theCUBE + NYSE Wired: AI & Retail Trailblazers | Leslie Lorenz, Snowflake

Clips
More from theCUBE + NYSE Wired: AI & Retail Trailblazers

Leslie Lorenz

Head of Retail

Snowflake

play_circle_outline Snowflake's impact on retail and AI transformation

play_circle_outline Importance of real-time data for customer insights and decision-making

play_circle_outline Data's role in driving value and innovation in retail

play_circle_outline Monetization of data and insights in retail industry

play_circle_outline Revolutionizing Retail: Agentic AI Leading the Way in Customer Service and Data-Driven Innovation

Info
Transcript

Leslie Lorenz, Snowflake

Leslie Lorenz

Head of Retail Snowflake

The Head of Retail for North America at Snowflake, Leslie Lorenz, discussed the impact of AI on the retail industry. Retailers are focusing on consolidating data and implementing AI strategies to drive operational efficiency. AI is transforming supply chain logistics and inventory control, leading to internal efficiencies. Real-time data insights are essential for understanding customer behavior and shaping buying patterns. Snowflake's data cloud architecture plays a crucial role in enabling real-time insights. Retailers are also exploring opportunities for m... Read more

explore Keep Exploring

What are some benefits of using Snowflake for retailers in terms of data organization and AI strategy? add

What benefits do retailers see in using real-time data for understanding customer behavior and improving customer experience? add

How should we think about the overall landscape and data's role in driving value for retailers going forward? add

What role is data playing in everyday decision making and how is it being accessed and utilized within organizations? add

What is a major topic of conversation at NRF this year regarding agentic AI and its potential impact on various industries? add

bolt Powered by CUBE AI

Leslie Lorenz, Snowflake

search

Dave Vellante

>> Hi everybody. Welcome back to our continuous coverage here at the NYSE. NYSE and theCUBE Wired community getting together and NRF week. It's all about retail, it's all about AI, really transforming that industry. Leslie Lorenz is here. She's the Head of Retail for North America at Snowflake. Leslie, good to see you. Thanks for coming down here in Wall Street.

Leslie Lorenz

>> Thank you for having me. This is awesome. It's really cool to be here.

Dave Vellante

>> Getting away from the buzz in Midtown.

Leslie Lorenz

>> The hustle and bustle.

Dave Vellante

>> What's it like over the Javits? I haven't been over yet.

Leslie Lorenz

>> It's been really cool to be there. It's ton of, ton of, ton of people, so it's really busy. But watching the general themes move through the days of what are retailers doing, what are they caring about to all the work that's happening in AI and the general themes around agentic AI. There's just a ton of really interesting, especially for Snowflake, a ton of really interesting things that we can engage in and talk about.

Dave Vellante

>> I remember at Summit in June, Jensen was up on stage and saying, actually, it was two years ago, he was on stage with Frank and he said, "We are going to supercharge the Snowflake data cloud." And he was all excited. The crowd went crazy. So how has AI affected the data cloud generally, but specifically in retail around... I said the data cloud, I meant the AI, injection into the data cloud and the Snowflake cloud. How has that affected retail?

Leslie Lorenz

>> Well, I think there's still a lot of conversation that we have with retailers about just getting your data in one place, removing data silos and focusing on that data foundation and a data strategy. And then AI has allowed us to build on top of that. We say it's Snowflake, you build an AI strategy off a data strategy or you need a data strategy before an AI strategy. But watching retailers transform their data journeys through using Snowflake and then starting to talk about the interesting things that can happen in AI because of that, we've seen a lot of growth in that space. So it's really exciting.

Dave Vellante

>> And so where are they seeing the bang for the buck? Is it the consolidation in that one place and being able to govern it? Where's the ROI?

Leslie Lorenz

>> If we quantify this in two different terms, there's the external ROI for a retailer. What customer-facing things can you do to really benefit the broader spectrum of spectrum of ROI for a retailer? I think there's also a lot of interesting internal applications to AI in general. We've seen a lot of efficiencies gained in supply chain as an example, logistics identity, inventory control. So there's a lot of interesting things that are happening internally that are probably less exciting to talk about because they're not super customer-facing, but it's driving a lot of operational efficiency across the board that retailers are, they're realizing the ROI in a different way. So it's these core operational areas where I get really excited about AI in general and how that can apply to just making business, making retail business more operationally efficient.

Dave Vellante

>> We had Duncan Angove on here just a couple of hours ago. He's the CEO of Blue Yonder. I know you guys are partners.

Leslie Lorenz

>> We are.

Dave Vellante

>> And he was talking about just an amazing amount of waste in the supply chain.

Leslie Lorenz

>> A hundred percent.

Dave Vellante

>> He was saying like a dump truck, every second of textiles goes into the dump basically. I'm like, "Wait, did you say every second?" He said, "Yes. That's how much waste there is in the supply chain." So what are you seeing, is AI having an impact yet or is it more just the Snowflake model of cloud infinite scale, separating compute from storage to simplify a thing and all that has probably taken a big bite out of that waste. So where are we at and how much further do we have to go, do you think?

Leslie Lorenz

>> I think it's going to be both. It's a good consulting answer. I think driving that, all of your data in one place for supply chain specifically, it improves visibility. So part of the challenge that we've seen over the last four years, five years is no one knows where anything is. So to get that right product to the right customer at the right time, it's really hard to even know where your products are. So from a foundational perspective, I think there's a lot of efficiencies that have been driven just in Snowflake's capability to be that kind of foundational data platform. AI is going to allow for that to happen faster and more intelligently and more efficiently and just add on top of that, right? Beginning to forecast in a better way, beginning to manage your inventory in a different way, whether it's replenishment or movement of inventory in and of itself. So there's a ton of opportunity in that space to just drive efficiency on top of understanding where your products are.

Dave Vellante

>> What about GenAI? I mean it's obviously all the buzz. Is it just a matter Leslie of well now I can talk to my data in human language? Are there other sort of use cases that you're seeing emerge beyond some of the fun experiments?

Leslie Lorenz

>> The fun experiments. I literally just at NRF had a conversation about the experiments versus the ROI that's being driven. It's funny you say that. It's also about accessibility. So it is those things, but it's also about accessibility of data. So being able to talk to your data in normal human language allows for not just the nerds, the data nerds of the world, the mes of the world to be able to query that data and understand it, but it allows for more accessibility across your organization to have conversations and make really good decisions off of that data as well. So I think it's more than that. I think we're going to also start seeing value conversations. As a business person, I tend to always want to take it back to what value are we driving, what business outcomes are we trying to solve for? What challenges are we facing? And right now there is a lot of conversation around the fun toys that are out there. I think you're going to start seeing, and my guess is this year in the coming years, actual ROI and business value begin to be quantified in a measurable way.

Dave Vellante

>> Are you a data nerd?

Leslie Lorenz

>> I am a data nerd.

Dave Vellante

>> Really?

Leslie Lorenz

>> I am.

Dave Vellante

>> So tell me more about your data nerdness because I love data, but I wouldn't... Are you technical?

Leslie Lorenz

>> I am.

Dave Vellante

>> So you could do some -

Leslie Lorenz

>> I used to be.

Dave Vellante

>> Okay.

Leslie Lorenz

>> I am.

Dave Vellante

>> But you could do some pretty serious things with data. You just love data. Like I love data. I'll take a bath in data, but I can't make magic out of data. I need help in doing that. AI is helping me make tables and analyze data a little bit more. But how did you become a data nerd?

Leslie Lorenz

>> Well, I was a swimmer growing up and I wanted to be a marine biologist like every good swimmer does. And I realized that science was not my jam. So I moved to technology and started my data journey very early. I was a consultant for a long time and decided to pick and choose my projects and data made sense to me. And so I went to work as a customer prior to coming to Snowflake, building Snowflake for our customer, a retail customer. And it just strengthened my love of logic. It's logic. It's not necessarily for me just data, it's logic and the application of that to business, which is really what kind of fascinated me across the board.

Dave Vellante

>> Data made sense and making data makes sense.

Leslie Lorenz

>> That's right. That's right.

Dave Vellante

>> Actually was what -

Leslie Lorenz

>> And then you introduce the people and process side to that and that's where I geek out.

Dave Vellante

>> So tapping into some of your geekness if I could.

Leslie Lorenz

>> Yes.

Dave Vellante

>> So you think about real-time systems. I was talking to Duncan about this. I've talked to a lot of people over the years about real-time systems because essentially we have today's historical systems of truth in the analytic data warehouse or data cloud as you guys like to call it. It's still not real-time, but we want to get to real-time. In order to get to real-time, you've got to have connection to backend legacy systems.

Leslie Lorenz

>> That's correct.

Dave Vellante

>> You've got to be able to harmonize the data. It helps to have it all in one place. Break down those silos. You've got e-commerce data, you have data from distributed networks, physical stores. Where are we with real-time insights and how is it shaping buying behavior?

Leslie Lorenz

>> So the one thing I'll say about real-time is right size, real-time. Not everything has to be real-time, in my not so humble opinion.

Dave Vellante

>> Can I just add, what is real-time? A lot of people are like, "What do you mean real-time?" I say I define it as before you lose the customer.

Leslie Lorenz

>> Well, so there's real-time which is within seconds. There's near real-time, which in retail land could be anywhere from 15 seconds to an hour. Range varies depending on who you are.

Dave Vellante

>> Right. Okay. Fair enough.

Leslie Lorenz

>> So I think when we are talking about real-time data, a lot of what retailers are referring to are how do I understand where the customer is buying and who our customer is and how they're engaging in a real-time way so you can pivot, right? You want to be able to pivot what your marketing tactics are, what your pricing structures are potentially, and how you're promoting to a customer. And for that, at this point, you need real-time data or near real-time data depending on what type of retailer you are to be able to keep up with the changes in customers buying patterns and their moods, and where they're buying and what they're buying. And understanding that in a faster way will only help with customer experience.

Dave Vellante

>> So since the AI heard around the world was introduced, I'll call it, have we compressed the real-timeness or I come back to an earlier question. Is it really the fact that it's in the cloud? You've got an architecture that allows you to put everything into a data cloud, be governed, have it all in one logical place even though it's potentially globally distributed. How much of the credit pie goes to that architecture versus AI? And is AI moving the needle yet in terms of real-timeness?

Leslie Lorenz

>> I think again, going back to my original, I want to take all the credit pie. I think Snowflake wants to take all the credit pie, we'll gladly accept it, at least based on my belief system. But the foundation of a lot of those conversations is not necessarily just getting to a modern technology, getting your data in one place. It's being able to have access to that in a way that you need it. So again, AI is just a layer on top of that and it's just logic hitting whatever that data foundation is. And data is only as good as the AI work that you're doing. The ML, the Gen AI is only as good as how good of your data is and how structured it is and how placed that is in that foundation, in that single platform. So I want to take the credit pie or maybe we, the royal we, but I think again, this kind of goes back to if you have crappy data in your data foundation and your data stack, you're going to have crappy AI. So one feeds the other, I think is my answer.

Dave Vellante

>> And that's always been the beauty of Snowflake. When I first encountered Snowflake around 2015 during the big data Hadoop days, and we were like, oh wow, this actually could work because Hadoop really didn't work well unless you had rocket scientists and guys and gals in lab coats that knew what they're doing. So that was really interesting. And the other really compelling thing was the whole security and compliance model. Put it in the data cloud, we're going to take care of it, data clean rooms and all that other stuff. Then all of a sudden, a few years ago, I guess it was open table formats. Everybody wants to have open table formats and we want to bring any engine to any compute to the data. Okay, great. We'll play, if that's what you want. Then I asked your customers, how are you going to govern that data? Are you going to use open source governance tools? Are you going to use third party governance tools? And they haven't figured that out yet. So what are retailers telling you about both of those things, their desire to have open table formats, and then how are they governing that?

Leslie Lorenz

>> I spend a lot of time talking to retailers about their customer data, which is I think probably the biggest area where privacy, security governance needs to be adhered to. When I bought Snowflake, I think I bought Snowflake in 2018, I had the same reaction. I was like, wow, *imagine this thing is sprinkled with fairy dust and the one thing sitting on the other side of the fence, the not customer side at this point, the one thing I'll say is, that continues to impress me is when we release a feature, when we release a set of capabilities, it's done with the Snowflake governance and security structure in mind. So we're trying to, as best as we can, take some of that governance and security conversation, at least from a baseline out of our customer's woes. We don't want to have to have them worry about it now, whatever data's in there is a certain amount of, we can only govern what we can govern and people have to take their responsibility. But Snowflake does its best to, with all of the capabilities, clean rooms, open table format, all of the cybersecurity work that we're doing, et cetera, et cetera. We do our best to keep a lot of those feature sets released as a part of Snowflake's capabilities as a whole in an ongoing way.

Dave Vellante

>> I'll play that back to you. You're doing your part. When it gets out of your control, there's not much you can control, but you can advise, and my advice, my consultants had would be, look, if there's value and you having openness, understand that, but understand the risks as well and manage those risks. It's like the shared responsibility model, that's on you now. Don't be blaming Snowflake or your data platform provider if something goes wrong. So just be careful and think through how that governance is going to play out.

Leslie Lorenz

>> And just as any other capability inside of an organization like be smart, be intelligent, and make sure you're doing right by your company. And we have a lot of these conversations in retail, yes, about customer, but also about monetization of data and other people having access to what data when we talk about data sharing. So it's always top of mind for us when we go into a conversation or providing advice or direction or some sort of guidance.

Dave Vellante

>> Big picture in retail, I mean, I don't know how you think about the TAM, how you think about the market. You've obviously got the e-commerce giants and Amazon and Walmart battling it out, but retail is vibrant. I mean it's exciting. I mean you go to the malls and there's actually a lot of action going on. Obviously e-commerce is booming. COVID changed the game in so many ways. How should we think about the overall landscape and data's role in driving value for retailers going forward?

Leslie Lorenz

>> That's a big question. I mean, here's the thing. Data continues to play a more and more and more and more and more important role in everyday decision making. And the interesting part about what's happening, I think from just a core foundational perspective is it's not just me who has access to it anymore. I get to actually give access to it to store managers, to marketers, to logistics providers, et cetera. So drawing that seamless view across every aspect of a retail organization or just an organization in general is I think a lot of where the power foundation is going to come from. From a market perspective, you continue to see innovation happen, and I think there's this core foundation of data stuff that needs to happen no matter what. You need to understand who your customer is and where your product is and how to get one into the other and and and. But then there's the toys that you talked about earlier, which is how do we intelligently integrate co-pilots into conversation, whether that be conversation with our customers or conversation with store associates or in training or in customer service. There's a bunch of different optionality in terms of where I think it's going to shift. And I think an interesting movement in this space is also how do you make money off of your data? There's smaller organizations that are wanting to buy insights from the larger organizations who have kind of won the game already. And so we're seeing, and I have a lot of conversations about the monetization of data and how we're making money. We're beginning to drive different ROI from that information as well. So the shifts are going to be pretty big and I think we're in for a bit of a ride over the next couple of years.

Dave Vellante

>> Any favorite examples? I mean you guys, a lot of case studies out there, but any specific to retail? How by putting your data in the data cloud, unifying your data, it's driven measurable results, what would you point to if I were a prospect saying, Hey, you could potentially do this. What's the expectation?

Leslie Lorenz

>> Yeah, one of my favorite examples, and I'll tell you why in a minute, is the work that Petco has done with Snowflake. So Petco was really struggling in kind of their broader omnichannel journey, not to get into too many details. And they invested in Snowflake, put all their data one place and began to change the way the customer experiences the brand and change their loyalty program and change the ideology of how they engage with a customer. And you think about Petco as a company, your customers aren't just pet parents, they're pets and pets don't have emails. And how you engage with a pet parent and a pet and a broader household is an interesting challenge. And marketing to them is an interesting challenge as well. And so especially if you have different kinds of pets. And so it's an interesting thing to think about when you're talking about leading products and promotions and loyalty to a set of customers that are actually really challenging to get at. You know what I mean? And pet parents are -

Dave Vellante

>> That's really interesting....

Leslie Lorenz

>> crazy. Pet parents are crazy. Right?

Dave Vellante

>> Best in show.

Leslie Lorenz

>> Best in show. That's right, that's right.

Dave Vellante

>> So manifesting the pet through the human.

Leslie Lorenz

>> That's right.

Dave Vellante

>> But it's understanding the human buying behavior as a function of the pets, the type of pets, the personality of the pets, potentially. The pets likes and dislikes that are inferred from the human. Is that right?

Leslie Lorenz

>> That's right, that's right.

Dave Vellante

>> I love Petco, by the way. I love going into Petco.

Leslie Lorenz

>> Yeah. Me too. As a double dog owner, I am a massive fan.

Dave Vellante

>> Yeah, we got two dogs, a couple of cats, so that's cool. What do you think is going to be the biggest breakthrough in retail over the next, give it a horizon, 12 to say 24 months?

Leslie Lorenz

>> Biggest breakthrough?

Dave Vellante

>> Tech breakthrough.

Leslie Lorenz

>> I mean, honestly, whether it's a tech breakthrough or something that we're already talking about that I think is just going to begin to actually become realized, and it's a massive topic of conversation at NRF this year is actually agentic AI. And I don't think it's necessarily the agents that we are going to see in customer service or anything like that. I actually think it's going to be the agents driving other agents. So you have an agent looking at inventory fulfillment and one looking at, let's say you have five different agents looking at your supply chain, there's got to be one agent that controls that. So it's going to be become more dynamic in terms of how agentic AI actually plays out in this space. And one agent isn't going to rule them all. And so how all that gets married, there's going to be a lot of development around that, and I am excited to be a part of Snowflake. So we're going to be a part of that story, but seeing how that adds value beyond just, hey, we're a customer supporter, we're a customer co-piloters, I think going to be really neat. And I think that is going to be where the innovation comes. A lot of it.

Dave Vellante

>> I love that answer. I mean, we could go another 20 minutes on agents if we had the time, but you're right. Did they say follow the money? I say follow the data.

Leslie Lorenz

>> Yeah. Yeah. Yeah.

Dave Vellante

>> That's it.

Leslie Lorenz

>> Well and follow the excitement too, right?

Dave Vellante

>> Yeah, for sure. Yeah, don't fight fashion.

Leslie Lorenz

>> That's right. That's right. That's right.

Dave Vellante

>> Leslie, thanks so much for coming in to theCUBE.

Leslie Lorenz

>> Thank you for having me. It's been a pleasure.

Dave Vellante

>> Great to have you. All right.

Leslie Lorenz

>> Thank you.

Dave Vellante

>> Okay, keep it right there. John Furrier and I, will be back right after this short break. NYSE Wired community in theCUBE's coverage of NRF here at our NYSE studio in theCUBE. Be right back.