Floor & Decor Holdings, Inc. (FND) Earnings Call Transcript & Summary

We love those lights. Thank you so much, everyone, for joining us today. Hope everyone can hear us in the back. We're here to talk about how data matters for retail, and we're just thrilled to have Jared Brown here from Floor & Decor. And what's exciting about this story is we're going to be talking about how their data management organization and what they did with data management overall, helped improve customer experience, it helped improve the bottom line as well as sustainability in the organization. So Jared, thank you so much for joining us today.

Thanks for having me.

And one thing before we really get started is there was originally Oriental Trading Company here, but due to weather she was unable to make it, but we really want to thank Jared for coming up here and doing this extra session with us today. So -- let's get started.

And can you tell us about Floor & Decor for everyone maybe that's not aware of your organization?

Yes, absolutely. I love talking about Floor & Decor. So we're a hard surface flooring company. We specialize in just hard surface flooring. And we were founded in the year 2000 and we are now 220 stores. We've made it public that we're going to grow to 500 stores in the upcoming years. And we're a STEP -- Stibo STEP customer, and we're going to talk about that.

Thank you so much. What's really interesting about retail, it's been a challenge for all of the folks that are in the stores in the last few years, but I think -- when you think about data, how much has changed in like 5 years and how data organizations have been able to go after that challenge and address that challenge. Can you -- you've done this twice in 2 different organizations. So you've been able to move the organization forward with your teams. Can you tell us a little bit about that and how you've done that in your career?

Yes. So we have to set the goals. So you have to have the company and the leadership aligned to the vision that you're trying to accomplish, understand the problems that you're trying to resolve with the solution. And then you need to build the team, having subject matter expertise internally around the data and the systems and then how to expose that out to the multiple channels and systems within your organization are a key that you want to understand where you want to get to from the beginning so that you have that path and you have that direction. And it allows you to move forward. And then the buy-in from leadership is important because you're going to have cultural friction, you're going to have historical processes and historical debt that you're replacing with a solution like Stibo STEP. And so there is work to be done there and a lot of complexity. But with that leadership by and then you're much better off.

Yes. Sometimes we talk about the technology, but we don't talk about the politics and the buying and stuff like that. So obviously, that's one of the challenges. But if you kind of go back to the beginning, what were some of the challenges, if you will, from the base camp when you first started before you got to where you are today with the organization.

Yes. So our data -- back in 2016, when I joined the company, that about a month then, they said, "Hey, we need a Master Data Management system, and we are in the middle of an RFP." One of the challenges was we had data [ strolling ] throughout the entire company. It was working, but we wanted to consolidate it, create a source of truth and then put some governance around it so that we could know that the start data for our factory vendor and product, we have all 3 of those in Stibo STEP could then get pushed through a workflow, and we would then have better quality throughout our entire organization, and then it would get out to the customers. The product relationships that we are allowed, we have over 300,000 attributes and data points within the system. I think it's actually closer to about $0.5 million now. That data is really important in our -- in the trends that we're seeing from the customers. They want to be informed when they're making purchasing decisions. And we also want to be able to track a lot more data than we used to have to. People are more eco-friendly. They want to know where the products are derived from. And so we have to collect that data and then be able to show that visibility out to our customer. So that's become more and more important with our customer experiences.

Yes. So thank you for that. I think some of the things you really don't hear that often is those product relationships as well as onboarding. One of the things, if you think end to end, what -- how important that product onboarding is. So can you kind of go through how you think about product onboarding and even supplier onboarding and how you bring that data into your organization and how it's been working from the beginning all the way until now?

Yes. So we leverage Stibo so that we have our factory data in the system. We have our vendors. We have our vendors in the system as well. So they're actually adding data in the system that our merchants can then collaborate on with the vendor, and we have a workflow that has approval clusters for multiple groups within the organization to streamline our onboarding and speed to market for our products to our customers. This has shown that having one system that can do this really speeds us up. And I think we're somewhere between 60% to 65% faster speed to market for our product onboarding. We're looking to increase that another 20% to 25%. And one of the things that's really important is the system is allowing us to see in which state we have processes that are outside the state that slow us down. One of the things that we learned very early in the process was if we got out of the system, we then slow down significantly from the processes being helped along the way with the system. So this way, we can see how long our products are in each state. And then we can really dive into quality and process improvements around those selected areas and get people back into the system so that we can go even faster. So if we look at it over a 4- to 5-year period, we'll be close to almost 70%, 75% total -- maybe even 80% total increased speed to market from a product onboarding perspective.

Yes. And I just want to drill down on one of them, I think the -- I think you talked about the significant efficiency gains. But one of the things with people working with data, they want to do a great job. They want to be efficient in their business. So how does the operational efficiency help like your employees and overall, the culture in your organization?

Yes. So the employees can go into the system and the task-based visibility that you get in STEP really helps us as far as pinpoint exactly the action that they need to do or an activity that they need their vendor to do in the system so that they can go and collaborate and talk to the vendor and kind of get them moving along or they can work internally to reach out to different groups that have activities in the system. So that really helps us from an operational perspective. And as well with the data and the relationships in the data, it allows them to facilitate a lot more conversations along the way for the data as well. I was going down a different route there, so I'll wait.

That's okay. And obviously, when you're [indiscernible] you're having business benefits such as cost improvements, getting more throughput to the system, but there's also other benefits in business. So can you provide some of the -- tell the audience some of the other better business benefits that you get?

Yes. So now we're at a point that we've implemented the solution, and it streamlined our processes. Now when we're trying to adopt to changing customer trends and we're trying to get new products in the system, we have kind of a template and a playbook already in the system so that it's very quick for us to add it into the system and then get it downstream out to our customers in the shopping experiences. So we don't have to start from scratch anymore. We have that playbook, which is really nice because the customer trends are always changing. We're a design taste -- a design business, if you think about how people want their kitchens and bathroom projects, that's a design type industry that we're involved in. So we're always trying to adapt to their changing expectations and opinions.

Yes. And we were on a podcast last week, wasn't it with RetailWire? And one of the things we talked about was going after new categories or launching new brands or new product into the -- into your organization. So -- can you talk about like that playbook? How had that evolved in terms of you getting out to market being first in certain brands or certain types of products? How important is good data management in that area.

Yes, it's vital. One of the things that it allows us to do is, as our vendors have more products that they want to present to the customers, they can put their product catalogs in the system, our merchants can then take a look with the data that they have and make a decision on what they want to potentially pull into our assortment. And one of the things that's nice about what we've set up is we don't necessarily need a full enriched list of data upfront. We have what we call a leaner level of data that we can make those decisions on if we want to add it to our assortment or not. And then if we decide that the product wants to go into our assortment, we then bring it background, enrich it for the full amount of data that we need for the product to be completely ordered and out to our customers, including e-com. One of the areas that we've added is we've brought that visibility to e-comm far -- much further in advance as far as what products might come down the line that they need to prepare for. So we have an e-com component right upfront in our workflow that allows them to see everything that's even potentially going to be offered and that they have to get ready for the website. So it's been a great experience from that perspective.

Well, so far in this conversation, you kind of talked about operational efficiency, employee satisfaction, being first to market and all these things. I'm going to pull a question that you don't know about was -- what would it be like if you did -- weren't able to do -- if you didn't have a data management solution right now, what would your business be like at right now?

Yes. So I think it would -- I'm starting to have a little bit of a reflection back to where -- before we put it in. When you don't know where your data is or you have multiple areas that can be updated, it really creates a challenge because you don't know where your source of truth is. So then you have data quality issues throughout your entire experience, which means you could go into the store and get one level of information. You could go online and get a secondary information. It creates a bad customer experience. It could be a bad customer experience at the register. It could be on a return. It could be in your design. So the data flowing through everything seamlessly is really important so that the customer gains confidence in what you're providing them and the products that they're researching to be the informed customer at the point of purchase. So it really builds that confidence from your customers, which is important. And it also takes a lot of pressure off of your employees to have to then troubleshoot through all of the challenges that -- that would bring to them. And a lot of times, we see it at the point of sale, which is not where you want -- you do not want that to be the point that the customers are trying to have those challenges. And for us, we have an application called myProject that seamlessly puts their projects together. So if you're having a kitchen or a bath project, you can put that together online or you can go in the store. Well, if that data wasn't the same, then you're trying to design and make a decision and it would just be a bad customer experience overall.

Yes. I mean home projects are stressful and can be painful if you don't have all that information right in front of you to make the right decision to get all the components that you need. So great points on that as well as -- I wanted to think about your team because I know you manage a team. So based on where you've been as an organization, what are you most proud of what you guys have been able to achieve the last couple of years?

Yes. So I'm very proud of our leadership and the buying that they had to allow us to go through the challenges to clean up with the data. That was one of the big challenges that we had was just because we consolidated it, we then had to collectively get together as a group and decide what that source of truth data was. That's not an easy conversation always to have internally. There's a lot of opinions and there's a lot of smart people that have to work to get that done, adding governance to a process and kind of the guardrails at times is found upon through certain processes. And the idea is that it slows the process down and everybody wants to keep going fast and get speed to market. My team and the teams that work on this internally, we were given the ability to get training, become subject matter expertise so that the intellectual property was internal, and we weren't dependent on external people all the time to help us with our solution. And then for us, early on, we were dealing with the challenge of reviewing our historical processes and trying to figure out how to get them in the system. And one of the things that we did really well and I think is one of the top things I'm proud of is we took a step back and we said, this is an industry-leading application. How can we not just follow it in its core competencies and challenge us and our processes and what we do to then align to the industry leading. And that's where we really started to see adoption, and that's when the project and our growth of leveraging our data and the data quality exponentially grew tenfold was when we challenged ourselves on why do we have to keep with our historical processes or the edge cases for what we had. So I think that's what I want to say.

Yes. Thank you, Jared. And the one thing -- I think we -- I love your story because we talk about better data. We talk about a better business. And I think some retailers are still grappling with what is the better rule. So I know you've been doing some work on the data side that helps with sustainability. Can you share a little bit of what your approach is and where you guys are going in terms of sustainability?

Yes. So one of the things that we did implement in our workflow is GS & C, our Global Sourcing and Compliance group actually is in the system so that we know from our factory level, we have visibility from the factory to the vendor or factory straight to us and out to our customers. So we're able to give that visibility to our customers a lot better than we could in the past, which is an improved customer experience. As well, one of the things that we did is we coupled right along the way, we started right upfront with automation testing for our processes. So we can automate and do full regression testing through the entire process. So any time that we add any modules or anything like that or if we're exposing the data downstream, they can tap into our automation testing, and we don't have to necessarily hold their hand through the process, which really helps us be efficient.

Thank you, Jared. The way you're like bringing it all together and making the customers make the right decision at that point. It's just really commendable, giving them the solutions to make the right decision. So we're going to wrap up, but we cover a lot of topics during this session. What are some of the key takeaways that you want to share for the audience? And for everyone in the audience, we're going to have a few minutes for questions. So think of some questions now, and we have some microphones going to be around. So -- but before we get to that, what are some takeaways you have for the audience?

Yes. So a couple of takeaways. So if you are in the process of thinking about implementing a PIM solution, there's a couple of keys. One is trust the system. If you're choosing Stibo STEP, which is an industry leader, trust the core competencies and then challenge yourself, I think that is something you can't emphasize enough. As well, lately, a lot of conversation around AI and machine learning. Well, we leverage this internally at Floor & Decor, where we have a test area that doesn't expose it out to the external world. And we're trying to figure out how to leverage the data because all of these systems have to have really good data so that, that technology can make and be leveraged at a really high level. And I think that goes unseen a little bit because we're all excited about what it's actually producing, but the data is really, really important to be able to make sure that we're getting actual solutions and actual responses from the technology. So I think that's really important. And then from a leadership perspective, build the team, trust the team and power the team to use the tool and challenge everybody else on why they can't leverage the tool to make things faster. It cleans up the data fight. You have the leadership that's willing to support the governance processes so that the data is clean. So these are things that are -- they're tougher conversations to have internally, but they really, really will benefit your customers and your company overall.

Thank you. And I think we covered this. But the one thing that I really like, and we're always focused on the customer experience. And I think the one thing to call out that you said was getting back to all the way back to the beginning of all this data, where it comes from and letting it go through to have visibility of that and make sure that you have the good data quality, and it's in the way that the customer needs it is -- is something that maybe is not said that often. So thank you so much. And we have about 10 minutes left for Q&A. I know there's a lot of talkative people in the audience that loves to ask questions. So we do have some microphones running around. So who is first with a question for Jared or myself?

How has this given you a competitive differentiator in your market? Or how has this allowed you to at least keep pace with the way customers interact?

Yes, great question. So one, the speed of market to product alone, increasing that between 60% to 80% has allowed us to get new products to our customers a lot faster. And therefore, that customer experience is better. As well as being able to show a lot more attributes and a lot more data to the customer, they become more informed, and therefore, they get the products that they're looking for. There are certain things around for us in hard surface flooring, you may purchase tile or something like that, but you're not thinking about all of the other items, whether [ your grout ], your installation materials, that holistic project, what is it that you need for that holistic project, molding types. Those are just examples. You're not necessarily thinking of that when you're just looking at your kitchen backsplash or your flooring in your bathroom. So being able to expand that visibility out to the customer, it's just a better customer experience, and that's how we're keeping up with our competition. And then the assortment. I mean, we have one of the most amazing merchandising groups that say, up-to-date with latest trends and then they leverage us to get it out to our customers as soon as possible. Den, answered your question?

Thank you so much, Jared. That was a very complete answer. Any other questions? Oh, I got one right there. The microphone is coming to you.

I'm Michelle from Parkson Consulting. My question is regarding the implementation, how long it took? And if there was any watchouts that you would recommend.

Yes. So for our factory, it took -- for factory data, it took us about 4 to 6 months to put in. Our vendor was about another 8 to 10 months and then product was somewhere around a year. And I think that was because we wanted to make sure that the data cleanup process was very, very important to us. So we put a lot of due diligence around that. Our automation testing took some time to couple in with it. The [ gadgets ] are not getting wrapped around the axle with all of the edge cases of either historical or technical debt that you're trying to solve. So that goes back to where I said, no, the problem you're trying -- the problems, like document the problems that is the reason you're trying to solve for the solution being implemented and then keep reverting back to those to make sure you're actually solving those and challenge the parties that want to try to keep that historical edge cases as a forefront too early. It's okay to review them once you have that core system and you have your data, but you can get wrapped around the axle a little bit and looking at a lot of the edge cases that may or may not be resolved right away. So that's probably the biggest thing I would say upfront.

That's a good point. And we hear sometimes -- we've worked with over hundreds of retailers. We've seen a lot of use cases. So sometimes you have to listen to your vendor to say, "Hey, maybe that's not the right way to use the system." So that's a very good point. Yes, we have another question back there.

How much effort was put on as a manual effort to correct the data or collect the product information data? And was there any automation used to correct the data?

So we didn't -- let's see, so the effort was pretty big because we wanted to make sure that we had the collective -- from the big -- the complex standpoint, it was getting the right people in the room to make the decision. We had to empower the people to pick which data. And because we have different categories and multiple categories, it was a cleaning up of making sure the attributes that we had could suffice for everybody that was using it. And if it couldn't, they had to bring a good reason to the table to create a new attribute because we wanted to decrease the amount of bad data we had, and we were able to do that. We did not leverage AI or I mean, automation for the data cleanup, it was a more manual effort for us in the beginning. We did multiple comparisons between systems and our source systems, though, to see where we may have downstream discrepancies in the data and that was allowing us to really dial into, okay, how do we now make sure we expose the true data to the systems. So we did that level of effort of cleanup, and we did use some automation testing for that visibility.

Thanks for the great questions. I've got another question on the left here -- on my left.

Yes. Did the implementation of the MDM system and the refined processes that you put around that impact your team and specific roles? And if so, can you talk a little bit more about how.

Yes, great question. So it grew. We brought an internal IT team to support the system that would partner with our data management group and our merchandising group and our GS & C group. So we really built that IT team that's under my purview and made sure that they had the subject matter expertise. And then we also made sure that we educated the business. So our business is actually pretty proficient with the technical aspects of our workflows and the folder structure in the system so that when we are designing or we're talking about changes in the system, everybody is on an even playing field. So we didn't hide anything in that regard, which I think was really beneficial. It wasn't, hey, we're going to talk technical talk and you're on the business, so we don't want to include you in that. They are educated through the entire thing, and it's the same thing. We educate our technical people on some more of the business side that we may or may not so that when they're helping make systematic solutions, they understand what the impact is to the customers, internal or external. And that's really been beneficial for us.

Any other questions? I'll give a couple of more seconds here, but -- thank you all for attending.

Thank you.

We are going to be at Booth 3938 after this. We could talk where retail data matters, anything you need in terms of data management or foundational data, we can help you in a lot of different domains. So thank you all for attending today, and we look forward to seeing you at our booth or somewhere else at the show. Thank you.

Thank you.