’s the big driver of this shift. Shoppers jump from wearing Allbirds shoes, to Warby Parker sunglasses, to shaving with Dollar Shave Club razors, to sleeping on Casper mattresses quicker than most retailers can respond. And there is some urgency associated with the sluggish pace that many retailers have in place right now.That urgency comes when you look at the turmoil as you pointed out in turbulent retail marketplace, which has been quite dramatic with record store closings and bankruptcies and mounting debt actually, which is a big threat in itself because there’s a long list of prominent retailers that are still operational, they’re still moving forward, but they have mounting dept because of inefficient processes.

    You know, we can see record years among Walmart, Target, Lululemon, Costco, Nike, Dollar General to name a few and that’s a wide variety of retail segments, so it’s possible that any segment you’re in can have a success story if you figure it out. I didn’t mention Amazon in that group and you almost can’t have a conversation in retail without mentioning Amazon. But make note here that Amazon is keeping pace with customer expectations and by doing that, it is setting the expectations that the other retailers have to follow.

    [Kevin Sterneckert]

    Yeah, and I think the challenge for apparel or consumer package goods is really quite different. For apparel, you’re trying to guess what the new hot orange will be, or what the new yellow is, and what the different lengths, or fabrics, or et cetera and that’s really, really hard. But for packaged goods, it’s about, “What’s the right assortment for this specific location? How many versions of olive oil do I need? Or how many types of cereal do I need? Or how much space do I dedicate to frozen versus fresh,” et cetera.

    Those decisions are increasingly required to happen at a specific location, but it’s really impossible for a retailer to expect that a category manager, or a category management organization could tune the specific assortments, and space plans, and pricing, and promotions strategies for individual stores or individual customers without some substantial help and technology in doing it.

    Really, that’s what the customer is expecting, that when they come into a specific location, when they decide they’re going to make a physical trip, that there’s specific things that they want and that they want now. They want them to be in stock, they want their decisions to be clear and straight forward, they don’t need more difficult decisions and they want to leave satisfied with the objective that they set out whether it’s physically shopping in the store, or picking up the items as they drive by the store. The last thing they want to do with their mission in mind of certain items that they wanted to buy is to have that store carry the item generally, but be out-of-stock, or to be not competitive in the marketplace on price, or to not have promotions that are encouraging loyalty and traffic driving events.

    It’s a substantially different challenge, apparel to consumer package goods, but a consumer package goods challenge is more granular than an apparel challenge. Right? In an apparel store, you’re thinking what size and what style do I put in one store versus another, and in a CPG, you’re trying to figure out the number of facings for a specific item and whether or not that item should be in that store at all.

    You have a lot of data, but a lot of companies don’t have the capability to analyze and drive an assortment plan that is customer specific for a single location with the right space, price, and promotion plans for that individual location. That’s really where I believe AI has a real role going forward, especially for CPG companies, retailers and manufacturers.

    [Joe Skorupa]

    AI driven merchandise management is an investment that retailers can make that helps them hit a possible sweet spot while catering to fast moving customer needs. We demonstrated the urgency of keeping up with a fast moving customer, but how do you do it? As you indicated, you have to do it at the store level. You have to do it at the individual store level, not at the 100 group store level and retailers just simply don’t have the capability to do that.

    You identify any meaningful patterns of customer demand, price sensitivity, omni channel complexity, and enable retailers to hit that sweet spot at each store, and deliver satisfying experiences to customers.

    When we talk about AI driven merchandise management and the efficiency that can be achieved through it, we’re not just talking about making sure you don’t lose your customers, or we’re not just talking about making sure that you scoop up customers that are available to you and they don’t go to your competitors. We’re actually talking about the role it can play in hitting your company’s financial goals. That’s an important point, because we have pointed out that retailers are struggling, there’s turmoil in the market and hitting financial goals is more important than ever.

    If you hit your financial goals now, you’re going to have the money to invest in resources in the future and make the big pivots and changes that you need to as customers continually change. What we’re talking about here, is, “Yes, you’re going to be driving more sales, you’re going to be improving the performance of your stores, but most importantly of all, you’re going to be hitting your financial targets and those financial targets are key to your sustainability future.”

    Let me just go over two quick points in this study by way of summary and note that there’s a two year window we believe, for grocers to get a jump on their competitors. If you start AI now, you’re going to be jumping into the marketplace two years ahead of most grocers in our study that say they are two years or more out. That’s significant for anyone to think about pulling the trigger on whatever project they’re working on next.

    I also want to say that, the two major strategic goals, and this is getting into the execution of what AI can do for you, is improving sell through of inventory, that was chosen by 59%, a big majority, and improving margins at 56%. These are specific areas where you can build your business case for developing an AI program in your merchandising area. Then, the final point I want to make is three specific applications that you can shift AI into that help improve performance and you’ve mentioned this a couple of times Kevin. Number one is demand forecast being chosen by 56%, replenishment 44%, and price management for 41%.

    [Kevin Sterneckert]

    Yeah, I think those are incredibly valuable areas to focus on. I also think it’s important that when companies are taking a look at who they’re going to partner with, that they look at organizations, providers, software companies, that deliver AI in a platform way and not just a few use cases where the provider might be just as inexperienced as the retailer is in using AI.

    At NRF this year, I would think Joe, you would agree, there were probably over a hundred companies that said that they had AI in their war chest. But knowing these companies the way I do, probably, really, about only 10% really have something that true AI. It’s a buzzword that a lot of people are using and it’s something that if you’re not careful and don’t look at companies that actually have delivered in enterprise scalable ways, you can get caught up in a project that’ll go nowhere.

    [Joe Skorupa]

    Yeah, just because a vendor has advanced algorithms it doesn’t mean it’s got AI and that’s for sure, and there’s a lot of companies making that claim and they don’t really have AI.

    [Kevin Sterneckert]

    Yeah, it’s a tough analysis and review process because everybody will say their algorithms are better, it learns better, whatever the case might be. At the end of the day, can you handle the massive amount of information and can you deliver the why, the what, and what now that AI can do. If the AI provider can do those things and have proven that they do it, then that’s an AI provider to take note of.

    [Joe Skorupa]

    It’s a good point Kevin. Yep, it’s something that we’re looking at in an upcoming study as well as how we define AI and what the applications are required when retailers need to deploy them. I guess that gets to the point of, can your platform provider demonstrate to you that big data can be handled in a way where natural language processing can be handled, can be prescriptive, recommendations handled and you don’t have to go to your tech team to do queries and find results.

    [Kevin Sterneckert]

    Yeah. I don’t think any retailer needs more BI tools. I think what retailers really need at this time is better recommendations that get them closer to their customers.

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