What is the “Rule of 17” and how is AI being set up to help retailers achieve an agile merchandising approach? Join Joe Skorupa, Editorial Director, RIS News, and Kevin Sterneckert, Chief Marketing Officer, Symphony RetailAI, as they discuss recent industry research that examines how retailers, and manufacturers, can survive and thrive in today’s market.
Interested to learn more?
- Read the full targeted research report from RIS News on AI-Driven merchandise management
- Learn more about the Rule of 17 and adopting an agile merchandising approach
- Read Podcast Transcript
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. One of the great ideas that came out of this study is that this big shift has to do with the fact that merchandise management teams simply cannot keep up with the speed of omnichannel consumers and dynamic competitors. By dynamic competitors, I mean the Amazon and Walmart’s of the world. They can’t compete because they may be sticking with their old methods and the legacy systems.
Yeah, and when I was looking at this challenge, I recognized that there is a rule that’s emerging and I coined it, “the Rule of 17.” The Rule of 17 says, that up to 17% of the category in a CPG retail environment is pure duplication. The trick here is, what is the 17% that’s duplicated, and why does it matter, and what does it mean if you’re able to use the Rule of 17 to your benefit?
The Rule of 17, also speaks to the paradox of choice, which says that “If you’re given too many choices, you choose not to choose,” which reduces sales when customers are not specifically focused on buying something. When the choices are too similar you choose not to choose unless it was your mission. Think about the last time you went down the cereal aisle and looked at buying cereal. When was the last time you bought something new? There’s just so many choices and so many different ways that you can do a certain cereal that you just buy what you buy and you don’t worry about different choices, because it’s too complex and you’ve already made enough choices and in the day, in your work life, or personal life to mess with it while you’re trying to buy cereal. Right?
Getting it right’s important, because you improve the decision process for the customer, you expand the number of differentiated products for the customer, you make your supply chain more efficient, you increase revenue, you increase profit, and you increase equivalent unit volume. Doing that’s important, but the Rule of 17 works when you’re able to apply it at each location, which means you need a unique application of the Rule of 17 to each store. You can’t do that without substantial intelligence and insights, and recommendations that are automated in nature and using AI and ML brings that to life.
As we look at tactics as to how AI can play that role in helping, especially grocers, but retailers in general get more profitable turnover and margins through their merchandise management functions. I just want to point out that the study noted that of the major segments that we tracked, that those that are the most in the lead with having a maturity level where they have many or most merchandise functions operational with at least some AI engines. It was mass market in general merchandise in retailers, and we think of those as being the Walmart’s, Target’s, Amazon’s of the world and they are the ones that have the decision making going on at AI speed.
The group that was the farthest behind and was the one you were talking about when you were talking about tactics here Kevin, and you were talking about the choices, and the struggles, and the turmoils that grocers have and they were farthest behind. As the study also looked at investment strategies over the next two years, we found that the apparel, specialty and mass market investment in AI merchandising far surpassed that of the grocery area. This is an area that I would urge grocers to look at, because if they jump in now and get their merchandise tuned to AI operational efficiency, they’re going to have a big leap up on their competitors because their competitors are going to wait two years to begin.
Well, you make an interesting point and I often have thought, “Why would grocers be behind in this area?” Grocers have some of the most rich data sets. Many of them have loyalty programs that are widely leveraged and adopted, so they’ve got lots of loyalty information. There’s great household information that’s available. Retail grocers are primed to have a great application of artificial intelligence and machine learning to the business model, but I think possibly, that what’s happened is, is that prior to maybe two years ago or three years ago, grocers felt like they were insolated from the challenges of eCommerce. I believe that apparel and mass saw the challenges of eCommerce and omni channel and they needed to get on the boat to get AI infused in their business decisions so they could make better business decisions and do better, so it was kind of a posture because of demand and need.
If grocers a couple years ago felt like they were insolated, which I believe many did, I think today, that they look around and see that they’re not insolated and that eCommerce is coming, and it’s hitting, and there are companies that are doing really well with click and collect and other forms and they were flourishing. Then there are some that are that not doing so well at all and they’re struggling.
I think it’s kind of like they felt like some of the other segments of retail felt like they had to use AI and maybe grocers didn’t feel like they had to, but I believe that many are beginning to realize they have to. Now, the challenge is, how do you do it?
Right, and I think that the interesting point here is that if, for our retail listeners, for our grocer listeners, if you’re analyzing what AI can do for you in your merchandise management functions, just recognize that most of your competitors are going to wait two plus years to begin testing their AI capabilities. If you have a faster acceleration to market, go to market with your strategy, you are going to achieve a very significant advantage.
One thing AI is very good at is thinking in terms outside complete datasets. That’s something that human beings have a lot of difficulty with, but something that AI is very good at. And the other thing AI is really good at, building on your comment, is thinking holistically. “How does every decision we make here impact all of the other decisions we’re making, and across all of our channels, and across all of our products, and across all of our stores?” AI happens to be very good at both of those things and the only way retailers can try to replicate that, I would guess, would be to hire thousands of merchants and somehow mash all the data and all the decisions they make every day somehow into action items and actually, that doesn’t sound very successful.
Well, it isn’t and really, this is not about hiring more people, because this is a challenge and a problem that people can’t manage. Let’s talk about some of these companies like Kroger, or Publix, or stores that have hundreds or thousands of stores and think about the one category buyer for let’s say frozen pizza. The category buyer cannot possibly understand and deliver a correct assortment for each individual location. Nor can they deliver a planogram for space for each individual location, and pricing, and promotions, and all of the aspects related to properly managing the category for each location.
If you had one category buyer for every store, you still would not be able to do the job that is required because there’s too much information to understand and it is not possible to see the correlations. In one store, fresh might impact frozen pizza. In another store fresh might not have any impact on frozen pizza. Then, the kinds of frozen pizza that you need on the beach are different than the kind that you need in the mountains, or in the center of the country, or in the middle of Europe. What you need in Italy is different than what you need in Germany, so it’s impossible to just solve this by adding more people.
I believe that what really should happen is, is that the people that are managing the job today need to be focused on, “How do you train the artificial intelligent engines? How do you help the machine learn? And how do you leverage AI…” as what we’re calling at Symphony, a personal decision coach, where AI is doing all the hard heavy lifting of all the analytics, understanding why things are happening, what’s happening and what should be done, and coaching the decision maker on the options and the outcomes, and allowing the decision maker to make a truly informed granular decision for the organization.
We believe that this model is a model that has a lot of legs and we’re finding fantastic success in the market as we share this idea of AI and ML as a personal decision coach for decision makers across the business. Not just category management, but the store, the supply chain, marketing. This idea of being able to understand this massive amount of information and deliver recommendations based on the facts, it really empowers the decision maker to do what they got in the business to do to begin with.
Kevin, it was great talking about artificial intelligence especially as it can help the merchandise management area. I certainly got a lot of information on hearing your ideas. I’ve known you a long and honestly, I always do get a lot of ideas in talking with you, but if I would summarize what I got out of our conversation, and it is that AI helps merchants deliver personalized assortments at scale. That’s the hard part for retailers. They have large groups, they identify customers in groups, they identify stores in groups. AI breaks that down into individuals right down to planograms, right down to shelves.
The other areas that I think that AI provides clear guidance at speed to retailers in their merchandise management capability is through price sensitivity and understanding omni channel complexity. I think we spoke about that earlier in terms of a holistic understanding while also being comprehensive with all the data points that are in play. For all of those reasons, I believe that the AI helps merchants to hit their most profitable sweet spot for each store while catering to their customer needs.
That was very well said Joe and I appreciate having the opportunity to visit with you. I think what you just described is what we’re calling agile merchandising. It includes all the aspects of taking from the conversation with the supplier all the way to the refrigerator or the pantry shelf of the customer. And it’s understanding the consumer and the changing behaviors of the consumer and leveraging a personal decision coach that’s AI and machine learning based that can help make those better decisions so that the retailer can be closer aligned with the expectations and not be one of those retailers that goes by the wayside because they lost touch with their consumer.
I really believe at the end of the day the stakes are that high, and this two year window that you’ve found in your report, I think is critical. For companies today, I think it is a call to action to find a provider that you can partner with, that you can begin meaningful improvements in how you connect with the customer now. It’s not waiting until NRF, it’s not waiting until next year, it’s not waiting until you have budget approval.
I think the call up to action is, is acting now, and acting now means you have a head start. But it also means that you begin a new level of relevance with your customer and that’s the level of relevance that will drive improvements in revenue and profit that will help these organizations, and these retailers, and these manufacturers succeed in this chaotic and tumultuous market that we are all in.