Lowe's Companies, Inc. (LOW) Earnings Call Transcript & Summary

Hello, everyone. I'm Amy Eschliman. I'm Managing Director of Retail Strategy for Google Cloud. We have an amazing group of panelists with us today. I'm going to go through a little bit of context about what we're going to be talking about, and then we'll go right into the questions. So to start, I want to address the elephant in the room, generative AI, you might have heard of it while you're wandering the floor of NRF. Retail has been through this before. These massive transformations is nothing new to retail. The Internet is a great example, change the way we shop, mobile phones, same thing, change where and how we shop. And now we're a -- generative AI, which has the capability of transforming everything from the customer experience to the associate experience. It's got tremendous potential, and it's a really exciting time in retail because of this technology. Possibilities of generative AI in retail are really everywhere. It's the ability to synthesize and analyze information that we did not have the ability to do before. So think about handwritten forms coming from the stores, product imagery. We're able to use that unstructured data in a way that we, again, have not been able to do before. Then the ability to generate content, massive implications in the world of retail marketing. When you think about not only creating campaigns, but then also being able to iterate on those campaigns, improve them with the insights that you got and then creating and automating processes. Again, every part of the retail value chain has the ability to have generative AI effect and really change the way that we do things. And then finally, engaging through conversations. We're not in the same world before robotic chatbots. These can be incredibly natural conversations that make a customer feel like they are having a very personalized, seamless experience. So the possibilities are really endless. This is some use cases. It's certainly not an exhaustive list of use cases. But if you look behind me, these are the use cases that we, at Google feel like really have the ability to drive more immediate value within retail. And within retail, time-to-value has always mattered. When we talk to our retail customers and look at the retail industry, 2024 is going to be a year of action. This survey, we had 81% of retail decision makers say that they feel urgency to adopt generative AI. And 72%, a pretty massive number are looking at implementing it in 2024. So the time is now. We ask how they plan on using it, you can see a variety of use cases, everything from the customer experience to the associate experience and making processes more effective. So customer service, the primary use case engine. Product descriptions, how do you speed the categorization of product, the description of a product, how do you personalize that to different sectors -- segments. Creative work is a huge opportunity. Conversational commerce was listed. And then also interesting is almost equal amount of survey respondents mentioned the associate experience as well. So not just the customer experience, but the associate experience. I know many of you in the room are at different places in your AI journey. And I think one thing stands true no matter what type of AI we're talking about, strong data foundations are absolutely critical. And before you can move on to those really innovative use cases, you need to have that data behind you in order to use it in an effective way. So -- and with that, I'm going to actually turn it over to our panelists, which I am really excited to speak to. I'm going to have each of you introduce yourselves, Murali, I'll start with you, and we'll go down the line.

Okay. My name is Murali Sundararajan, I'm the Chief Information Officer at Victoria's Secret.

My name is Jessyn Katchera. I'm leading E-commerce for the Group Carrefour and the Innovation as well.

Okay. My name is Chandhu Nair, SVP of Technology for Data AI and Innovation at Lowe's home improvement company.

Thank you, guys, very much for being here. So I'm going to actually ask the first question of you, Murali. You at Victoria's Secret are leveraging AI and generative AI in lots of exciting ways. I'd love you to talk about how you're leveraging generative AI in the digital commerce space. Can you share more details?

Yes. I think like any other retailer, Victoria's Secret is focused on going through the journey with the AI and generative AI. So typically, we focus on 3 categories: One, is the customer experience for the customers and the associate experience, both in the store as well as for the corporate office. And improving operational efficiency across various functional areas. So the use case is the one we started with last year was the focus on as customer experience for the digital one. We wanted to bring the customer experience what the customer typically go through in the store, and we want to mimic the same operations in the digital, that's what we were focused on, how do we can able to fill the gap. The example is when the customer walks into the store and then if she is interested in buying a bra, if the bra, the one she has used it for, 5 years before, she wanted to come back and say, I want to buy the same bra. Can you help me out? So the associate who's in the store are experienced, they know what exactly the question they need to ask. They know how to exactly work through the customer to navigate exactly what she's looking for across all the 50,000 SKUs that we have. But if you want to simulate the same experience in the digital, we're just exploring how we can able to do? So that's when we had a conversation with Google and Google who is helping us to say, can we leverage the virtual -- visual AI on using their Vertex platform. So we started the journey sometime in March. The use cases came in early into March, and then we could able to work through the prototype. We put it in production in June of 2023. So the reason I'm mentioning the time line is because this is how we can able quickly to the prototype, see what the value is and then put them into production and then get some feedback on the customer because AI or generative AI, the feedback is very, very critical. And as you talked about, data is also critical. We need to understand what kind of the data we get, what kind of experience we are trying to solve. So this one, we could able to literally see -- we saw the use case, and we can able to bring it to fusion very, very fast. And then that created a lot of momentum in terms of how do you build the use cases. And now we are focusing on -- a lot more on customer experience, a lot more on creating more productivity and efficiency for the associate. So that the associate can reduce their mundane work in the store, focus more with the rest of the associate for the customer experience and we can able to improve the work effort.

Great. And you guys, I don't know how you did it, but you sat in order of the questions I wanted to ask you. So Jessyn this next one is for you. I know Carrefour is doing some pretty amazing things with generative AI from marketing use cases, even HR use cases. For the purpose of today's conversation, I'd love to have you talk to us a bit about the marketing use cases you've started and any results that you've seen thus far would be great.

Yes. And just to play back a little bit, I mean, similar to you, as soon as we heard about all the buzz that GenAI was carrying it around, we knew we wanted to be part of that journey. Why not? Because it's fun, because it's sexy, because we saw the potential it had to reinvent the way we operate to transform our operations. And so the first thing we did to come up with what are those right use cases that we developed with Google for some of them. We started to talk with all of our operational teams to really understand what are the pain points today? What are the use cases we really need to solve and where GenAI can really play a role to transform those operations. And so by doing so, we realized there were 3 types of use cases that could be relevant. There are the everyday use cases like all the low value-added tasks that you can automate, that you can realize some small productivity gains. Think about sending minutes automatically, following up on actions, smart composing your e-mails and for all of them, to be fair, that's not where we wanted to focus because we thought there are a lot of brilliant players like Google and many of your competitors that will solve that for us. We just have to wait for the next release of those innovations in the tools we use. On the day-to-day to bring that to the best of our employees. Then there was a second type of use cases, which was around where can we get a competitive edge. Because we're reinventing part of the experience where GenAI can help augment the way we serve our customers by being more personal, by being more tailored, by better empowering our employees to do the right thing. And so that's one of the use cases we developed there is around shopping assistant. And then the third thing, which I think is the truly crux of the issue for us is how can we reinvent end-to-end some big chunks of functions that we have in our business. And that's where marketing played -- entered in our reflection. We realize that there are a lot of functions that you can radically transform by using GenAI to be to better empower your employees, to better serve our customers, to better propel our growth, and that's where we played in the -- in what we call the marketing studio. The goal there was really to think about how do we use GenAI to be smarter, faster at generating assets for our marketing campaigns, whether it's audio, whether it's tax, whether it's visual. These are some of the use cases we're exploring. And why does it matter? It depends on the companies. For us, it's really about accelerating the agility, the time to market. So instead of having to wait for many weeks to get a customer-ready product from a marketing standpoint, we can develop that in a matter of hours or a matter of days, which means you can certainly decide to expand and tailor the content you have to really feed the media you want to use, to start to deploy local nuances to the content to develop globally because instead of having to wait for weeks or months to deliver their 1 asset at a time. you can have done that in a few days. So you can start to really expand the reach and the personalization of your marketing campaign. And that's what we're really proud about and some of the use cases we developed with Google on that front and that are displayed in the booth are really exciting for us.

Thank you. I've been in retail for 20 years. And I think we've been talking about personalization all that time. It's really exciting to see some of these use cases come to life because they truly change the way that we deliver personalized content. Okay, Chandhu, I'm going to send this to you next. You have been an early adopter of Google retail search. I know you're using generative AI as some merchandising use cases. I'd love to hear your perspective on kind of what -- how it's going? Any results that you've seen thus far?

Sure. Thanks, Amy. Absolutely. But I'll start with kind of the common theme that's there. I think generative AI is not kind of the differentiator by itself, your data is, right? So that's -- I think what -- how I think of generative AI as to how it will unlock value for retail, especially is you got to think about now data that we could not process before. Think about all of your operating procedure documents, the PDFs and things like that, that is out there that is typically hard for you to process and understand and really kind of build out experiences for your associates, for your customers or kind of help improve in generally your productivity as a company, right? So I think that's kind of the broader premise by which we started looking across the entire retail value chain and see where generative AI use case could apply. So -- and merchandising was definitely one of the areas that we picked on. And the reason why picked on is if you look at home improvement, we sell anything from appliances, you sell appliances very differently from lumber to paint. I mean people curate -- I mean, if you're a merchant, you curate those assortment very, very differently compared to like your next category of assortment, right, that you need to bring to the table, right? So what we started was with very basic stuff. We noticed a growing problem that we had was when it come to product onboarding like onboarding a SKU or onboarding a vendor. It was a laborious process, very manual process. A lot of the data quality that is involved in capturing them upfront, it was not there. So it was kind of exposed to the customer, all the store associate explain that. So it was not that we never tried to solve that problem. We have applied a lot of AI models before to solve that problem. But now with generative AI, we almost saw about 60% reduction in like the amount of manual labor that is needed to upload these initial product descriptions. Think about all of the attachments that needs to -- the images, all of that, that needs to go into that, right? So that's one part. Then we thought about, okay, how do we productize this? And that's a theme that we're working on because [indiscernible] any other thing. It does require a different way to think about productizing generative AI products. But -- now think about how you shop for a vanity in the bathroom, right? So if you go to a store, you kind of see a curation of things that actually tells you how it could look like, the backsplash, the faucet, I mean, everything that is in there. How do you kind of bring that? How do you curate that in a very dynamic way? Usually, it is all very manual where somebody is a marketer or a merchant is working together to kind of pull all of that together. But with generative AI, you can actually curate that with a lot of -- not just with generative AI, I should say, we use generative AI alongside a lot of our model because it has to understand our data, and we bring that together to curate what the vanity could look like. What are the options, right? So -- and we are in that process to kind of evolve and that will be a customer-facing experience. So we have both associate-facing experiences like helping onboard products with better data quality, better images, better graphics to kind of help sell, help kind of -- help tell the message to the customer the right way. At the same time, we also want to kind of have the right experiences to our customers with curated collections of [indiscernible] that can actually enable sales digitally or in-store, right? In-store or associates are doing that. So that's been the part. Now I'll kind of share some of the learnings though, as far as to how we applied it. To me, like I think large language models, all the buzzes around generative AI, language models are, in a way, commoditized. I mean there's a lot of options, open source or otherwise to go and get different large language models. Like I said, the data is foundation. I think my job from a technology perspective is to look for what is the best and the most accurate model at the lowest price, right? Because these are expensive ones to run. And you have to kind of figure out what are the right use cases to apply. So you kind of have to think about what is the most accurate model at the lowest price. Two, like we said, your data is your foundation. And you have to have an ecosystem where your models will work in tandem with the LLMs to kind of give the right experience that is in there. The third learning is really in terms of when we talked about -- we are a product-driven structure in terms of our technology organization. But when we started to bring in generative AI, we had to kind of disrupt our model to kind of make sure that we can build the right experiences for the customer or our associates. The reason I say that is these models are not good at giving you definitive answers, right? They are 80% good. 20%, they can do things that we kind of don't know or they are not accurate. So you have to now work with -- you have to change an engineer's mind to work with something that is not very definite, but it's 80% there, right? So how do you make that into your process of rolling out experiences to your customers. Now you have to also make sure that the customer is not exposed to that stuff. So -- those are some of the things, both from how we rolled out the different products, from the use cases to different products and how we thought and continue to learn as we evolve this evolve into this journey, if that makes sense.

So you talked a bit about prioritization and as you were describing those use cases, which is great. Thank you. I'd love to hear a little bit more like any advice you have for prioritization across an organization, how you get the organization on board with prior -- with your -- the use cases that you've decided? I know you kind of addressed in each question, but I'd love to go a bit deeper into that question of all the use cases you can do, how do you pick the one that you want to start with? Murali, I'll start with you again.

I think the right way to do is what you said is correct. I think we have to understand the use case scenario is what makes more sense. I think the reason we picked up customer experience as a critical one for Victoria's Secret is because that's where the value for what we are trying to do, and that's where we can able to learn a lot of the insight from the customer, learn from that for certain experiences. But the other experiences, we are focused on associate focusing because specifically when you go to the store, there are multiple situations where we can able to have an impact of the associates. Because in retail, the labor of the store is always the premium. How much are they being able to reduce their work behind the curtain, put them in front of the store, work with the customer to make them process easier, that's going to make the life of the customer much, much easier. So we look at for the business value, we look at for where all we can add value back to the customer. So that's where -- that's the priority that takes precedence over the other one. Doesn't mean there are other areas like supply chain or merchandising or finance, it doesn't make some sense. But we do them, but it's going to be a little different priorities. But for all of them, the focus is going to be the data and the experience that is going to have add value.