Thank you for joining today. We've got some exciting content to dive into at our retail webinar. So I'm going to go ahead and get started. So to start off, my name is Alex Pallotta. I'm a Senior Product Marketing Manager here at SAP Emarsys. I am coming up on one year with the Emarsys team. So let me tell you, I am absolutely thrilled to see the work, the research, the expertise that we've invested and are bringing to the retail space. Which is really the reason I'm excited to moderate the webinar today. So I've got some standout retail experts and my colleagues here, Matt and Eloise. I am going to pass it off to you for some introductions. Hi, everyone. I'm Matt Gardner, an Executive Advisor for retail at SAP. I've spent over 20 years in various leadership roles at retailers like Office Depot, Sports Authority, and Kroger. And I'm excited to share the findings from our new research report. And hey, guys. Thanks, Matt. Hi, guys, I'm Eloise Burton. I'm a Product Enablement Manager here at Emarsys. I've been at Emarsys for about eight years this month, although only been in the product enablement team for about five of those months. I was a customer trainer before that. So I'm kind of moving over to the internal training side. But yes, so later on today, I'll be showing you a live Emarsys demo. So sit tight and wait for that. We're going to have a story and a live demo in the platform. But yeah. I'll speak to you later. Thank you both. So excited for both of your pieces of content that you'll go over. So before we jump in, let's see what's on the agenda today. So like Matt said, he will be sharing some insights from the retail space. We'll get some highlights from our AI and retail report. And then we'll hand it over to Eloise to run through our retail customer journey and, like she mentioned, a live demo. So we're going to be covering every step from commerce to service and, of course, Emarsys. And we'll show all of that and bring it to life in our demo. So then we'll be on to Q&A. So for Q& A, go ahead and throw your questions in the chat. We'll cover either the questions live at our Q& A session, or we'll connect with you in the Chat. So pass them over there. And I'm actually going to pass it back to Matt so he can start on his content. Thank you, Alex. So let's start with a quick look at the foundation of today's discussion. Our new 2025 report on global AI and retail, it had us survey over 10,000 consumers and more than 1,200 marketing decision makers across five key retail markets, the US, the UK, Germany, UAE, and Taiwan. And our focus is to understand how AI is shaping the future of shopping. What you'll hear today is grounded in that data, giving us a truly global view of how expectations and behaviors are evolving. Let's look at five results aligning with trends we're seeing across the industry. Number one, omnichannel is no longer optional. Consumers are evenly split between physical and digital channels, 55% shopping in stores and 54% buying through marketplaces, like Amazon Marketplace or where there's third party sellers. And this means that channels are not competitive, but rather continue to converge. We kind of shop at different touch points along the customer journey on different channels and they synergize together, rather than replacing one another. Number two, AI is delivering real value. 71% of marketers say it's helping them launch faster campaigns, saving over two hours per launch. And that's really tangible efficiency. Number three, personalization is working, but not perfectly. While nearly 70% of consumers are satisfied with AI driven product recommendations, that still leaves a gap for brands to close. Number four, AI budgets are growing. Nearly two thirds of marketers plan to invest more in AI next year to boost engagement. And number five, trust is lagging. Only 37% of customers are confident in how brands handle their data. A clear call for transparency and responsible AI. So what does all of this suggest? Well, speed and reach are improving with AI, but genuine connection requires a cultivation of trust. What we see is top retailers achieving this by balancing efficiency with empathy. As the tools become more efficient on the AI front, it's important to keep humans in the loop. So let's take a look at what experiences consumers are having. The most common is self-service checkout. Maybe not surprisingly. I think we've all seen that at various locations. Nearly half have used it and 76% rated it positively. That's followed by chatbots, AI phone agents, and even robot delivery and restaurants. What's interesting is that while adoption is still relatively low, sentiment is high. In every example, the majority who did engage with AI said it was a positive experience. What this tells us is that there's an opportunity. Even small scale AI experiments can generate delight and build trust if executed thoughtfully. So how are shoppers using AI to enhance their own shopping experience? 34% search for products to buy. 33% use it to compare products. 27% want to find the best prices or deals. And 21% are searching for stores that carry a product. What have all of these have in common? Well, searching, comparing, gift finding, they're all research-based tasks. This is where AI can offer real utility to consumers by surfacing relevant options faster. And we're also seeing use cases like budgeting and deal hunting and even helping shoppers write great personal messages and greeting cards. All signs that consumers are open to creative AI help if it feels useful, timely, and personalized. So for marketers, this is about connecting AI tools to real shopper intent and doing it in ways that delight the consumer through the buying process. So what is the consumer demand for AI adoption? When we asked respondents this question, the thing I just mentioned on the last slide is exactly what we heard. Consumers want AI to extend throughout the shopping journey like providing faster customer service or ensuring products being shown are actually in stock, which I might argue is more of a data connectivity effort than AI alone. I found it interesting that Taiwan was leading so much here so did some other research on the side just to see that the shopping experience in Taiwan is driven. Largely by mobile first culture and consumer expectations and demand are just a bit higher than obviously in the other regions we see there, about twice those in the US, Germany, and the UK. And while the US consumer AI adoption is rising and brands are aligning strategy accordingly, the demand is actually decelerating due to rising AI hesitancy. There is growing consumer concern around data security and privacy today as we have more data breaches in different public spheres. AI is one of the categories that gets hit by just people being a little hesitant with exactly how the data is being used. I know in some cases there's court orders to retain customer data and interactions and all of these tools just should be fully vetted as you're working with vendors to make sure that it's following the guidelines that you as a company are offering yourself to your own customers. As you can see here on the slide, 63% are not confident in the data privacy standards of AI tools and that's up from 44% the prior year. I thought it was interesting that the younger the generation, the more trusting they are, though this likely has to do with the younger adults having less time exposure to data breaches than their grandparents. What can you take away from this? I would say at least two things when you ask customers for data. First, be transparent with customers about how your brand uses AI and how you're protecting their data. And second, make sure you put it to use in a way that signals clearly that you're not just listening, but adapting to what they've shared. Collecting data and just having it go into an abyss and not really having anything come back to them as an experience, just... Isn't the experience that customers are comfortable with when they're sharing data with companies. So how do consumers feel about the impact of AI? Here's where things get interesting. More than half of consumers, when we combine the two bold rows, you can see here on the left in bold text, they say they can't identify how AI influences their buying decision. So that's either a signal that the AI is too subtle or that it's not impactful enough. On the right, when asked how they feel AI impacts their retail experience, just 17% reported a negative view. That's encouraging, but it also means brands need to work harder to demonstrate value, not just use AI behind the scenes. Clearer value exchange and transparency are key. If you want consumers to trust the tech, you have to show them how it's helping. We'll see in some of the demo that's coming up here, a lot of it's invisible. I mean, a lot it empowers us to do things and personalize in a way that when the consumer receives it on our behalf, a lot if it's already taken place. And again, we'll see some of that a little bit later. Shifting gears, let's talk about the sentiment of marketing professionals in the survey. 92% say they're using AI this year, with 70% optimizing campaigns in real time and 66% using it for forecasting consumer behavior. These aren't future state numbers, they're actually happening now. We're also seeing AI playing a major role in copywriting, customer targeting, content optimization and task automation. The takeaway here is that AI isn't just about scale, it's about precision, helping marketers spend less time on low impact tasks and more time on strategy. And if 92% are already on board, the real question is, are you using AI everywhere you could be? What impact are marketers seeing with AI? Well, it's twofold, increased productivity and improved customer experience. 72% say it frees their team to focus on more creative and strategic aspects of their job. 71% note the efficiency of building and launching campaigns, saving over two hours on the average campaign launch. 60% report an increase in consumer engagement and 58% note an uptick in customer loyalty. Given results like these, it's not surprising that, as we saw earlier, nearly two thirds of marketer respondents plan to invest more in AI in the future. I know from my own experience, I find it feels less like I'm a copy editor and more like, or a copywriter and more a copyeditor. There's like a level change in the way of eliciting ideas that can then be tailored and most of my time can be tailoring and reacting to what's come back rather than trying to create it, always on tap at any time it might be needed. But the question remains, what AI tools and use cases should you invest in and where do you start? So to gain trust from consumers through AI, we need to connect the dots, bringing together data, solutions and integrations to link customer insights with every touch point and connect consumer demand with teams, the teams that are serving it. When that happens, you redefine intelligence, putting the consumer at the center of your business. And this means that you know your best customers without endless manual work. You can do connecting and personalized experience across all of their channels when the data is unified. And you can scale those experiences across teams through that shared data set. This is how you start to earn customer trust using AI because it takes more than just data. You need tools to make it actionable and shareable so your team can deliver seamlessly. That's where SAP solutions come in. With SAP Business AI, everything we do is relevant, reliable and responsible, working together to deliver real business results. By relevant, we mean embedded into SAP's enterprise applications, delivering AI in the context of your business processes so it's always future ready. One of the challenges a lot of our customers have is making sure that the data is all relevant. Like it's all like exactly what's needed in the right context. And it's through a lot of data engineering and data cleansing that sets it up to be maximally useful to AI to reference when it's bringing together the best ideas you have from your own trusted first party data. The second is that our Business AI is reliable because it's powered by the industry's broadest contextual business data set, ensuring it's accurate, consistent and grounded in reality. SAP has been trusted for decades. I think we're up over 50 years at this point with customer data. And that obviously makes it so that trust laced with AI. Very effectively, like at the core really gives a potency that all the other integrations of one-off tools just can't achieve. It's something I'm personally really passionate about when we talk about our solutions at SAP. Third, this is responsible because it's designed with security, privacy, compliance and ethics at its core. Meaning you know what data is used, how outputs are created and you have controls to guide tone, creativity and results. Together these principles make SAP Business AI a trusted context for turning insights into impact, helping you earn and keep customer trust at every interaction. So with that, thank you for exploring this new report with us. And now let's see some practical examples of these principles playing out and the customer journey and demo we've prepared. Alex, back over to you. Thank you, Matt. The AI and retail report results are really eye-opening. So like you said, you'll get a copy of that after the webinar. But with no further ado, I think we hand it over to Eloise to give us the best run retail customer journey and then show us some of that demo in the product. Thanks Alex and thank you Matt. I loved those slides and that data. I think it's really well presented and yeah, really interesting. Going, kind of alluding to that last slide Matt talked about how everything is interconnected. What I'm gonna start with now is show you a customer story. Now this is a story about a guy who buys a bike. And after we've done the story, I'm going to show you in the platform how you could build that in real life to show how accessible that is. So let's talk about best run bikes. They're a US-based e-bike manufacturer who aspires to instill a love of fitness and outdoors into their customers. And like many of you, they have a complex business model. They're building relationships directly with their consumers through e-commerce site, through the stores they own across the United States. They also must build relationships with their retail partners throughout the world, ensuring their success and helping them connect and build loyalty with their own customer basis. So we're gonna dig in today into a direct to customer example. And in this portion of the demo, you are going to see a few things. You are going see how SAP Commerce Cloud allows them to understand the customer preferences and deliver personalized experiences. You'll see how what you can take to learn about your customers in this customer data platform in the CDP. And how with that information, you can activate an omni-channel program in Emarsys. This will be the second part of what I'm showing you today. And you'll see how ERP is integrated every step of the way to ensure you and your customers are benefiting from the use of all your inventory data. So let's start with our story. Here is Michael. Now he is an avid traveler. And on his last trip, he rented an e-bike and he loved it. And he made a vow that when he comes home, he is gonna buy his own e-bike. So he goes into Google, Google's best e-bikes and finds the first hit, which is a best run website. Now he starts browsing around the site. Now at this point, he's still anonymous, but with as few as six clicks, Commerce Cloud can understand his intent and start to make personalized recommendations, which is pretty amazing. Anyway, so he's browsing around and he sees some bikes he likes, has a look at them, quite likes blue. So he starts playing around here on the website, looking about, clicking around, deciding what it is he wants to do, has a good look at the bike. Then he has an idea. He's like, actually, I'm going to look for the exact bike I used on my trip. So he decides to do a search. And in doing so enters the bike and finds the very bike he saw on the trip. But it's a little on the steep side, not exactly how much he was willing to spend. But then he spots the bike finder. He's like, right, what can I find on here by following some preferences? So he puts in what he likes. He doesn't mind hills. Yes, he wants to have an e-bike and he finds a really good bike that he likes the look of. And he notices... He can actually try it out and he's going to go for a test ride. So he fills out his details here, puts his address in, it finds a local shop and he makes an appointment at a time and date which suits him. And here he has his test ride booked. When he gets to the store, he goes and logs in at their console. You'll notice it's recommended loads of things to him based on his behavior before. And he goes in and does his ride and he is now... Very happily a new bike owner. Yeah, all good. Now, a few days later, he downloads the mobile app and using his social credentials, signs in. And since we know Michael, we can start to personalize some options to him, accessory recommendations, that sort of thing. Just like all of you, Best Run wants to turn all first time buyers into loyal engaged customers. So how can we do that? Well, let's take a look at what the Best Run marketers did using SAP CX. First thing they did is they used the CDP, the customer data platform. And this connects from every possible source in the business. So we're talking your e-commerce site, your store sales, marketing engagement, service consent data, anything and everything is all. Held in here. And because of the CDP's direct integration with ERP, Best Run marketers can understand who their highest value customers are. And we're not just talking here about who spent the most, but everything. Who has done the most returns? Who's downloaded the most podcasts? Who's clicked on the most videos? Any data you want to bring in, it can help you build a really good profile about your customer. But today we're focusing on first time buyers. So here in the CDP. We can find more about Michael and other customers like him. So we're going to create a segment. And in the segment we look at people who've bought once, downloaded the app and had at least 15 or so rides with us in the past. Once we have our segment here, we can then use this segment as an entry point in an Emarsys automation program to start a whole flow of actions to basically start the whole journey that Michael went on. I'm going to show you this in the platform in a minute. But just to show, here is an example. We have first time buyers, they get an email. Look, he also gets a WhatsApp. And then finally, another email. So let's just go back to Michael. And here he is in the WhatsApp and he's just messaging his friends. And he's like, oh yeah, I had a really nice time when I was away. And then gets a message from Best Run saying, hey, would you like us to send you a message to communicate? Sure, he says, please. And then they tell him, because we know that he is a valued customer, because of our segment we just built in CDP, we are offering him to come to our VIP event. And he is like, yeah, I would very much like to attend. And he gets a little ticket. And he comes to the event. So fast forward to the event, here he is. He's in the event, he buys some accessories, has a great time. And on the way out, sparks a conversation with one of the co-founders. Now, based on Michael's whole CX experience, he's been so impressed with it. And with Best Run, he decides to become a brand ambassador. And the ambassador pass comes in the form of an NFT. Minted by the SAP Web3 Cloud. And this gives him early access to future Best Run products. And it represents his ownership in the brand, giving Michael and other ambassadors the ability to influence the company's future direction. That's the journey. And you might be thinking, yeah, fine, pretty fantastical. I'm not entirely sure how that's really gonna play out at our side. Let me show you the Emarsys platform and show you how actually simple it is to recreate this whole journey. So let's start at this point. I just showed you actually a similar version of this program. This is actually a slightly better one I bought because I've involved more channels. Now this program here is built in our automation part of the platform. It's essentially a series of nodes which connect and they perform essentially a flow of action. So we have here in the actions, things like A, B test, you can run a segment, you can wait, you can do a trigger, trigger in action, something along those lines. And further down here, we have channels, which is where the Omnichannel comes in, of course. We have multiple channels in this account. But in order to add a channel, it's just a matter of picking it up and dragging it where you want it to go into the program. I'll talk through the program very quickly. Then we'll take a look at the channels in turn. And at the end, I forgot to mention this, it's gonna be slightly interactive. You can download your very own Wallet Pass. So... Here is the entry node, which is the customers coming in from the CDP. They all get an email. We wait three days, see if they've used the app at least once. And if they have, he gets that WhatsApp message we saw. But I've also thrown in a web channel campaign just to show you another channel. And then eventually they get an invite to the event if they engage. We know this entry point is a CDP segment, but just to show you, while we're in Emarsys, that if I were to create a segment, you don't have to use a CDP segment. You can actually use any segment that's in here, including an AI segment. So these AI segments are predictive. So you might say anybody who is a lead, who is likely to convert in the next 30 days, I want them in the program. Maybe anyone whose predictive revenue is high, based on your revenue and your account, they go in the program. There is incidentally coming up, I think in the next few months, a new segmentation tool with AI, where you can use natural language. I wanna build me a segment, finding any blue bikes under a thousand pounds, name it, and it does it all for you. But for now we have these predictive AI segments, which is also nice. Going back to our program, by the way, and also just to let you know, in case you're looking at this and you're already like too much, I can't, our team cannot have, we haven't got the time to build these programs. We do also have tactics. These are pre-built automations over a whole host of different scenarios that you can search for and use. Just wanted to say that. Going back to our automation, we then have our email. Now let's take a look at email number one that Michael receives. So here is our email builder. You'll notice, again, templated. I mean, how simple. It's just a matter of dragging in the blocks into the tool and adding images, et cetera. Now we have first name personalization here. We can personalize in many ways. As you'll see, we have recommended bikes based on his preferences, his browsing, et cetra. They're served when he opens the email. But what I want to talk about here is this. You'll notice I'm not much of a copywriter. I want this block to look pretty cool. I want it to grab his attention. I don't have the time to write something. I'm no very good at writing something. Let's use the AI builder. So what we're going to do here is I am going to put some text in. Now you don't to put much text because I actually want to look for specific things. I want have first name personalization. I want to say congratulations. And I want it to say click more to get inspiration. It's going to give me some ideas. Well, here we go. Celebrate your milestone. Okay. Way to go. I mean, this is great. I wanted it a little bit more fun though. So I'm actually going to say here, use bike puns as well. Let's make it a a little more fun. And in a minute it will give me some, here, we go, pedal into new adventures. You've hit 15 rides. Celebrating your first 15 rides, keep rolling. That's not so bad. So maybe I'll use this one here and insert it into my block. So super simple. You can use anything in here. I had a lot of fun playing with this before I did this session, doing funny Shakespearean speak and all this sort of thing. But you don't have to use many words and it will build it for you based on your instruction. The other thing I haven't done is this subject line. So let's go back to email basics and notice I can build my subject line from my existing content. It knows exactly what I want because the content is in the email. I don't even have to prompt it. But I might actually want some emojis. So use emojis and let's see how that pans out. So here we go. Perfect. I'm going to add this one in there and the same with a pre-header. I can also add some variants here but I don't want no first name. So let's remove that. And get something perfect. There we go. Wonderful. So there is my basic email built with AI. I mean, there are more things in here you can do. If you have your product catalog, you can a product search where you can say find bikes under a thousand pounds which are blue, add and then it will add the text. You can even say things like find my best-selling bikes, find bikes in the sale. Bikes which haven't sold for six months, anything and it will find them, present them and create the content for you. So that's also something you can do as well. Nipping quickly back to our program. Let's see where we are. So wait three days. If he's used the app, two things happen. He gets sent a WhatsApp. He also gets sent to a web channel campaign. The WhatsApp is here. I mean, it's very simple, not huge amount to show. Notice the personalization as well, I've added the logo. The web channel. We didn't have this in our story actually which is why I put it in, but you can see I've added further personalization. I've add event details and this will be a pop-up on the website and it will happen at the point in the program where he is, when he goes online. Finally, he will eventually get a second email. Now this email is where he gets the Apple Wallet invite. So pretty cool. But again, this seems a little bit. Abstracts, like how do we do the wallet? Well, again, it's just a templated tool. It's literally this. You just have to add the image. You can choose what type of code you want in it. I've added all of this information. I've added a personalization token. Takes no time at all. And let's make this fun. You guys can download this invite. I'm gonna test the pass. You can scan the QR. The top one is Apple, please note. The top QR is for Apple devices, the bottom for Google. Feel free, scan it, add it to your wallet. Please though, don't turn up to the event next Saturday because it's not a real event that you will be waiting outside the bike lane shop with nowhere to go. But yeah, there we go. So feel free to download that and test it out at your end. That's essentially the end of our story. There is one, while we're kind of a little bit AI heavy on this, I wanted to also just talk about the AI assisted report builder. Now I'm gonna put a prompt in here, the sort of prompt you would probably do if you were using this in real life. I don't expect it's gonna find anything because this isn't real data, but let's see. I've put in, find anyone who has downloaded the wallet, clicked on the bike wallet campaign and purchased. Unsurprisingly, there's no data in here for that. But I wanted to show you that you can just, we can do a different one here. Like what's the average order value belonging to a program? And again, it will give you data. So here we have a report. So there's a lot more AI stuff coming into the platform in the next months actually. That's the end of the demo, but I just need to talk through a couple of things. So we're talking about turning retail AI, retail AI strategy into reality. So on this slide, we talked about audience and segments. I showed you how we can build an AI segment using predictive AI, but there is the segment generator coming and it literally in the next few months, which will be able to build segments based on natural language. We did content and channels. I showed product recommendations in the email. You can do send time optimization, which just sends the email when it's most likely to be opened. You simply do that in the last step when you go to send. Subject line generator I showed you as well. Measurement and insight. So customer lifecycle, so sorry, using smart insight. I didn't show you this. This is our intelligence tool, but it allows you to do things like predictive affinity, who is a lead, who's spent the most, what are they buying, what are defecting customers buying and market productivity where we talk about the product finder, which I mentioned. And the segment description, which is part of the generator, which again, will create all of that for you just using natural language. So before I hand back over to Matt, who's got a nice customer case study for you, I just want to bring it back to show you that none of this can happen for retail brands without the right solution in place. So our mission here at Emarsys is to give you that using SAP CX. You can connect across the entire experience. You can make insightful and data-driven decisions. So understand your customers, really important in order to drive profitable growth and adapt to market needs. So scale your business with a composable platform and automate omni-channel campaigns. So with that, I'm going to give you a customer success story of City Beach. The challenges we had with City Beach were connecting online to offline, scale personalization, nurture existing customer base and increase average order value. So we launched a new loyalty program called City Beach Rewards for them. Integrated City Beach rewards into in-store POS for end-to-end experience. Delivered personalized recommendations, based on their customer insights from loyal customers. So again, it shows the benefits here of knowing your customer really well. Personalized the omni-channel experience. And predicted customers who are likely to churn and make a purchase and then active and engage. Well, we just saw those very AI segments, how you can build them using predefined templates. Super simple. The impact there, you can see on the right top of the slide, there was a 37% open rate from loyal customers, 4.2% conversion rate from your customers, 18% redemption rate from rewards. And a 48% win back from defecting customers within 90 days with SAP Emarsys AI. So using AI and predictions to understand when people were churning and defecting was the key takeaway for City Beach. The main thing I wanted to point out on this slide was the 48% win back from defections. Like this is, I mean, unprecedented in the space of what AI makes possible. It's so good at synthesizing nuances from customer data that even lost customers can be brought back into the fold quite successfully. And again, just an amazing example with this customer story here. And so I think Alex, back over to you. Well, now that we're all here, I think we can get to Q&A, which is perfect timing. What I wanted to start with, I think this was sent during your session, Matt, if you wanna go ahead and cover. When searching with AI for products or comparing them, which tools are most commonly used? Is it chat GPT or other tools? Yeah, chat GPT stands out as one of the most frequently mentioned tools for product comparison. It's particularly effective for feature brainstorming or roadmap planning related to how these things can integrate into our lives. I've used it quite a bit for that. Perplexity is another one that's also highly regarded because you can look at the citations. We also have Google AI mode, which has been rolled out, I know to some regions at least, which is trying, I think, to catch up with perplexity in some ways. But I'll say my favorite use is in just asking AI to put products next to each other in a series of tables showing differences and similarities and let the AI figure it out. But again, those three, chat GPT, perplexlity and Google AI Mode are what we're mainly seeing consumers using. In some cases, it's right where they already are and things that they're using for other use cases as well. And they just, of course, realize they can use it for product shopping as well Interesting. And I'm sure we'll see so many more use cases and users are gonna get smarter with how they use AI going forward. So that tool set will certainly expand. Another question, we'll go back to Eloise and then Matt, I've got another one for you. But for Eloise, what AI features are the most popular with your customers? I mean, the ones that we show today are the key ones people are using. So the content builders and the, that's been super popular and the product finder. They're the ones people really are running with. I think the key things as well to take away from some of the content tools is the time savings it affords you. Because you might look at that automation that we had and just think, well, we don't have designers, we don't have developers, we didn't have copywriters. How can we even begin to create this? But actually it just does it all for you. And also another thing to mention is it's not just using natural language, it can search tags and ESL. So the prompts don't need to be particularly in depth or concise even. It can actually learn really well with all the information you have in the platform. So yeah, so basically, time savings and time to value and building campaigns to market faster are really the key things. That all makes sense. I think your question is actually kind of related, Matt, so I'm interested to hear your take. But how do you think AI or AI tools are being underutilized by retailers? The main thing we're seeing in the market is ensuring, it's important to ensure that clients, retailers, are aware of the full breadth available in the tools they already have in many cases. You wanna be looking for solutions that have AI embedded. There's generally two categories of pay for credit. If you think of chat GPT just as an example or one of the chat bots, you can hit like a daily limit. That's a certain amount where you're paying a monthly fee and there's a cost, there's the computing cost related to how those things are utilized. When you get embedded AI, things that are just built into the tools you're buying and there kind of sky's the limit, you can find leverage to where you can do things more than just maybe writing creative marketing copy. You can use it for dynamic merchandising, rapid A-B testing and automated assortment optimization. Extended use cases, you don't know where any given retailer is going to find the real results in the sense of there's a lot of levers we can pull and there's lot of distraction I would say with hype around AI, but being able to find and test the right levers for your specific company is really key to making sure that you get more utilization out of the tools you already have and also will highlight where there's gaps in new tools you might wanna try. Some of the most valuable thing to share with customers is research. And when I was on the other side of the table prior to this role, you don't have a lot of time as a retailer to surface the things and the insights you need. So no, we did cover a lot in this research today, but there's quite a bit more and you can download this research report for yourself, I think obviously through the QR code at the top. I would imagine it's probably in the side chat as well here and you can also of course find it on our website. So definitely take a look when you can in between other things you might be busy with just because there might be some things there that give you some new roadmap strategy as you continue through 2025. Well, we'll send you the report. We'll also send you this deck. There was a ton of good content that I wanna make sure we get into your hands. So thank you, Matt. Thank you Eloise so much for sharing today. Everything you went through was super helpful and informative and I wanna thank everyone else for joining the session today. So don't forget to grab your copy of the AI in Retail report and we will see you next time.