At the 2024 National Retail Federation Big Show, RetailNext hosted a conversation with retail expert Jonathan Jimenez, exploring the transformative role of data in shaping the future of brick-and-mortar stores. During the discussion, Jimenez highlighted how advanced analytics have redefined decision-making in retail. A prime example is the concept of the "decompression zone," an area just inside a store's entrance. Through A/B testing, retailers discovered that customers often bypassed the first display, needing time to acclimate to the store environment. By repositioning these displays, brands could enhance flow and engagement, creating a seamless shopping experience.
Beyond layout adjustments, data has also informed operational strategies. Integrating analytics with point-of-sale systems enables real-time decision-making, such as optimizing employee schedules based on traffic patterns. For instance, one store shifted opening hours to better align with customer behavior, boosting efficiency while reducing costs.
The conversation also delved into the nuanced expectations of today’s shoppers. Retailers like Nike excel by creating environments where customers feel comfortable exploring without pressure. This experiential approach builds loyalty and generates positive word-of-mouth, even among non-buying visitors. In beauty retail, where personalization is key, data-driven insights are invaluable for tailoring store layouts, product offerings, and customer interactions.
Jimenez advised retailers to start small by analyzing traffic patterns in a handful of stores before scaling insights across locations. He emphasized the importance of recognizing regional differences, adapting strategies for diverse shopper behaviors, and aligning internal teams around data-driven goals.
As RetailNext continues to lead the charge in retail analytics, conversations like these underline the importance of harnessing data to not only drive sales but to craft meaningful and memorable shopping experiences.
EXPLORE: Crafting Immersive Store Experiences With Digital Signage
Welcome, everyone, to The Pulse, a series of thought-provoking conversations with leading retailers and industry experts on the state of retail today. My name is Jenna Suhl, and I am the Account Director here at RetailNext. RetailNext is the first analytical platform to bring e-commerce-style shopper analytics to brick-and-mortars brands and malls. We focused primarily and entirely on entirely on optimizing the shopper experience.
Through our centralized SaaS platform, we automatically collect and analyze shopper behavior data, creating the insights retailers need and use to improve the shopper experience in real time and at scale. We are The Pulse of The Store, trusted by more than 450 retailers across 90 countries globally, have adopted and come to trust. Joining us today we have Jonathan Jimenez, who has worked in fashion and the retail space for over 15 years across five different retailers. His expertise is in retail tech and innovation.
Welcome, Jonathan! - Thank you. So, our first topic for today is to find out what has changed or been accelerated in the beauty industry that requires brands to better understand the shopper journey. We've been partnering together for quite some time now.
How did you become involved with RetailNext? - Back at one of my prior companies, a team was created to come up with a solution to better understand what our customers were doing and how they were experiencing our stores. That’s when I connected with Varun, one of your account directors, and he was integral in showing us what the tool could do and the kind of data we could expect to get from installing these cameras in our stores. Before we dive into our next question, I'd like to point out a few trends we're seeing in the health and beauty industry.
If you look at the chart above, you'll notice a comparison of year-over-year traffic data for 2022. Now, you may notice 2022 has a significant increase, but we must remember that this was the year coming out the global pandemic. What are you seeing in terms of shopping behavior in the beauty space right now? Right now, we're seeing that our customer base is changing quite rapidly.
Our reaction to that is to make sure that all our stores, depending where they are around the world or in the U.S., our product line, our store design set up is configured to that specific shopper. So having this data helps us make reactive decisions, to say, okay, this location wants to shop this way, we need to design a store specifically for this shopper type. As our demographic changes, our shopper ages change, we're kind of ready to go for new store openings or retrofits. The shopper journey has never been more important.
Today’s customers remain deeply loyal, but they also have an elevated expectation level and many options to choose from. When you started down this road with advanced data, what did you want to understand about your shoppers? I can answer this two ways. From a personal perspective, I like to go and explore different retail locations.
Let's use Nike, for example. I'll go to a Nike anywhere in the world because I enjoy that experience. They have nailed their store design. I may not buy anything, but I like to explore and just see how the store is designed and see how freely I can walk around the shopping environment.
And then from a business standpoint, it's kind of the same thought process. How do I go ahead and design a store where a shopper can just spend time in there? Maybe they won't buy anything, but there's something to them peacefully being able to navigate and just hang out and spend time in there. Maybe they'll go back and talk about how nice it was to be in that shop and visit us in other locations.
But I think it's important to really create an environment where the customer feels comfortable and not pressured and they're not overrun with product and merchandising items. They're just kind of there to explore and learn. One of the key benefits of partnering with RetailNext is the ability to use the data to craft a more personalized shopping experience for customers. That is especially valuable in the beauty space, which is such an experiential visit.
Throughout the rest of our conversation, we'll focus on how retailers use our data to create that experience for their customers. Being able to launch a solution and collect data is only part of the process. More importantly, it's using the data in a way that allows brands to make decisions that impact that shopper journey. The right analytics with the right data is very powerful.
So I'm going to ask you, Jonathan, what were the more informative decisions that were made based on using the data and were there any surprises or anything impactful that stood out? There's a lot of impacts from the data, but there's always a couple that kind of stick out more than others. Immediately, the first change that we saw was we learned about this term the decompression zone, which is just when you first walk into the store. Retailers kind of slip a new product to the table there, and through A/B testing, we kind of learned our customers weren't stopping there.
They were getting there eventually, but they needed time to almost transition into the location and get out of the outside, wherever they were, into this new store. They didn't want to be in the way, and so through that, we actioned it. Stores that were already built or stores that were coming online were designed to have that front table moved to the left or to the right to allow the customer to get in, walk around, not feel like they're blocking the door and see our new product, but not directly interface, maybe off to the side a little bit so that we can still give them that story. And as far as more back-end findings that we got, we integrated to our POS in a couple of scenarios, and that is when you really see sales per working hours.
I think that's the right term. To know, okay, we have six employees on now. This is how much money we're making at the moment. Do we know if we can send someone out to lunch now?
Can we send someone home earlier? Do we shift our opening hours? Because we're seeing that folks are actually coming in later in the morning, but they're staying later at night. And through that, we can kind of react and change as we go.
So because we get the historical data, we know Tuesdays people aren't coming until 11, so on Tuesdays, let's open at 10. But on Fridays, people are coming earlier and staying later, so maybe we'll stay open two hours longer, one at the beginning and one at the end of the day. So that data really helps us make on-the-fly changes weekly, monthly, however you choose for the cadence. Those are great takeaways, Jonathan.
That decompression zone is always eye-opening for customers when they begin using full-path analysis. Oftentimes, customers use full-path analysis data, much like you have, to determine the optimal flow to the floor set so they can improve sell-through as well as the shopping experience. While this data is helpful in increasing revenue, it also helps prevent unnecessary expenditures. Another RetailNext customer used full-path analysis to determine whether a particular new fixture was worth the investment and ultimately saved them significant and unnecessary expenses by realizing early on that it wasn’t generating the results they expected.
This is just another example of how this data can make an impact on better business outcomes. With that said, Jonathan, I have to ask you about how you provide data or information to the field teams, and how do they use that information when managing stores? From the field side, our store managers are getting live traffic data and all the historical data. So they can understand, usually on a Tuesday, I can schedule lunch for these employees from 12 to 1.
And so we give them the power to adjust their non-sellable tasks and adjust their break times that day if they choose. It's not necessarily always going to be done weeks prior. And then from a business standpoint, we ask them to provide us feedback if we're changing the layout. If you want to change the layout for six weeks, let us know how you guys feel about it.
Let us know if your customers are reacting. And then they give us that data back. And obviously, with the tool, we can go ahead and understand how the customer journey has changed, and based on the feedback and what we're looking at, we can go ahead and make another change a couple of weeks after that if needed. So do you feel as though working with RetailNext enhanced your effectiveness as a business and your skill set personally?
And if so, how so? Personally, yes. It's ruined my shopping experience because I expect every retailer to design their stores with me in mind. I walk in, I have to see a camera.
If I don't see a camera, I'm kind of a little bit disappointed in the brand for not taking the time to invest. And then on a business side, just talking about what I was saying before, just allowing the customer to be at peace when they're in the shopping space; is nothing I ever really thought about. I can't put a monetary value to it, but there is value in just having them be in your location. I'll bring someone to a store because I'm like, hey, look, I'm going to the store.
Let's go look at it. I'm not going to buy anything. But there's something there that's important to building a connection with your customer. Is there anything you would have done differently?
And do you have any advice for other retailers and brands that are wanting to deploy full-path analysis data? And are there any final words of wisdom for the audience? I think there are kind of two or three things here. First, I started with just traffic, and I think that that is key.
With just traffic, you're going to get directional entrants. They're walking in left, right, and center. And then just understanding who's coming and when they're coming is just so impactful for making changes, understanding hours and employee headcount, and who's going to be there when. And just using that was kind of a catalyst to say, okay, what else can we get?
Now let's think about putting more tech in to understand the full journey of the customer. And regarding that journey, full-path analysis shouldn’t be in all your stores. It should be in a couple of them. I did four stores in 130 locations, chose four stores.
Emerging market, hometown, big city, and a random other location. And those four stores are then tested for however long you choose, six weeks, let's say. And I know based on the results there, I can go and choose 10 other like stores, and I can make those changes. Did it work again?
Great. It’s super successful. So I think it's really important to know that your shopper is different in every city. I had stores in Wichita, Kansas.
I had stores in New York. They do not buy the same products, and they do not walk around the store the same way. New Yorkers have their headphones on, and they want to be by themselves, right? So how do we design a store to fit their style of shopping?
From an organizational standpoint, that was the most difficult part I found. I was excited about the tech. I worked on the tech side. I wanted everyone to be like, "Look, check this out." And I was almost too excited.
But I had good business partners and a good team, and we were all aligned on what the goal was. We got all the traffic data, we made it digestible, we sat on it, and then we presented it in a way that we can show "If we make these two changes, move the tables back and change store hours, this is the result we're going to get financially, and, just in general, a happier customer." And from then on, I had business teams asking me for more data. And I was like, "Yes, I can give it to you. Give me a week.
I'll give you more data." And from then on, it kind of just took off. And it was super successful. Perfect. Well, I wanted to thank Jonathan for today’s informative conversation about the beauty of shopper behavior analysis.
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