Snowflake Data Cloud Summit 2024 | Rosemary DeAragon | Snowflake & Anil Kumar Paila | Foot Locker

Clips
News
More from Snowflake Data Cloud Summit 2024

Rosemary (Hua) DeAragon

Global Head of Retail and Travel

Snowflake

Anil Kumar Paila

Director, Data Engineering, Platforms and Architecture

Foot Locker

Four insights you might have missed from theCUBE’s coverage of Snowflake’s Data Cloud Summit

The Snowflake Data Cloud Summit in San Francisco last week offered an in-depth look at how artificial intelligence, data and open source are continuing to reshape the enterprise world.As Snowflake Inc. moves from its Data Cloud into the AI Data Cloud, the company is seeking to write its next chapter under the leadership of new CEO Sridhar Ramaswamy, supporting an AI-driven environment where customers are demanding data simplicity and value for the business

Insights from Foot Locker’s innovative use of AI and data in retail

With data’s continued surge as a growth driver and competitive factor, harnessing it long-term will determine whether businesses sink or swim with the tides.In a proactive response, Foot Locker Inc. is revolutionizing its data management strategy by integrating Snowflake Inc.’s Data Cloud to transform operations, enhance customer experiences and reap the benefits of AI in retail.Discussing Foot Locker’s operations overhaul using Snowflake Data Cloud.“We have around 2,600 stores; we’re a major retailer in footwear, but Snowflake is the backbone for us at Foot Locker,” said Anil Kumar Paila (pictured, left), principal architect at Foot Locker

play_circle_outline Footlocker's use of Snowflake as a backbone for data analytics and driving business

play_circle_outline Customer-centric approach to data and personalization

play_circle_outline Store of the future and live commerce as trends in retail

play_circle_outline Gathering data and using insights to meet customer needs in the store of the future

Info

Rosemary DeAragon | Snowflake & Anil Kumar Paila | Foot Locker

Rosemary (Hua) DeAragon

Global Head of Retail and Travel Snowflake

Anil Kumar Paila

Director, Data Engineering, Platforms and Architecture Foot Locker

search

Rebecca Knight

>> Good afternoon, everyone, and welcome back to theCUBE's live coverage of the Snowflake Data Cloud Summit here at the Moscone Center. I'm your host, Rebecca Knight, sitting alongside my co-host, co-analyst, Dave Vellante. You much have a sneakerhead, Dave?

>> I wouldn't say a sneakerhead, but I have a lot of sneakers.

Rebecca Knight

>> All right. Okay. Well, then a perfect way to introduce our next guests. I'm going to start with Anil Kumar Paila, he's the director, data engineering platforms and architecture at Footlocker. Welcome. Anil.

Anil Kumar Paila

>> Thank you.

Rebecca Knight

>> And Rosemary De'Aragon. She's the global head of retail and travel at Snowflake. Welcome back to theCUBE, Rosemary.

Anil Kumar Paila

>> Thank you.

Rebecca Knight

>> So, I'm going to start with you, Anil. I think most of our audience is aware of Footlocker and knows what you do. Tell us a little bit about how Footlocker is using AI data cloud today?

Anil Kumar Paila

>> Yeah, sure. You know about Footlocker, it's not new to many of you, I guess. Footlocker, we have around 2,600 stores. We're a major retailer in footwear, but Snowflake is the backbone for us at Footlocker. Snowflake enabled us to move from passive data analytics to rather use data as a core asset to drive the business, right? So if Footlocker Snowflake enables the data, it enables insights. Ultimately, it enables business, right? So that's how we use. So we are bringing the data from all of our systems and we infuse AI into the data, and then make it intelligent data. We build intelligent data apps. We have a ton of reporting, data and analytics. We have self-service that is running on Snowflake, which is powered by Snowflake, and we have apps that are built on top of Snowflake. Yeah, it's huge. And we are happy with what we have, particularly the SaaS, right? There is no need for me to worry about the managing the infrastructure, the scalability aspects of it is really working out well with Footlocker.

>> Can we zoom out? And I want to ask you about the dynamics in your business. Retail business, very competitive, a lot of choices out there. You've got e-commerce as multi-channel. What are the forces in your business that are the main drivers, and how is that affecting your data strategy?

Anil Kumar Paila

>> So needless to say, customer is at the epicenter for us. Customer's interest, sorry, epicenter, right? So we bring data from our point of sale system, data from a e-commerce, bring data from other channels. We build a customer-centric view of data within Footlocker. And from there, we drive personalizations, we drive marketing campaigns, targeted inventory ads, a lot of stuff that we do on top of. So we follow the approach of indexing the data, unifying the data, complementing with the identification. We have our partners who can bring in the household information about the customer so that we can drive much more efficient strategies around the customers.

>> Rosemary, what are you seeing in the retail space, and what are the similarities with travel? I'm curious about that.

Anil Kumar Paila

>> Yeah, maybe the first question first, what am I seeing? I think maybe two big topics of interest recently, the first being how generative AI actually gets applied at the business level. So starting with chat bots for shopping or assistance for shopping to things like using generative AI for the product's blog and generating descriptions for items, helping customers search more easily. And then the other part of that, the monetization side, retailers trying to find a new way to generate revenue. So more than just selling products and items, how can you package up different data products and sell those to hedge funds that are validating investment hypotheses or third party data aggregators. If a hedge fund is thinking about investing in a smart nightstand, how are you going to know if nightstands are selling recently and whether they should actually invest in them. And so it's quite fascinating. So there's ways now on Snowflake to generate revenue from data and not just items.

>> That's really interesting.

>> Yeah.

>> So when you hear them say channel checks,-

>> Yeah....

>> this could be one of those. so-called channel checks.

>> Exactly. Exactly. And then lastly, the connection between retail and travel is that customer 360 kind of guest 360 experience. So understanding when someone lands from Beijing, and maybe they're a luxury retail shopper, being able to share that data between an Hermes or LVMH with British Airlines or China Airlines, that connection is really powerful on the data side.

>> I know more about you than you know about you.

Rebecca Knight

>> No kidding. But it is pretty wild and a little creepy, but anyway, we're not going to go there. Talk about some of the biggest data challenges that retailers are grappling with today.

>> Yeah, so first and hot topic of the week is security and governance, especially for retailers with personally identifiable information.

>> If I can interrupt, you're saying it's a little creepy, but you have to worry about creepiness-

>> Oh, yeah.

>> And you have to mitigate that.

>> Oh yeah, for sure. I actually teach a big data ethics class at UC Berkeley, and one of the topics is always, which companies are tracking me, and what kind of data do they have on me? And from a retailer perspective, you have to have the ability to expunge that data anytime the customer requests it, or expose that data if the customer requests that. So those security and privacy requirements then translate into technical requirements for the retailer to implement. And that becomes quite a challenge.

Rebecca Knight

>> I'm interested in that. Teaching at UC Berkeley to Gen Zers, who are perhaps more concerned, more alarmed about security and privacy than older generations, what is your take?

>> Well, they get really alarmed during the class, and then they're like, "Oh, I'm going to go on a social media fast." And then I come back and I ask them, "So who actually went on a social media..." And no one has actually the guts to go on a full-on social media fast. And so it's funny because there's actually more awareness that we're all being surveilled, but there's still not the desire to let go of all that internet surveillance.

>> Anil, how has data affected your pricing strategies?

Anil Kumar Paila

>> That's a great question.

>> This has been an ongoing conversation for years, but I feel like we're at a point now where you can actually do some real-time adjustments-

Anil Kumar Paila

>> Right....

>> of value.

Anil Kumar Paila

>> Good question. When a product is launched, it is hot and heavy. At that time, people really want... It's the best of the model. Then it'll go through a phase where it will mature, and then it comes to a phase where it's marked down, and then it comes to a phase where it makes , right? So we have built-in AI data apps, which is a dynamic pricing that we send it to our stores and e-commerce channels. So what we do is we enable our merchandising business more from an intuition-based to a science-based prices, right? So what it does is it'll maximize the sell-through inventory ,and that ultimately dictates the profit and the top line and bottom line sales, right?

>> So thinking about that spectrum of launch, a new iPhone, everybody wants one, this end, and then sort of tail end-

Rebecca Knight

>> Or a new pair of sneakers.

>> New pair of sneakers. Right. Tail end, but same dynamic. So at the front end, you don't have to... You price it for margin. You're going to get your margin. At the tail end, you might just want to get the product off the shelf so you can make room for the next new hot product. Is it true that it's that fat middle that really is the hardest to optimize, or is it really the end points as well?

Anil Kumar Paila

>> It depends. A lot of factors will come into play. Seasonal factors will come into play. Now Olympics is coming up. You might have launched a product, but even though it is seasoned out, Olympics might drive the sales of your product. So typically toward the tail end, we do see a drop in the sales. That's when we take these markdowns driven completely by AI, and that will basically provide much more sales, right? So I want to sell through some aged inventory. So I have some markdowns, I'll put it in the system. It'll tell you if you your margin... Sorry, if you bring your price predicted through, so-and-so value, you will be selling so-and-so units. So we typically see it towards a tail end, not in the middle end, and that's where this pricing engine will play a huge role in selling through our aged inventory and bringing new markets onto the shelf.

>> Okay, so you guys both former Walmarters, right?

>> Yeah.

>> So remember beer and diapers? Okay? Do you know the story, right?

>> Yeah.

>> You know the story, right? Oh, so Walmart was selling beer and diapers and they didn't understand why. Well, they realized that men would go out to the store to get diapers for the baby and they'd pick up a six-pack of beer. So they'd put them right next to each other and the sales went through the roof. I don't know if that's urban legend or what, but it was a beautiful story back then.

>> Yeah.

>> So we evolved from that, and then online, just spamming you with ads that weren't relevant. You said something on the phone, and all of a sudden you get an ad about it and you go, "Oh boy." Okay, we've evolved well beyond that. Where are we today in terms of the modern-day equivalent of beer and diapers?

>> Well, they will still do those analyses today. So the correlation between red cups and ping pong balls and beer, which are traditionally in two different categories on the e-commerce site. But in the store, you'll want to place them strategically in the beer aisle. We see retailers still doing that analysis today to understand which items have affinity towards each other. On the e-commerce side, it's even more granular because you can get the background and the shopping behaviors of the customer, and that is an additional data point layered on top of, okay, I know this person typically buys, I don't know, red solo cups and ping pong balls and likes, I don't know, vegan beers or kind of small batch beers as opposed to Coors Light. And that is definitely something that we do at the customer level on the e-commerce side. But yeah, on the retail side, that analysis is definitely still alive today.

>> Right. And so now, you can do, on the e-commerce side anyway, mass customization at scale. And really, that's powerful.

>> You can use generative AI to create the content that's targeted for particular customer segments as well. And then you can also use third-party data to understand what are young folks doing. We see a trend towards non-alcoholic beverages lately with Gen Z. And so how does that translate into the content, into the emails that you push to that age range? It's all interesting. You can use generative AI for that.

Rebecca Knight

>> When Gen Z starts having kids, it'll be bubbly water next to the diapers.

Rebecca Knight

>> They have bars here that are completely non-alcoholic.

Rebecca Knight

>> Tell the hedge funds that.

>> Exactly.

Rebecca Knight

>> So thinking about customer optimization and optimizing pricing and retention, where do you see the future of retail going, rosemary?

>> Well, there's some interesting futuristic things happening. So one is the store of the future. I know Footlocker is doing store of the future initiatives. So Amazon has these stores. You walk into a dressing room, it knows your profile, and there's an endless aisle behind the door for all the different colors and sizes and shapes and everything, and it's just like a magic mirror. And you do all your shopping in this room, and it's totally tailored to you. So that's an interesting kind of futuristic-.

Rebecca Knight

>> You are physically there doing the shopping.

>> You're physically there.

Rebecca Knight

>> So it's a real store.

>> You open the door and there's a whole supply chain in the back that's automatically bringing the item to you, delivering-

>> That's cool....

>> it to the mirror behind the door. Then the other side, there's also live commerce, live selling. So through TikTok or Instagram, you have someone in the store explaining live to all the people who are watching, the different items. And they're touching the items, they're feeling the items, and they're walking through them. And that is definitely a futuristic version of what buying might look like as well.

Anil Kumar Paila

>> Yeah. So Footlocker is... Like Rose just mentioned, we have started coming out of the offline more to a store of the future concept. We are driving a core concept, which we're collaborating with our partner vendor, trying the concept together. You can bring the virtual reality into the stores. We can feel the product and how does the product fit onto you through virtual reality. So we are driving the customer experiences eventually through all of these.

Rebecca Knight

>> Excellent. So thinking about the store of the future, which is just mind-blowing, frankly, how are you gathering your data? Because as you said, you're seeing these trends, Gen Z not wanting non-alcoholic beverages. And then there's got to be some sort of athletic wear equivalent to that trend that you're-

>> Crew socks versus... Did you know this?

>> What's this?

Rebecca Knight

>> Let's hear it.

>> Okay. The way that young people tell if you're old is if you wear ankle socks or no-show socks. Did you know this?

Rebecca Knight

>> Okay, I don't want to-

>> So if you wear crew socks, you're officially young. And if you wear ankle socks or no-show socks, you're officially old.

>> No-shows are old now. Oh my God.

>> No-shows are old now. So anyway, in the footwear world,-

Rebecca Knight

>> Right....

>> that would be an example.

Rebecca Knight

>> Okay. So that's the definition of cool according to Gen Z, apparently.

>> Apparently.

Rebecca Knight

>> So how are you gathering this and making sure that you are on top of this? And obviously you're getting that data that's happening now, but then predicting the future. Tell me more, Anil.

Anil Kumar Paila

>> Yeah. So you are asking more about gathering the data? Can you repeat that question one more time, please?

Rebecca Knight

>> I'm really just curious about how you are developing the store of the future and-

Anil Kumar Paila

>> Oh, okay....

Rebecca Knight

>> making sure that it does hit all the right futuristic buttons for your shoppers.

Anil Kumar Paila

>> It's understanding the needs of the customer. We have the data, what our customer wants, what their behavioral patterns are, what their needs are, and we get the market trends too, what the young people want, because what the basketball trend looks like. If it's the Olympics season, what kind of inventory, that assortment that we should carry in the store. So with all these insights together, we ultimately bring the inventory into the store, put it in such a way, right product, on the right shelf and organization in such a way, and talk to the customer, explaining the features of the product, and also bring the retail virtual reality into play where the customer can truly feel it before he or she can wear it. That's how... It's all about data, gathering the data, bringing the data and knowing the trends, and then making it happen inside the store and make a customer feel that. That's all we are.

Rebecca Knight

>> Anil, Rosemary, thank you both so much. I need to go get some new socks.

>> Gen Z's are dorks. That's my conclusion.

>> I went straight to this store after I saw that analysis and bought some crew socks. I know. I didn't want to be old.

Rebecca Knight

>> No, definitely not. Thank you both so much. Great conversation.

>> Great being here.

Anil Kumar Paila

>> Pleasure talking.

Rebecca Knight

>> I'm Rebecca Knight for Dave Vellante. That wraps up day three of the Snowflake Data Cloud Summits. Come back tomorrow for more of theCUBE's live coverage. You're watching theCUBE, the leader in enterprise tech news and analysis.

>> That was-

Rosemary DeAragon | Snowflake & Anil Kumar Paila | Foot Locker

search

Rebecca Knight

>> Good afternoon, everyone, and welcome back to theCUBE's live coverage of the Snowflake Data Cloud Summit here at the Moscone Center. I'm your host, Rebecca Knight, sitting alongside my co-host, co-analyst, Dave Vellante. You much have a sneakerhead, Dave?

>> I wouldn't say a sneakerhead, but I have a lot of sneakers.

Rebecca Knight

>> All right. Okay. Well, then a perfect way to introduce our next guests. I'm going to start with Anil Kumar Paila, he's the director, data engineering platforms and architecture at Footlocker. Welcome. Anil.

Anil Kumar Paila

>> Thank you.

Rebecca Knight

>> And Rosemary De'Aragon. She's the global head of retail and travel at Snowflake. Welcome back to theCUBE, Rosemary.

Anil Kumar Paila

>> Thank you.

Rebecca Knight

>> So, I'm going to start with you, Anil. I think most of our audience is aware of Footlocker and knows what you do. Tell us a little bit about how Footlocker is using AI data cloud today?

Anil Kumar Paila

>> Yeah, sure. You know about Footlocker, it's not new to many of you, I guess. Footlocker, we have around 2,600 stores. We're a major retailer in footwear, but Snowflake is the backbone for us at Footlocker. Snowflake enabled us to move from passive data analytics to rather use data as a core asset to drive the business, right? So if Footlocker Snowflake enables the data, it enables insights. Ultimately, it enables business, right? So that's how we use. So we are bringing the data from all of our systems and we infuse AI into the data, and then make it intelligent data. We build intelligent data apps. We have a ton of reporting, data and analytics. We have self-service that is running on Snowflake, which is powered by Snowflake, and we have apps that are built on top of Snowflake. Yeah, it's huge. And we are happy with what we have, particularly the SaaS, right? There is no need for me to worry about the managing the infrastructure, the scalability aspects of it is really working out well with Footlocker.

>> Can we zoom out? And I want to ask you about the dynamics in your business. Retail business, very competitive, a lot of choices out there. You've got e-commerce as multi-channel. What are the forces in your business that are the main drivers, and how is that affecting your data strategy?

Anil Kumar Paila

>> So needless to say, customer is at the epicenter for us. Customer's interest, sorry, epicenter, right? So we bring data from our point of sale system, data from a e-commerce, bring data from other channels. We build a customer-centric view of data within Footlocker. And from there, we drive personalizations, we drive marketing campaigns, targeted inventory ads, a lot of stuff that we do on top of. So we follow the approach of indexing the data, unifying the data, complementing with the identification. We have our partners who can bring in the household information about the customer so that we can drive much more efficient strategies around the customers.

>> Rosemary, what are you seeing in the retail space, and what are the similarities with travel? I'm curious about that.

Anil Kumar Paila

>> Yeah, maybe the first question first, what am I seeing? I think maybe two big topics of interest recently, the first being how generative AI actually gets applied at the business level. So starting with chat bots for shopping or assistance for shopping to things like using generative AI for the product's blog and generating descriptions for items, helping customers search more easily. And then the other part of that, the monetization side, retailers trying to find a new way to generate revenue. So more than just selling products and items, how can you package up different data products and sell those to hedge funds that are validating investment hypotheses or third party data aggregators. If a hedge fund is thinking about investing in a smart nightstand, how are you going to know if nightstands are selling recently and whether they should actually invest in them. And so it's quite fascinating. So there's ways now on Snowflake to generate revenue from data and not just items.

>> That's really interesting.

>> Yeah.

>> So when you hear them say channel checks,-

>> Yeah....

>> this could be one of those. so-called channel checks.

>> Exactly. Exactly. And then lastly, the connection between retail and travel is that customer 360 kind of guest 360 experience. So understanding when someone lands from Beijing, and maybe they're a luxury retail shopper, being able to share that data between an Hermes or LVMH with British Airlines or China Airlines, that connection is really powerful on the data side.

>> I know more about you than you know about you.

Rebecca Knight

>> No kidding. But it is pretty wild and a little creepy, but anyway, we're not going to go there. Talk about some of the biggest data challenges that retailers are grappling with today.

>> Yeah, so first and hot topic of the week is security and governance, especially for retailers with personally identifiable information.

>> If I can interrupt, you're saying it's a little creepy, but you have to worry about creepiness-

>> Oh, yeah.

>> And you have to mitigate that.

>> Oh yeah, for sure. I actually teach a big data ethics class at UC Berkeley, and one of the topics is always, which companies are tracking me, and what kind of data do they have on me? And from a retailer perspective, you have to have the ability to expunge that data anytime the customer requests it, or expose that data if the customer requests that. So those security and privacy requirements then translate into technical requirements for the retailer to implement. And that becomes quite a challenge.

Rebecca Knight

>> I'm interested in that. Teaching at UC Berkeley to Gen Zers, who are perhaps more concerned, more alarmed about security and privacy than older generations, what is your take?

>> Well, they get really alarmed during the class, and then they're like, "Oh, I'm going to go on a social media fast." And then I come back and I ask them, "So who actually went on a social media..." And no one has actually the guts to go on a full-on social media fast. And so it's funny because there's actually more awareness that we're all being surveilled, but there's still not the desire to let go of all that internet surveillance.

>> Anil, how has data affected your pricing strategies?

Anil Kumar Paila

>> That's a great question.

>> This has been an ongoing conversation for years, but I feel like we're at a point now where you can actually do some real-time adjustments-

Anil Kumar Paila

>> Right....

>> of value.

Anil Kumar Paila

>> Good question. When a product is launched, it is hot and heavy. At that time, people really want... It's the best of the model. Then it'll go through a phase where it will mature, and then it comes to a phase where it's marked down, and then it comes to a phase where it makes , right? So we have built-in AI data apps, which is a dynamic pricing that we send it to our stores and e-commerce channels. So what we do is we enable our merchandising business more from an intuition-based to a science-based prices, right? So what it does is it'll maximize the sell-through inventory ,and that ultimately dictates the profit and the top line and bottom line sales, right?

>> So thinking about that spectrum of launch, a new iPhone, everybody wants one, this end, and then sort of tail end-

Rebecca Knight

>> Or a new pair of sneakers.

>> New pair of sneakers. Right. Tail end, but same dynamic. So at the front end, you don't have to... You price it for margin. You're going to get your margin. At the tail end, you might just want to get the product off the shelf so you can make room for the next new hot product. Is it true that it's that fat middle that really is the hardest to optimize, or is it really the end points as well?

Anil Kumar Paila

>> It depends. A lot of factors will come into play. Seasonal factors will come into play. Now Olympics is coming up. You might have launched a product, but even though it is seasoned out, Olympics might drive the sales of your product. So typically toward the tail end, we do see a drop in the sales. That's when we take these markdowns driven completely by AI, and that will basically provide much more sales, right? So I want to sell through some aged inventory. So I have some markdowns, I'll put it in the system. It'll tell you if you your margin... Sorry, if you bring your price predicted through, so-and-so value, you will be selling so-and-so units. So we typically see it towards a tail end, not in the middle end, and that's where this pricing engine will play a huge role in selling through our aged inventory and bringing new markets onto the shelf.

>> Okay, so you guys both former Walmarters, right?

>> Yeah.

>> So remember beer and diapers? Okay? Do you know the story, right?

>> Yeah.

>> You know the story, right? Oh, so Walmart was selling beer and diapers and they didn't understand why. Well, they realized that men would go out to the store to get diapers for the baby and they'd pick up a six-pack of beer. So they'd put them right next to each other and the sales went through the roof. I don't know if that's urban legend or what, but it was a beautiful story back then.

>> Yeah.

>> So we evolved from that, and then online, just spamming you with ads that weren't relevant. You said something on the phone, and all of a sudden you get an ad about it and you go, "Oh boy." Okay, we've evolved well beyond that. Where are we today in terms of the modern-day equivalent of beer and diapers?

>> Well, they will still do those analyses today. So the correlation between red cups and ping pong balls and beer, which are traditionally in two different categories on the e-commerce site. But in the store, you'll want to place them strategically in the beer aisle. We see retailers still doing that analysis today to understand which items have affinity towards each other. On the e-commerce side, it's even more granular because you can get the background and the shopping behaviors of the customer, and that is an additional data point layered on top of, okay, I know this person typically buys, I don't know, red solo cups and ping pong balls and likes, I don't know, vegan beers or kind of small batch beers as opposed to Coors Light. And that is definitely something that we do at the customer level on the e-commerce side. But yeah, on the retail side, that analysis is definitely still alive today.

>> Right. And so now, you can do, on the e-commerce side anyway, mass customization at scale. And really, that's powerful.

>> You can use generative AI to create the content that's targeted for particular customer segments as well. And then you can also use third-party data to understand what are young folks doing. We see a trend towards non-alcoholic beverages lately with Gen Z. And so how does that translate into the content, into the emails that you push to that age range? It's all interesting. You can use generative AI for that.

Rebecca Knight

>> When Gen Z starts having kids, it'll be bubbly water next to the diapers.

Rebecca Knight

>> They have bars here that are completely non-alcoholic.

Rebecca Knight

>> Tell the hedge funds that.

>> Exactly.

Rebecca Knight

>> So thinking about customer optimization and optimizing pricing and retention, where do you see the future of retail going, rosemary?

>> Well, there's some interesting futuristic things happening. So one is the store of the future. I know Footlocker is doing store of the future initiatives. So Amazon has these stores. You walk into a dressing room, it knows your profile, and there's an endless aisle behind the door for all the different colors and sizes and shapes and everything, and it's just like a magic mirror. And you do all your shopping in this room, and it's totally tailored to you. So that's an interesting kind of futuristic-.

Rebecca Knight

>> You are physically there doing the shopping.

>> You're physically there.

Rebecca Knight

>> So it's a real store.

>> You open the door and there's a whole supply chain in the back that's automatically bringing the item to you, delivering-

>> That's cool....

>> it to the mirror behind the door. Then the other side, there's also live commerce, live selling. So through TikTok or Instagram, you have someone in the store explaining live to all the people who are watching, the different items. And they're touching the items, they're feeling the items, and they're walking through them. And that is definitely a futuristic version of what buying might look like as well.

Anil Kumar Paila

>> Yeah. So Footlocker is... Like Rose just mentioned, we have started coming out of the offline more to a store of the future concept. We are driving a core concept, which we're collaborating with our partner vendor, trying the concept together. You can bring the virtual reality into the stores. We can feel the product and how does the product fit onto you through virtual reality. So we are driving the customer experiences eventually through all of these.

Rebecca Knight

>> Excellent. So thinking about the store of the future, which is just mind-blowing, frankly, how are you gathering your data? Because as you said, you're seeing these trends, Gen Z not wanting non-alcoholic beverages. And then there's got to be some sort of athletic wear equivalent to that trend that you're-

>> Crew socks versus... Did you know this?

>> What's this?

Rebecca Knight

>> Let's hear it.

>> Okay. The way that young people tell if you're old is if you wear ankle socks or no-show socks. Did you know this?

Rebecca Knight

>> Okay, I don't want to-

>> So if you wear crew socks, you're officially young. And if you wear ankle socks or no-show socks, you're officially old.

>> No-shows are old now. Oh my God.

>> No-shows are old now. So anyway, in the footwear world,-

Rebecca Knight

>> Right....

>> that would be an example.

Rebecca Knight

>> Okay. So that's the definition of cool according to Gen Z, apparently.

>> Apparently.

Rebecca Knight

>> So how are you gathering this and making sure that you are on top of this? And obviously you're getting that data that's happening now, but then predicting the future. Tell me more, Anil.

Anil Kumar Paila

>> Yeah. So you are asking more about gathering the data? Can you repeat that question one more time, please?

Rebecca Knight

>> I'm really just curious about how you are developing the store of the future and-

Anil Kumar Paila

>> Oh, okay....

Rebecca Knight

>> making sure that it does hit all the right futuristic buttons for your shoppers.

Anil Kumar Paila

>> It's understanding the needs of the customer. We have the data, what our customer wants, what their behavioral patterns are, what their needs are, and we get the market trends too, what the young people want, because what the basketball trend looks like. If it's the Olympics season, what kind of inventory, that assortment that we should carry in the store. So with all these insights together, we ultimately bring the inventory into the store, put it in such a way, right product, on the right shelf and organization in such a way, and talk to the customer, explaining the features of the product, and also bring the retail virtual reality into play where the customer can truly feel it before he or she can wear it. That's how... It's all about data, gathering the data, bringing the data and knowing the trends, and then making it happen inside the store and make a customer feel that. That's all we are.

Rebecca Knight

>> Anil, Rosemary, thank you both so much. I need to go get some new socks.

>> Gen Z's are dorks. That's my conclusion.

>> I went straight to this store after I saw that analysis and bought some crew socks. I know. I didn't want to be old.

Rebecca Knight

>> No, definitely not. Thank you both so much. Great conversation.

>> Great being here.

Anil Kumar Paila

>> Pleasure talking.

Rebecca Knight

>> I'm Rebecca Knight for Dave Vellante. That wraps up day three of the Snowflake Data Cloud Summits. Come back tomorrow for more of theCUBE's live coverage. You're watching theCUBE, the leader in enterprise tech news and analysis.

>> That was-