CDW Corporation (CDW) Earnings Call Transcript & Summary

Yes. I think very similar to what Jill has just described. I think some of the things on the slide really resonate with me. The workforce analytics, we all know the pressures of hiring people, the turnover of staff, the ability for people to onboard and manage that workforce at scale is a huge problem. And that's definitely maturing and the technology in that space and actually, the use of AI is some really interesting use cases that AI can help in that space. Just generic AI in retail, all up, whether it's -- I hesitate to say optimizing price because that's probably some of the research I've been looking at, probably the least value because I think retailers are pretty good on pricing, and it's probably something that they can't really control themselves. It's more dependent on whether people will purchase at that price. So it's a bit more of a fine art. But in terms of supply chain execution, inventory optimization, there's so many different areas, as you mentioned earlier, where AI has relevance. I think the other thing I would add is as you go through that maturity, the software matures the knowledge in your business of how to leverage that software matures, understanding the impact of some of those technologies on your business and how to manage them and introduce them, you start to get better at doing all of that. All of that is part of why that you go through that maturity curve and that's an important factor that we're seeing in the maturity in this space.

Absolutely. So David, Obviously, there's a lot of great stories about what this looks like out in the real world. And want you to talk a little bit about that?

Yes, sure. I mean -- we always get -- I work for a company that has an ingredient in a device that many people use and as a brand, we've done a great job of helping people understand the value that Intel can deliver out in the marketplace. But it's solutions that really people are looking for. And we worked with this company over the last 12 months. Actually, we first met them here at NRF last year. We got really excited about the solution. What they do is help you select the clothing that fits or best suits you. And you can create that clothing, you can decide that you want loose fitting clothes or tight fitting clothes, you can kind of create, it's not -- it's tailored to you and your personal preferences and choices -- But what it does is it scans your body. It doesn't keep an image of that -- of your body. It keeps a digital avatar of your body, a digital twin, if you like. And then it compares that to the clothing that they have on stock and makes the recommendations for you based upon your personal preference. And that does a couple of things. We all know part of the problems with selected and buying clothing and apparel, if you're not in a store environment is, well, is it the right color? Is it the right size? Does it -- how does it look when I put it on me? And this stock -- so what people then do is they say, well, I'll buy both the medium and the large because I'm not sure which one fits me, knowing that you're going to return items and that's obviously a huge issue for the industry. And actually, in some cases, causes damage, which means it's not a very sustainable model either. So really, this AI solution is trying to tackle some real issues that retailers have in the industry, which is making the right recommendation, minimizing the refunds, being a more sustainable operations. So it hits a lot of different sweet spots. And it was a great way where this AI technology that we all talk about shows how we can provide new value to retailers, both in terms of new revenue opportunities, but also optimizing their operations.

I love this example, David. I am so glad you brought this up because 2 sides on this, what retailer that sells online is not talking about what to do about returns, it's costing a lot of money. And they are even throwing out, can they charge the customer a return fee and nobody wants to do that. That is not very customer-friendly. I don't think the customers are going to be happy about that. And the other side that people don't talk about as a customer, when I buy something online, I want it to fit me. I want to be so happy when it comes because I chose the best thing for me, it looks good. And it fits. I don't really want to return it. So I love this example of how you can use AI to -- it's also good for the climate, reduce those returns and in the end, make your customers happy.

Yes. No, it's a great point because there's so much data out there that shows that -- return -- the impact that return policies have on the purchase decision, especially if it's a new brand that a customer may not be as familiar with how the fit will be. So this is absolutely a great example of how technology can be a game changer. Jill, obviously, we talked a lot about how AI can be used, what are some of the use cases, some examples. But I think, obviously, right now, people certainly need to justify their spend. I think there's a pragmatic approach coming to how do I invest, where do I invest? And so it's more important than ever to understand where to place your bets and how to prioritize what you're looking at. And we talked about, obviously, looking at not using AI or any other technology for that matter, just throwing it at something because you want to use it, but really starting with that business objective. So how does your team look at prioritizing those projects? And how do you decide on what business objectives and outcomes to really focus on?

Sure. We use this model -- we've used this for probably the last 8 years. We presented it to some of our partners and vendors and they said, you're a little bit unique in how you guys transform these, but its fantastic. And so the key here is that we start with the business folks. This is a business-first, technology second. We see a lot of people start stumble and stop because they pick a technology and they have some of their team build a pilot, but it never gets to production. IoT is a pretty high and pretty low pilot to production rate. It's like 16% of projects make it from pilot to production. So we looked at those factors. We looked at why people stumble. We tried to build it back into this workshop. And so we start with the outer ring. We start with their business goals. What are you solving for? So we get a group of 10 people from our clients and their business people. They're typically not IT people. We talked about business goals. We've had goals like I want to become a market maker. I'm like, that is a good goal. So we start with 4 goals. We look at value drivers. The 10 folks that are in the room, we ask them, how do you think you can meet these goals? What are some of the ideas you have. We then take those. We look for duplicates. We put them through a funnel. For the top 12, we then do further analysis. So we looked at what business process is associated with this idea, what business systems are tied to this idea. We then start looking at the data itself. Data is often a stumbling block for folks. And so we say, how much of this data that you're now sourcing is relevant. You need to peel it off at the edge. How much of it is revenue, how much could you sell to your clients or make products out of it. We then also look at systems of systems. Are there other things outside of your existing ecosystem that could help your solutions such as the weather channel data or Google maps. We then do a 12-point technology gap analysis to say for this particular idea. Do you need a new sensor? Do you need to improved networking? Do you need analytics? What does that gap look like? We also look for IoT technology and then security in that ring as well. The next phase is we look at if this project was to go live in year 1, how much -- what's the ROI impact in year 1? Are you creating a cost avoidance scenario? Or are you creating a new revenue stream? And how big is that for your company? Last time we did this for a client, we had 80 different ideas that we came up with. Once we boiled it down to the top 12, it was $10.5 million in year 1, if they were to implement all the projects, return to the client. So those are some of the things that we saw clients stumble with. And so we kind of put it together in a workshop. It takes us a day of the client time, then probably a week to turn it around, so it's a micro service for us. But we found it to be very successful. So that's how we help clients with products transformations, with business transformations with operational transformations.

Excellent. Gina, obviously, again, as we mentioned earlier in the past, you've been with some amazing brands on the IT leadership side with brands like Nordstrom, REI, Blue Nile. And you've actually been in charge of doing a lot of this work. Tell us when you were on that side, when you were with those retail brands, how did you look at getting started and building that plan and how do you get going?

And as Andy said earlier, it is overwhelming because AI is the buzz and you just hear, we have to do AI. Please show AI, put in our quarterly reports. Let's talk about what we're doing with AI. But it doesn't have to be overwhelming. Don't get caught up in the hype. I think Jill showed a great chart earlier and we've been seeing it again and again, start with your strategy, start with knowing what problem are you trying to solve? David's example is a great one that they're trying to solve the return problem that's popping up, and that's hot right now. You can't solve everything at one time. So focus, know what's important for your business to move forward, what's important to our customers, what's important to your employees, start with that strategy and your business problem. Otherwise, you will start way too much. And then the next thing, my favorite slide, I got to reference it again, is that race car Formula 1, very cool. I wish that was my slide. But understand your data, again, so overwhelming. We've been collecting data for so long. And every retailer has so much data and you're thinking, oh, my goodness, is my customer data right? Is my product data right? I switch systems. I have data all over the place. Once you understand your problem, then only look at the data that is relevant. If you're trying to clean all your data, everything is sludge, it's going to take you a long time, and it's very overwhelming. Start small, identify your business problem, identify the data that is needed for that and then turn that into the oil. That's where you want to focus. And then finally, if you're struggling, go get help. There's companies out there like CDW, but there's others that have done this before that can help you get started to follow processes, to make it easier for you because it is -- when you just hear AI, everyone's talking AI, it is overwhelming, but I think just breaking it down, going smaller, understand what your target is, working on that. You can go get this. And then you can be part of those retailers that we're seeing that experience 2x the sales growth, right? Who doesn't want to be in there.

Absolutely. A lot of great points there. David, what are your thoughts? As we start to wrap up and kind of come to a close here on our time in a few minutes here, what are your thoughts on where should retailers go from here?

Yes. I can't overemphasize the importance of data in this, right? So we've talked about this from the beginning here. Artificial Intelligence is a set of technologies that builds insight from that data. And if your data is not in the right form or you don't have the right data or if it's not as clean as it needs to be, that's definitely the #1 starting point. And if you don't this out before you start your project, you'll figure out very quickly into the project. And I think that's one of the areas that's holding back a lot of these projects because the data is clean enough in most retailers for them what they need to do today, but it's not always clean enough for them to do what they're trying to try and do with AI. So that's definitely a big area for them to think about. And it also goes back to those things that we've also talked about in retail for many years, which a lot of that data is siloed, but you need to break those silos to really try and get the value out of the data that you're really trying to do. So that would be the second point that I would make. And then I think the other thing that we've seen is when it's just the technology pilot. When it's just trying to understand the technology and you have to go through that phase as you start to scale up. We understand that that's never going to be the final stage point. You have to start with the business problem. And there has to be a business problem that is significantly valuable for you as a brand or as a retailer, but also has the right data in support of your business. So I think this slide is really a great kind of checklist for what you need to do in terms of the next level. And in most of the times, when we're having discussions with either our partners or our customers in this topic, that's always where we start.

I know it's hard to believe, but in the retail space, there are some other exciting technologies and other things happening outside of AI. What's caught your eye so far at the show here? And what are you excited about for the coming year and beyond?

It's going to take that [indiscernible] first.

Go for it.

I think in 2024, I am most excited about video analytics. I think it's you look at where it's at on the Gartner chart, it's at that 30%. So just out of curiosity, how many people are using video analytics in their environments today? How many plan to use them in 2024? There we go. Good. So I think video analytics is prime for adoption. I think audio analytics will be the next thing that we see to sense everything. So I think that's going to be a pit in 2025 or 2026, but for next year, I think a lot of people are going to start an AI project of some sort. I think we're all jumping in the pool together. I don't know that we can all swim, but I think we're going to jump in. So I think there will be a lot of swimming lessons next year as well.

Yes. I think that we're certainly hearing from a lot of our clients and partners about the power that video and computer vision is starting to unlock. And I think one of the great things about that is it's an example of a technology that basically every retailer has invested it in some way, but in many cases, now -- I hate to go back to AI, but layering AI on top of that, allows you to take that existing investment that you have. You don't necessarily need to replace all your cameras. You don't need to upgrade them. You don't need to change the placement in some cases. It's just a matter of actually layering AI on top of that to gain more insights and use it in new ways, whether that's to understand your traffic flows, whether it's for loss prevention use cases, understanding your store productivity and optimal staffing levels. We're seeing so many new use cases that are coming on board for ways of using it.

Yes. And I'm like so excited you [indiscernible] let me go next. Let me go next. Sorry, David. I am so excited about RFID. I don't know. I am a big in-store shopper. You can tell by the brands I worked for. I'm all about that customer experience going in. And I love what I've seen here where you pick up something off the shelf and it changes your display. And this is a combination of RFID and computer vision, so exciting. And we've heard about going into -- you take your children store where some mom is bringing or dad -- you're bringing your kids along and can I have an easy checkout. We're looking at self checkout in grocery stores, exciting, love it. But I just love that smart card, smart bag, smart basket idea, and we're seeing that how nice, especially if your arms are full, but I don't have to hand every single item over to be scanned. It's like here's my basket, scan it, get in a bag and let's go, love it, love it. And one more is the smart dressing rooms. Saw incredible examples of this. I hope you guys saw it too, where I don't have to wait for a sales rep to come in to change a different size, right, or different colors. A lot of people, we saw examples where they're putting it in the mirrors, you can touch a tablet and just say, hey, here's what I want. This frees up your labor, which we heard, what short, right? Everyone's having problems there. And it doesn't have to have someone can keep coming to the customer and say, hey, can I help you? Can I help you? And as a consumer, I don't have to wait for that interaction. So I'm just all excited about, like I said, the RFID also computer vision.

Yes. I think it's another great example though of a technology that could be leveraged across multiple use cases, right? We talked earlier about that pragmatic approach to investing in technology and retail and want to justify that ROI. And really, when you think about RFID, there's certainly an expense in getting started the tagging of your items, if your partners are not actually doing that tagging for you already, although I think we're starting to see more and more manufacturing suppliers doing that work. You see mandates from companies like Walmart and other places in food safety. You're seeing the Food Safety Act coming online in 2026. So a lot of drivers about the adoption of using RFID. And it initially started obviously with inventory management, quickly realized that could be used for loss prevention as well. But now we're starting to see a lot more of those innovative use cases. We think about how do we bring the customer back to the store, create better brand engagement and customer loyalty and creating those experiences and those moments that make a customer want to come to a store as opposed to feeling like they need to come to the store to buy something. RFID really unlocks a lot of that with those immersive experiences, the connected fitting room like you mentioned and everyone is trying to solve for the labor shortage at checkout. And how do I -- we're seeing customers who are rebelling against scanning. I think there was a stat and I apologize, I don't recall who made it, but I think it was 1 in 5 consumers in the U.S. have admitted to not scanning all of their items at checkout, which is staggering to think that 1 in 5 actually admit it. So how many are actually happening. And part of that is not even always on purpose. Sometimes it's their frustration with not being able to scan a barcode or find a barcode on their item. RFID solves for that by utilizing the RFID technology, dropping it into a scanning bin, everything is scanned all at the same time, no delay, no fumbling and they can move on and still actually achieve the reduction in staff. So I can go on and on. But David, how about you? Any last thoughts before we can wrap up here?

Yes. So -- by the way, I think that's the coolest piece of in-store technology I've used is RFID checkout. I think all the other stuff is great and computer vision, I think, is awesome sets of technology. But just as a shopper being able to just go in and just dump my bag in a box and a number come up and pay and go, it was just so cool. I was even in there with my family in a store in Europe. And yes, all of them are going. It's not that easy, is it? It's not that easy. So yes, that's really cool piece of technology. I think the other thing that we haven't really talked about too much, but I think sustainability and the circular economy, I think that's another area we're going to see a lot of take off as people are more conscious, especially in the fashion space. I have a daughter who's 20 years old. And her older sister is all about the brands, and she's all about I want to buy reuse -- recycled clothes. So I think there's a generational shift where people are expecting more of a circular economy impacts lower power, less of an impact on the future of the planet. I think there's going to be a lot more. And for retailers, of course, that also goes with new business opportunities and also reducing costs. So I think there's going to be some interest in play. And some of the technology I've seen into that space is very good, especially around food waste, how can you manage -- minimize the food wastage.

This is a big area. Absolutely. I think food waste, we're seeing a lot of companies do amazing things with technologies to help the environment and social issues. And Macy's, I saw, I've been watching some of the work they're doing around, how they're handling samples during their fashion seasons and digitizing that and the amount of waste it reduces by using digital samples and still allowing people to actually not compromise on the actual review process. And there's just so many examples out there of how companies are using technology to help you a better global citizen. So with that, we're just about at time here. I think we have a few minutes left, but we all recognize how difficult it is to get across the floor here in the show. And so we want to make sure we get everyone out on time. So we're going to start to wrap things up. We really appreciate you being here with us today and spending the time. We know you have a lot of options of things to go do today, and we appreciate you spending that time with us. If you're not familiar with CDW, we really didn't talk about CDW too much because we're so passionate about talking about the technology and how it can help customers do amazing things. But if you're not familiar with us, we are a global systems integrator and services provider, helping retailers across the globe do amazing things. And if you're not -- we'd love to have you stop by our booth, take a look at some of the services we're showcasing there, but also if you are a customer of ours, thank you for that. We greatly appreciate your business. If you're interested in having a follow-up meeting with us, there is a QR code on the screen where you can request a free consultation or just stop by the booth. And if you are a CDW customer, of course, reach out to your account manager to set up a meeting and you could also visit cdw.com/retail. So thank you again. We hope you enjoyed the session, and have a great show. Thank you.