← The Agile Brand with Greg Kihlström®: Expert Mode Marketing Technology, AI, & CX
The Agile Brand with Greg Kihlström®: Expert Mode Marketing Technology, AI, & CX
#712: Compounding returns on your marketing campaigns with Chris O'Neill, GrowthLoop
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- [00:02]
Liberty Mutual Spokesperson
And Doug, there's nowhere I wouldn't go to help someone customize and save on car insurance with Liberty Mutual. Even if it means sitting front row at a comedy show.
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Greg Kilstrom
Hey everyone, check out this guy and his bird. What is this, your first date?
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Chris O'Neill
Oh, no.
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Liberty Mutual Spokesperson
We help people customize and save on car insurance with Liberty Mutual. Together. We're married. Me to a human, him to a bird.
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Greg Kilstrom
Yeah, the bird looks out of your league.
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Liberty Mutual Spokesperson
Anyways, get a quote@libertymutual.com or with your local agent.
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Chris O'Neill
Liberty Liberty. Liberty Liberty.
- [00:31]
Strayer University Announcer
At Strayer University, we help students like you go from Will I to why not? For over 130 years, we've been innovating higher education to make it more affordable, accessible and attainable so you can reach your goals. Go from thinking can I? To Yes, I Can. And keep striving. Visit Strayer. Edu to learn more. Strayer University is certified to operate in Virginia by Chev and its many campuses, including at 212115 Street north in Arlington, Virginia. The agile brand.
- [01:13]
Greg Kilstrom
Welcome to Season seven of the Agile Brand where we discuss the trends and topics marketing leaders need to know. Stay curious, stay agile and join the top enterprise brands and Martech platforms as we explore marketing, technology, AI, e commerce, and whatever's next for the Omnichannel customer experience. Together we'll discover what it takes to create an agile brand built for today and tomorrow and built for customers, employees and continued business growth. I'm your host Greg Kilstrom, advising Fortune 1000 brands on martech, AI and marketing operations. The Agile Brand podcast is brought to you by Tech Systems, an industry leader in full stack technology services, talent services and real world application. For more information, go to teksystems.com to make sure you always get the latest episodes, please hit subscribe on the app you listen to podcasts on and leave us a rating so others can find us as well. Now onto the show. If your marketing grew like a dividend reinvestment plan, would you still let a quarterly target dictate every decision? Agility requires stacking, returning gains faster than the market changes. Think compound interest. But for marketing campaigns, today we're going to talk about the compound marketing engine, agentic AI and why being data driven still needs greater adoption among leaders. To help me discuss this topic, I'd like to welcome Chris O', Neill, CEO of GrowthLoop. Chris, welcome to the show.
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Chris O'Neill
Great to be here Greg. Thrilled for our conversation.
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Greg Kilstrom
Yeah, looking forward to talking about all this with you. Definitely a lot to cover, but I think we'll get to it all. So before we dive in though, why don't we start with you giving a quick, quick background on yourself and your role at Growth Loop. Sure.
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Chris O'Neill
My career spanned a bunch of leadership roles. I've been very fortunate to work at some amazing brands. Google, Evernote, Glean, Xero and a few others. And I'm the CEO of. Yeah, I am the CEO of Growth Loop, a company I followed for a while. I had the good fortune of working with Growth Loop co founders at Google and have known them for over a decade at this point. So really thrilled to be here and really lucky to lead such a great team and pursuing a meaningful mission here.
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Greg Kilstrom
Great, great. Yeah. So let's dive in here. And so Growth Loop recently introduced what you call the industry's first compound marketing engine. So let's start there. What exactly does that mean? I know I briefly teased it in the intro, but what exactly does it mean and why should marketers be paying attention to this?
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Chris O'Neill
Yeah, so compound marketing derived from my fascination with the concept of compound interest. So Albert Einstein famously coined it the eighth wonder of the world. And from a very early age I became obsessed with investing. This notion of compound interest was really just at the heart of it. So I got to thinking, what's going on in marketing or business more generally, that's preventing the type of gains in growth that we all want to aspire? It often isn't that we try to get a little bit better. We always try to get better. But what's missing is the speed, the iteration speed. So the difference between compounding at a weekly basis versus a quarterly basis or a monthly basis is not a little bit. It's a lot similar to how compound interest works in finance. So really, when we thought about it, marketing cycles are too darn slow. There's manual steps at every, every one. Every step of the, of the cycle is manual. They have to be stitched together manually and that really holds companies back. So we thought there's a better way. And that's really what a compound marketing engine is really all about. It's applying AgentIC AI to your data in your data cloud to reduce the distance between. I have an idea and insight to impact. That's what we're doing.
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Greg Kilstrom
Nice, nice. And so I want to talk a little bit about how that works with agentic and stuff like that in a second. But I mean, first, I mean, this is really possible because things move so quickly, right? I mean, this is, you know, we have access to data. You know, big data was like the thing, what, like 15 years ago now or something like that. So everybody's been stockpiling all this stuff and data lakes and lake houses and all that kind of stuff, and now we actually have the ability to move quickly. But is that kind of the genesis of this, is just that need for the speed of marketing?
- [05:48]
Chris O'Neill
Yeah. Well, I just happen to believe the more agile you are as a marketer or business, you're going to win, you're going to take more shots on goal, you're going to take better shots on goal, you're going to be able to learn from previous efforts. But it really does start with the data. Like this is all possible because of the rise of data clouds. Part of the challenge is the fragmentation of data all over the place. So you got to kind of stitch things together. It's a very good thing. And it's very obvious to us that either through serendipity or luck and maybe a little bit of intelligence that things are going to be in the data cloud, they're going to start and end in the data cloud. So that's very much a part of this, is getting your hands on data, having a very clear data strategy, having a semantic layer on data so that you can do important things, in this case, lifecycle marketing, and really personalizing the journey, which has been the holy grail for many years, of course, and we've really fallen short for decades. Really?
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Greg Kilstrom
Yeah. So could you walk us through either a real case or a hypothetical example where we've got several AI agents handing off tasks and this idea of compounding over time. And how do the marketers also factor into this, this scenario?
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Chris O'Neill
Yeah, so. So it's very important that marketers are in the loop. Indeed. So I think of agents as teammates and the very beginning is really understanding the data. So agents are good at that. So understanding the schema, understanding what's in the data itself, what worked in the past in order to suggest experiments, starting with who to talk, who to talk to to target. Right. So the very first agent, well, there's, there's a coordination agent that basically wakes up and says, what is the person asking? So we very much believe in outcome back war, meaning what are you trying to accomplish? You're trying to reduce churn, you're trying to increase lifetime value, trying to, I don't know, grow a specific category, whatever it is. You then turn that into specific ideas by first looking at the data to say, hey, what have we learned in the past? In order to say, okay, what does that look like in terms of an audience? Okay, previously, just a Little bit of background. The before to now, it was metaphorically like the marketing teams would line up outside the data team's door like a breadline in the depression, asking for them to fulfill their needs. To say, I have an idea, who should I target? Let's run some SQL and bounce back and forth.
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Greg Kilstrom
I've been part of that line.
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Chris O'Neill
Yeah, I mean, there's gotta be a better way. And that's where we started to be clear, really being precise with democratizing that data so the marketing teams can do that themselves, no lines needed. Okay, so we can now do that by actually having agents do that work too. Not only do you have to translate insight from a marketing person to an audience, they do that instantly. But now we're surfacing, proactively surfacing. Some, some suggestions which are, are literally served up to you, which then are able to be activated through a journey or orchestrated through different channels and then actually executed across channels. Whether that's sms, push email, a paid ad, you name it. Hundreds of different surfaces campaigns run results read back into the data cloud and then you lather, rinse and repeat. This is the notion of growth loop, the name of our company. But at each of those steps we have agents. Now some of the agents are better and more fully developed than others, let me be clear about that. But that's very much our vision that that end to end happens with agents at every step. But a human also in the loop to sign off and inject creativity and spontaneity into the mix, to make sure it's on brand and make sure it's, you know, it's really, you know, resonating with, with the audiences.
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Greg Kilstrom
Yeah, so it sounds like, I mean it, it would make sense that it works best when it's, you know, full funnel, you know, start to finish or I guess it's, it's hopefully it never finishes, but you know, full, full life cycle and omnichannel and all that. Are there places where, you know, if you start seeing momentum first, so you know, in, in, in that funnel, you know, are there, are there certain parts where you start to see the results more quickly?
- [10:23]
Chris O'Neill
Yeah, it is in the audience area and it happens to be where we have the deepest level of domain so that, that domain expertise. So perhaps that's not coincidental. One of the agents that is happening and developing more, far more quickly than I would have anticipated is on the image creation. So somewhere in that loop you basically have to say, what are we going to say? What words, what content are we going to put? And even now what video or image? Right. The models are getting so good so fast, so that part of the loop is really starting to elevate quickly. We had that on a roadmap sometime next year in startup land, that means pretty much never because there's so much to do right now. But we're really pleased with what we're seeing around the ability of these models, multiple of these models, to translate what we're trying to accomplish, outcome orientation into a creative brief, creative brief into copy, copy into actual images. And now with VO3 and other things like it like full like motion and video, it's pretty astounding. So it's exciting to be in this business.
- [11:37]
Greg Kilstrom
So, you know, agentic AI, I mean it's, you know, it's the thing right now, kind of the shiny object right now, so to speak. And yet, you know, as you mentioned, lots of actual practical applications for it. What do you think over the next, you know, let's say I know 12 months is like forever in the future at this point, but you know, over the next six to 12 months, what kinds of like agentic workflows are going to be generally, you know, accepted kind of the norms and what are some things that, you know, maybe CMOS skip for now?
- [12:10]
Chris O'Neill
Yeah, I think the full end to end will take that full time. I think we'll start to see people pulling it all the way through in that time horizon. We're doing that more manual automated way, but not necessarily with the assistance of AI at every step right now to be clear. But I think the full loop will be in realm. A couple other things that I'm paying attention to is really the intersection of machine learning and more specifically reinforcement learning and AI. Machine learning is propensity modeling and really predictive based upon previous patterns and reinforcement learning to say, hey, what are you trying to solve for? There's a lot of innovation in that realm that's just starting to take hold. It's really more promise at this stage than quantifiable reality. Although that's happening quickly. One possible iteration of that is we're talking about agents to manage workflows for the marketers. What we see is the opposite to be true too. In other words, an agent's for the actual consumers that will actually help personalize in a way that's highly relevant. And then these agents will talk to one another to start to deliver fundamentally better experiences. That's something that's important. I alluded to the image stuff that's happening at a breakneck pace. I wouldn't have expected that that's happening. Another one we're paying attention to and quite excited is basically simulated data where you're basically proxying real life profiles of humans so that you can actually test with synthetic audiences, synthetic panels, to say, hey, we think this collection of activities, the combination of who and what and when, will have a desired impact against an objective we care about. But we can run it against synthetic data and you know, it's getting pretty good. So we're really excited about that as well, but that those are the things we're paying attention to. But boy, oh boy, in my life I've not seen the pace of innovation, the pace of change, and that's what makes it exciting. It makes it daunting for people to say, try to stay in touch with it all. But we're doing our best and that's part of the relationship we have with marketers and the data teams that we are so fortunate to work with.
- [14:29]
Greg Kilstrom
Yeah, yeah, definitely. Yeah. Well, we'll have to have you back on the show and talk about the synthetic stuff because that's definitely a top of mind for me right now for a few things. But definitely everything that you mentioned, lots of exciting stuff there. You did mention the importance of keeping humans in the loop as well. And I think that's an important thing to underscore too because we're all talking about personalizing personalized journeys and I'm excited about the predictive and the machine learning plus gen AI that lets us actually do the segment of one stuff that we've been talking about for quite a bit too. But how do you look at putting the guardrails in place with the agents to make sure that yes, customers want personalized experiences, but we still want that brand control. We also don't want our customers to find it creepy. Right. You know?
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Chris O'Neill
Yeah.
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Greg Kilstrom
How do you, how do you recommend finding that, that balance?
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Chris O'Neill
Well, humans do need to be in the loop, as I said, but it's not altogether different from suppressions that we currently do in the platform today.
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Greg Kilstrom
Right.
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Chris O'Neill
So there's suppressions for regulatory reasons, there's suppressions for all sorts of privacy to comply. We've been built with enterprise in mind from the very beginning. So we have those and also, also suppressions as mundane as, hey, you know, Greg's already purchased something, let's suppress that. Like that offering of the same thing he just purchased. Like that's the benefit of having, you know, all the transactions and all the data in one place. I don't think it'd be very different than that, really. Provided there's a human in the loop. And I think part of what's happening with, again, content creation and the suggestions is that they can be. They can pick up the essence of a brand, they won't get it all the way. Right. The agents aren't quite there yet. That's where humans will need to override it. I think ultimately in the short term, who knows where that goes in the long term, but it really is guardrails in terms of adherence, suppression of certain things, again, just like what we do today with the platform. So I don't think it's going to be too far, too big a leap at that point.
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Greg Kilstrom
Yeah. And I mean, I think it's makes sense that it also. A lot of that needs to be able to be automated because of the speed that we're taught. You know, like it's. Even if we wanted a human to be involved in some of this stuff that, you know, the speed at which things need to happen, you know, humans need to be involved in setting those, those guardrails up, but then we need to be able to trust the machines to automate it or else we can't move in real time, near real time, stuff like that.
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Chris O'Neill
Right? Well, precisely right. And this is the issue, the point, isn't it? It's not just that things are manual, they actually don't scale. There's a reason why there's limited number of segments that people usually carry around. There's a reason why there's a limited number of stimuli or creatives. It's like you have to go get approval and all that stuff. And really the reality is that leads to lowest common denominator kind of thinking and execution. So it's not only about iteration and fast, higher velocity, it's that, yeah, it can scale, so you can do thousands of things. Right. We do need to use algorithms to help with this. Much the same way as Netflix makes suggestions to you for the next show you want to watch, or Spotify with the next song, et cetera, those algorithms with the underlying data and propensity models and new AI LLM are getting astoundingly good. And it'd be as. It would be as absurd as thinking that there's a human behind there making a recommendation for every single one of the shows. That's not the way the world works. The permutations are literally measured almost towards infinity. Infinity, Right. When you think about all the different permutations. So it has to be automated. There has to be guardrails which can be consistently applied. And then you have to let the machines and the algorithms do their work.
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Greg Kilstrom
Yeah, yeah. Well, because I mean, to. The other thing that you briefly touched on as well is we're on, I mean, you know, MasterCard, Visa, PayPal unveiled shopping agents. Right. For consumers. So you know, that's. We're not only on the cusp, like it's kind of here. So how do you move so quickly? You know, when I have an agent shopping on my behalf, it's less about that the brand, color is right. Then I still get what I need, but I still want everything the way that I want it. So it has to move even more quickly. Which means, again, we have to be able to get the data quicker and make all these decisions even quicker. Right?
- [19:14]
Chris O'Neill
Yeah. So I serve on the board at Gap. So I'm thinking about these problems through apparel deeply. And what's going on is equivalent to what happened when Google disrupted Yahoo. Yahoo used to be this directory and along comes Google. It's like, no, this is what I want. And here it is. It's not some directory. It goes like, understands intent, something similar and I think far more profound is happening. It's not like I want a pair of blue jeans. It's like pretend for a minute I'm a, I don't know, a teenager going to Coachella, right. I have something very specific. I pay attention to certain influencers. I'm going to Coachella, right. I want it to match with my cowboy hat. I mean, I could go on for a long time and making this fictitious example up and it's kind of ludicrous, but you get the point, right? Those are not a pair of jeans. Those are something that does something far different. I'm looking for something far different that cannot easily be discerned and boiled down into a simple taxonomy. It really is about exploding the variance of what the product is. That's what the agent's going to do and it's going to really serve me as a consumer. And the brands that actually tap into that effectively are the ones that are going to win. And that's not easy to do. It's hard to do. It requires significant investments in data and teams and algorithms and the tech stack that allows it all to happen seamlessly.
- [20:37]
Greg Kilstrom
Yeah. And I think the other part of that is just becoming. I mentioned at the very top of the show this data driven decision making. Everybody talks about it, everybody says it's important. And yet, you know, I, I work with some very large companies. It's really hard to do that. Right. It's hard to let Go of kind of the human intuition and, and all those kinds of things. Sometimes when the data says one thing and, and stuff. How are, how is the average enterprise doing as far as this, this stuff goes? Like, are we still like well, you know, have a lot of, of work to do still and, and being more data driven or where's the average enterprise these days?
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Chris O'Neill
I'd say we're in the early innings. I'd say average enterprise is very poor at this and there's a lot of good and bad reasons for that. Again, it does start with basically the underlying tech stack and systems, the fragmented nature of the data, the lack of investment in the data. Not just putting it in one place, like in a cloud or fewer places. It's about investing in the semantic layer to impart meaning in that layer. It starts there and then it is okay, how do you modernize that? So it's not just these one size fits none platforms that really promise everything, but actually in the end are very slow, cumbersome and costly as heck and that don't ultimately allow the flexibility to move at the pace that's sort of what we're aiming to do better than like there's modern tech stacks that are very composable. You can mix and match them. You're not locking in all that, all that good stuff. So historically companies come in and they, they buy, they buy the belief. They, they say oh, this tool is going to solve, you know, right. World hunger, cold fusion. And every marketing needs you every. Except for it doesn't. That person gets, you know, moves on or it's invited to leave and then another person comes in with another tool and all of a sudden you have all these tools.
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Greg Kilstrom
It's always the tool, right, that's going to solve her. Yeah, yeah.
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Chris O'Neill
And then layer on top of that like the longest poll of all change ever. And this is certainly true with AI when people have deep fear about their jobs, this is going to disrupt is okay, yeah, I can, I think I can do better. Now that is a common thing. Even though there is incredible historically. I know look at a lot of these different companies have propensity models that say, you know, whether it's a supply chain or assortment or marketing that are quantifiably better and yet the human reaches in and says no, I could do it. And again for a whole bunch of reasons, maybe trying to justify their existence, their job or they sometimes do think it's better, except for they're usually not. Right. So it is about doing compare and contrast. Right. Sometimes there's Situations where it is, it is smart to do that, but it is having the courage to actually trust the machine first. Right. Humans in the loop, but trust the machine, it's going to do a better job and then also go on the change journey. Right. It's not about, it's going to, you know, there will be some jobs which will indeed be disrupted, of course. But the bigger story is how do you actually use these tools to reinvent the actual workflow? That's what we get excited about. Like, you know, look at all these disruptions that have happened, have changed the consumption of media. This is true of AI. We're consuming media in different ways, information in different ways, ChatGPT, Cloudy, you name it. But the bigger opportunity, I think is that it changes the work. It's a supply side thing. It's like how marketing gets created, how work goes from here to here. And you have these agent swarms with MCP and agent to agent protocols which allow you exchange context in very rich, nuanced ways. The similar way to what TCP IP did back in the day with the Internet. So with the packets. Right. It's repeating itself with bigger stakes and at a faster pace with, you know, I think, more transformative potential. Having talked about business models and all this good stuff where you can start to use outcome oriented business models and we're playing with some pretty powerful stuff all at the same time, at a pace we've never seen before. So it's really, really, really fun. But I have some empathy for these companies because there is a lot, there's a lot. Both technical data and human.
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Greg Kilstrom
Yeah. And it's, you know, it's, it's giving up a little, or it's, it feels like giving up a little bit of the control. But I mean, to your point and AI, there's plenty of talk about AI being biased and all that, but there's, you know, humans have plenty of biases as well and you know, cognitive, you know, anchor bias and all those kinds of things. And so, you know, I think the partnership idea makes a lot more sense than, you know, it's, it's us or them or something like that. Right. It's, it's. And I, that, that must be hard as a leader to, to let go of to an extent to say, okay, we're gonna, we're gonna let the data lead us. But it's not really giving up on being a leader. Right. It's, there is some kind of, you know, middle ground, right?
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Chris O'Neill
Quite the opposite. I don't think it's Giving up. It's actually being even a better leader. Right. Just, you just have different team members and they're called agents.
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Greg Kilstrom
Right.
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Chris O'Neill
And even, even more, I think individual contributors are actually now managers too. Why do I say that? Well, they're going to have like metaphorically a thousand interns called agents. And what is good management entail? Well, good management means you need to set good goals. Right. You need to set expectations. What do you expect? Like these agents, if they don't have context, they're useless. But, but if they don't know what they're solving for, they're also useless. Well, guess what? Agents need feedback. They're not going to be right right away. They need to get feedback, mechanism, et cetera. So these are all hallmarks of leaders or managers. More specifically. So I think it's quite the opposite, maybe counterintuitive to say like you actually everyone's going to become a manager, they're just going to be managing these things called agents and together this is what it's all. And I actually think of these agents as like glue people. They're going to glue together or create these agent swarms, like these workflows that don't require manual stitching together. They'll happen automatically.
- [26:45]
Greg Kilstrom
Yeah. Love it. Love it. Yeah. Well, Chris, thanks so much for joining today. One last question before we wrap up here. What do you do to stay agile in your role and how do you find a way to do it consistently?
- [26:58]
Chris O'Neill
Well, I try to ride my bike every once in a while to stay reasonably fit. But I like to experiment with all these different tools. I have just started college age son and a high school aged daughter. I like to look at the world through their eyes. They teach me stuff all the time. It's amazing how they're using AI so I learned from them. And then I make it okay for the team to experiment and fail with these things. That's how I try to do it. I don't pretend to keep up with it all. I think that's, that's really, really difficult. So those are the things I try to do and try to have a sense of humility about it all. But boy, it's an interesting time and a fun time to be in business with all these tools and models that are shaping our world at breakneck pace.
- [27:45]
Greg Kilstrom
Yeah, absolutely. Well, love it. Well, again I'd like to thank Chris o', Neill, CEO of Growth Loop for joining the show. You can learn more about Chris and Growth Loop by following the links in the show notes. Thanks again for listening to the Agile brand brought to you by Tech Systems. If you enjoyed the show, please take a minute to subscribe and leave us a rating so that others can find the show as well. You can access more episodes of the show@theagilebrand.com that's theagile brand.com and contact me if you're interested in consulting or advisory services or are looking for a speaker for your next event, go to www.greg kilstrom.com that's G R E G K I H L S t r o m.com the Agile brand is produced by Missing Link, a Latina owned, strategy driven, creatively fueled production co op. From ideation to creation, they craft human connections through intelligent, engaging and informative content. Until next time, stay curious and stay agile.
- [28:50]
Strayer University Announcer
The agile brand.
- [28:58]
Greg Kilstrom
Refresh your space for summer during PURA's Summer Savings event. Enjoy 20% off site wide with exclusive savings on our smart home fragrance Diffusers. It's the perfect time to upgrade. Visit pura.com
- [29:13]
Liberty Mutual Spokesperson
and Doug there's nowhere I wouldn't go to help someone customize and save on car insurance with Liberty Mutual. Even if it means sitting front row at a comedy show.
- [29:23]
Greg Kilstrom
Hey everyone, check out this guy and his bird. What is this, your first date?
- [29:26]
Chris O'Neill
Oh no.
- [29:27]
Liberty Mutual Spokesperson
We help people customize and save on car insurance with Liberty Mutual. Together. We're married. Me to a human, him to a bird.
- [29:33]
Greg Kilstrom
Yeah, the bird looks out of your league.
- [29:34]
Liberty Mutual Spokesperson
Anyways, get a quote@libertymutual.com or with your local agent.
- [29:38]
Chris O'Neill
Liberty Liberty Liberty Liberty.