#282 Chris O'Neill: How GrowthLoop is Using Agentic AI for Real-Time, Personalized Marketing

Marketing is changing forever. In this episode of Eye on AI, host Craig Smith sits down with Chris O'Neill, CEO of GrowthLoop and board member at Gap, to explore how agentic AI and GrowthLoop's Compound Marketing Engine are transforming the way brands connect with their customers. Chris shares how GrowthLoop applies AI on top of modern data clouds like Snowflake, BigQuery, and Databricks to automate audience targeting, personalize campaigns in real time, and accelerate experimentation loops. He explains why speed and iteration matter more than ever, how companies like Allegro doubled their return on ad spend with GrowthLoop, and why the future of marketing belongs to brands that embrace agentic AI. If you're a marketer, technologist, or business leader looking to stay ahead in the age of AI, this conversation is packed with practical insights you can't afford to miss. Stay Updated: Craig Smith on X:https://x.com/craigss Eye on A. I. on X: https://x.com/EyeOn_AI

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Speaker 00

  If you think about what's happening with Google News, Spotify, Netflix, these recommendation engines are getting so much better. And why is that? Well, it's actually not just old fashioned machine learning and reinforcement learning. It's actually artificial intelligence in combination with machine learning. Humans have a very important role. They'll be the gatekeepers. Ultimately, these tools are in service of the relationship between consumers and brands. So getting the data in the right place, investing in a culture of experimentation, making it okay to fail, and velocity is probably even more important. It's always important in business to move quickly and in the right direction. But now with the models changing so quickly, the underlying technology, you gotta move fast.

• 0:39Craig S. Smith?

  Build the future of multi-agent software with AGENCY. That's A-G-N-T-C-Y. The Agency is an open source collective building the Internet of Agents. It's a collaborative layer where agents can discover, connect, and work across frameworks. For developers, this means standardized agent discovery tools, seamless protocols for inter-agent communication, and modular components to compose and scale multi-agent workflows.

• 1:16Craig S. Smith?

  Join Crew AI, LangChain, Llama Index, BrowserBase, Cisco, and dozens more. The Agency is dropping code, specs, and services. No strings attached. Build with other engineers who care about high-quality, multi-agent software. Visit agency.org and add your support. That's A-G-N-T-C-Y dot O R G.

• 1:49Speaker 00

  Hi, Craig. I'm Chris O'Neil. I'm the CEO of Growth Loop. I also serve on the board of Gap and been really fortunate to have a great career in technology. Big companies like Google, smaller companies like Glean, I've started a few things of my own. So I'm delighted to be here with you today.

• 2:05Craig S. Smith?

  Can you tell us what allowed you or drove you to leave Glean and become CEO of Growth Loop? And just give a quick thumbnail of what Growth Loop does.

• 2:21Speaker 00

  Yeah, so Growth Loop applies Agentic AI on top of a data cloud like Snowflake or Google BigQuery, Databricks, whatever data cloud or lake or warehouse you choose. And the whole point of that is that you as a marketer or a data team can drive growth faster. And we can unpack more specifically what that means. But this is a company I followed, invested in a while back. I was fortunate enough to partner and work with the two founders of the company, Chris and David, dating way back to my time at Google, when they were looking at Google and seeing that there just had to be a better way to do marketing.

• 3:05Speaker 00

  And this is a company that was bootstrapped from the very beginning. It's not raised a dollar primary capital. Google was our first and our largest customer to this day. So it's been a fun ride. I've been investor advisor, joined the board, and most recently was asked to lead the company. So it's been a fun ride and Growth Loop is really at the right place at the right time, really trying to help out marketing and data teams solve some really complicated problems about how to get, how to get personalized relationships with their customers up and running much more efficiently and effectively than has been possible before AI.

• 3:42Craig S. Smith?

  Yeah, can you tell us, tell listeners how Growth Loop works? I mean, it started as a From the notes I've seen as a composable customer data platform. First of all, what is that? And then how's it grown since then?

• 4:02Speaker 00

  Yeah, so all these terms that get applied, like that's not really what the company cared about from the beginning. The company really cared about, but just very specific business outcomes. So it started off trying to figure out how to reduce churn or make it much easier to acquire new customers. Right? So it was very much from, hey, what business problem are you trying to solve? And then it was a necessity or luck or good fortune was built on top of data clouds. Right? At the time, there was this belief that of course, customers are important and therefore customer data is important. Therefore, we should build these separate pools of data on customers. We should copy data all around and really, you know, queue up a bunch of workflows off of that. As it turns out, again, out of necessity or good fortune.

• 4:49Speaker 00

  The company built on top of data clouds from the beginning, right? Not trying to have a separate pool of data, but to say, hey, smart companies in the future are going to put all their data into data clouds. That's why we've seen the rise of Databricks and Snowflake and Google's cloud business. So number one, it sits on top. That's the concept of composability. It's not some monolithic trying to do all things all people. It sits on top of the data. And then it's an intelligent layer on top of that. It basically applies AI different steps of a growth loop. Or you say, hey, I want to try to maybe acquire a new customer. Or maybe I want to make an existing customer more loyal. I want to expand the number of products or relationship in some meaningful way. So the AI basically sits on top of the data and understands the data first and foremost.

• 5:34Speaker 00

  And then it suggests ideas, experimentation to say, hey, maybe you should try to offer Craig this. Whether this is a specific service or a product. And then it launches that, learns from that, and then iterates quickly. I'm simplifying a great deal, but ultimately it's about how do you reduce the complexity? For in this case, a marketing team trying to partner with their data and technology team. So they could get through iteration loops or experimentation. And lips faster. So you actually learn better and faster. And now AI does a really nice job at many of those steps, not all of them yet. But in the future, I'd imagine that that will very much be the case.

• 6:12Craig S. Smith?

  Yeah. And you said at the beginning that it's agentic. So it does it. Do you have a conversational interface? Where people can can say what they want in natural language. And then does the AI layer? Find the data, compose the email or the the online campaign or whatever it might be and then execute that?

• 6:40Speaker 00

  Yes, it can. So so a lot of questions in there. It is an interface that you'd recognize for now anyways. Like chat, I think is an imperfect. It's an imperfect interaction mechanism, but it's the best we've got. We could talk if you're interested in terms of future design mechanisms, but absolutely starts. It starts with asking a very simple question. What are you trying to accomplish today? Again, trying to increase lifetime value of a customer. You're trying to reduce churn. What are you trying to do? Trying to grow? Grow category sales, whatever it is. That's the first question. And then the first agent goes to work to understand the query, understands what you're trying to accomplish. And then it starts to wake up the different agents and says, okay, the data agent is going to say, okay, great. Based upon the data schema, the underlying data in the data cloud.

• 7:26Speaker 00

  What are some ideas to augment the human idea generation facility? Say, hey, look, in the past, when you're trying to do a similar thing. Here are three campaigns or three specific segments that seem to work. Would you like to build on those? And so suggesting some ideas. From there, you can basically say, yeah, I want to work on that segment. Then you say, okay, great. What journey do you want to take them on? So in other words, what do you want to offer them? Is it an app? Is it an app experience? Is it an email? Is it a paid advertising? Whatever it happens to be in terms of the actual journey itself. And then it does indeed coordinate with all the different destinations, hundreds in fact, through APIs to basically say, great.

• 8:11Speaker 00

  These different channels, let's just go get it done. Okay, removing complexity, which is currently handled typically by teams, by channel, by tool. Alright, so every one of these steps that we're describing are manual and they're time consuming and they usually involve interaction across multiple different functions, usually a data team and a marketing team that are going back and forth. So this can often take weeks, months, gosh, sometimes longer. And really, that's just, to me, is unacceptable. So the problem is like, how do you reduce complexity? How do you increase the speed and reduce the manual nature of any one of the tasks, including the handoffs from one, one step of the process to the other? So that's really what this is about and agents increasingly are at every step.

• 8:56Speaker 00

  And as I said earlier, some of the agents are really working great. Others were really experimenting with right now. Because the models are getting better, but they're not quite there. An example of that would be like generating creative briefs and the actual images and the copy itself, like that's pretty good, but it's not all the way there yet, if we're really honest. The other agents, choosing who to focus on, we're getting really good at that. And that's been our bread and butter from the beginning to say, hey, we're about who is it and then how can we orchestrate this conversation? We're really good at that right now and it's just getting better at a, at a really pretty impressive rate and not, not necessarily just due to our good work, although I'm very proud of the team's work. It's actually the underlying models.

• 9:41Speaker 00

  And we're comparing and contrasting all the different L L Ms to see which model is great for which task. And of course, there's cost effectiveness and security and all these things that factor into that decision too.

• 9:53Craig S. Smith?

  Yeah. And how, how do you reach out to, to customers of, is it, do you have various channels that then the AI or whoever is managing the AI can choose between? This is something I've been interested in for a while. I mean, how do you reach the customer and their? I remember talking to a company that's based out in Bozeman. I can't think of its name right now. They're an app, an AI ad company. But they would, you know, they certainly had direct email. But, but they could position ads that would

• 10:38Craig S. Smith?

  reach the, the customer through social media or through uh some other maybe uh Google. I don't really understand the tech behind that, but you know. So, so how many different channels do you have?

• 10:54Speaker 00

  Hundreds, hundreds and hundreds and, and even more if you think about the extension and reach through networks. So that's, um, I think that's table stakes, right? So the connectors or the destiny, we call them destinations or sometimes we call them surfaces. Right? The services are really up to, to the, the marketing team themselves. So they decide, uh, often it's just an email or an in-app, uh, mobile experience. But it, but it really is any permutation of any of the destinations that are relevant to their customers, whether it's paid advertising, whether it's social, whether it's in-app email, push notifications, it's any or all of the above. So that's pretty much table stakes uh for what we do.

• 11:39Speaker 00

  And you know, of course, those channels grow and contract and collapse, but the real benefit isn't just the reach of all them. I think again, that's table stakes. It's really understanding. What's the sequence, right? What's the timing? Ideally, you want to engage an existing customer on your own channels, your own in-operated channels, right? So we're seeing a huge business uh with, with retail media as an example. So the Walmarts of the world, um, increasingly Costco and Gap and all these other companies, these big brands that have relationships with their customers. And they're actually facilitating advertising revenue from their partners that want to get in front of their customers. So it really depends on the context, but really table stakes is to have that reach.

• 12:23Speaker 00

  The real benefit is the intelligence to say, hey, what is precisely the right place? In the right time to have a message that would resonate across the entire life cycle of that customer. So that again, it doesn't feel like an interruption or doesn't feel like something that is, you know, antagonizing them, right? Often, you know, these experiences more often than not are just kind of kind of awful, really. And it's, it's, um, we're out to, to, to stop that, right? And if, and if you're offering something at the right time, in the right context, it can be a delightful experience, right? And we're trying to aim and help, um, enable teams to do that more often. And that's really how growth is going to happen, right?

• 13:05Speaker 00

  You're going to have more loyal customers that care about you because you're actually, it feels like you're listening to them. As opposed to just bombarding random, not relevant messages to them all day and every day.

• 13:15Craig S. Smith?

  Yeah, and, and the, the, the classic, uh, example of, of, uh, that, uh, you know, online marketing that drives me and I'm sure everybody crazy is, you know, I do a search for outdoor umbrella. And, uh, you know, I find one and I buy it. And for the next two weeks, I'm getting ads for outdoor umbrellas. And, you know, I only needed one and you don't need to keep serving. How do you know when someone has responded so you don't, uh, keep serving them the same marketing, uh, over and over?

• 13:53Speaker 00

  Yeah, it's a great example and that example repeats itself in lots of different flavors all over the place. But this is, we're going to talk about the importance and the paramount importance of data clouds. Right? So when you have these siloed data pools, that's how you end up with an experience like that. We should know, a brand should know that you bought the dark umbrella. In fact, all brands should know that, but that's a separate matter. But how it works is when, when you have purchased that, the transaction data sits alongside the customer data. In this case, so no, you purchased that product. And guess what? You will not receive that ad. As quickly as the database can be updated, up to and close to now, real time, right? Like we're literally in real time, we're talking about seconds.

• 14:36Speaker 00

  So that's a very good example. You'd want to suppress that for obvious reasons. There's a whole bunch of other reasons you'd want to suppress things and you can do that in automated ways for compliance reasons. Right for all sorts of like privacy reasons, like there's a whole bunch of things and it's really difficult and manual to do that now. And it doesn't need to be right. That's what we do. We basically allow you to basically have context. And that context is, hey, Craig's already bought the darn thing you're trying to sell him. So perhaps either leave him alone, or perhaps maybe talk about some other thing that you can do for Craig. That would be of use to him.

• 15:16Speaker 00

  It really, I have all sorts of ideas in terms of what that kind of could look like, both form and function. But we'll get there in the conversation if it's, if it's interesting to you.

• 15:26Craig S. Smith?

  You've recently launched the Compound Marketing Engine. What is that?

• 15:31Speaker 00

  So it's interesting, right? So you mentioned the term composable CDP. This is an industry that's very crowded and there's lots of alphabet soup and there's lots of acronyms and so forth. And that's all well and good. In fact, growth have created what is considered the composable CDP category. And that, all that means is it's just on top of the data cloud. It's very flexible and composable so you don't get locked in. What we're talking about with compound interest is kind of simple, right? If you think about the problem we're solving is the marketing cycles are simply too manual. Because they're too manual, they're way, way, way too slow and they result in suboptimal experiences like what you just described as one example. But there are millions of examples where that is.

• 16:14Speaker 00

  So if you think about that, there's a better way, right? If you actually were to centralize your data and improve that data such that it could receive and participate with both marketers and agents. You apply agentic AI on top of that, on top of that data, you could then have a much faster, more relevant context for the entire marketing loop. Think about the term compound interest. And so I I'm obsessed with the term compound interest, going back to my days as an investor. Right. We we want to say, hey, what are the benefits of compound interest applied to marketing? Right. So fundamentally, this is about how marketing happens.

• 16:55Speaker 00

  It can happen both better and faster to ultimately drive business outcomes. So in in essence, that's what compound marketing is. It's about how do you drive growth faster by basically getting people through iteration cycles and experimentation better and smarter so that you learn faster as a business leader and a marketer. So that's really in essence what it is, and we're really pleased with the reception. People tend to get it right away, perhaps because of the term compound, which is a very powerful word in of itself.

• 17:25Craig S. Smith?

  Yeah. Can you walk us through an example that that illustrates it for the for the listener?

• 17:33Speaker 00

  Yeah, we're really fortunate to have a number of customers who are who are have been on the journey and continuing to lean in. The most recent one I'll talk about is is a company called Allegro. For those not familiar with Allegro, they're basically Amazon in Europe. They're one of the largest marketplaces. They're a very successful company, very, very analytical and detailed. They have been using our compound marketing engine to, and they actually announced something really interesting today. It's the first end to end agentic campaign that's that they've run.

• 18:06Speaker 00

  More specifically, they're applying Growth Loop or Compound Marketing Engine to automate and apply AI to the selection of audiences. Right. So remember I was talking about who you actually want to address at any one point in time? Historically, that was a very manual process for them, right? That so they basically had humans going back and forth with ideas and SQL to say, well, we think we want to target this person. And that would take a lot of time. Right now, they do it instantly, right? It sits on top of the data cloud, in this case, BigQuery, and it basically serves up specific ideas for audiences to help. And what has that done for them, right? Well, they're they're using, in this case, to partner powered their retail media network, right? So that

• 18:48Speaker 00

  Means there's a brand that sells goods in the Allegro marketplace. They want to get in front of agencies. They want, sorry, they want to get in front of audiences, specific customers. Uh, and then uh, find them wherever it'd be most relevant in their journey, whether that's on their mobile phone. Uh, in an app or whether that's in a paid um, advertising environment like TikTok or Facebook or whatever. They were up and running in a matter of weeks and then uh, over the course of two months have improved their return on ad spend by two X. Right. We're really increasing the amount of volume through their marketplace and doing so more efficiently.

• 19:27Speaker 00

  So that's one example uh, that uh, we're really proud of and they've been very public about sharing these, these results and really, you know, think that they're one to watch because of the investments they've made in the data teams and uh, agents at every step of their marketing cycle. So that's one example. I'm happy to share others if, if it'd be, if it'd be interesting to you.

• 19:47Craig S. Smith?

  Yeah, well, well, on that example, so um, Allegro, you know, if they, if they want to push, I, I don't know what the what the, the range of products they have, but if they want to push uh.

• 20:01Speaker 00

  Think of them as Amazon. I mean, they have everything, right? It's a huge marketplace.

• 20:04Craig S. Smith?

  Yeah, a shoe, if they want to push a certain shoe, uh, sports shoe. Uh, they, they first, uh, the, the, your growth loop first would find customers in their data. Is that right? That are most likely uh, to be open to uh, sports shoe purchase? And then uh, we would, the, uh, your, the growth loop player would then uh, design a campaign and decide on um, a vector, a channel to reach that customer.

• 20:44Craig S. Smith?

  As you said, whether it be mobile or social or whatever, and then execute that is is that am I on the right track?

• 20:53Speaker 00

  Yeah, you're certainly on the right track. Let's let's let's let's build on that a little bit. Okay, so not just any shoe, right? There's always different trends happening. Like I serve on the board of Gap with lots of different brands. So it's amazing to me how quickly different fads and trends come and go. So it would be informed to say it's not just any old shoe. It'd be, you know, this type of shoe, it could be a platform. Don't ask, don't quiz me on my my fashion, shoe fashion knowledge. But but suffice it to say, there's usually a trend or two that really is is an important input. The other thing would be you'd have a data team that would do propensity modeling. They would say, hey, Craig, Craig's propensity to purchase shoes is either high or low.

• 21:34Speaker 00

  So maybe you should talk to to to you say it's high and as for this particular type of shoe. Their propensity models, and this is M L, this is machine learning, not A I. It'd say they'd have a lot of models based upon all the previous transactions. They say, hey, we we think that people that look like Craig have a high propensity to purchase this shoe. And then you automate all the other things you described. Okay, what might the offer be? Where do we where should we talk to Craig, etc. You get the gist. And that's very much what we're working on with them. Where I'm really proud of what they've done. They've basically leaned into actually the actual creative itself. So they have a team that's using the underlying models.

• 22:16Speaker 00

  So Google launched V O three last week, which is a video one, but there's no shortage of these different image generation models, which are really darn good, like mind-blowingly good. So they're they're actually able to not only just take all that upstream intelligence on propensity. They're basically able to have an ad that basically they created themselves, not some third-party agency. And then there you go. So it'll be really fun to watch the type of results that they're going to continue to get. The early results were astounding, really, like two two X improvement for a company that is already really good is pretty, pretty impressive. So we're really, really lucky to have them as customers.

• 22:56Craig S. Smith?

  Do you augment customer data for your clients? So let's say it's Allegro and they have a

• 23:08Craig S. Smith?

  A profile for each of their customers that includes everything they've ever bought and I don't know what, but I had a conversation about a year ago with a company that uses

• 23:25Craig S. Smith?

  AI to help political fundraisers target individuals and it was amazing. They had built out,

• 23:38Craig S. Smith?

  you know,

• 23:40Craig S. Smith?

  I don't know how many columns in their in their data field, but everything from what magazines people subscribe to to

• 23:52Craig S. Smith?

  their zip code. So, you know, which gives some information about the political leanings and, you know, just all of this information. And then they could, the AI would sort through and identify people that it was, you know, the ROI was high enough for them to spend time reaching out to. Does, do you help augment that customer data or is that a different part of the pipeline?

• 24:23Speaker 00

  Yes, yes. Short answer is yes. Longer answer is we do it in a variety of different ways, typically with partners. If you think about these data clouds, there's a lot of enrichment. So just getting the data in both for the first time. And then, you know, what I'm talking about, and we talk about loops, it's feedback loops so that you actually provide feedback in so that you learn, not just the first time, but as you continue to do things. But I'll give you some examples. So

• 24:51Speaker 00

  we partner with companies like TransUnion to basically enrich a profile to understand more about a potential customer or an existing customer. Companies that do a lot of performance marketing use, use companies like LiveRamp or they'll use third-party information again to augment and get a holistic picture. The term, the buzzword is, you know, customer 360. You want to have a 360 degree view of your customer. So there's no shortage of people that offer the ability to do that. We've chosen not to lean specifically in the act of doing that for reasons I won't bore you with today. But, but suffice it to say, we help through partnerships to get people to ingest that information and get it in a centralized spot.

• 25:34Speaker 00

  We then kick in at the orchestration, so the identification and activation orchestration of that data. But it's a very interesting field. And if you think about, it's not just things like magazine. If you think about what's happening with Google News, Spotify, Netflix, these recommendation engines are getting so much better. And why is that? Well, it's actually not just old fashioned machine learning and reinforcement learning. It's actually artificial intelligence in combination with machine learning. So that it's not just a magazine. It might be a tone. It might be a regional, it might be a political leaning. It might be a thousand different things that are really almost imperceptible

• 26:16Speaker 00

  to humans that constitute surface area to then experiment. And gosh, it explodes your brain to think about how companies like Walmart and Gap and others think about a product. A product isn't just a pair of jeans, right? And it used to be, it's as equivalent and maybe even more profound than the difference between Yahoo and Google. If you recall, Yahoo was this directory. It was like, it was a dropdown. It had to categorize something into a directory. Along comes Google, right? And doesn't have a directory at all. It basically understood signals by using backlinks in this case, but but other things too, to say, this is really what you're you're want. That same thing is happening in all elements of

• 27:01Speaker 00

  business today. Think about a product again, a pair of jeans. It might be the cut of the jeans. It might be a vibe. It might be who they're connected to as an influencer. I mean, a million different things potentially. So that's really the fun here is to say, this isn't just about random blue jeans and trying to say, hey, Craig needs jeans. No, no, no, it's much, much more nuanced than that. So it's about understanding that context. And I believe that, you know, each prospect and customer will have their own agent too, to basically represent what you're trying to accomplish, both in commerce or entertainment or any walk of your life. So it's kind of a nutty thing to think about it when you stop and think about it for a minute. But it is exciting to me because it's going to

• 27:46Speaker 00

  mean more meaningful engagements, more personalized, because the context has really exploded, I think, in exciting ways.

• 27:53Craig S. Smith?

  Yeah. And you mentioned early on about not being intrusive. And hopefully,

• 28:03Craig S. Smith?

  Marketing systems are becoming increasingly sophisticated through things like Growth Loop, where you're not just being bombarded with irrelevant marketing. Because there's so much coming at you now, how do you, how do you manage that? So you're not exhausting the, the customer or adding to the noise so that, that you're, you're really delivering a message at the right time that's going to resonate? Build the future of multi-agent software with AGENCY. That's A-G-N-T-C-Y.

• 28:46Craig S. Smith?

  The agency is an open source collective building the internet of agents. It's a collaborative layer where agents can discover, connect, and work across frameworks. For developers, this means standardized agent discovery tools, seamless protocols for inter-agent communication, and modular components to compose and scale multi-agent workflows. Join Crew AI, LangChain, Llama Index, BrowserBase, Cisco, and dozens more. The agency is dropping code, specs, and services, no strings attached.

• 29:31Craig S. Smith?

  Build with other engineers who care about high-quality, multi-agent software. Visit agency.org and add your support. That's A-G-N-T-C-Y dot O R G.

• 29:47Speaker 00

  I mean, there's two, there's two main ways. Ultimately, humans have a very important role to play. They'll be the gatekeepers. Ultimately, these tools are in service of the relationship between consumers and, and the brands that they serve. Or, or in a business-to-business context, we also support, you know, dozens and dozens of professional sports teams, which is more of a business-to-business setting, or, or a combination thereof. But, but be that as it be, it's like, basically, the human will decide, right? What is the right level and quantity and quality of engagement? And then the, the agents come in to say, look, it'll be business outcome oriented, right? And the data will help inform important human decisions. Right? So it is, it is.

• 30:33Speaker 00

  So interesting. The reason bombarding happens because every one of those bombardments have some incremental gain. But what is not modeled is the overall impact of the relationship. That is hard to model. It is no longer that hard to model. It's still obviously complex, but it's not impossible to do. What do I mean by that? Or said differently, I think it is, if you're trying to optimize for the lifetime value, right? You will not do those stupid things. You will basically understand context and be in a position to intercept and be welcomed in at the right moment to basically extend that relationship in an additive way. It won't

• 31:14Speaker 00

  just be guessing or shooting in the dark. So you'll have humans first and foremost, setting the context. And then these agents and tools will basically allow you. And that's what very much what we help companies do. We talked about the suppression. We talked about just basically signals back. It's really exploding in terms of the ability to do that effectively. So that's, that's how I see it evolving. And you know, that's, that's an aspiration. I think not all brands will get there. But just, just same thing, you know, some brands are clever and really tuned into this and others aren't. And that's just the way it'll work. I think the separation of the haves and have nots will happen, I think faster than even it has historically. Those don't get this will die.

• 31:55Craig S. Smith?

  And, and you talk about the relationship with the customer. There, I've been talking to a lot of customer service, customer support companies that are using agentic AI or generative AI with chatbots or whether it be voice or text. And to me, that seems really valuable from a marketer's standpoint, because you're actually talking directly to the customer. And I know I do some work with Boston Consulting and they have worked with Oriol, L'Oreal,

• 32:41Craig S. Smith?

  The cosmetics company and they have a chatbot on L'Oréal's website where you can, people can ask for advice about, you know, their hair or makeup or whatever it is. And that, that seems incredibly powerful. I mean, do you, do you do any of that? Or do you partner with any companies that are doing that to, to get that kind of data into your system?

• 33:10Speaker 00

  We don't do that ourselves. We would view that as one of the potential destinations and or inputs back into the data clouds. And a very important one at that, what you're talking about is one of the clear early killer applications for generative AI or more generally AI. That encoding, right? So, so it turns out, AI is pretty good at coding. It's pretty good at customer support. Those are two very clear early killer applications. And Glean we were talking about before we started recording is another one. Marketing, we believe, is, is, is another one, not just because, because we're in this business and we're biased. But if you look at Benedict Devens' most recent report, or some of the stuff that Mary Meeker just put out.

• 33:53Speaker 00

  She's highlighting the need for, you know, both of them are highlighting the need for marketing to be next. But, but on, on the chatbot or the customer support, it's such a good use case. You know, there's companies like Decagon and Sierra, Brett Taylor's company, that are doing a fantastic job. And it's really, really clear, like it's, you know, just as websites in the first, you know, wave of web one dot O. Like a website was like your way of interacting with your customer. Like now it will be agents. There'll be agents and you don't need to proxy. You don't need to guess. Every interaction that you have with your prospects and your customers can be in the form of agents. And well, okay, well, that sounds, you know, to some people probably weird. But then allows you to dial in and get feedback. So you're not having to do expensive surveys and guess and proxy.

• 34:38Speaker 00

  You're actually, you're actually able to, to, to pull sentiment analysis. You're basically able to see every interaction you have. So it's very clear that that's one of the early good use cases. And we think that will be one of the inputs to say, are we doing this right? Like, that's one of the signals that will be, will be helpful for brand marketers and just teams and business, businesses in general to figure out if they're doing it right.

• 35:02Craig S. Smith?

  When you talk to, and I presume you do speak to a lot of other CEOs, but in their search for marketing solutions, or what's the advice you give them, not necessarily to sell growthly, but just to look at marketing as an increasingly important part of their their business, whether it's B2B or B2C?

• 35:34Speaker 00

  Yeah, I think I think the things I tend to have conversations, and you're right, I'm so fortunate to spend time with some incredible folks, not CEOs, CIOs, marketing leaders, data scientists, you name it. You know, I always come back to like, your job is to basically ensure that you're building brand value and enterprise value over time. And that's usually a function of, hey, are you growing lifetime value, or whatever the proxy for a business outcome that you care about is. Like, focus there, like, and be clear about how you're going to measure value. And then create use cases off of that. That's one. Two, in the early days of Growth Loop, it was really much of like investing in helping people invest in their data, right? So data was all over the place, and it was a mess.

• 36:19Speaker 00

  Companies have gotten that memo. But that said, putting your data in a centralized spot, nurturing it, building feedback loops. You know, we're just talking about ingesting customer support information. That'd be a really important thing if you have agents. So getting the data in the right place, investing in a culture of experimentation, making it okay to fail, right? And velocity is probably even more important, right? It's always important in business to move quickly and in the right direction. But like, now with the models changing so quickly, the underlying technology, you gotta move fast, right? So that's, those are some of the things. And then really think about the underlying workflows that really

• 37:00Speaker 00

  are going to be dramatically simplified, right? And one of the key themes across all these conversations, their teams are drowning in complexity, right? They're all trying to do their best, they're all trying to deliver a business outcome. But then they have to wade through all this manual workflows. We talked about some of them in the marketing context, but every, every workflow has a lot of mundane, a lot of manual nature, and this is where agents can come in. So to me, I think these are the sorts of things. And then the longest pull of all, Craig, is always change management. It's always how do you get people to change their behavior? And that's about making it okay, or really more than okay, to basically experiment with these tools.

• 37:40Speaker 00

  There's tools to help in every pocket of, of, of a company right now. So those that will succeed, I think, create an environment of experimentation, velocity, and trying out these tools to find what works and make it really easy for teams to do that. So, no, that's, that's really the advice that I'd offer, or really what I hear as the things that are more indicative of the, the successful teams. Less on just chasing AI for its sake and the buzz and the nice demos and all that jazz. It's really about driving business impact, which is really about lifetime customer value and anything that doesn't directly contribute to that over time is frankly a waste of time in my view.

• 38:22Craig S. Smith?

  You guys are, are part of the solution, but how business leaders, there are so many different tools or solutions or

• 38:35Craig S. Smith?

  products out there. How does someone decide what to use Growth Loop as opposed to, I don't know what, what the competitor would be, but

• 38:49Craig S. Smith?

  what do you tell business leaders? Do they set up a different team to just do that market research and, and sort through? Do they rely on relationships and other companies or executives?

• 39:04Speaker 00

  I mean, many, many of the ways in which decisions are taken aren't that different. I think the benefits of, of some of the more modern tools and AI-powered things is the speed with which you can actually determine whether it works or not. So again, I'll come back to you. Often you have to be really clear of what you're trying to do. You know, it's a little bit like, I think it's Alice in Wonderland and the Cheshire Cat, like Alice is asking me, I've lost, can you tell me where to go? And the Cheshire Cat asks, like, where are you, where are you going? She says, I don't know. And then she says, well, any old road will do, right? Same is true in business, right? You gotta be clear where the hell you're going, what you're trying to solve. So if you don't have that, then like, you know, you're kind of lost. So figure that out first.

• 39:49Speaker 00

  Look, the benefit of these tools are you can basically do proof of concepts in days and weeks, right? You can create synthetic data if data is particularly sensitive, or if it takes a long time to get security all set up. These things can be done in hours and days, and then just run them. Like, the days of these legacy systems where you have a systems integrator that needs to come in, and you're talking weeks and quarters and months, and like, before we even go, that's just comically bad. Unfortunately, a lot of legacy players, I don't necessarily name them by name unless it's helpful. That's just, you just don't need to do that, right? There's much more flexible architectures. And yes, the partners, we partner greatly with Google and Snowflake and Databricks.

• 40:30Speaker 00

  And we're multi-cloud because we want to be a value-added partner to them, right? Yes, we drive compute and consumption and drive their business, but we do it because we solve, you know, help them solve problems for their customers or joint customers. And that's true of the Costcos and the Albertsons and the Google themselves. Like, we're helping them, and these channels are how we actually influence. So it's a little bit more unbiased. We have these players saying, hey, you should check out companies like Growth Loop. We think they could, they could help you. So that's how, that's how it usually goes. And look, I just, I just think the other advice I have is be clear about that process.

• 41:09Speaker 00

  Set it up. Like, yeah, bring in a lot of different tools to test, but be clear about what you're trying to solve for. Be clear about how you're going to measure intermediate success and make a quick decision. This doesn't need to be days, you know, months, months and quarters. It can be done in days and weeks.

• 41:24Craig S. Smith?

  And with Growth Loop, what are the metrics that you use to gauge success beyond?

• 41:34Speaker 00

  It's really simple. Sorry to interrupt you, Craig. I mean, it's velocity, it's uplift typically lifetime value or some top line revenue and then efficiency and cost efficiency. Like velocity, how many, how many shots on goal can you take? You take more shots on goal faster and better, smarter shots on goal and more of them. Typically, that is anywhere from three times faster. Indeed is on the record saying we've helped them do eight X more faster experimentation. So that's huge. And then everything's about impact, top line growth, ideally. And if you can proxy lifetime value or actually measure it, that's even better. And what people are realizing is if you centralize on a data cloud, you can reduce

• 42:15Speaker 00

  a lot of other tools and certainly reduce a lot of costly, costly processes of moving data around, copying it. So you can actually get efficiencies there. And then AI has a role to play, of course, there as well. But it's really velocity, top line growth, and then cost efficiency.

• 42:33Craig S. Smith?

  And for a company that wants to work with Growth Loop, you were talking about this, you know, high speed iteration to decide what, what works best. How do you guys charge? And how does someone engage with you? Do you have a freemium layer where they can, you know, run a couple of loops? I don't know if you call them loops.

• 43:02Speaker 00

  We do, we do use the term loops. Typically, we work with partners. So that'd be a Google or Snowflake or Databricks. We have a team that does it. Part of where we have, we have chosen to work with larger customers and we've chosen to be very, very mindful from the very beginning of security and governance and compliance. So we don't have just kind of come to our website and try it out, right? That's, that's not a model that we support in for that reason, right? We want to work with the technology and the data teams to make sure that everything is done in concert with and respectful of all the compliance. It's why we have a nice business in Europe because it's like it really.

• 43:44Speaker 00

  Matters. And then, yeah, you know, oftentimes we set up synthetic data and a proof of concept in a matter of days. So that's, that's typically how it works. And again, you know, that, that is often with partners, but you know, if people are interested, of course, they can you can just reach out to one of our team members and we'd be happy to help.

• 44:02Craig S. Smith?

  Well, that's interesting. So you use synthetic data to run a proof of concept. Yeah. And then when you move into their data cloud and you guys aren't in the business of consolidating data, or you know,

• 44:22Craig S. Smith?

  and that sort of thing, right? You're a, yeah. And so when you move into their data cloud and start running these iterations to measure, to figure out what's, what works. How many, is there some metric of how many different iterations you can run in a week or in a month? I mean, I'm just curious about this. You talk about velocity, what?

• 44:49Speaker 00

  There's no limit. I, I mean, this is, this is, this is the interesting thing is we start to think about SaaS versus agentic, whatever, in this case, marketing. You know, SaaS is a seat-based model. It's usually limited. The rate limiter is a human's number of seats, number of like, when you start to have agents that work autonomously in 24/7, right? That, that breaks, that breaks apart or separates. Why am I telling you that? Well, I, I, I guess like there's, there's no limit. Yeah. We, we, we talk to customers about enriching their data and getting a really good data from which to start. Then we talk about the number of audiences that they're using, and that's usually measured in hundreds or in some cases, thousands of audiences. In the fullness of time, that number is just going to continue to go up.

• 45:35Speaker 00

  So it's really only limited by consumption in a data cloud, right? That's really the limiter and, and, and the creativity of the actual practitioners. So we do look at that, but ultimately, you know, it's about coming back to lift. We have experimentation built in. It's A B testing at every, at every, you know, step, wherever you want it. So that's how it typically works. So it's not, it's not limited by anything other than compute creativity on the part of the practitioners and the number of agents that the, the customer feels like spinning up.

• 46:05Craig S. Smith?

  From your side, since you're, you're, you're seeing what works, uh, do you, uh, do you advise them? That, you know, th- this is what we've seen work in the past. Why don't you try this? Or, or is it really up to them to experiment?

• 46:26Speaker 00

  I mean, it's mostly up to them. I think we're, we're, we're starting to, to see more, it's all new territory, right? In terms of the agents, to be clear, like, you know, the first waves of success and value was really about democratization of these data tools. And then putting it in the hand. So, you know, we say we're built by marketers and loved by data teams and, you know, started with, like, data teams don't like doing SQL queries all day. And you're going back and forth. That's not value added work for anybody. So we, we've started there, but the real, the real, we're not saying, okay, you should do this or this specifically at that tactical level. We're there to facilitate and surface insights that they can then draw those conclusions on their own. And/or the agents can help them do that, to be clear. What we're finding to be helpful, Craig, is really almost like a playbook or really like a bit of a, bit of an approach.

• 47:11Speaker 00

  If you think about how HubSpot started to think about inbound marketing, right? They, part of their success was they actually helped people in the early days understand what that meant. And like, okay, it's this step, then this step, then this step. We're writing that book as we, as we speak, and it's, I don't, I don't want to create the image that it's some, you know, it's chiseled in stone and it's deterministic. Cause it's far from that. But, you know, we are finding that there's, there's some waves of different, um, and sequences of things that are more successful customers are tending to do. So it's so fun to be writing that alongside with them. So that's how, that's, I'd say how we think about it. We're, we're here to enable, right? Humans. And, and again, I, I'm unapologetic.

• 47:53Speaker 00

  About saying, look, you know, yes, some of the roles will be changed or taken away. It doesn't stay, but there's going to be far more things and far more creativity that's going to be unlocked. Far more cost effectively with these agents. It's not even close to me. So that's sort of how we think about advising and partnering with our customers and not just our customers, our partners like Google and Snowflake and Databricks.

• 48:13Craig S. Smith?

  It seems that marketing as a function in an enterprise has grown. That in the old days, uh, marketing was just about establishing a brand. There were, you know, half dozen channels.

• 48:32Craig S. Smith?

  And, uh, the real business was on the sales side and, and, um, and, you know, the production. But it just, my sense is that marketing is, is kind of leading now in an enterprise. Do you think that's true? How marketing has changed? And, and then the other question is, what's your roadmap over the next 12 to 18 months? Do you have, uh, things coming up that we should be watching for?

• 49:05Speaker 00

  Yeah, for sure. And yeah, I mean, part of why I'm super excited to be back doing what I do. You know, I spent 10 years at Google and it was a very interesting time when digital was taking over. It was very much thought of a fad or it's like, oh, that's just going to be an e-commerce thing or, you know, and, and we typically tend to overestimate things in the short term and underestimate them in the long term. That's certainly true in that run. And, and this is, this is, this is definitely the case here. Like, I mean, as much as the hype is, is here, I think in the long term, we'll, we'll say, oh my gosh, this was totally transformative. Um, so, so yes, I mean, there's, there's so many, you, you talked about it before, like there's so many, there's literally, we support hundreds and probably over time, thousands of different destinations.

• 49:45Speaker 00

  There's so many different fragmented ways in which people are consuming and interacting with, with information. And that's only going to atomize even further. So like, that's, that's part of it. So it bursts the bounds of what humans can do. You need tools, you need agents, you need these like things to basically keep track of it and much less be relevant with the context at the individual level. So like, that is what's going on. It's, you know, yet the same thing remains, right? You know, the old adage, water maker, right? Half my marketing works. The problem is I just don't know which half it is. And like, that's unacceptable, right? That, that will be solved. It is being solved as we speak. So I, I, I think there is complexity to say it's led. I think it depends on the business model.

• 50:28Speaker 00

  You know, some models are product led purely. Some are more marketing and some are more sales. I'm not here to generalize and say it is. I, I am here to say that marketing is an increasingly important role because it's, and it's more complex. That requires the, the symphony of orchestration of people and, and in this case, AI. Our roadmap is really, it's funny, you know, at our scale, you know, a 12 to 18 month roadmap. We have a vision for the next couple of years. And it's really clear and exciting. We, we do two week sprints. So, so if you ask me what like is on the roadmap in 18 months, I can't tell you. It's just not relevant because the pace, I'll give you a specific example.

• 51:10Speaker 00

  We weren't thinking, we, we do partnerships with like companies like Typeface that are doing really interesting things around creative. And we're thinking that that's going to be off in the future sometime next year. So we're not going to think about it. I'm telling you, OpenAI opens ImageGen, VO3, and five other things happened in the last couple of months. It says, holy cow, we can do things with that now. So it's such a dynamic time to be in here. But what, what, what we're really aspiring to do is first and foremost, make things more real time in nature, right? So like instant context understood so that the, the agents can adapt real, real time. We're applying agents at every step, starting with understanding the data, then having an audience agent.

• 51:53Speaker 00

  And an outside world, a world data agent that goes out and say, if I'm a marketer running a brand, tell me about my competitors and then give me ideas. Like we'll have agents at every step and then we're trying to. Just tune those agents to make them more effective. Those are the real guardrails for what we're doing. The near term, we're just iterating like hell. We're doubling the size of our engineering team. Even though we're getting enormous efficiencies from AI, we're really, really understaffed relative to the opportunity that we're seeing from our customers every day and now prospects. So I guess the last thing, this whole commerce retail media thing continues to be a growing business. It's like literally close to a billion, a hundred billion dollars.

• 52:34Speaker 00

  You have brands like Walmart and Amazon and Best Buy and, you know, many, many other retailers or commerce players that are starting to see those businesses grow at north of 50% globally and very high margins. So when these businesses are struggling with macroeconomic uncertainty and tariffs, these types of businesses where they can broker these personalized effective communications and monetize their actual first-party data, it's really attractive. So we're leaning into that because people are pulling us into those conversations that wasn't initially what we thought would be the direction. But we can't avoid that. That's an exciting thing that has caused us to win business recently.