AI Decisioning & The Future of Marketing Personalization with Hightouch | Ragnarokast #20

I'm Steven and I'm Spencer. Welcome to Ragnarokcast, your podcast for all things marketing in Martek. Hello everyone, we're the co-seos of Ragnarok. Welcome, welcome. All right. Here we are. Tadius. Great to have you. Great to be on. A little disclaimer that this is not like other podcasts that you've been on. Yes, we will be talking about marketing stuff. We will be talking about AI, we'll be, you know, generally doing podcast-y things, but I mean, you've met Steven in person, so you know, he's a silly guy. I bet both of you also. Yeah, I'm prepared for this. Yeah. It's going to be a little silly, but we think still impactful. Well, also, welcome, Steven, I guess, a. Well, thank you for not welcoming me back. Welcome back. Welcome back. I'll take it. I'll take it. Go birds. Let's go birds. Tadius, we're a few days out here from the Super Bowl, Steven's representing his current city's team, the Eagles, which are, you know, hopefully they'll win the cheat. Come on. We got it. You know, like, it's not that I don't like the Chiefs. It's just the one last year. Let's give the Eagles a shot. You know what I mean? Yeah. Yeah. I wouldn't pretend to be following and I tell them this year. That's fine. That's fine. It's just like, it's more like Travis Kelsey, you know, he's got Taylor Swift and it's just, the Eagles got to get something, you know? And Philly, if you've ever been to Philly, is a sports town. Actually, no, it's an Eagles town. It's an easy set. Yeah. Like, they have the Philly's and everything else to sort of tie them over. But really, it's a, it's a football town. It's an Eagles town. Philly, she's staked. It's not relevant to the culture and stuff, right? It's just football. Yeah. I mean, I don't know like, how often the average Philadelphia actually eats a cheese steak. So even do you know, they all come in from Jersey. So it's hard to say. It's kind of like a, I mean, I'm sure they know the good spots, but I think it's still like out of a touristy thing because it's a lot to eat too. I can relate to that. I'm from Nashville and, you know, everyone asks me, now that I live in San Francisco, I've had Nashville hot chicken, gee, did a ton. It's like, actually, I don't even remember hearing about Nashville hot chicken, or had EBS when I, when I grew up. I think all that stuff's in here when it's really touristy. It was just chicken. It was just chicken. It was just chicken. It was just chicken. Exactly. All right. So Tage, do you mind giving us a little bit of a background on yourself and high touch as the real intro to this conversation? Yeah. Sounds good. So I'm Tage's founder and co-CEO at High Touch. High background is in engineering and product and technical stuff grew up programming and got into like coding and stuff pretty young and came out to San Francisco about 10 years ago and joined a company called Segment, which was a super tiny company that not a lot of people knew about back then, but is now one of a big, big players in the marketing technology space, CDP, custom-ready-to-platform and owned by Twilia. I started High Touch with a couple of friends, Cushion Josh, about five years ago and we have been growing super strong, work with some big brands like Pet Smart, Warner Music, Grammarly, Woop, so forth. And the high level idea is that we want to help brands make all their custom-ready data as valuable as possible to improve marketing, personalization, make it more effective and help them use AI in that process in a real way, right? So no BS, all real ways, all easy to implement, all easy to try out and yeah, we'll talk more about it, but that's a little bit of intro on myself. No, no, wait a minute. You weren't the guy who was telling my friends to tell me that I shouldn't go by in CDP. You weren't that guy, were you? I was the guy who was telling everyone to tell their friends to not buy a CDP, both in person and on t-shirts at conferences, that somehow our marketing team let us print, which I think is pretty cool. Usually the marketing team is supposed to tell people not to do crazy stuff, right? But our marketing team is like, I have to tell them not to do crazy stuff sometimes. They had an in-person, they had a conference, an online in blogs. Yeah, so we published a pretty hot take, I don't remember how long ago, maybe like three years ago, called friends don't let friends buy a CDP on our blog, it's still up if you Google friends don't let friends buy a CDP or probably just like don't buy a CDP or why I'd have a CDP here or whatever, you'll probably find it online. And really it was putting out this thesis that CDP as a concept is really valuable. Yes, marketing teams should have a database with all their customer data and they should have tools to build audiences or journeys or segments on top of it and get it into different different marketing and platforms. Like that is a very valuable concept, they shouldn't be bottled and they could buy data teams or IT teams or not knowing SQL, not being an engineer for every task. But the whole idea of collecting all your data or copying it from different places in your company and restructuring it to fit it into a traditional CDP like segment or ourselves for a data cloud or Adobe RTCDP or whatever it is, there's so many, honestly at this point, is a bit cumbersome and for a lot of companies, they're already investing in these data warehouses like a snowflake or Databricks, Google Cloud or whatever it is for analytics and there should be a new model of CDPs. We call it the composable CDP, that's kind of our original product here at high touch, sits directly on top of the warehouse and just enables those same capabilities and doesn't require a perfect warehouse. We can help you, help you get there too, but there's no need to go create another sort of source of truth. So yeah, that was our original product at high touch, hundreds and hundreds of companies using it today. And now we're thinking what's next and jumping on the AI train here and trying to build some real value for customers. I love that. Do you know how many people I talk to every week or half of them say, we're thinking about a compostable CDP, not a composable, but a compostable one. You gotta throw the lettuce on there. You gotta be environmentally friendly these days, you know. Yeah, actually, I mean, it's important, you know, if we were a public company, we'd definitely have to rename it to composable CDP, I think. Honestly, like, I think if you're talking about, do increase yourself with your marketing team, definitely steal that and do something with it, you know, on earth. Maybe, you know, yeah, maybe April Fool's Day. We never done a good April Fool's joke. There's been lots of good ideas that I've had to shut down. I think they're a little bit risky, but we've never done a good one from a company brand perspective. It would be a good one. You know, iOS frequently corrects composable CDP for me to compostable. You actually, I fix what I type, but when I say it aloud, it's still like, what the heck is this? Like, let's put composable CDP and if I do, it's speech to text and I just embrace it. So, yeah, I don't correct it anymore. I just go with it. I love it. You'll be a good April Fool's joke is, you know, you're like, oh, like throughout a random large tech company that no one's ever heard of saying, we're being acquired by Tullio or whatever. Because like, you see, there was like three CDPs and recently that were bought by random large companies that I was like, I think I've heard of that. I don't know what it is. So. Very reliable. And I think it's more, we're going to be counting what are the CDPs that haven't been acquired. It's inverses. The ones that recently got acquired is, uh, is my take. It's like how like the onion's parent company is global Tetrahedron, which I just think is awesome. I'll act like I'm smart enough to understand what you just said. What? Global Tetrahedron. The newspaper. Oh, yeah. It is global Tetrahedron, which is just awesome. I think I've seen that. I haven't read an actual article from the onion for a while. I do see it. See them in my feed here and there and just read the headlines. It's at the article right there. Before we get into our, our main topics here, something I did a few episodes ago. It changes. I feel like you're a good guinea pig for this, which is, uh, I know. All right. So we've got a challenge for you. Uh, we've got someone outside your door. They have a clown suit and you're going to have to run around the block. We're going to time you know, um, luckily I'm traveling for work. So I don't think I'm untraceable right now, uh, you, that's what you thought. We, uh, so I want to ask you, do you have any hot takes, more tech, hot takes? Something that, you know, it doesn't necessarily have to be like antagonistic or contrarian just to be that. But if there's something that you're like, I don't know if I think the market doesn't agree with me on this, but I feel very strong in my conviction. Yeah. I mean, composable was definitely a hot take and that like marketers, we're going to embrace the data warehouse and actually just use tools built on top of it. It's a super hot take a few years ago. A lot of people ask me like, Hey, why aren't you guys talking about composable all the time anymore? It's like, I think it's not a hot take anymore. It's just about what product does it well, new hot takes. I mean, I would say AI agents is a, is a hot topic right now. Right? Everyone's ever talking about it. Everyone, you know, Salesforce is helping out with that right there talking about agent force left right and center not a lot of people know what it means. I think the concept is pretty simple. It's like, how do we use AI to not just give us the answers on the screen, whether it's a predictive model or like asking chat GPT a question. How do we use it to actually do work that people would otherwise have to do manually? And in some tasks, just at the same scale as people, so that's like customer support and stuff. But I think in marketing, it's like, how do we do that in a much greater scale than people and give, give marketers a lot of leverage, which is super interesting. My hot take on it would probably be that in the domain of marketing, I think the most popular AI agents will actually use traditional machine learning and data science and reinforcement learning more than they'll actually use this generative AI wave we're seeing with large language models and tools like chat GPT. Obviously that stuff's super valuable, but I actually think the first generation in first class of AI agents that will really take off and do things like marketing and orchestration and journey building, stuff like that will actually use reinforcement learning and more data driven machine learning technologies versus you know, operating at the language level. This all sounds very familiar to you, just it sounds like you're putting your money where your mouth is on that hot take. Yeah. It is a product of ours that we recently put out, call AI decisioning, it's kind of built on that hot take, I may say, but the idea is that, you know, in the future and in the present, I think for a lot of campaigns, it doesn't really make sense for marketers to be having to not just define this strategy for the campaigns, I think they need to do that. That's great. They need to work and define the continent creative, but a lot of time, a lot of people on the marketing team is spending that can really change the efficacy of the campaigns is building out these very specific audience segments of who to send this content to or building out a calendar of, you know, how you should spread out your content, which is very, very common in retail companies over the next weeks, months, quarters or building out journeys in a journey builder to say exactly how many times you should follow up with content A versus content B. And what we're finding is that a lot of those tasks can actually be automated with reinforcement learning where you tell an AI agent to give it some goals basically, like, hey, I want to drive, you know, for my batch and blast marketing calendar, I just want to drive clicks and I want to drive conversions and just keep optimizing to figure out what does that best for a use case like trying to help the customers cross sell into a new product category. You can, you know, specify a goal around cross cells and wait it on which cross cells are most important and basically give the AI some boundaries, like don't email customers more than X factor and unsubscribe rate and basically have it, you know, do automatic experimentation and figure out what types of content follow up, frequency, timing, all those dimensions that marketers have to think about themselves right now and do guesswork or do an A/B test to understand what works better. Do that all for you and get smarter and smarter at optimizing your company's marketing over time. So we've packaged up some of the technology that powers things like TikTok's feed where, you know, flip through and you get a really personalized feed for you and it's just getting smarter and smarter to personalize it for you in a kind of scary way. We've personalized over that technology and packed it up in a nice UI and platform for marketing teams that sits directly on their data, wherever it is, like a snowflake, etc. And or a CDP and also plug it directly into their marketing channel tools and just allows them to really build these one-to-one journeys with AI. When you're sitting in front of your toughest customers like the CFO of a large brand, they say, wow, this all sounds great, but how does this, like, you know, we have, um, insert CDP name already or we have this in-house thing or whatever or maybe they don't, but just how does this make me money? How does this save me money? Well, you know, what are your, when CFOs are throwing these like very revenue and efficiency oriented questions at you? What are like the biggest value ads, uh, from a, from a dollar perspective? Yeah. Actually, there's kind of another hot take embedded in this one, but I think those CFOs and even a list of a lot of discussion about AI right now, like if you want to get pressed on AI, you'd probably need to talk about how it's helping you save money or cut jobs or replace human work. Um, I think AI decisioning is interesting because, you know, that obviously we do save marketing teams from having to do some of this tactical monotonous work, but it's not a majority of the time marketing teams are, are spending because you just can't do this stuff efficiently as people. So we end up doing it inefficiently by building these one-off journeys and not rigorously experimenting them until they're perfect or building these calendars and just calling that the calendar for the month or calendar for the quarter. So really the goal of AI decisioning isn't to save work. It's a part of it. It allows you to be more strategic, but it's to make more money. Um, it's to, to, it's under the notion that you actually have really good content as a brand most likely, but you're not using it super effectively. Like you, you've created all this content over the last 10 years and like, you just need to send it to the right customer at the right time, uh, and actually factor in everything you know about a customer and how they responded to content in the past to give each of them a really effective individualized journey. And what I like about it to you is that we can actually prove the efficacy of the, of the technology. So some of the, one of the things that we've baked into the product is the ability to pick a part of your marketing, whether it's a cross-sell initiative that you have at your company. So I'm trying to get customers into the store more or to use a new product on your, you know, multi-product mobile app or you can pick another initiative like converting your leads database into signing up or, or windbacks, whatever it is. You can actually do a whole lot of tests, uh, or, or an A/B test. So we can just split, you know, split the audience, take, take 10% of it. For example, don't send any marketing to them or still send your traditional marketing programs to them. And, and then compare after some time what's actually the performance of AI decisioning versus the traditional marketing. Now this, this can be, this is a good way to get started and see like, is this driving real value? What's the best way to use this technology? Well, I think the future is probably not running these A/B tests for every single, uh, marketing program because it's just tedious to set it up twice, one via AI and one manually. Yeah. You, um, you know, I think one of the big pieces of this with anything with, with agents at craying like that, that's using more, we'll say, statistic, statistical level, uh, or have, has more statistical rigor than maybe machines are a little bit more bound to use statistical rigor than humans are or as humans have a little bit more intuition, right? And there's obviously some limitations to this, like, for example, if I wanted to launch something new, is agentic AI like the path I would go down to launch something new? Or is it more like, you know, as a marketer, I should be focusing on the new things and then, you know, spend the portion of my time letting the, like, setting up the agent, so to speak, to work on the stuff I've already launched. Like, at what point, where do you feel like the, the agent will be the most helpful? Like, should it be, you know, like, I think back to like, you know, 10, 5 or 10 years ago when everybody bought mixed paddle to look at, you know, a, uh, to look at the people who like weren't going down the conversion funnel and then like digging into literally everything that they did. Like, that was like the marketer's exploration path and it kind of caused this like, well, not cause it was a good thing, but it like had people think more intuitively about what is the real customer journey and not like the one that I predict people are doing because they click off my email, then they go do this thing that I told them to do and then they do this and they convert, right? It's actually a lot, it's much massier in between the different ends. And so do you think the, I don't know why that's such a tangent. But do you think the, the, the agent is going to be better at like the, the mixed panel side of the house, which is like, here's an interesting thing I found that we could, we could go and build a program off of, or is it going to be a lot better at, you know, here's something you've already established. Let's, you know, optimize that and squeeze another two, three, four, five percent incrementality out of it. Yeah. It's a, it's a good question. I think at a high level, what, what we're, what we're talking about here is what are the right use cases to apply a technology like AI decision in a marketing program and where will we see the most efficacy? So this is something I've actually been thinking about a lot. Um, I think there's basically three kind of paradigms for marketing orchestration, a lot of different terms, a lot of different tools. But I think it all buckets in these three things. One is just like batch, batch and boss communications, which, which for what it, it's worth, aren't bang. Um, I'll talk about that in a second. Uh, but batch and boss communications, which is basically, you know, I'm going to send out a calendar of communications to my customers. I'm going to be proactively paying them, even though there's not a lot of intent or anything like that. But I just want to stay top of mine. And that's important, especially for like a retail company that, you know, may not have the frequency of interaction with their customer without, um, something like that. And second is like triggered communications. So these can be built in audiences, like an abandoned cart audience, right? Customers who added to make the car didn't check it out. It can also be built through like journeys, oftentimes, like, let's not just ping them one. That's when they didn't check it out. It's pinged multiple times on, on multiple different channels and kind of followed them until we really drive a conversion. But they all start with a trigger. So customers who added something to the cart, customers who finished a purchase, et cetera. These are actually not too bad, oftentimes because while you have the guests of the steps of like, you know, what you are emailing the customer or texting them, like, it all stems from some level of intent. So it's likely going to be much more effective on a, um, on a percentage basis than something like the batch and blast communications. And I think a lot of brands aren't yet capitalizing on those. And then lastly, uh, is this new paradigm that we've introduced with AI decisioning where, um, you're actually not building out a triggered sequence and not building out a marketing calendar, but differing to AI to figure out what's the best way to orchestrate this content for my business goals. With batch and blast communications, I actually think there's a lot of opportunity for AI decisioning there. And a lot of value to be created, not for every campaign, right? You're always going to have your big brand push, which you're not trying to optimize conversions on, frankly speaking. This would be like your new arrivals, like your merchandise, that type of stuff, yeah. New season, you know, huge new store in New York that you want to tell everyone nearby about like, there's just communications where you're not necessarily going to, yeah, you're using as a brand channel, using as like almost advertising and media, even though it's on email or text. We'll have those done manually. We'll put them on the calendar, keep them as is, but what do I send to like every other day? Well, what I see right now in the status quo is that we create a lot of new campaigns to kind of stay fresh and stay top of mind, push those out to customers, but we're not using like everything we have about, you know, what's worked in the past for a different customers. We don't even know because we're not running experiments right as marketers all the time of the batch and blast campaigns. It's super tedious to do and we're not using all that info of what's worked, what might resonate with different customers to decide the calendar, right? We're just kind of building a calendar a lot of times while high agents aren't going to make batch and blast, you know, marketing, have a 2% conversion rate instead of a 0.5% conversion rate or something like that 0.03, I think 0.03, yeah, probably they can increase the conversion rate significantly, right? And if the volume's high enough, it can make a real difference, not by making it like amazingly personalized in this one-to-one way and predicting rights on the mind, right? What's on the mind of your customers? It's not possible for a lot of brands, but just making it incrementally better in a lot of small ways that humans wouldn't do otherwise. CTA changes for different customers, tone changes for different customers, automatically incorporating things like product recommendation type insights into the mix. So some of this is getting into a little bit of generative that when you talk about tone changes and things like that, or is that more- We're not doing the generative ourselves. I said management. If you put in a bunch of content of different tones, we can, you know, we actually do use Genai to scrape that out and generate more features on that to say content of this kind of tone works well for customers, if that makes sense. But it's like, it's pre-written in this case. Like if you have seven different segments, you would have seven different versions of that tone or versions of that copy. Exactly. Except you don't need to think about the segments. You can just say subject line, here's seven different ones, AI decisioning, go figure out, which ones are not good at all, like they're just not good ones, which ones are good for certain customers, et cetera, and we'll just continuously optimize. So just allow as markers to throw a ton of content into the system. And then AI decisioning is actually running continuous experiments on your Bash and Blast program, which is something not a lot of brands do, right? You'll have global holdouts to understand the program's working at large, but what specifically worked to last quarter versus this quarter, I don't know. So AI decisioning constantly running experimentation. And then versus traditional experimentation, we're not just picking one winner, we're kind of seeing if there's patterns so that there can be a winner for certain customers, a winner for other customers. Honestly, on the Bash and Blast program, what do we found is that there can just be a lot of long tail optimizations that add up that don't make your Bash and Blast program like crazy amazing and write what's relevant for the customer, not possible in a lot of context, but makes a significant difference and can drive significant amounts of revenue. And we've shown that with pretty large brands, like a Fortune 500, kind of specialty retailer. So we've talked about, you know, the CFO, who is the, if not the decision maker on what tools to bring in is the decision maker on if the contract is signed or not generally. And we've talked about how these features will, it's almost like adding a new layer. Like, and I saw your article the other day too, but it's like, there's AB testing versus AI decisioning. And it's like, it's almost like another, it's another way of doing it, rather than it's an alternate method, basically for our marketers out there. Who are the ones that are really, ultimately, really interested in high touch and the ones that are going to be saying, hey, come present to my leadership. How is, you know, AI decisioning specifically, how is it going to change their day? The day over the next few years? Yeah. So the way I think about it is that one, it makes your program is more effective. I think you need that so you can pitch your CFO, not just as it makes them more effective. If a CFO asks a marketing team today, like what out of the campaigns we sent last quarter really worked, like what changes have you made that are really incremental? Those are like hard questions to answer today. With using AI decisioning to orchestrate your campaigns, you just are answering all those questions by default. You have a ton of insights that can help you build better campaigns in the future and build better content, but also helps you present internally on what's actually working, what's effective, and just look like a way smarter marketers, be a way smarter marketer. Second, I would say, changes to workflow more materially is today at a lot of enterprises, what we see is that there's a lot of people that need to coordinate together to get every campaign out there. A lot of CRM marketers that I speak to on a database almost feel like they're just rushing to meet deadlines on this marketing calendar, especially for batch and blast programs where just the expectation is really high to get new content out there all the time. And really what AI decisioning allows them to do is take a little bit of a step back. Be more strategic. Reuse a lot of the content they've sent years ago. A lot of people didn't open it anyways, if you can send it back to them, add content to the system that is based on seeing insights like the CTA really resonated with customers in the past and actually add content when it's interesting, when it's valuable, and instead of thinking a lot in terms of how should I balance these communications out, et cetera, and just throw more content into the system, more creative, more campaigns into the system, and let the AI system actually decide who to send it to. So it really just allows everyone to set a level higher up in the stock than they previously did. And instead of thinking about going into a braze or iterable or sales for us and configuring every specific journey or deciding exactly, should I send this email on a Tuesday? Because Tuesday overall has the best efficacy of my email, so I should send my best email on a Tuesday. I've seen this at Fortune 500 companies, right? We're going to send our offer email on Tuesday because it's going to be at like 2.30 PM too. Exactly. Instead of thinking through all that, like put the God in the system, we'll figure out that it's that the promotion offer you're sending out is extremely effective and we'll send it to customers on the day that makes sense for them and yeah, a lot of them will go out on Tuesday. Maybe most of them will go out on Tuesday, but a good chunk of them will go out another day is maybe 40% of them and in your campaign will be automatically more effective and you don't need to think about it. Really it just takes every job from the people creating content and creative that didn't have a lot of insight into what was really working before, gives them insight, allows them to be more strategic from CRM specialists who are in tools configuring campaigns instead of configuring journey sequences and multi-step sequences and where we all know we're kind of guessing to an extent. It allows me to instead be looking at insights, changing goals to tell the AI how to optimize differently. Maybe let's double down and cross cells. Let's go ask my data team if I can get another feature into high touch so that it could look at that, like weather data, right? We've seen customers incorporate things like weather data and just be more strategic and be more like things about data instead of just thinking about knobs. And then at the highest level, like, you know, the lifecycle marketer isn't the directors of lifecycle, don't have to be asking if we can experiment on, you know, a versus b, the system does that. They can think more about campaign strategy, what type of content we should create, what type of programs we should run as a marketing team at large. The way you're sort of talking about the goaling and the optimization are, is this particularly looking at it at like a campaign by campaign basis? One of the things that we've seen in a lot of our experience with our clients is, you know, one of the challenges a lot of lifecycle marketers have or even analysts have when they're looking at the program is they're looking at it with too much of a lens on, was this campaign better than this campaign? As opposed to is the program as a whole from a business perspective doing better, right? Am I shifting, not just shifting my, you know, attribution over to my email or my SMS or my push. I'm not also just pushing a purchase forward that probably would have happened anyway, but I'm truly incremental on the business side. How does the agent understand that what it's optimizing to is actually incremental and is not just the way that we've been doing it forever, which is, is this campaign better than this campaign? Yeah. Really good question. So, the only way to really understand incrementality, you guys debate me if I'm wrong here, you're the experts, but it's experimentation, right? So it's to actually run an experiment where you do something on group B, you don't do something on group B or you do something different on group B, you compare the results. We really do that to levels. One, if you're just trying out AI decisioning versus, you know, historical campaigns or journeys you've created, we can do a split on your audience of customers and put some of them through the traditional path, put others of them through the decisioning and compare the lift to see if AI decisioning is actually being incremental or just doing what customers would have done in the journeys and audiences you built and your email tool will work. And the second thing is that AI decisioning, when it uses reinforcement learning, kind of has experimentation baked into the system that we can constantly be experimenting and figuring out if certain variant or certain content or certain campaign tactic is better for different customers and serving those insights to you. We can always tell you, you know, what would be the outcome if you did something different? It's way too difficult and not possible, but we can tell you, like, what's actually better? Like, is it, in what cases do customers prefer to receive emails on Tuesday when say, for example, is it a correlate to gender, is it correlated to, to age, is it correlated to you? FICO score or hit what's the correlated to that's in your, in your data warehouse? And this is all, they would be having, they would be at the same volume, ideally, right? Or whatever's in your control piece here, your AI decision treatment group, like both would be sending five emails in total or four emails in total, or do you see that AI decisioning might actually send less or send more, depending on how it's optimizing? So sometimes that's the question we get, right? Like, oh, isn't, you know, is an AI decisioning just sending more emails? And that's why it's more effective. And I would say that's one of the value props to is that we can figure out how many emails we can actually send a customer about a certain topic without them unsubscribing. So no, it wouldn't necessarily be the same volume. One of the value props that AI decision can figure out, like, yeah, how many emails should you email a customer about X or Y or Z without them unsubscribing? So you can actually have unsubscribes and opt-outs and any other negative signal in your business as an negative reward in the ML bottle as well. And track that. You know, yes, sometimes more emails is more effective, but if you really measure these over a long-term basis, you can see if it's actually more effective. It's not an easy challenge. Like, if a Mario mouse was all we needed to do and was easy and we weren't worried about unsubscribes, we'd all just send twice as many emails today, but obviously we are worried about unsubscribes. So that is actually one of the value props of AI decisioning as well as to figure out that kind of optimal balance. But within some guard rails, so that doesn't go crazy experimenting. Right. With our remaining 10 minutes or so here, I wanted to bring up one more buyer, so you're going to TFO, the marketer, taking a step back, you know, there's also times where, you know, the CTO or the data team or even the IT team are the ones that are making the decisions or have the budget or whatever. And so we've talked about the, I guess, the campaign layer, but taking a step back, like, how does the, in AI tools and AI decisioning, how does the, you know, the data warehouse or whatever, a cool term, I know we're not supposed to call it data warehouse anymore, we're still going to close it down, it's called the data warehouse. And the, you know, how does the data warehouse come into play in AI decisioning? Yeah. So you're totally right that especially with anything relevant to AI data, but especially AI, the technical teams aren't also going to get involved in this. And it's for a good reason. I think there's a lot of ML and AI features and a lot of different, you know, ESP solutions are marketing solutions that advertise themselves as they'll just work in a click of a button. And sometimes they do work, but sometimes they don't work. And there's nuance to that, for example, what's the underlying data that the models built on. And those aren't always advertised for an incentive when you hear, when you're buying these solutions. So it's good that the technical teams get involved in my opinion. And one of the things that the technical teams really like about AI decisioning is that what are, what are data teams spending a lot of the time on an organization? What do they want to spend all the time on? It's they don't want to be heading people reports all day. That's why we have self service BI tools. That's why they like to reverse ETL or activate data from the warehouse into marketing tools like they want the business to self serve. What they actually want to spend their time on is building a lot of interesting data products in a repository of data, interesting attributes about customers in a predictive LTV model. That's what data engineers data scientists, data analysts want to spend their time was creating really interesting insights about customers that then the business teams can use. And AI decisioning sits directly on top of a company's data warehouse like Snowflake, Databricks, School Cloud, we don't care which one, where the, where the data teams are creating these insights. And that means two things. It means one, the data teams, IT teams are always like happy about it because they don't have to move the data into another system that's going to force the data into a different format and limit the potential of these ML models. They're going to get control of the data that's feeding into them right there in the warehouse to you. It actually means the models perform better than a lot of out of the box models that are created in a tools directly like a Salesforce or a different email platform and not because the ML technologies those email platforms use aren't good, they're good technologies, but what features are fed into them, how data is feeding into them. That stuff makes a big difference when it comes to any sort of ML and AI technology. In AI decisioning as a product, both are usage of reinforcement learning. So the type of machine learning that actually experiments and tries things with different customers and learns from that. That's a very, very, very critical component that allows us to automate customer journeys and automate marketing calendars. It wouldn't be possible with just predictive models or predictive audiences like you see in a lot of marketing tools. That's really important, but the second thing that's important is that we sit directly on the data warehouse and get direct access to you to the best data and can even incorporate data science models, product recommendations for Penn City scores if your data team is advanced enough to be creating those today. The other hot topic right now that an AI, at least in MarTech, is a CDP, CDP is getting a bad rep for various reasons, at least traditional ones. So how does a CDP play a role in this AI decisioning and data warehouse, I guess, relationship? So CDP means a lot of things to a lot of customers and people out there, but one of the things that it means, especially in traditional CDP context, like if we take a segment, for example, one of the big value props is helping you collect a lot of digital data about what your customers are doing, interesting attributes about them, different pages they're visiting on the website. We can do this as well in our high-tech events product. Anyone can do it. A lot of companies can do it. Honestly, that's a great foundation of interesting data to feed into any ML model, including the models that we create in AI decisioning. If you just have a table of contacts and some basic attributes about them, that's all AI model can be based on that and how they respond to your emails. If you have interesting data from your website and mobile app and stuff as well and clicks and carts and all that kind of stuff, we can bake that into the decisioning of what to send customers. The more data you give the model, the better it performs. That's kind of the whole idea behind JGPT and a lot of the AI ML stuff these days. And CDPs have a really good source of data to give customers in this case. And it's nice when there's a little work that's gone into organizing it, it's not a prerequisite or working with customers who don't have a CDP, use high-touch as their composable CDP obviously taking this to a lot of our existing customers, use something like a Salesforce data cloud or whatever it is. But if there's any solutions upstream of us in the stack that provide good structured data, that makes everything easier when it comes to AI. A lot of the applications you've talked about seem to be skewed towards B2C, but mentioning sales force. Is there also a B2B application you think for AI decisioning or is the volume large enough to really be successful? I'll answer in two ways. Right now we're super focused on B2C and like anything where you have a high scale of consumers that I'm talking about, at least 500k customers, but probably millions of customers to be honest. And you want to optimize how you're interacting with them digitally on some sort of own channel or a channel where you can address them one-to-one. So email, text, push, web interactions, et cetera. We're expanding our outlook to paid media as well and CRM, advertising. That's something that we're going to work on this year. But we are staying away from like sales and also some of the B2B's cases right now. Unless you're like a super product-led growth company with a huge customer base of individual users like Notion or Grammarly here. Companies like that are great, but they're not the typical B2B company if we're honest. And I think there are very interesting applications of AI in B2B and in sales and in everything I mentioned. But because of the volume of interactions, because a lot of the interactions are not digital, a lot of times, because you often want to optimize on the account level and how the accounts are progressing versus the user, there's a lot of nuances that I think the technical solution might be quite different and are kind of straying away from it right now. I could see there being like maybe a use for the companies that have like self-serve models like Cliveo or MailChimp Shopify where that is quite like even though it's B2B, it's kind of B2C and US like SMB, B2B, high volume B2B, we can do that for sure. Like on the CDB side, for example, we work with some banks that serve SMBs and there's just millions of them and they basically operate consumers at some point. There's like a business owner that's managing the relationship of the bank or maybe plus one person. Quick books, even if you click QuickBooks Online, it's like that. TurboTox, I guess is B2C as well, but yeah, for sure those types of businesses are fair game and can operate the same way and it oftentimes use the B2C marketing tools, right? A lot of them use Salesforce marketing tools and iterable and braze and those tools sometimes in parallel to like their Marquetto and their ABM motion for the upper tranche of accounts set. Companies using the B2C and our tech technologies where we're focused right now, you know, there might be a company that comes out and just does some really cool stuff in AI for the B2B side. I think there's a different set of interesting problems there. We'll tackle it in a couple of years, but this stuff's hard, so we're staying focused. There's quite a, quite a green field here for you to tackle. You got a lot of ground to cover, so I think B2C is focusing on B2C is okay for the foreseeable future. I agree. That's probably, you know, if that's what we call focus and we have another conversation. All right, guys, well, we are at the one minute to the end here. It's wanted to thank you, Tages, for coming on. Yeah. Tages. Go birds. Go birds. That's go birds. Do you guys have anything that you wanted to impart before we leave or, you know, closing words, final words? Last words. Last words. I talk a lot. Last words. I talk a lot. Steve, and you have to say something crazy. Okay. I think from my perspective, and I, you know, when I got to talk to Tages about this late last year was, you know, this is something that companies have been building or trying to build internally for many, many decades before, not decades, but years before, like this. There has a 20s. 19, 20s. They're like, ah, I'm saying. It's underneath. It's underneath. Right. It's underneath. Right. Over the last decade. And, I think one of the interesting things is like nobody's really ever like thought like, man, we should just build a product that does this, right? Like, I think some CDPs kind of touched into it, some ESPs kind of did. But nobody really like built, you know, a system that just be the AI layer, right? And, you know, because they either they wanted to own more of the vertical or they were like, I don't know, I really don't understand like why it wasn't built before was always something you needed a large data science team to build. So, you know, Tages, I think just like in from a business perspective, it's vastly interesting that you guys one identified like this as an opportunity and two, like solves a pretty big gap in what you would be from a personnel perspective to do it yourself, right? And so I think you've got a nice carve out here where like, which once you're in, like, I can't imagine unseeding yourself, right? I mean, maybe they'll be competitors at some point, but like the only other way to do this is like higher of 20 to 40 data scientists, right? Like it's not easy to do. And I think it's just really interesting that you are bringing this and making it more attainable for marketers to actually like make their find ways to actually make their program improve without needing to either hire more bodies or hire a completely separate function. Yeah. And actually usable by the marketing team Steve, right? So, there you go. Yeah. All right, guys. Well, that's all for today. Tages, again, thank you so much. Steven. Thank you. So, yeah, and welcome back, Spencer. Where did I, oh, to the podcast? To the podcast. Okay. Thank you. Thank you. No one welcomed me. What the hell? Thank you. Thank you. All right, guys. Bye.