282 Audio.mp3: Audio automatically transcribed by Sonix

282 Audio.mp3: this mp3 audio file was automatically transcribed by Sonix with the best speech-to-text algorithms. This transcript may contain errors.

Chris:

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. The 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.

Craig:

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 interagent 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. Build with other engineers who care about high-quality multi-agent software. Visit agencyorg and add your support. That's A-G-N-T-.

Chris:

Craig, I'm Chris O'Neill. 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.

Craig:

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

Chris:

Yeah.

Chris:

So GrowthLoop 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.

Chris:

But this is a company I've 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. 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 an 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 personalized relationships with the customers up and running much more efficiently and effectively than has been possible before AI.

Craig:

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

Chris:

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 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. 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, the company built on top of data clouds from the beginning, 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.

Chris:

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.

Chris:

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 relationship in some meaningful way. So the AI basically sits on top of the data and understands the data, first and foremost, 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 loops 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 will very much be the case.

Craig:

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

Chris:

Yes, it can. So a lot of questions in there. It is an interface that you'd recognize. For now, anyways, chat, I think, is an imperfect interaction mechanism, but it's the best we've got. We can talk if you're interested in terms of future design mechanisms.

Chris:

But absolutely 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 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 category sales, Whatever it is? That's the first question.

Chris:

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. It says, okay. The data agent is going to say, okay, great, based upon the data schema, the underlying data in the data cloud, 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 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, across all these different channels, let's just go get it done, okay, removing complexity, which is currently handled typically by teams, by channel, by tool, right.

Chris:

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. It's 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 step of the process to the other? So that's really what this is about, and agents, increasingly, are at every step and, as I said earlier, some of the agents are really working great.

Chris:

Others we're really experimenting with right now because the models are getting better but they're not quite there. An example of that would be generating creative briefs and the actual images and the copy itself. 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 how can we orchestrate this conversation. We're really good at that right now and it's just getting better at a really pretty impressive rate, and not necessarily just due to our good work, although I'm very proud of the team's work. It's actually the underlying models, and we're comparing and contrasting all the different LLMs 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.

Craig:

Yeah, and how do you reach out to customers? Do you have various channels that then the ai, or whoever is managing the ai, can choose between? Uh, this is something I've been interested in for a while. I mean, how do you reach the customer? And they're. I remember talking to a company. It's based out in Bozeman, I can't think of its name right now. They're an AI ad company, but they certainly had direct email, but they could position ads that would reach the customer through social media or through some other, maybe Google. I don't really understand the tech behind that, but you know. So, how many different channels do you have?

Chris:

Hundreds, hundreds and hundreds and even more if you think about the extension and reach through networks. So that's, I think that's table stakes, right. So the connectors, or the we call destinations, or sometimes we call them surfaces, the surfaces are really up to the marketing team themselves. So they decide. Often it's just an email or an in-app mobile experience, 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 for what we do. And you know, of course those channels grow and contract and collapse, but the real benefit isn't just the reach of all of them.

Chris: 11:46

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 owned and operated channels, right? So we're seeing a huge business with retail media as an example. So the Walmarts of the world, 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.

Chris:

The real benefit is the intelligence to say, hey, what is precisely the right place and 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 awful really, and it's we're out to stop that. Right, 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 enable teams to do that more often, and that's really how growth is going to happen. Right, you're going to have more loyal customers that care about you because you're actually feels like you're listening to them, as opposed to just bombarding random, not relevant messages to them all day and every day.

Craig:

Yeah, and the classic example of that? 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. 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 keep serving them. The same marketing over and over.

Chris:

Yeah, it's a great example, and that example repeats itself in lots of different flavors all over the place. We're going to talk about the importance and the paramount importance of data clouds. 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 darn umbrella. In fact, all brands should know that, but that's a separate matter. How it works is when you have purchased that the transaction data sits alongside the customer data in this case. So, oh 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, so that's a very good example.

Chris:

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 in 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. 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 interesting to you.

Craig:

You've recently launched the compound marketing engine. What is that?

Chris:

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, uh, growth loop created what is considered the composable cdp category, and that all that means is it sits on top of a data cloud. It's very flexible and composable, so you don't get locked in. Um, what we're talking about with compound interest is kind of simple, right.

Chris:

If you think about the problem we're solving is the marketing cycles are simply too manual. Because they're too manual, they 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. 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 data, you could then have a much faster, more relevant context for the entire marketing loop.

Chris:

And if you think about the term compound interest so I'm obsessed with the term compound interest, going back to my days as an investor we want to say hey, what are the benefits of compound interest applied to marketing? So fundamentally, this is about how marketing happens. It can happen both better and faster to ultimately drive business outcomes. So 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.

Craig:

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

Chris:

Yeah, we're really fortunate to have a number of customers who have been on the journey and continuing to lean in. The most recent one I'll talk about 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. In detail, 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 they've run. Something really interesting today is the first end-to-end agentic campaign that they've run. More specifically, they're applying Growth Loop, our compound marketing engine, to automate and apply AI to the selection of audiences.

Chris:

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? So they basically had humans going back and forth with ideas and SQL to say, well, we think we want to target this person. That would take a lot of time. Right now, they do it instantly, right? It sits on top of their data cloud, in this case, bigquery and basically serves up specific ideas for audiences to help. And what has that done for them, right? Well, they're using, in this case to help, and what has that done for them? Well, they're using in this case to power their retail media network.

Chris:

So that means there's a brand that sells goods in the Allegro marketplace. They want to get in front of audiences, specific customers, and then find them wherever it'd be most relevant in their journey, whether that's on their mobile phone, in an app, or whether that's in a paid advertising environment like TikTok or Facebook or whatever. They were up and running in a matter of weeks and then, over the course of two months, have improved their return on ad spend by 2x. We're really increasing the amount of volume through their marketplace and doing so more efficiently. So that's one example that we're really proud of, and they've been very public about sharing these results, and I really think that they're one to watch because of the investments they've made in the data teams and agents at every step of their marketing cycle. So that's one example. I'm happy to share others if it'd be interesting to you.

Craig:

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

Chris:

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

Craig:

Yeah, a shoe. Think of them as Amazon. I mean they have everything right. It's a huge marketplace. Is that right that are most likely to be open to sports shoe purchase? And then the growth loop player would then design a campaign and decide on a vector or a channel to reach that customer, as you said, through, whether it be mobile or social or whatever, and then execute. That is. Is that am I?

Chris:

on the right track. 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 shoot 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.

Chris:

So it would be informed to say it's not just any old shoe, it'd be this type of shoe, it could be a platform. Don't quiz me on my shoe fashion knowledge, but suffice it to say there's usually a trend or two. That really 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, so maybe you should talk to say it's high and it's for this particular type of shoe. Their propensity models and this is ML, this is machine learning, not AI. Their propensity models and this is ML, this is machine learning, not AI. It'd say they'd have a lot of models based upon all the previous transactions to say, hey, 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 should we talk to Craig, et cetera. You get the gist, and that's very much what we're working on with them.

Chris:

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. So Google launched VO3 last week, which is the 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 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 2x improvement for a company that is already really good is pretty impressive.

Craig:

So we're really lucky to have them as customers. Do you augment customer data for your clients? So, let's say, it's Allegro and they have 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 AI to help political fundraisers target individuals. They had built out, you know, I don't know how many columns in their data field, but everything from what magazines people subscribe to to 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 was, you know, the ROI was high enough for them to spend time reaching out to Do you help augment that customer data, or is that a different part of the pipeline?

Chris:

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 what I'm talking about when 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 we partner with companies like TransUnion to basically enrich a profile to understand more about a potential customer or an existing customer.

Chris:

Companies that do a lot of performance marketing use companies like LiveRamp, or they'll use third-party information again to augment and get a holistic picture. The term, the buzzword, is 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 suffice it to say we help through partnerships to get people to ingest that information and get it in a centralized spot. We then kick in at the orchestration, so the identification and activation orchestration of that data.

Chris:

But it's a very interesting field and, if you think about it, 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, 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.

Chris:

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 other things too to say this is really what you want. That same thing is happening in all elements of business today.

Chris:

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

Craig:

Yeah, and you mentioned early on about not being intrusive, early on about not being intrusive, and hopefully, 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. Uh, 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, uh, 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. 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 interagent communication and modular components to compose and scale multi-agent workflows Join Crew, ai, langchain, lama, index, browser Base, 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 agencyorg and add your support. That's A-G-N-T-C-Y. Dot O-R-G.

Chris:

I mean 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 the brands that they serve, or, in a business-to-business context, we also support dozens and dozens of professional sports teams, which is more of a business-to-business setting or combination thereof. But be that as it may, it's like basically the human will decide what is the right level and quantity and quality of engagement, what is the right level and quantity and quality of engagement. And then the agents come in to say look, it'll be business, outcome oriented and the data will help inform important human decisions. So it is so interesting the reason bombarding happens, because every one of those bombardments have some incremental gain.

Chris:

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?

Chris:

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 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 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 how I see it evolving and that's an aspiration. I think not all brands will get there, but just the 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 who don't get this will die.

Craig:

And you talk about the relationship with the customer. 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 L'Oreal, the cosmetics company, and they have a chatbot on L'Oreal'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. Do you do any of that, or do you partner with any companies that are doing that to get that kind of data into your system?

Chris:

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 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 it turns out. Ai is pretty good at coding. It's pretty good at customer support. Those are two very clear early killer applications. Glean, we were talking about before we started recording, is another one. Marketing, we believe, is another one, not just because we're in this business and we're biased, but if you look at Benedict Evans' most recent report or some of the stuff that Mary Meeker just put out, she's highlighting the need for both of them are highlighting the need for marketing to be next On the chatbot or the customer support. It's such a good use case. There's companies like Decagon and Sierra, but Taylor's company that are doing a fantastic job and it's really, really clear.

Chris:

Just as websites in the first wave of web 1.0, a website was like your way of interacting with your customer. Now it will be agents. They'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 okay, well, that sounds to some people probably weird, but that allows you to dial in and get feedback. So you're not having to do expensive surveys and guess and proxy. You're actually able 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 um 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.

Craig:

When you talk to and I presume you do speak to a lot of other CEOs, but in their search for marketing solutions, what's the advice you give them? Not necessarily to sell growthly, but just to look. Cios, marketing leaders, data scientists, you name it.

Chris:

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 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 about like investing and helping people invest in their data. Right, so data was all over the place, and it was a mess. Companies have gotten that memo. Right, so data was all over the place, and it was a mess. Companies have gotten that memo.

Chris:

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 OK 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 got to move fast, right? So those are some of the things. And then really think about the underlying workflows that really are going to be dramatically simplified.

Chris:

Right, and one of the key themes across all these conversations the 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 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 is always how do you get people to change their behavior?

Chris:

And that's about making it okay, or really more than okay, to basically experiment with these tools. 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 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 how does someone decide what to use?

Craig:

growth loop as opposed to I don't know what, what the competitor would be, but, um, what do you tell business leaders? Do they set up a different team to just do that market research and and sort through?

Chris:

they rely on relationships and other companies or executives 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. Um, so again, I'll come back to you. Often, you, you have to be really clear what you're trying to do. You know it's. It's a little bit like, uh, I think it's allison in wonderland and the cheshire cat. Like I was asking you, I, I'm lost. Can you tell me where to go? In the cheshire cat house house, 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, the same is true in business, right, you got to 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 so figure that out first. Um, 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 it takes a long time to get security all set up, these things can be done in hours and days, uh, and then just 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 even you're going. That's just comically bad.

Chris:

Unfortunately, it's a lot of legacy players. I don't necessarily name them by name unless it's helpful. You just don't need to do that, right? There's much more flexible architectures and, yes, we partner greatly with Google and Snowflake and Databricks 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, our joint customers, and that's true of the Costco's and the Albertsons and the Google themselves. Like, we're helping them in these.

Chris:

These channels are how we actually influence. So so it's a little bit more unbiased. We have these, these players saying, hey, you should check out companies like growth loop, we think they can. Saying, hey, you should check out companies like Growth Loop, we think they could help you. So that's how it usually goes. And look, I just think the other advice I have is be clear about that process. 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, months and quarters. It can be done in days and weeks.

Craig:

And with growth loop. What are the metrics that you use to gauge success beyond it's?

Chris:

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 efficiently Like velocity. 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 8x 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 a lot of other tools and certainly reduce a lot of 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.

Craig:

And for a company that wants to work with GrowthLoop. You were talking about this high know, high speed iteration to decide what, what works best, uh, how, how do you guys charge, uh, and, 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.

Chris:

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 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 not a model that we support. 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. That's why we have a nice business in Europe, because it's like it really matters. And then, yeah, oftentimes we set up synthetic data and a proof of concept in a matter of days. So that's typically how it works. And, again, that is often with partners. But if people are interested, of course, they can just reach out to one of our team members and we'd be happy to help well, that's interesting.

Craig:

So you use synthetic data to run a proof of concept, uh, yeah. And then, uh, when you move into their uh data cloud, uh, and you guys aren't in the business of consolidating data or you, you know and that sort of thing, right, yeah. And so when you move into their data cloud and start running these iterations to measure, to figure out 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. There's no limit.

Chris:

I mean, this is the interesting thing. If you 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 limas 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. Well, why am I telling you that? Well, I, I guess, like there's no limit.

Chris:

Yeah, we, we talk to customers about enriching their data and getting 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. So it's really only limited by consumption in a data cloud, right, that's really the limiter and the creativity of the actual practitioners. So we do look at that. But ultimately, you know it's about coming back to Lyft. We have experimentation built in it's A-B testing at every you know step, wherever you want it. So that's how it typically works. So it's not limited by anything other than compute creativity on the part of the practitioners and the number of agents that the customer feels like spinning up From your side, since you're seeing what works, do you advise them that this is what we've seen work in the past?

Craig:

Why don't you try this? Or is it really up to them to experiment?

Chris:

I mean, it's mostly up to them. I think we're starting to see this is all new territory, right, in terms of the agents. Let's 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 going back and forth. That's not value added work for anybody. So 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 an approach.

Chris:

If you think about how HubSpot started to think about inbound marketing, 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.

Chris:

We're writing that book as we speak and I don't want to create the image that it's some. You know it's chiseled in stone and it's deterministic because it's far from that, but we are finding that there's some waves of different and sequences of things that our 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 here to enable right Humans and again, I'm unapologetic about saying look, you know, yes, some of the roles will be changed or taken away at Dunst Day, 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.

Craig:

It seems that marketing as a function in an enterprise has grown, that in the old days marketing was just about establishing a brand. There were you know half dozen channels and the real business was on the sales side. Uh, you know the production, but uh, it just my sense is that marketing is is kind of leading now in an enterprise. Uh, 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?

Chris:

yeah, for sure, and yeah, I mean part of why I'm super excited to be back doing what I'm doing. 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 gonna 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 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 say, oh, my gosh, this was totally transformative. Um, so, so yes, I mean, there's so many you talked about it before like there's so many. There's literally we support hundreds and probably, over time, thousands of different destinations. There's so many different, fragmented ways in which people are consuming and interacting with information, and that's only going to atomize even further. So 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.

Chris:

It's um, you know, yet the same thing remains, right. You know the old adage and want to make her right uh, 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 think there is complexity to say it's led. I think it depends on the business model. You know, some models are product led purely, some are more marketing and some are more sales. So I don't, I'm not here to generalize and say it is. I am here to say that marketing is an increasingly important role because it's and it's more complex.

Chris:

That requires the, the symphony of an orchestration of people and, and in this case, ai um, 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 years, uh, and it's really clear and exciting. Uh, we, we do two week sprints. So so if you ask me what, like uh, 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. We weren't thinking. We do partnerships with 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. That says holy cow. We can do things with that now.

Chris:

So it's such a dynamic time to be in here, but 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 agents can adapt. Real time. We're applying agents at every step, starting with understanding the data and having an audience agent, 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. 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.

Chris:

In 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 understaffed relative to the opportunity that we're seeing from our customers every day and now, prospects and, I guess the last thing, this whole commerce, retail media thing continues to be a growing business. It's literally close to $100 billion. You have brands like Walmart and Amazon and Best Buy and many, many other retailers or commerce players that are starting to see those businesses grow at north of 50% globally in 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 connections 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.

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