CDW | NRF 2024 Replay - Technology Innovations Transforming the Retail Customer Experience
(upbeat music) - Hi everyone. I'm Andy Szanger, Director of Strategic Industries here at CDW. Thanks for joining us today for a session titled Technology Innovations Transforming the Customer Experience: What to Know and How to Get There. I'm thrilled to be joined today by CDW Senior Industry Advisor for Retail, Gina Cox, Jill Klein, Head of Emerging Tech and IoT at CDW, and David Dobson, Global Industry Director and Retail COE lead at Intel. So it's great to be back.
Last year at NRF 2023, we spoke about a lot of different topics about what we expected to see in the coming year. We spoke about trends that we expected to be hot, such as experiential shopping, frictionless checkout experiences, doing more with less, and loss prevention solutions as some of the areas that we expected retailers to be leaning into. David, it's great to see you again.
Can you tell me what's your feeling about the things that we spoke about last year and how the year actually played out? - Andy, yeah, thanks so much for inviting me back and yeah, I think we did a pretty good job. You know, when I look at this list, experiential shopping, it became really important, as we all know, people return back to the store. Everybody wants to go back to the high street or the mall and do some shopping. You know, frictionless checkout experience, we saw a huge investment from retailers in making it simpler for the shopper to transact with them.
Do more with less, what retailer isn't challenged with that? Especially given difficulty they have in hiring staff these days, as well as the rising costs that we've all been facing globally over the last 12 months. So I think that was definitely a theme that I was discussing with retailers. And then, yeah, we all know the loss prevention story. I'm not sure technology is the true golden bullet to solve that problem, but definitely we used technology in some very creative ways last year to help retailers address that loss prevention problem.
So I think we did a pretty good job. - Yeah, I agree. I think we saw things play out a little bit different in some ways than we expected, but I feel like the main themes that we were speaking about really were relevant throughout the year.
But Gina, obviously you were actually a leader for some pretty large retailers over the last few years and last year in particular, you probably saw a lot of this firsthand. Can you talk about what you saw as far as how the year played out? - Thanks Andy, excited to be here and share what we did see. You look at digital decision making, very exciting that we were able to see that consumers were willing to engage with chatbots. And according to PWC, 46% of consumers are willing to do that, which I think is great news, which speaks to some of the things you were saying last year. Also, so happy as all retailers are to have consumers returning to the stores. And we do have from Scanbot SDK, that 70% of consumers actually prefer in-store experiences as long as they have the digital services and all those things to help them shop better.
So so happy, so happy to see consumers back in store. We talked about David, thank you for mentioning, we still are doing more with less. It's still hard to find the people to fill the positions.
36% of the retailers, according to Insider Intelligence, are telling us they are still having problems filling those positions. So again, another trend that continues year over year. And finally, I think one of the favorite things to talk about, AI is showing a competitive advantage. So retailers who have embraced AI and the machine learning, they're seeing sales growth up to two times more and that is according to IHL Group. So David, can you tell us a little more about the AI? - Yeah, that's so important, right? We did talk about this a little bit last year on our get together and we did predict that AI was gonna be important.
I think there was a lot of metaverse, AR, VR hype last year at the beginning of the year and that almost disappeared and AI has taken all of the oxygen out of the conversation I think. It's a great piece of research that Greg and the IHL Group have done at looking at the economic impact, right? As opposed to we are technology companies, we tend to look, well how much are people spending on this? 'Cause that's our path to revenue, which is super important for our company. But what Greg's research is showing is those who are really investing in the right type of AI and the leaders in this space, they're already seeing business value and that's coming back to a measurable increase in their performance from a revenue perspective. So that's a really useful piece of research in understanding the true benefits 'cause talking about it from a technology perspective is super important, but does it deliver value to retailers? And Andy, I'm sure you're seeing something similar from your work as well. - Yeah, there's no question, David. I think without a doubt AI captured the world's attention last year, right? We did absolutely speak a little bit about AI and some of the use cases when we were talking last year, but no one expected the explosion of popularity and interest that the world would have around AI.
And based on some of the stats that you mentioned from IHL Group, it's not surprising that the top two industries making AI investments right now according to IDC are banking and retail. We're seeing retailers use AI or attempting to use AI I should say, in all areas of their business, right? We're seeing them looking at it for task automation, we mentioned chatbots already, but personalizing the customer experience, demand forecasting, inventory management, exploring dynamic pricing, the use cases around integrating AI along with computer vision and cameras has potential to be a game changer for that loss prevention issue that everyone's been battling with. And even at checkout, we're seeing retailers trying to use it. So it's one of those technologies that it's not new obviously, right? I mean, we've been talking about AI for years.
More than 20 years it's been around but now all of a sudden the world's eye is on it. And so we're seeing people trying to leverage this technology in many different ways. And it's gonna be exciting over the next couple years to really see how that plays out. I think one of the challenges though is where do you start? When you want to take a look at leveraging a new technology or new ways to use a technology is probably a better way to say it. You know, how do you get started? What is Intel advising clients about and what are you talking in and out about? - Yeah, thanks for bringing me back in there, Andy.
Just reflecting on some of those use cases you were describing and thinking about how we were prepping for this session, it's almost what can't you use AI for as opposed to, and it's kind of skimming that list down. And from my perspective, almost every conversation with retailers today, yeah, there's conversations around infrastructure, there's conversations around solutions, but mostly it boils down to how can we take advantage of this technology? And as you rightly said in your question to me, where do we begin? You know, Intel's done some recent research and you can see that on the graphic that we're showing at the moment around where we're seeing businesses in terms of their AI journey. And it's fair to say the majority of them are still in the early, they're in the foothills of the mountain climb towards AI. They're either researching in terms of trying to understand what areas of their business they're gonna have most impact, trying to identify those opportunities that AI is the best fit for. And actually also where in their business are they ready for AI and I know we're gonna come onto this in a little bit around what it is needs to be in place to really leverage the power of AI but kind of working through that, what technology do we have, what's our business like in terms of all the operational data and other things that we've got and where's the improvement gonna come through from in terms of what's the value you're gonna see from a customer? And then the other piece is having gone from either they've identified the use case, the second piece is just trying to understand the technology, trying to prove that use case in terms of early demos. The number of people who've actually got to the stage of deployable solutions is very small in comparison to those people on that journey.
So we feel that this is an early phase of the evolution of AI and we launched some internal messaging and we went global with it, actually in New York just before Christmas where we're talking about AI everywhere. And Intel as a company, we're committed to solving some of these difficulties that customers are having, making AI available to people in the way that we made internet available to everybody, the way that computing was made available to everybody in the last 20 to 30 years, we believe you need to go on that same journey with AI. How do we make it easier for people to use this technology and how do we embed it into everyday life? And that's why we start to talk about AI everywhere, which comes back to that use cases that we talked about before. So I know Jill, you've been doing a lot of work inside CDW around AI. What's your thoughts in this area? - Yeah, thanks David.
You know, I personally see AI as a Formula One race car for businesses that didn't have it before. I mean, imagine going from the Vega we have today to a Formula One race car, I think that's exciting. I think our future is bright and I think it's gonna be fast. Some of the similarities between the two of 'em, speed I think is the ultimate goal here with your AI model. Obviously that speed doesn't happen without performance optimization so those models have gotta be optimized and the car has to be optimized.
I think they're both dependent on good fuel. You know, both the AI model and a race car, imagine putting E85 gasoline in your Formula One race car or imagine putting unscrubbed data into your AI model. That's how we get biased outputs and reduced accuracy and unintended behavior from these models. And so the point is the AI relies on the availability of high quality data, well-trained models and the appropriate inputs for model optimization.
So it's promising. - Yeah Jill, and I think one of the things that we see sometimes is that too many people get excited about a new technology like AI or again, a technology with new use cases like AI and they start trying to force a reason to use it, right? People are too quick to say, "AI is gonna solve the world's problems and we need to make sure we're using it." And I think sometimes there's a challenge with that because when you start with the technology and not the strategy, you're far less likely to achieve for the true ROI than if you start by looking at the business outcome you're trying to solve for and then actually finding the right technology to actually utilize. And so when I think about AI and how it can be leveraged and what else associates with AI, you talked about AI being the engine and I agree with you.
I think of AI as really at the end of the day when we simplify what AI is, it's a platform or technology that's taking inputs and data from all over the place and it's taking them and making insights or creating things in general from them. Can you talk a little bit about how you're seeing some of those technologies play in and what should retailers be thinking about because it's not always jumping right into AI, right? There's some other things probably that you can be thinking about that will ultimately feed into that engine. - Yeah, I think one of the phrases that we often use is that it's a business first, technology second approach.
And you're right, I agree. A lot of folks, you know, there's a lot of shiny unicorn solutions out there. And so picking the right one for your business, picking the one that has a great ecosystem, picking the one that actually can help you meet your goals is often where we kind of direct clients.
The promise of emerging tech is great, it's just a matter of which ones are ready for the market. - I agree. And I know that Gartner does a lot of research on this, on looking at the hype cycles of technologies and there's a lot that's happening in the retail space, I think of some of the emerging tech that really holds a lot of promise. Can you talk a little, what are you seeing there and where are you seeing retailers looking at and where we are in the journey on that technology? - Yeah. I think the chart we have here is actually the retail hype cycle but there's an AI hype cycle as well. And if you look at it, the first hill that it climbs is full of opportunity.
It's full of solutions that are there and then it kind of plateaus off to not a lot of solutions. If you look at the one that we have here with retail, there's a lot of things that are real time. When I look at hype cycles, I look at two things. I look at where the solutions are on the chart, if they're all the way through to the end, that means that 20 to 30% of enterprises have adopted.
So if we're looking for things that are ready, I'd look in that space. The other thing I look at is the color of the dot. I think white indicates something that's less than two years out. So look through the chart, look for things that are white.
The common theme that I'm seeing is video analytics, and I think we talked about this a lot and it's a game changer from checkout to store monitoring to inventory to supply chain. I mean, it's everywhere. So we're excited to bring some of those solutions to bear as well. - David, what are you seeing from your standpoint at Intel? - Yeah, a lot of what you guys have been saying have really resonated with me what Jill was talking about.
Getting your foundations right, making sure you pick the right business case, et cetera. I think in truth, going back to that kind of piece of research that we talked about before and why are people in these early stages of AI, and this is where I'm gonna be a little bit counter to Jill's analogy of a Formula One car. For retailers, maybe a Formula One car for every store, that cost point is not really what we're aiming for. We want that level of value, we want that level of precision and accuracy, but we don't want a cost of a Formula One car because this is retail, right? So I think one of the things that Intel is trying really hard to do is to bring the price point for some of these technologies down to make it affordable so that we can truly scale. Computer vision is a great example.
If you need a heavy infrastructure, a lot of hardware to actually enable that computer vision use case that you're trying to deploy across your thousand plus stores, it is gonna be really difficult to prove the business value, to prove the business case, loss prevention being an exception 'cause it's such a burning bridge for retailers today. But if you wanna move that same technology into other areas, the price point is gonna have to come down and come down significantly. And that's one of the areas that Intel is working really hard on. Can we bring the cost of this technology down? Can we bring the value without impacting the value to retailers? So that's kind of one comment I would say. And then I think the other point that was made really well was in the number of the solutions that we talked about and the different business areas.
If you look across this chart, how much AI has got an important component of realizing the value of some of these investments, right? Workforce analytics. Okay, we've talked about workforce analytics for a long, long time, but AI optimization built into that workforce analytics is gonna create that incremental value that everybody's looking for. Just as a simple example. - Yeah, no, that's great. And Jill, are there any particular ones that you're keeping your eye on when we think about this journey along the hype cycle? - I would say the area that I would focus on is not necessarily a technology, but anything that's operational based. I think that emerging tech is looking for things to automate.
So non-value added work that people are doing, we're seeing a lot of emerging tech move into that space and kind of help accelerate that for clients. So that's the area that I see a lot of movement in right now. - Okay, great. And David, when we think about how this is really translating out in the real world, can you think of any examples of how retailers are actually starting to execute on this? - Yeah, yeah, great thought 'cause I think the theory is great, but what is it that we're seeing that's really showing some business value? I would say a lot of what we've talked about here so far is more on the operational side.
And for a lot of retailers, that's their first foot into technology because it's easy to show the return on investment of saving costs in your business, to kind of focus in on those operational systems is always kind of the default place that retailers go to. But I wanna share with you an example that we have with a partner that's more on the customer experience side. And now it does feed into operational efficiencies, which is good 'cause it's great to have both. But one of the problems that we've been seeing and has been reported widely in the industry is if you're buying things in an online environment or in a digital environment, how do you make sure, let's take clothing as an example. How do you make sure that the item that you're gonna purchase is gonna be the right item for you? And by that I mean fit my body shape and it's the most difficult problem for e-commerce around the world. It's driving huge amounts of return activity and I'm sure you guys see the same and hear the same stats as we do.
A lot of online operations in the non pure-play space are running at a loss today. So how can you reduce those returns, reduce that cost, and actually give people the right garments in the right way? And we've been working with a partner, actually a partner that we met at the beginning of last year at the NRF event in New York, a partner called Fit Match. And they showed us some of their technology they were using where they can do a scan of your body, not store an image of that scan, but create a digital avatar of you as an individual, and then taking the similar digital avatars of clothing or apparel, tell you what is the right size of product for you, including by the way, not just does this 14 match to your body size or does this large match to your body size, but also what's the shape and fit of these garments? And you can select that if you particularly like tight fitting clothes, they can recommend what the tight fit is for you. If you like loose clothes, they can recommend what a loose fit would look like for you. So they can make really accurate recommendations on the garments.
And that means that your choice that you make of the garments is you can have in confidence. So you're not gonna buy two sizes knowing you're gonna return one, you're just gonna buy one. And that's a saving for the customer, it realizes real value for me as a shopper, 'cause I'm gonna go back to there 'cause I know that you guys are gonna find the right fit for me and also provides all that great insights and analytics data for the business around the shopper. And I'm not talking about body size, I'm talking about making sure that you're fitting the right garments to the right people and seeing the right selections and the Fit Match guys work with both the suppliers and the retailers to provide this service.
So it's a great example where given a piece of data from you, the shopper, you can really start to accurately make recommendations and that will improve your operational efficiencies and create a happy customer at the end of the day. - I would love to add on to that, David. I think that is such a wonderful example because we are hearing from retailers about returns and that the cost returns are very costly to them. And as we're trying to do more with less as we talked about, anything to reduce those returns is huge. I know retailers are talking about charging their customers for returns, which is never a consumer favorite activity. So I just love that you brought forward this example.
I think it's true to almost every retailer today. - Yes so Gina, it's great that you talk about the charging and it was an example that really shocked me when I heard about it, but during Black Friday in the US, some of the retailers were actually doing no returns on policy. And by that, I'll give you the money back, but don't send it back to me. And that was because the returns were so huge for some retailers that they one, couldn't process them, and two, the cost driven into the business, it was easier to just leave the product with the customers. So it's another good example of how much this is costing. Now that's not a sustainable model, that's only a temporary thing that retailers are gonna do, but it shows you the nightmare of larger numbers of returns come in after these sale periods or high purchase periods.
So yeah, it's a real big problem. And obviously we all care about the environment and we also know that returns generates a lot of non-recyclable returns. They just have to be thrown away and we don't like that either, right? So it's not only a cost, it's not good for the environment. So anything that we can do in that area we believe is gonna be beneficial for everybody.
- Yeah, no, and I think it has such potential. It's a great example of innovation that has the potential of really helping to solve for a major business problem. When I think about the cost of returns and then the potential negative customer impact of charging for returns or not accepting returns, that could be the death of a lot of online retailers if that were to happen and there'd be many customers who might not order from them to lean into places where they could more easily return an item. So I think absolutely a great example.
So thanks for sharing that David. You know, when I think about a lot of what we're talking about, I think one of the challenges though that many retailers face, especially under times of economic scrutiny is how do I launch into some of these projects? How do I implement some of this technology when I'm being scrutinized for what I spend and what our budget looks like to make sure that we're profitable. And so how do I get started? And every retailer is at a different place in that journey of where they are today and where they want to go and how do I make an impact along the way as I build a plan to get to the future? And Jill, how do you look at that? I know your team spends a lot of time working with clients on just that, about assessing what they're doing and how to get to where they wanna be. And so can you talk a little bit about how you're looking at that? - Sure, sure. We've got a model and a framework that we've used for probably the last seven years.
This is something that we've built and fine tuned over those seven years. We've put it in front of our vendors, we've put it in front of our partners in the industry and they've all come back and said this is a fantastic tool to get people started. The other thing we looked at was how do people fail with transformations and what's behind that? Is it data? Is it security? Is it not understanding the art of the possible? And so we kind of built our workshop around that. And so what we do is we start with the company's goals. You know, you've heard me say before it's business first, technology second.
So we start with your goals. What are you solving for? What do you wanna do? We once had a client say, "I wanna be a market maker." And I thought that was a great goal to have. And so from there we kind of lean into what are the value drivers? What are the ideas you have to make that goal happen for your company? Once that's complete, we go through a funnel process.
We typically get 60 or 70 ideas at this level of the funnel. We go through, we cut out duplicates, we kind of merge some together. We then take the top 12 and say what business process is associated with that particular idea? We look at business systems, what systems is this tied to 'cause that adds complexity to your idea. We also then take a look at the data that we're producing as part of this project. So in some cases if it's an IoT or if it's a new data source, it's doing one of two things. It's creating a cost avoidance scenario or it's creating a new revenue stream for your company.
So we look at that data and say who are the data value beneficiaries? Who can benefit from this data? That's another thing that we see as a challenge or shortcoming with folks when they transform is they have a new data set, but they don't know who else in their company needs that data so we document and lay that out for the clients. We also look at other systems that may be an input. So are there other systems to add to this solution that could be examples like the Weather Channel data or Google Maps. The next thing we do is we look at the technology you need to make this project work and we look for the gaps.
We look to see if you have to upgrade what you have, if you have to replace what you have or if you have to add something new. When we do that, we look at complexity as well and say how much complexity is that adding to your project? And why do we look at these things? Because at the end of the day, we have probably 12 major projects that are a way for your company to transform their business. We wanna make sure that you understand the ROI, you understand the technology gap, you understand the complexity that you're getting into with this new project and then where we think you should start. So we provide a roadmap that says here's what you should do on Monday, here's what you should do in the next 30 days, here's what you should do in the next 90 days.
We've done this for several years. Average team size is about 10 people. It's a microservice. We have designed it that way on purpose because we know clients don't have eight weeks for an in depth workshop.
And so it's about a day and a half of your time, it takes about a week for us to turn it around. We also estimate what the return is on these projects in year one. I think the last time we did this, we had 80 different transformation projects before we put 'em through the funnel. And the top 12 projects were worth 10.5 million in return to the client in year one.
So it's a great way for us to say what is the return on this emerging technology. Another fun fact that we often see is that IoT projects, people underestimate the return on investment. So it's exciting to kind of walk through what that means to our clients.
- Excellent. Thanks Jill. And Gina, you've spent significant amount of time working for some legendary retail brands that are customer focused and so what are your thoughts on where retailers should go from here? - Yeah, thanks Andy. And I loved what Jill said. I think she's spot on. It is business first.
You wanna start with what is your problem you wanna solve? Go after that and rank them because you can't solve everything at one time, but what is the number one area you wanna go after? And then when you look at that, what data do you need? I think so many retailers are really nervous about their data. You have customer data, you have product data, you have store data, you have your e-commerce data that you've collected for years. And as Jill stated earlier, sometimes the state of that data isn't so great, it's more sludge than her oil example. So once you identify what you wanna go after, just look at the data you need for that area. It doesn't have to be so big. You can start small. You can look at and say hey, maybe I'll get the operational fixes first.
Maybe I can go for after some of the customer experience like David mentioned. There's so many steps you can do, but it's all about that strategy and then looking at the data, the state of the data you need to support that. And finally, I just wanna say you don't have to go it alone.
There are people out there, CDW one of 'em and Intel also who can help you there. If you feel like you don't know where to start, there are people out there that can help you do this, who have successfully done it in the past. - Great. Thanks for that, Gina. David, any last thoughts? I know we're getting close to the end of our time here.
- Yeah, I think that there's always more that we can talk about on this topic, Andy. I think I would say, I think just reflecting on what Jill said first, what I loved about the diagram that you showed was that security at the center. So I think that's an important thing that you need to design into your systems.
Let's not forget that these systems are using your data and that the results that come from that, we need to make sure that security is at the heart of these solutions when we're deploying these solutions at scale. And so I think that's an important piece. And then I think that the other thing is that we haven't really talked and this is a whole conversation in itself, but is a term that we talk about, ethical AI. Is it actually giving you the results that you want? And is it any biases that we bring to the table with our expectations and our assumptions in advance? Really be careful about how you define them and how you bring some of those pre-thinking to the solution that you're developing. And we work a lot with our partners on how to do that. Some of it is from a data perspective but a lot of it is actually just bringing preconceived notions and ideas.
So the business problem, don't try and prejudge that business problem and come with an open mind. And that ethical AI approach, I think is super important for retailers to think about for them to be successful in this space. - Well, great. Thanks David. And it's always great to get together with you to catch up on just talking about retail in the industry and how technology is helping to transform it so it's always a pleasure.
So I appreciate you joining me again. And Jill, Gina, it was great to have you on here with us today. It's a great addition to David and I so thank you so much. Thank you all to everyone who's watching us today, and I'd like to thank you all for joining us, but if you are a existing CDW customer, thank you for your business as well. If you'd like to learn more about how CDW can help you make amazing happen, reach out to your CDW account manager or just visit cdw.com/retail to learn a little bit more about how we're helping customers make amazing happen.
So thanks again for joining us. (upbeat music)
2024-02-21 17:23
