Hello, everyone. I'm Melissa Gonzalez, host a retail refined and market scale podcast, and I'm coming to you from Shop talk twenty twenty four. I don't even know how it's March already and Saint Patrick's Day on top of that. I am sitting here with Christopher Thomas Moore. He's the chief digital office are at Domino's, and he is busy. He leads digital, experience in loyalty, customer care, retail technology, delivery technology, operation and of and international digital marketing. So it's busy. And has a really interesting diverse background with experience in e commerce and marketing and digital communication, which is all critical when we think about the customer experience. We are also going to be on channel today at at Shop Talk. So give you guys some inside, conversation for those who couldn't be at the conference today, talking about AI and the opportunity store operations and the customer experience. So, Christopher, thanks so much for sitting with us. Yeah. I'm excited to be here. Yeah. So you wear a lot of hats, but I don't even think I did it just So why didn't you tell the audience a little bit more about you and your role of Domino? Yeah. First off, thank you again for having me on your podcast. Can, really excited to be here at Shop talk. This is actually my first time in a long time being here, so I'm really, really excited to be here. But, at Domino's and my role as chief digital officer, I do have a number of hats. So everything from our e commerce experience, which really is more than just our websites and our apps. We also have Domino's type of thing that we call our Anywhere solutions where you can order a pizza in, like, ten different ways, from Alexa, from Google Assistant, from SmartWatch. This is dangerous. We're in. All of that. Talking to Alexa Moore. Access to pizza. You know? And, so then our loyalty also our, you know, personalization program or targeted targeted offer programs, bounce back programs, also all of our technology in our store. So everything from our point of sale, so all the different screens and devices that individuals use in our store. So all of the technology like GPS and all those innovations, and autonomy also is a big part of what we focus on in the delivery side. And then international from a digital marketing standpoint, from a product standpoint, our source systems, and the list goes on and on. But it's fun. You know, I get to do something different every day. It never gets boring. And so it's really exciting site work. I love that. Well, there's so much we can dive into. So let's see where we can scratch some of the surface Yes. At least. Why don't you tell us a little bit more about when it comes to AI, which is the topic we're taking, on the stage today. When it comes to AI, what is Domino's invested in? Yeah. I mean, it's a lot. I mean, first off, the topic of AI is so broad. Right? AI encompasses everything from machine learning to generative AI. Right? Yeah. And Spectrum is pretty wide. And so you know, on the spectrum of machine learning, we've been in that space for a number of years, you know, finding ways that we can build models and algorithms that help bring efficiencies or do they act at, activities, but also ways that we can help become more targeted and focused on how we're communicating and addressing our centers. Right? And so tons of different models from our, you know, marketing programs, to our experience within our web variance. Where we're moving and what we're really excited about is now how do we leverage some of that technology in store? And we've done a lot of that as well, right, from trying to find the smartest routes for our drivers, to trying to find efficiency in the operation, but we feel like we can take it a step further. And so we're really excited that last year we partnered with Microsoft in building out kind of a center of excellence on how do we build the future of AI technology as relates to store operations, as well as our consumer experience. So we're in the throes of that working on different projects and having a number of conversations to find where are some of those opportunities where we can bring some proof points to that full spectrum of AI. Everything from machine learning to generative AI and how it applies to both the consumer experience, to the customer experience on the web in store, but also how it applies to our team members that are working in our source data well, knowing there's so much opportunity, right? You just kind of spoke the the spectrum of that. Tell us, like, let's let's break it down for the audience. Like, what's the steps that you're taking in order to even decipher and decide what avenues you wanna pursue. Yeah. I mean, I think one of the things that has been really critical for the Domino's culture is a test in order mentality. Right? We, first off, come to everything with the level of skepticism. Right? If we don't trust it because everybody else trust it. You know, we have to see it for ourselves. So everything needs to be evaluated. So I understand, hey, this might be working good for another client, you know, marketer, and this is gonna work well for us. And so in in that testing, it's very internet. Right? So we like a call walk run strap you're right. How do we build towards something that's more sophisticated? So we're taking our learnings, you know, step by step to understand you know, let's first start off with a simple model and understand what that does. Right? And if we're finding efficiency, how can we actually lease that up and take it to the next level? How do we deploy that in a way where it's self running and, you know, learning and growing by itself? And so we're taking these progress steps as we learn the business and how the business impact it as a result of some of these newer technologies that are built. So how do you set up? I mean, there might be a variance of answers, but how do you set up the success metrics? Like, you know, those milestones that you're hitting along the way to say, okay, we hit this this level of conversion of success, and then it'll let us go to the next level of investment. I I really, it starts with having a hypothesis around, like, what are we actually striving say, a podcast result of whatever change that we're doing. Right? And then we have amazing analytics and insights team that does all of the number crunching and all the data to help us really understand the reality of those things that we're deploying. Right? So sometimes it's, like, on the consumer side, what are we doing to either address retention? What are we doing to address conversion? You know, there and it's always the same. Right? It's really different depending on the scenario. And so we have these preset KPIs that we work against that we then look to see. Did we actually have a thoughts that we thought we did. Yeah. And if we did it, what can we learn about this testing that we did? So try in a different way or try something different to have that impact that we're work. Yeah. So that's an interesting conversation then to think about, like, the stakeholders. And obviously, there's gonna be different use cases, but, you know, a lot of this isn't just the tech There's the hypothesis. There's the technology tool, but there's also the human elements. Absolutely. So can you talk a little bit about that side as well when we think about you know, the change management, the training, and then taking a level deeper, thinking of Domino's, like, you're you also have franchisees. Right. So how how do you get to that next later too to make sure the right train is happening. Absolutely. You know, like, this space of AI is really interesting. There are a lot of stakeholders involved with everything that you're doing. Right? So, like, the consumer is a stakeholder, but the operations is also a stake Right? And when you think about our franchisees, we've been in business for over sixty years. Some of our franchisees have known this business inside and out for almost their whole life. Right? Yeah. Over ninety percent of all of our franchisees started off as a driver in the business. So these people have grown up through the business, and they know it really well. Right? And so when I say to them, you know, I wanted to play a model that replaces some of that cognitive load that you and your team members have had for years. That requires some proof points to validate that it's actually better than they are. Right? And so we have to be very transparent and, right, so show, not only is it working, but break it down so that there's a level of trust that we can build with our system. So I'd be able to kind of trust that those things are working and actually working better. And so you can't just be a deploy watch this performance walk away. It's more of a communication. It's huge management really to, like, get people out of these processes that you've had. Forward for decades really. Yeah. And so, you know, you talked about a spectrum of opportunities generated by machine learning. We think of machine learning the other aspect of it is the importance of, I mean, the inputs you put in is is going to highly impact the output that you get. So can talk a little bit about that too as you create your hypothesis. How are you making sure that you're having the right structured data sets? Having a diligence, and rigor in process around that too as well in order, you know, to position you for success. No. It's a really big topic and and big focus, you know, the same garbage data and garbage data out. Right? And so we have to make sure that we're sitting on a foundation that is strong and we feel really comfortable with. Right? And so we're going through our own evolution with continuing to tighten that for our business. Right? But where we are really comfortable, you know, it's making sure that we eliminate bias that we have, you know, just really kind of clear data that can be leveraging these models to make those decisions, and then continue to feed that data into those models to help improve their, you know, function. And, you know, I think that this topic though is a really important one. And when when I think of the broader spectrum of AI, we're, you know, I have some concerns in question. Right, being, an individual of a minority community. You know, I question, you know, the biases and things like that that exist in these platforms. Right? When you're thinking about who's developing, how they're developing, we have the opportunity to develop things that aren't necessarily fully inclusive. Right? Right. And so ensuring that, especially as we get to the world of Genrodab Yeah. That we're thinking of how do we bring diversity to tay hole as we think of both the people building these programs and models, but also how they run to make sure that they're really inclusive and representative of the full community that during the afternoon. Yeah. Absolutely. I mean, it's critical. For sure. Well, so as you've deployed some of, some of the current AI initiatives. Can you share with us any successes learnings? Yeah. You you also could share the other side, like, don't ever do this again. But any yeah. Anything to that name. You know, I mean, they're they're gonna number on both sides. Try it. We've definitely deployed some things like that we thought were gonna have really big impacts to bring about efficiencies, you know, from you know, let's take it from, standpoint of, like, how we're deciding what orders to wear and things like that. And not all of those models work the way that we thought they would. Right? And so the the question is, you know, is this a deficiency in the approach? Is it a deficiency, the dataset that we're using to frame the scans, you know, where is the opportunity for improvement? I think we do believe that there's a path where we can find an approach for models to actually do a lot of removing your cognitive load and bringing out efficiencies. They're not just push and play. You know, sometimes they take a little bit of fitness. Yeah. You know, some manipulation to get them there. And so that's why that iterative approach is really important. Right? We can't just take a implementation, the success or failure of one implementation and let that be the only story. Right? We have to continue to understand where these opportunities exist because sometimes it's a small tweaking small refinement that actually gets sitting on the pond. No. Absolutely. It is like a twist on the the use case. You know, it's it's it's not gonna work in this direction, but if we actually reapply it here, it could be a really positive outcome. Absolutely. Absolutely. And so Like, we have to have a culture that's willing to have that opportunity for failure. Right? For us to deploy and have the opportunity to learn. And that's critical. Because if you don't want that, this is not the space for you to give us. Right. Yeah. Well, especially because while AI is hitting every headline and it's been in development for your still in very early stages. Absolutely. And it's the tools are still in its infancy. The people using its tools are still in the empathy of understanding them. Yeah. And that's why I'm like, we can't just take a few use cases. A few case studies in Villas, though, we have the silver bullet now. Right? Because it's not there. What works for one industry, one company doesn't necessarily work the same in others. And so there is a necessity to really understand, right, to learn, that you learn through testing, right, and how you can apply the to your business because it may not be the same as someone else. Right? Yeah. It might not be the same use case, but like I said, it might have another impact somewhere completely different that you aren't thinking about. So being open to that, you know, time that it takes to learn, the investment in resources that it takes to learn is really credit for this this grace. Yeah. Absolutely. And having a way to share. Absolutely. So that you could understand. Oh, maybe there's a different opportunity with what we just did. Maybe another group of stakeholders need to be brought in. Absolutely. Then when we do find that it works, how do we socialize that? And, again, get that trust and buy it from all of our stakeholders? Yeah. So that we can, you know, find what the next thing is and move on to the next opportunity that we have. Well, we're in early stages too of the consumer understanding AI, but that's also wrap at the changing. Absolutely. We think of alpha. It was funny. I was, in my phone yesterday with my daughter who's a gen alpha. She's eight. And she looked at piece of content that showed up in my feed. And she goes, oh, mom, that's AI. And I go, oh, how do you know? She's like, it's not real. Look. Look. Look. She could point out all the reasons why. And I said, when you something? Do you think you can tell the difference? Yeah. And she was like, yeah, duh. And so it was just interesting because it's novel to us is like what's like, just intuitive and everything for them. So as we continue to evolve in this younger generation is growing up. Like, what do you see as the big opportunity in the future? Like, you know, if you could dream five years from What do you get excited about? You know, I'm I'm really interested in just this constant state of change that we're in. That's why I've been in the digital space for over two decades and I'm here because I love the fact that it's not the same tomorrow as it was today. Right? This constant change. And we as consumers are constantly evolving based off of the experiences that we have right in life. And to me, it's really funny. Do you think that you know, maybe a decade ago, maybe even not that far where, you know, the having too much precision and targeting personalization from a consumer standpoint was pretty freaky. Right? Like, what do you have about the Yes. So you're listening to everything in my phone even when I think you're not. In the world. But now I feel like, generationally, but also just predicated on the experiences that we've had, there's a higher bar of expectation. Yeah. Right? I kinda expect that you do know me a little bit. Right? Yeah. I was giving you information I purchased through your your platforms and through your experience. So why are you showing the information that's not relevant? For. Right? So I think that there's been an evolution in the mindset of a consumer, right, as far as what their expectations are. And so I think it's not as freakiness and the scariness, as long as it's relevant in bringing about a level of efficiency and some level of benefits, me as a consumer. So I just think that those expectations are different. Right? And so I do feel like people, can also spot when, an attempt is there not a good one. Right? Like, you could see right through it. Like, that's how Exactly. Exactly. Like, this has absolutely nothing to do. You tried. Good try, but it failed. You know, And so, so, like, it's kind of the inverse of where we were. Right? The level of precision when you get into these applications, and in tools is actually even heightened to me because you really want to speak to that consumer based off of who they really are. You know, I call this, I look at this whole space of personalization, leveraging, you know, AI as no different than any a relationship that you have at this point. Right? First time I meet you, know little, very little about you. Right? Yeah. But there's a level of expectation that you're gonna address me differently. You're gonna talk to me differently the next time we have a conversation, and that should grow as a relationship grows. And so we as brands, you know, have a relationship with our customers. And so we need to show that maturing relationship through our conversations and leveraging technology like this provides that opportunity for you to do if you do it right. Yeah. No. I love that analogy. It's true. Because in the beginning, it's probably a lot of blanketed assumptions. Yep. But it's it's a brand and retailer's job to actively listen. Yes. So that you're always learning, and then you can better serve even if you're a private company or service company. So better be in service with the customer. Regardless of what you do. Right? If you can speak to me in a way that shows that you get me you understand what's important to me. Perhaps with you, I expect that I will be more loyal to you as a result. Yeah. Absolutely. Well, I think there's so much exciting ahead. We're just scratching the surface here at Shop Talk, having these conversations. But, thank you for sharing what you've been working at, in your leadership role at Domino's, and I think we're just gonna continue to watch to see you know, probably some magic behind the scenes, you don't even realize it's happening, but it feels, you know, but if that's what makes the magic. But we're getting out of Domino's. It's better and better. And we say pizza's magical. So why not make it magical? That's exactly. That's right. Well, thank you again, everybody. This is Christopher Thomas Moore, chief digital officer at Domino's coming to you from Shop Talk twenty twenty four.
