Episode 188: What AI Really Means for Loyalty Programs: Speed, Personalization, and Data Protection

Speaker A: Foreign.

Speaker B: I'm Dan Correll with DCA Card Links and this is Commerce Code, a bi weekly digital commerce podcast for leaders in card linking, loyalty and digital marketing, mobile wallets and payments, and financial data.

Speaker A: Thanks for joining this running conversation with

Speaker B: leaders in the industry. And if this podcast is helpful to you, come join us at a DCA Card Links event.

Speaker A: You can learn more at www.digco.

Speaker B: This week on Commerce Code, I'm talking with Mark Lessu, the chief Commercial officer at Olive. Olive builds digital commerce platforms for fintechs and those platforms offer consumers fundraising, financial wellness and membership and loyalty solutions. Today, Mark and I are talking about how he recently used AI tools to build a sophisticated customer loyalty app from start to finish in about 40 hours. The kind of thing that used to take months. It's a sort of experimental rocket, not something that replaces Olive's current technology, but this rocket flies and it shows what's possible. So stay tuned for a great conversation with Mark to learn about it and to hear what he and the team at Olive decided to do with what he made. Commerce Code is brought to you in part by VantageScore. Eight of the top 10 banks and over 3,400 leading banks and fintechs use VantageScore to predict and manage repayment risk. Learn more about, uh, the latest advances in credit scoring and how to grow your lending business by leveraging financial inclusion@vantagescore.com.

Speaker A: Mark, great to have you back on Commerce Code. Looking forward to a terrific conversation today. Where are you joining us from?

Speaker C: Hi, Dan, it's great to catch up with you again. I'm joining you from just outside Toronto, Ontario, um, in a little town called Burlington.

Speaker A: Terrific. Well, look, we are about to start upon a very interesting conversation that I think probably everybody at this point can relate to in some way and everyone will certainly learn from, which is about AI and how it's starting to, you know, how it's sort of integrating in. So we're no longer talking about theory of AI, uh, which I think is a conversation that's been going on for some time. We're talking about how, you know, it has impacted your business and how you're working with it and alongside it and all that. So I know everyone's got a different story about kind of how that came to them, how AI came to them. And I would just love to start off with, you know, how did you come to be involved more directly in AI? What sort of got Olive involved?

Speaker C: Absolutely. You know, for me, it actually started with a lunch with a mentor about two months ago, who I've been following on LinkedIn with his re entry into technology and development, Mark Ruddock. And, uh, you know, it was one of those things where I met him for lunch and he was a couple minutes late. And in the span of the 5 minutes waiting, I tried out one of the, uh, tools he had suggested I use. And in five minutes, I built the shell of a new loyalty app. And I was blown away. And so I decided that for Olive, let's go see how this applies to our business and set it upon myself to see, uh, if I could rebuild our demo as a client app. So we, as a B2B company, we have a platform for organizations to do their loyalty programs. But in that demo, we hadn't been doing what our customers do, which is showing the actual upside, because that's usually the business and the domain of our clients to build. So I wanted to see if I could do it. And it was an interesting, fun experiment. That's great.

Speaker A: Yeah. Well, I mean, and just to start off with, I mean, the fact that, you know, we're sitting here and it's September and your story starts, you know, two months ago, uh, we've been talking since the 90s and about, you know, the pace of change, and, boy, the pace of change is so fast. That's going to seem quaint and anachronistic, you know, in the 90s and 2000s that we thought that things were changing fast. The idea that we're now talking about a thing that's significantly underway that started with lunch two months ago is absurd. That's not what we meant by fast pace of change in the 90s. We thought if it was like two years ago. Right. Was real quick. That's amazing. So, you know, so Olive. Olive is a B2B platform for others to sort of build loyalty applications. And so why did you build your own AI loyalty program? And kind of. What can you tell us about that project?

Speaker C: Like I said, I started out to kind of create a demo application for us. And it was a bit of a thought experiment. Right. I was curious, but really we did want to be able to showcase to our clients what you can do with our platform. And so, you know, it started with about maybe about 12 hours, eight hours on, like, design effort with I'll, uh, call the bots. And then, you know, we spent about 12 hours connecting the APIs and kind of working with the tools directly to have it interact. You know, I had to relearn coding. I haven't coded in 10 years, let's say. And I needed to understand database structures again, and yet the AI bots kind of did that for me. So it was kind of neat. And, you know, the last kind of third of it really became about testing and enhancements. And so this little project about can we build an, uh, app became, you know, now I'm building a client application, and now I've taken that client application, which I can now demo, and it works and it interacts with our APIs and it's live and it's real. And I could probably put it out to the world if I really wanted to. It's not our business, but I could. But instead we turned it into an onboarding tool where we actually had the AI look at every view in that application and say, here's how you would build it. So we put little instruction sets all the way through the app. It was kind of cool. And so again, it became a thought experiment that now is a tool we're using as our demo, um, with clients in the real world.

Speaker A: A follow up on kind of everything you've said. And it relates to something that I've done some writing on, but I'm curious about. We're all still learning. This is sort of under the banner of like, how hard hard is this really? Like. And so it's. How technical is it? You know, you talked about, Well, I kind of needed to remember, you know, like, what I knew from when I was a coder. How necessary is it for someone to have, uh, had a technical background? Or are we at a point where with a bit of, like, to your point, with a bit of guidance and prompts, like, not, you know, you can kind of just. You don't need that. What would you say about that?

Speaker C: I think anyone with a good understanding of the fundamentals of their business can get started pretty quickly. You know, the AI tools are very good at getting you a starting point. I think like any tool, you, if you don't understand the inputs you're putting in and the outputs you're expecting, that's where it gets challenging. And so when I say I didn't, I needed to go back to my coding days, it's because I needed to remember the fundamentals of how database structures worked. Because AI was making recommendations and it was making table suggestions and it was coding the SQL to populate them. And so I was kind of going, right, is that the right structure or is that what I really want it to be building? And so a few times I had to step back and the hard part of it really was, you know, asking the right questions. Once you ask the right questions, it comes very easy and it's very fluid. But it took a bit of changing the way you speak to your AI bot, if you will, if you want to call your AI employee, some people say, but it's, you know, you talk to it differently than you would another human being.

Speaker B: Yeah, for sure.

Speaker A: And it's kind of. I'm imagining sort of distinctions between sort of design, architecture and engineering, you know, and there's something in, you know, even if. Even if a technician is going to carry this out for me, like, I have to have a decent sense of what I want and what might be happening, maybe in a general way, you know, when I make the request. I wonder what kind of surprised you about this. I mean, uh, you must have been surprised, at least initially, I would think, after you spent 20 minutes and had something. But, like, what was in the fullness of all of it, now that you've sort of done the thing? What. What is surprising about it?

Speaker C: Speed, obviously, is the biggest surprise. I couldn't believe how quickly and accurately it had created a design for me. So, I mean, the first command I think was, could I build a loyalty app to showcase, you know, cashback rewards and a roundup program, you know, with a consumer wallet designed for people to put money towards something they care about, whether that's donations or a spending wallet. I think that was the initial prompt. And what surprised me is it then went over the web and found, you know, chime and acorns, and it kind of picked a list of these very popular consumer apps and said, you know, I've cross referenced all these different tools and consumer applications to give you the best design possible. And it came back with a really impressive first pass on a front end. And that's what surprised me the most, was that it not only did it quickly, it went and did it at a deeper level of research than I could possibly do over the two to three weeks it would have taken me to do the equivalent baselining. Um, and so I think. I think people really underestimate the speed at which it can perform tasks if given the right guidance and instruction set.

Speaker A: Yeah, it's remarkable. It's the amount of change that will happen. It's just hard to. I mean, it's. We're. We're humans, we're limited. So there's only so much we can sort of imagine going forward. There's a lot of conversations to have about this. I'm going to chase it in this direction. We. And I'm not going to chase it in the technical direction. So we could spend the rest of, you know, whole time talking about like, oh, what does this mean for programmers, whatever. I'm going to go ahead and just set that aside and say a lot, uh, I think for technicians. But let's talk about the consumer experience because I think for the commerce code, uh, listeners, that's probably the area that they might have already started thinking about. I'm curious about effects. Like consumers presumably are going to see different things coming at them, presumably at a faster pace. So what do you think consumers can expect, you know, in the next, I want to say year or two, but heck it could be month or two. I don't know, like in the. What's the loyalty sector going to produce for consumers now?

Speaker C: I think for those. And we're kind of seeing it in our business. There's, there's been sudden like uptick, I'd say, in companies that are trying to bring loyalty to market in a unique way. And so I think consumers are going to see more choice for how they earn rewards, how they earn points, how they earn cash back or some combination thereof as they look to put those towards now more, you know, purpose built, if you will, or like, you know, goal specific, you know, objectives. So, you know, like I, I think to date like, you know, you earn a bunch of rewards and there's a big kind of push to how are we going to burn them, how are we going to redeem those rewards? Right. I'm seeing a lot of, you know, kind of across. As I look across ecosystem, there's a lot more apps kind of popping up where it's like, hey, you know, here's something you can use to help donate your money towards your school or here's something that can help earn you money towards that trip you're saving for or here's how you can earn rewards for this other purpose. Right. And so I think people will start aligning and I think there's going to be an increase in competition for that consumer's attention, um, because they have choice. So that will in turn, I think lead to more personalization. And I think the builders of those loyalty programs are going to have to be very mindful of how do we get more m. Personalized, how do we get more specific to what's important for the consumer. And I think AI will help enable that in some ways.

Speaker A: So the expectation for personalization and tailoring, you know, it's reasonable to think it'll rise. Right. And so you're just saying, look, we got to Be very on top of, you know, is this tailored, is this personalized in some way? So if that's kind of the, uh, part of the way forward, at least one aspect of it, there's always, it's natural and to think about new technologies and then immediately go to, ooh, is something we're doing right now going to die or go away or is it threatened or whatever? I mean, do you have an opinion on that? So are there sort of technologies, are there things that we do now that you figure probably we won't be doing in a year or two?

Speaker C: I don't think the technologies will change immediately. I do think every technology has to start thinking about the impact of AI. And, um, we already kind of see it across the ecosystem. Every product seems to be introducing some AI component, right or wrong. There is right now a big push to, you know, AI enable everything because it makes you feel relevant or you need to do it to be relevant or seem relevant. But, uh, regardless of that, I'm not going to go into that direction. But I think every company's got to start thinking about their policies, the way AI will interact with their tool or their product or their features. So it could be as simple as, you know, especially in our world of loyalty and card link data and what have you. It's, you know, what are the policies you need to put in place to protect consumers from AI? So, like, if I'm, you know, a network with card data for a consumer, right? Am I making sure that the agreements I have with my consumers or my customers who are themselves working with consumers, are protecting that data? Because once that data gets out there in the wild, it's now part of a bigger ecosystem of information that AI bots are, you know, the AI tools are relying on to do their modeling, to do their stuff like that. So protecting consumers, you know, I think will become more important how you interact with, I think the vendors and the suppliers that are thinking about how data will be consumed by AI and used by AI to produce the outcomes will be the ones that will get that edge up over time. Because, um, you know, as things iterate quickly, where are the protections? Right. Where are you, um, working with the data responsibly? Right. And how are you building trust with the consumer that their data is protected? So I think if you talk about which technologies are going to suffer or kind of, uh, maybe be killed off, I think it's the ones that don't adapt to that concept of protecting the data, working with the data responsibly, but also building for these AI tools that are coming in, knowing that they're going to go take liberties, right? Because they can do it at light speed compared to what we've been doing in the past. And so you have to build from the start those protections in, knowing that this is coming. It's like imagine you threw a hundred developers at a project immediately, and everyone's looking at it a different way, right? They're going to look for the loopholes, they're going to find ways to get past the protections. Right? So you got to think of that way from the start if you're a technology company, um, so you don't get caught down the road.

Speaker A: We've talked for a long time about building relationships with customers. It's been decades of that. From the customer perspective, it's been. Imagine, uh, like with airlines or whatever. From the customer perspective, it's been. Yeah, uh, I think most people are aware I've been click consenting to giant adhesion contracts for ages now that gave some unspecified amount access to my data. I don't even know because nobody reads that stuff to them. And then every so often, I will notice something that feels like it was tailored to me, and probably at least as often I will notice something that was supposed to be tailored to me but totally wasn't and didn't work. So it's been an extremely kind of primitive thing. And so it wasn't really like you. I'm just imagining now, both with speed and accuracy, with AI, the possibility that people would then be thinking, I think, in a more real way. Right. Because, I mean, I like everybody else, I've signed away my. My data or whatever to all goodness knows how many organizations, because really, it didn't feel like they actually ever did anything with it. Or maybe every so often I'd be surprised and they would, and it's just how life was. Whereas now I think it could feel an awful lot like, is this an organization I want to, like, build a relationship with? Because basically it's. It's like the moment I sort of give my information to them, it's going to start feeling like we're in an interchange of some real kind.

Speaker B: And that just.

Speaker A: I mean, that just hasn't happened, honestly, until AI came along. It just. I think the data was overwhelming and the systems and all that kind of stuff was. And so it's interesting to kind of consider, like, how it could maybe change consumer expectations or behavior or whatever. Right?

Speaker C: Yeah, absolutely. And I think it was one of our previous podcasts, or I think you and I were chatting at one of the events. It's also that level of, well, how creepy can you get with the data to the personalization level. You also don't want to spook the consumer. But, you know, their expectations are also changing around that too. Right. Um, because they are seeing it happen more and more prevalently. I think at the end of the day where these tools can go, where they can help consumers with that personalized experience, you know, it's, it's going to come down to how they interact with each other. I mean, they're trying to stitch together a lot of different services to get that personalized experience. Right. We know that if you're working with a screen scrape tool versus a direct network versus some other data source, they're all providing information in a different way. The AI tools are very good at meshing that together. And so there is going to be some gray area. I also think that, like I said, there are a lot of tools we're seeing coming out. If you're not focused on the fundamentals, if you're not building a product that matters to the consumer. Right. And it goes back to that personalization of outcome, not just the personalization of, hey, hi, Dan, it's nice to see you again. It's. Hi, Dan, it's nice to see you again. And how are we doing on your goal to, you know, help your kids? Right. I think, you know, if these companies are coming forward, everybody's using AI to build new tools and new experiences. If they don't have the fundamentals, as I said at the start, they're not necessarily going to build great products. And the best analogy I've been given, and I kind of chuckle every time, is it's kind of like when calculators first came out, you kind of were learning math in school, right. And people who didn't, you know, who were kind of just introducing calculators and they were losing it for the first time. They're like, if you don't know math, if you don't know the fundamentals of, you know, algebra and order of operations, right. When those first calculators came out, you don't put in the right inputs, you're not getting. You're not getting the right answer. Right. I think with the AI and building these different apps, if you don't understand the fundamentals of the business or the experience you're trying to create, you may not get the right answer from the AI either. And so you're going to see that cause some problems as well in the market.

Speaker A: I should Say for our younger listeners that what Mark is referring to is a device that is shaped like an iPhone, that is almost entirely buttons and barely any screen that did one millionth of what an iPhone did. Uh, but you know, it would give you numerical answers if, and this is his point, you knew all of the magic sort of buttons to press and such like at the time it seemed quite miraculous. And then there were also graphing calculators. And I'll just get too excited if I start talking about those, so I'll have to leave alone. But I assume you had a Texas Instruments graphing calculator at some point or another mark, is that true?

Speaker C: Oh, absolutely. Those were, those were a lot of fun to learn.

Speaker A: There was more screen, the buttons were more confusing and nobody knew what the hell was going on. But they gave you things, you know, they gave you answers of sorts.

Speaker C: They gave you things, right?

Speaker A: So I'm, I'm, you know, I'm thinking about maybe let's turn to leadership, I guess and thinking about kind of that like for, you know, again, most of the folks that are listening to commerce code I think are running a chunk of, or maybe a whole organization somewhere in this sector. And I, uh, wonder what you think about, you know, how should leaders think differently about, you know, say let's start with customer engagement, right? We've talked a little bit about this, but like from a. There's, then there's the extra level of like, well, what does the organization need to do? Or what? Like, what do we have to be good at inside an organization to be able to thrive?

Speaker C: Now I think the first step is think about that customer engagement. Have that be top of mind. You know, personally, I think every person, let alone your employees, I think every person out there should start playing with the AI, uh, tools. And I'm not just talking about ChatGPT. I mean that's a great starting point, but I think like look across your industry where you are, look for the tools that are purpose built. So for example, for our demo I was using a tool called Lovable, which is focused on proof of concept, MVP type building, you know, for kind of, you know, using legal work and using documents and research. I'm using like a Claude kind of thing. Claude AI. And then I'm about to explore things like perplexity. And there's, there's hundreds out there, but they all kind of do have. And as I dig more into it, there's this concept of models they're trained on. Right. So, you know, is the model aligned to the type of work you're doing. And I think every employee out there should start thinking about if you're, if your management isn't asking you to do these things. I think every employee should take it upon themselves to at least play with and learn the tools. And you know, it can be as simple as, you know, uh, we were talking earlier about my daughter and playing soccer. You know, she wanted to get better at soccer. And I asked Claude to come up with a 30 day training plan. Yeah, right, 20 minutes a day, something simple. And I'm not a soccer guy, right? So now I don't know anything about soccer. I was like, how do I teach my 9 year old how to be a better soccer player? And so, you know, again, the tools are there if you ask the right questions. Well, I need a 20 minute a day program for 30 days. Could you give me videos, you know, as examples so I can teach her? And it did, it went, found me the YouTube videos and found me things. And I think, um, you know, that's a personal example. But in doing that exercise, it helped me learn to ask better questions to get to the outcome I wanted. And I think every employee can do that in their day to day jobs. And then that's where as a leadership team, right, to support that engagement, you know, making sure you've got the right policies in place. So now it's like, okay, I want you to start doing this in your day to day job, but let's make sure we're not introducing proprietary information. Let's make sure we're introducing consumer data like so now you got to start being, thinking about the risks, risks, um, from a business perspective that you're, you're now opening up to your company as well. So you want to encourage that growth and you want to encourage that exploration so that you can find better ways to engage your consumers. But now you have to also think about the backside of it as an organization. What are our corporate risks? What do we have to protect against? And so um, you know, that's where we're at right now. Even with all of is like we want our team, we want our, our employees to start exploring and playing with this in safe ways that can provide a benefit to us and how can we create a better experience for our customers but at the same time protecting the data, especially because we're trusted with data in our role.

Speaker A: Your discussion of asking questions and then the example you provided, it has me thinking about questions and answers. And the super basic human model is that we start by asking questions and then we Graduate as we become more senior into the pursuit of that provides answers. And that's always been, I think, known to be a too primitive and not right way of thinking about leadership. Right. So if leadership is like, I'm in charge and so I provide the answers and you people are doing stuff and so you can ask questions as you need, that's not even close, right, to like a mature model in the past even. But now the fact is the real craft is to ask the right kinds of questions or to ask creative questions or questions that lead you in the right direction. That sounds so kind of abstract and, and blah, blah. But I think it's just true now that given how much can be generated in terms of answers, the skills that matter now. I mean, the first time I really dealt with AI, I just instantly thought the only thing that matters now is the art and intuition and knowledge that allows you to be able to distinguish between good answers and bad ones. And the fact that there's a lot of gray area in there and the ability to go from that to asking better questions along the way so that you're getting the things that you need. And like that is a really hard thing to teach.

Speaker C: Right.

Speaker A: Um, we're again have wandered into a territory that I think is, is too much to try and unpack here. But you know, the idea, like it's a lot easier to teach people the answers to questions than it is to teach them how to know when answers aren't looking good and to develop or you know, their ability to ask good questions. And it's terrifying because that's kind of, uh, that's, that's really what matters now. And it's super hard.

Speaker C: I would definitely emphasize that. So you asked earlier in this discussion around, like, what did I learn most and what were some things I learned? Definitely one of the things and uh, you kind of raised right now is that the AI is not always right. So even just working on a presentation, you know, the other day it provided me some stats, if you will, and I looked at them, I'm like, I know that's not a correct stat. And so I asked it for a reference and it's like, oh, you're right, I can't find a credible reference for that. Yeah, well then go find me a credible reference that makes sense.

Speaker A: No, it's really interesting. It's really interesting. I have an old and this is working with extremely competent and well educated humans. But I came over time doing kind of management consulting with data benchmarking. Uh, I had a saying and I Still fall back on it, uh, often, which is. I realize this is accurate, but I'm asking you if it's right. So the other sort of form of the AI getting it wrong is that they come back with something that maybe there is a credible source and the data is correct, but it doesn't, it leads you to a wrong conclusion. Because in total context, it makes, you know, it invites you to think something that's not quite right. And so there's. That's a very judgment. That's leadership. Right? Is being able to say, like, okay, I understand that it's accurate, but I'm just telling you, is it right? And there's a difference, you know, and that's, that's a tough one too. It's more valuable work. Right? Like, it's more, it's better than grinding through the boring stuff. The AI can do that now, which is great. But it put, it really puts an emphasis on the art of leadership skill and being able to, like, really be inquisitive.

Speaker C: Absolutely.

Speaker A: So you, you have talked with me a little bit about a sort of a paradox that you see developing here for loyalty companies and maybe for businesses more broadly. And I'd just love to kind of hear you talk a little bit about that in the AI era.

Speaker C: Yeah, I think we've touched on a little bit through the various topics here. But, you know, the paradox is that, you know, things are evolving and the development, the speed of development is really increasing. Right. And it's accelerating at a pace we've never seen before. And the paradox is that, you know, the fundamentals really haven't changed, though, because in order to support that speed, you have to be fundamentally sound, which seems kind of paradoxical in that, you know, here you are, you know, trying to run faster, but you still gotta, you still gotta go back to the basics at the end of the day and understand those basics. And, and, you know, can you engage your consumer? Can you build that trust? Can you build that loyalty? Can you protect them, um, from themselves? Sometimes, but also, you know, just make sure their data is protected and they're safe from, um, the world. You know, those fundamentals haven't changed, but the speed at which you can deliver products for them to fulfill those needs is just going lightning fast now. So.

Speaker A: Yeah, well, I'll close with this, and I'll make a confession as I do it. Like, I want to close on the question of feelings, because, you know, unlike, I think unlike most technologies, uh, at least recently, um, you know, AI pretty quickly evokes feelings from people and they might be scared or they're excited or they're, you know, all of the above. Um, apparently in the. In the 19th century, as steam engines came along, there were. There were literally religious movements, people who prayed to them. I mean, you know, can imagine how shocking it would be to see a train. Right. Decades before anybody even imagined what a car could be. And, you know, so you can imagine how shocking it would be. So people had feelings. And we've gotten to another era where there's been such a huge shift in what's going on that it evokes. And so for me, it's, you know, I have regarded myself as the guy that's like, well, you know, ah, stuff changes. It's fine. I have moments with the AI where I think, holy crap. Like what? Like this is really. I don't know, you know, what world we're in. I think maybe everybody's had sort of that. So I wonder when you think about feelings that you have as you. Because, you know, you've engaged pretty deeply now with this over the last couple of months in the context of your business, like, what does it make you feel?

Speaker C: It's a great question. I think the feelings change. I think excited, scared, maybe not so much scared is just like, curious. I think more than anything, I feel curious about where this is going and where it's going to take us. And I think in the cur, you can be excited, curious, you can be scared, curious. Like, it can be a bit of both, but it's the curiosity, because I think we're just at the tip of the iceberg here. Gosh, yeah. Like, I. From what I've seen it do, I'm very excited about what it can enable. I think that, uh, it's going to be incredible. Like, people are going to produce things they've never produced before. But I'm scared for the next couple years that we're going to produce a lot of bad stuff as people rush into it and they don't have the training or the understanding of how to interact with it. And so I get a little scared that we're going to train the AI and train the model models the wrong way in some regards, at least in the kind of more global, broader sense. If we're not thinking about it and acting with it responsibly today, that's more what it is, because it is. It is, in a lot of ways, going fast and reflecting back on us the outputs that we're allowing it to create. And so we have to, you know, we kind of have an onus on ourselves as a human species. If you want to go that esoteric as a human. Easy to get these early days. Right. In order to help us in the future. And that's, I think, where I get curious and scared and excited all at the same time.

Speaker A: It elevates what we're doing. That, that for the first time in my career, uh, you know, you can have, I mean, unless you worked in like the field of atomic sciences in the 20th century, there hasn't been a moment until now, I don't think for most of us where just doing your job, almost insistently said to you also, we have to think about like literally what does this do to humanity in the world? It's, you know. Right. You didn't really have to think about that before unless you were working on the Manhattan Project. And uh, now it's kind of obvious that that's part of the whole thing. And that's crazy. But it's also, you know, a call to leadership and it's. And frankly it's more interesting work. That's sort of a small way of describing it. Right. But it's more interesting work by far than some of the stuff we've done before, so. Well, look, Mark, this has been a terrific conversation and just uh, really fun to have you on. Grateful for that and thank you for uh, you know, stepping out. I guess what I'm hearing also, and this is that you picked something up that you strictly speaking didn't really have to in order to sort of advance the ball and then, uh, really think hard about it and want to do it well and right for the industry. So, um, great to talk to you about it. Thanks so much and we'll see you again soon.

Speaker C: Thanks Dan. I appreciate being here and thanks for the opportunity to talk.

Speaker D: Commerce Code is sponsored by Pentadata, the all in one financial data API. Whether it is bank account data, credit card transaction data, or credit reports and credit scores, Pentadata has it all in one simple and easy to use API. With coverage of over 6,000 banks, over 200 million credit files and 60 million merchants, you can get all the data you need for your apps at, uh, pentadata inc.com commerce code is a bi weekly podcast bringing you conversations with executives who are leading the way in digital commerce. If you like Commerce Code, your company should join the Digital Commerce alliance and become part of our mission of advancing trade for good through standard setting, industry networking, conferences and best practice sharing. Check out our website at, uh, www.digcomall.org. on behalf of DCA. Have a great week.