In this episode
Matt LeBaron, founder and CEO of Revvim, explains where enterprise Google Ads budgets leak money that standard reporting never shows: uncontested auctions where a brand is the only bidder and still pays Google’s per-bidder minimum bid. Greg Kihlström and Matt work through the difference between generative AI that writes ad copy and agentic AI that makes bidding decisions on its own, where the line between human strategy and machine execution belongs, why Performance Max and AI Max charge advertisers to learn what their paid search team already knew, how to trust and verify autonomous spend when decisions happen in milliseconds, and why ChatGPT advertising serves research intent rather than the engagement intent that drives branded search revenue.
Key takeaways
- Agentic AI decides and acts; generative AI remixes. Generative tools assemble a new mix of what people have done before. Agentic (or deliberative) AI evaluates its environment, makes a decision at computer speed, and implements it, which Matt frames as going beyond analytics to action.
- Humans own strategy and goals; agents own tactical implementation. The handoff point is the repetitive, time-sensitive execution a person cannot do from a screen, not the setting of objectives.
- Google sets minimum bids per bidder, not per auction. Since quality score arrived around 2006–2007, two advertisers on the same term can face different floors. Being the only bidder does not get you the click for a penny, the same way a Sotheby’s auction with one bidder still has a reserve price.
- Uncontested branded auctions are where the trapped capital sits. Revvim’s AdAI detects auctions with no competitor present in close to real time and lowers the bid. Matt reports bids falling to a penny per click on terms that previously cost 10 cents to a dollar, with impression share held at target.
- The savings are material at enterprise scale. Revvim’s largest single-brand customer saves about $1.3 million per month, and the company passed $100 million in cumulative customer savings shortly after Black Friday/Cyber Monday last year.
- PMax and AI Max are “another layer of black paint around the black box.” They need training data, and the advertiser pays for it. When AI Max launched, accounts began testing conquesting that experienced managers had already switched off because it was not cost-effective.
- Many brands run manual CPC for brand bidding. Once an ad shows every time at a penny a click, a smart bidding strategy has little left to add.
- Paid search is dynamic, so treat it that way. Brand holdout tests have been the main incrementality tool for twenty-plus years, but in an agentic environment AdAI can detect a competitor dropping its brand-defense ad and launch a conquesting ad within 8 minutes 45 seconds on average.
- Measure revenue, not clicks. Matt’s test for any new system: a visible lift in the actual outcome, with costs held or reduced.
- ChatGPT ads serve research intent, not engagement intent. Over a million daily holiday-season Google searches for “Nike” are people trying to reach Nike; the same query in ChatGPT returns the history of the swoosh. Treat AI search advertising as a long-term play, not a way to make this quarter’s number.
Chapters
- Introduction: trapped capital inside the Google Ads campaigns you already run
- Matt LeBaron’s background and what Revvim does
- Agentic vs generative AI in ad management
- Where the human hands a decision to the agent: AdAI and uncontested auctions
- Why paid search reporting hides trapped capital: per-bidder minimum bids
- Performance Max, AI Max and the black box
- Trust and verify when spend moves in milliseconds
- Do the same principles apply to social, programmatic and retail media?
- Ads in ChatGPT and research intent vs engagement intent
- One year from now: agentic purchasing
- How Matt stays agile
What agentic AI does in ad management that generative AI doesn’t
Generative tools are built on models that look at what people have done in the past and produce a different mix of it: images, ad copy, variations. Agentic AI, which Matt also calls deliberative AI, evaluates the environment it finds itself in, makes decisions at the speed computers can make them, and then implements those decisions. Marketing has had decades of tools that provide data, insights and visualizations for humans to act on. The agentic shift is letting the system make and execute the decision, and capturing the benefit of precise, data-driven choices made at high speed.
Where the human should still hold the controls
Humans set strategy, set the goals, and decide in broad terms how to pursue them. Agentic AI earns its place in the tactical implementation of the processes that serve those goals. Matt’s example is deliberately unstrategic: AdAI identifies when a brand is the only advertiser in a Google Ads auction, which Revvim calls an uncontested auction, and acts on it immediately. No person is sitting at a screen watching the auction and reacting in real time, so the machine does something a human cannot, and the result is a sharp reduction in branded search costs for companies that do not always face a competitor.
Why being the only bidder still costs you money
Since Google introduced quality score, it has been able to set different minimum bids per bidder rather than per auction. Two advertisers can want the same term; one bids a dollar and is told it is not enough, the other bids 50 cents and runs. The mechanism is driven mostly by click-through rate, because Google only earns when someone clicks. The same logic applies when no competitor is present. Matt’s analogy is a Sotheby’s auction for a Ming vase: being the only bidder does not mean you get it for a dollar, because there is a reserve price. Google applies that reserve on every auction, per bidder. That is the fact AdAI exploits, and the savings are large: about $1.3 million a month for Revvim’s biggest single-brand customer, and $100 million across the customer base reached shortly after last year’s Black Friday/Cyber Monday.
Why Performance Max and AI Max leave money on the table
Matt built his first automated bid management system in 2006 at SHOP.COM, so he hears new AI-wrapped bidding products as another layer of black paint around the black box. Those systems need training, training needs data, and the advertiser pays to generate it. When AI Max launched, Revvim saw an influx of competitive bidding as accounts tested conquesting that the paid search professionals managing those accounts had already turned off for being uneconomic. Tens or hundreds of thousands of dollars later, the AI may conclude the same thing. He allows that PMax or AI Max might occasionally find a missed term, but on uncontested brand terms where an ad already shows at target impression share for a penny a click, there is little a smart bidding strategy can add, which is why a fair number of Revvim customers run manual CPC for brand.
How to trust and verify autonomous spend
Have your own tools that operate at the same speed as the systems spending the money. The brand holdout test, where a CMO turns off branded ads for a week or a month to see what happens, has been one of the few incrementality tools available for branded search for more than twenty years. In an agentic environment that pause is visible to competitors’ agents: AdAI’s conquesting feature detects a removed brand-defense ad and launches a conquesting ad within 8 minutes 45 seconds on average. Paid search has always been dynamic, but it is often managed as if it were static. The fundamentals still hold: measure the actual goal, which for most companies is revenue, and confirm that a new system produces a visible lift in that outcome with costs maintained or reduced.
Do the same principles apply beyond Google search?
Largely yes, with nuances per auction environment. Google built the most effective revenue-generating machine in advertising by helping millions of businesses generate revenue in a measurable way, and social, retail media and Microsoft Ads are implementing auction environments that mimic it closely. Similar auctions can be acted on in similar ways at agentic speed.
What changes when ads arrive in ChatGPT
The intent is different. Matt’s experiment: type “Nike” into ChatGPT and you get the history of the company and the swoosh, where the same four letters on Google during peak holiday season draw over a million searches a day from people who want to engage with Nike, driving tens of millions of dollars in revenue. AI search engines serve research intent, which suits considered purchases and longer sales cycles and is less useful for a pair of shoes. Matt is still waiting for a LinkedIn post from someone who advertised on an AI search engine and made their quarterly number because of it. Invest, but treat it as a long-term bet that may or may not pay off.
FAQ
What is the difference between generative AI and agentic AI in advertising? Generative AI produces a new mix of what people have done before, such as ad copy or images. Agentic AI evaluates the live environment, makes a decision at machine speed, and implements it, moving from analytics to action.
What is an uncontested auction in Google Ads? An auction where a brand is the only advertiser bidding on a term. Because Google sets minimum bids per bidder, the brand still pays a floor price even with no competitor present.
How much can agentic bidding save on branded search? Matt LeBaron reports bids on uncontested branded terms falling to a penny per click from 10 cents to a dollar, with Revvim’s largest single-brand customer saving about $1.3 million a month.
Why is Matt LeBaron skeptical of Performance Max and AI Max? They are AI systems that require training data the advertiser pays to generate, often relearning at the advertiser’s expense what experienced account managers already knew, such as that conquesting was not cost-effective.
How should a marketing leader verify results from autonomous ad systems? Use tools that operate at the same speed, measure revenue rather than clicks, and confirm a visible lift in the outcome with costs maintained or reduced.
Should marketers advertise in ChatGPT? As a long-term investment, yes, but not to make this quarter’s numbers. AI search serves research intent, not the engagement intent behind high-volume branded searches on Google.
About Matt LeBaron
Matt LeBaron is Founder and CEO of Revvim. He began his career in 1997 at CatalogCity.com (later SHOP.COM), starting in the mailroom and rising to Director of Database Operations. He was part of the team that built what may have been the first multi-vendor shopping cart, which Amazon studied as inspiration for zShops. In 2004 he moved to marketing as SHOP.COM’s first Head of Customer Acquisition, where he uploaded six million keywords from search logs to Google Ads and, within three days, wrote his first automated bidding algorithm, which SHOP.COM pursued a patent on. He founded digital marketing consultancy New Day Consulting in 2007 and Revvim in 2011, bootstrapping it with consulting revenue. In 2020 his team built and patented AdAi, which onboarded over $5B in annual Google Ads spend in its first two years. He lives in San Juan Bautista, California, with his wife and kids.
———- Resources ———-
The Agile Brand podcast is brought to you by TEKsystems.
Need to evaluate martech, AI, or CX platforms and don’t know where to start? The Agile Brand Guide List is now in beta. We’re building the most useful, free-to-use, and up-to-date way to assess what truly matters when evaluating platforms.
Reach your customers with Reddit. Spend $500 in ad spend, get $500 back in ad credit!
Chaser is the only Slack-native project management platform that helps teams turn messages into tracked tasks, automate follow-ups, and maintain team-wide visibility, without adopting another tool. Now integrated with Claude and other GenAI tools. Use code AGILEBRAND for a 3-month free trial (normal trial is 14 days).
Start building your own apps with Replit and get $20 off.
Enjoyed the show? Tell us more and give us a rating so others can find the show.
Connect with Greg on LinkedIn.
Don’t miss a thing: get the latest episodes, sign up for our newsletter, and check out Martechipedia, the wiki for marketing technology.
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.
Transcript
Greg Kihlström: Hi, I’m Greg Kihlström, host of the Agile Brand, and here’s a question for you. What if the biggest source of new marketing budget this year isn’t a new line item from your CFO, but millions of dollars already sitting trapped inside the Google Ads campaigns you’re running right now? Agility here isn’t just reacting faster to the market, it’s having the intelligence to anticipate and act on opportunities inside a live auction thousands of times a second without losing the thread of your larger strategy. Today we’re going to talk about the differences between AI that writes your ad copy and AI that autonomously makes financial decisions on your behalf, how to fund new initiatives by claiming the 12 to 18% of trapped capital sitting in your current paid search spend without asking for a bigger budget, and why set it and forget it automation leaves an edge on the table in live auctions, and what it actually takes to capture it. To help me discuss this, I’d like to welcome Matt LeBaron, CEO at Revvum. Matt, welcome to the show.
Matt LeBaron: Hey Greg, thanks so much for having me.
Greg Kihlström: Looking forward to talking about this, but before we dive in, why don’t you give us a little background on you and your role at Revvum?
Matt LeBaron: I’m the founder and CEO of Revvum. Revvum is an agentic AI provider focused on the search marketing space. My personal background is engineering, software development, figuring out the intersection between what computers do really well and what humans do well, and how can we do those things well together. So that’s the premise behind the company, automation and revenue focus around search marketing and marketing in general.
Greg Kihlström: Great. Well, let’s dive in then and talk about a few things today, but I want to start with maybe definitions, and certainly we talk about AI on every episode here. Now we talk about agentic on every episode here, so they don’t take things away, they just keep adding more. But I want to talk about what agentic means in the context of what we’re talking about today. Again, it can do a lot of things and mean a lot of things to a lot of different roles, but in the context of ad management, what specifically is agentic doing that maybe a generative tool isn’t? Talk a little bit about that for us.
Matt LeBaron: For contrast, generative tools are oftentimes based on large language models or other types of models that are looking at things that people have done in the past, bring them together, and creating something that is a different mix of what people have done in the past. That’s essentially the generative side. And we can have fun with tools like Gemini to create images, generative images. We can use that to create copy, if we’re talking about ad copy and things like that. There’s another type of AI, agentic AI, sometimes referred to as deliberative AI, which is evaluating the environment in which it finds itself and making decisions at the speed that computers are able to make decisions, and then implementing them. And so I like to think of it as going beyond analytics. There’s been historically tons and tons of tools out there in the marketing space that provide data and insights and visualizations for us humans to then make decisions on. In the agentic AI space, we’re able to essentially have those computers make those decisions for us and reap the benefits of precise data-driven decisions at high speed.
Greg Kihlström: Great. And then I guess I know there’s a lot of different use cases potentially, but where does the human activity often hand over a decision to the agent? From an ad management standpoint, let’s say, where is it best to start drawing that line between what should a human still kind of manage and control versus what’s possible to hand over to the machine?
Matt LeBaron: Humans should be in charge of strategy, most definitely. Humans should be setting the goals, and in broad terms, identifying how to go about capturing those goals. Where I believe agentic AI is most effective is in the tactical implementation of the processes that are put in place to achieve specific goals. I can give you an example from our flagship product, AdAI, which is an agentic AI within this space we’re talking about, and it does something very, very simple on the surface. It essentially identifies whenever you’re the only advertiser within a Google Ads environment, when we call it an uncontested auction, when there’s no other competitor there. And because it’s able to identify that in close to real time, it can then act upon that, and we can take action that a human couldn’t take, because we’re not always just sitting at our screens, I hope, staring at the Google auction and reacting to it. And because it’s able to react at such a high rate of speed, we’re able to essentially dramatically reduce the costs of, in particular, branded search for companies that may not always find that there’s a competitor present in the auction. So that’s one example of how agentic AI can make life a lot better. And there’s nothing strategic there. It’s actually really, really simple. But it’s not something that humans are going to be able to do, and there is some sophistication around the implementation of this type of AI so that it does the right thing.
Greg Kihlström: Well, and that scenario you described, you’ve referred to that as a lot of the enterprise ad spend being trapped capital, money that’s essentially invisible in standard reporting and yet able to be leveraged like that. I guess you talked about this a little bit, about how paid search enables this, but what is it about the way paid search is normally measured that kind of keeps that capital hidden?
Matt LeBaron: Well, it all comes down to how the auction environments work, and since the advent of quality score by Google back in, whatever that was, 2006, 2007, something like that, Google has essentially been able to set different minimum bids, not per auction, but per bidder. So in other words, you and I could both want to have an ad show on a given search term, and Greg, you bid a dollar and I bid 50 cents, and because of quality score considerations, Google says, “Sorry, Greg, a dollar isn’t enough for us to show your ad. Matt, go right ahead. 50 cents, we’re happy to have that ad show.” And it’s mostly click-through rate, and Google’s doing that in order to maximize its own revenue. It wants to have ads that people want to click on, because if they don’t click, then it doesn’t make any money. So it all makes rational sense. And that extends even to uncontested auctions where there’s nobody present. So we would think, “Hey, if I’m the only bidder,” for example, let’s take an example with a Sotheby’s auction where you and I are bidding on a Ming vase, right? And I’m the only one there. You said, “I don’t really like that vase,” so I bid a dollar. They’re not just going to give it to me for a dollar, right? They’re going to say, “I’m sorry, sir, there’s a reserve price, a minimum bid. You have to bid at least a thousand dollars or something.” And effectively, Google’s doing that on every single auction with minimum bids, and not just per auction, as we mentioned, but per bidder. And so just because there’s nobody there in the auction doesn’t mean that you can just get it for a penny or whatever the minimum is in the local currency. So that’s the fact that occurs within these auction environments that we’re taking advantage of and able to suss out those savings. And they can be very material. Our largest single-branded customer, we save about $1.3 million per month. And last year, we hit 100 million in savings across our customer base shortly after Black Friday, Cyber Monday. So there are literally millions of dollars in enterprise ad spend that can be unlocked with smarter bidding.
Greg Kihlström: Well, and speaking of that, Google has rolled out Performance Max, smart bidding, things that make bidding a lot easier. I know I’ve used it myself. But maybe to your last point, they’re not always necessarily maximizing things for the advertiser. They are making it easier to use. Where are some of the opportunities where those tools are leaving money on the table and where an alternative might behave a little differently?
Matt LeBaron: Another great question. I’ve been doing this for way too long, and so I’m a little bit of a skeptic. I also have a history building automated bid management systems. I built my first automated bid management system back in 2006 for a company called shop.com that I was working for at the time, and they pursued a patent on it. So now 20 years of building automated bidding systems and such. So when I hear about PMax and AI Max and other things like that, what I hear is, “Oh, well, now there’s another layer of black paint around the black box.” And it’s AI-driven, which means it needs training, which means it needs data, which means it needs somebody to generate that data, and who’s paying for that data? Well, of course, the advertiser is going to pay for PMax or AI Max to learn to do things or not do things that are, in many cases, very similar to what the individual who’s managing the account has learned through their own time spent, right? So I’ll give you an example of this. We saw an influx of competitive bidding when AI Max was launched because all these accounts began using AI Max. AI Max said, “Hey, let’s test conquesting. Let’s try that out. Maybe that’ll work,” right? Now, the actual paid search professionals that have been managing the account didn’t have conquesting on because they knew that it was not cost-effective. Well, a few tens or hundreds of thousands of dollars later, AI Max or PMax might say, “Hey, we figured something out. We shouldn’t do this.” Right? And so we’ve essentially paid for Google’s AIs to learn not to do the things that we’d already figured out not to do, but hey, Google made their quarterly numbers, right? And so I’m a bit of a skeptic in that I’m expecting to see new AI-wrapped features or capabilities rolled out every quarter or two as ways to continue to drive revenue growth. Google as an enterprise, and in the search space in particular, has seen unprecedented revenue growth, right? How many quarters in a row has Google seen growth in its search and other line item? I don’t know off the top of my head, but it’s a lot. They never miss a quarter, right? So that’s my view. All that said, maybe there’s some incremental value to be found there. Maybe there are instances where PMax or AI Max are able to identify a search term that, wow, somehow had been missed by these professionals that manage the account, and they found a way to bid for it and get a little bit of incremental value. But color me skeptical. I don’t see that happen too often. The last thing I’ll say on the subject is that with AdAI, we’re able to dramatically reduce the cost of these uncontested auctions. It’s not uncommon for us to get bids down to a penny per click on an auction where there’s no competitor present, and you may have been paying 10 cents, 50 cents, a dollar per click before the technology, right? And we’re able to keep the ad in the auction at whatever the target impression share is. So once we’re at a very high impression share, and we have a very, very low bid, the question then arises, well, what is the value that a smart bidding strategy could offer on top of that? If my ad is showing every time I want it to show, and I’m paying a penny per click, what would a smart bid strategy do that would actually benefit me? And that’s where I think we see some of our customers, a fair number of them actually, using manual CPC for brand bidding, just because the smart bid strategy isn’t going to do a whole lot more. And maybe if you have very specific audiences, people that are coming to log into a site versus those that aren’t, maybe a smart bid strategy could help bifurcate between those and be smarter. But in many, many cases with many brands, that’s just not the case.
Greg Kihlström: When autonomous systems are spending a lot of money in milliseconds, right? So you mentioned kind of the black box in general, and it’s hard to understand some of these decisions that are happening already. But when things are moving so quickly, how does a marketing leader trust and verify, as well as separate impact from things like seasonality, creative refresh? How do they get a handle on things like incrementality and all that when things are just moving so quickly?
Matt LeBaron: It’s another great question, Greg. I would say that you need to have your own tools that operate at the same speed where possible, and that’s something that our customers benefit from. I’ll give you kind of a simple example of this. There are still people today, CMOs, that might come in and see a search for their brand and see an ad above an organic listing and say, “Hey, why are we paying for that ad? What’s the incrementality? Hey, shut off all branded ads for a week or a month, and let’s see what it does to the numbers.” Right? That’s sometimes called a brand holdout or brand incrementality test, and they have been one of the few tools that we’ve had to test incrementality on a really important channel like branded search for the last twenty plus years. However, in an agentic environment where we have AI agents that are monitoring all these different terms, and in fact, AdAI does this, there’s a conquesting feature within the tool, where if a competitor removes their brand defense ad, AdAI can detect that and launch a conquesting ad automatically within eight minutes and forty-five seconds on average of that ad being removed. We already know that paid search is very dynamic, and we’ve said it’s dynamic, but sometimes we treat it like it’s static or it’s inert, where, “Oh, we’ll make a change and check back in a week,” or something like that. Doesn’t make any sense in an agentic AI world because things are happening just too fast, and they should be. Anybody that’s not moving at that speed is going to be left behind. They’ll be making poor decisions. But you asked specifically, how do you measure and trust and verify when millions of dollars are being run through every millisecond? I think you have to go back to fundamentals and make sure that you’re measuring the actual goal. The reason that my company’s called Revvum and not Clickum is because I’m obsessed with revenue as a success metric. I don’t want to just pay for clicks. I want to pay for results, for revenue. And revenue’s a pretty good metric for most companies. So I think if you’re able to measure that, parse it out as best you can, and make sure that once you’ve implemented some new system, that you can see an actual lift in your outcome, revenue, whatever that might be, and maintained or decreased your costs to generate that, that’s success in marketing to me. And whatever the tool or process that’s implemented needs to support that success.
Greg Kihlström: Well, and so we’ve talked a bit about how Revvum is approaching this in Google paid search ads. Do the same principles apply in other areas? Social, programmatic, are there things to be unlocked here? Not giving you any product ideas or anything, but do the same rules or same principles apply?
Matt LeBaron: In many cases, yes. I think it depends on the nuances of the auction environment itself. And over the last twenty-five, thirty years almost now, Google has created the world’s most amazing revenue-generating machine for themselves. And they’ve done so because they’ve helped millions of other businesses generate revenue in a cost-effective way. And so that’s very true, right? These are not billboards where you put a big sign up in the field, and you hope that the passing motorists see it and take action. We’re able to track with great precision the impact that advertising has. And so I believe that people are going to mimic and continue to mimic success, and those other environments, social and retail media and things like that, are implementing auction environments very similar to Google. Certainly, Bing appears to literally implement every single thing that—I should call it Microsoft Ads—implements every little thing that Google Ads does shortly thereafter. And it’s a good system. Imitation is the sincerest form of flattery. And so all these systems, I think, are going to have similar auction environments and can be acted upon in similar ways with the agentic speed.
Greg Kihlström: Well, and last question then, too. How does ads in ChatGPT and other things change the dynamic here? Does it? Is it just a different platform and another channel? But how does this kind of either complicate things or keep them the same?
Matt LeBaron: I think, obviously, it’s a different platform. I do believe that there is broadly a different intent that marketers need to be aware of when they think about ChatGPT versus Google Ads. And I’ll give you a specific example here. There are over a million searches every day during the peak holiday season for the four letters Nike. Just that word Nike, right? People search for it, and the intent behind that within Google is to engage with Nike. That drives tens of millions of dollars of revenue for the company. Now, how many people have gone to ChatGPT and typed in Nike? If you do that, and I did actually as part of an experiment, you get the history of the swoosh, and you get the history of the company and some key points that have happened. But at least until recently, and as far as I know even today, there’s not a focus within the LLM search engines, the AI search engines, on engagement-type searches. They’re research intent, right? And so I think marketers need to understand that they are entering a research intent environment by and large, and that they should have the expectations of a longer sales cycle, more considered purchase, and that may be good for certain types of businesses, maybe not for a pair of shoes. I don’t know, maybe some people research shoes before buying them. If you’re a marathoner, maybe you want to find out the exact shoe that’s going to give you the best cushion and support for a marathon. Sure. But I think that’s not going to be useful for less considered purchases. And certainly the volume. I keep waiting for a post on LinkedIn to enter my feed and say, “I advertised, or I did something on an AI search engine. I used the tools that promote me, and I made my numbers this quarter.”
Greg Kihlström: Yeah. I know.
Matt LeBaron: Right? There’s just nothing about the results at all, and I’m not suggesting we shouldn’t put time and energy into these things, but we also need to understand that this is a long-term play, and it may not pan out in the way that we expect it to. So if you’re trying to make your numbers this quarter or next quarter, I wouldn’t rely on AI search or ChatGPT ads or anything like that. There’s just not going to be the return. Doesn’t mean you shouldn’t invest in it, but just keep in mind it’s a long-term investment, and it may or may not pan out.
Greg Kihlström: Makes sense. Well, Matt, thanks so much for joining today. I’ve got two last questions for you as we wrap up here. First one, if we were having this interview one year from today, what is one thing that we would definitely be talking about?
Matt LeBaron: Well, I would not be at all surprised if we find ourselves pivoting a little bit from agentic AI and AI search to agentic purchasing. The idea that you can send agents out and buy things, to have them buy things for you. I remain skeptical about how important that’s going to be for our society. I think a lot of people enjoy the shopping, shopping therapy, as it’s sometimes called.
Greg Kihlström: Right. Agreed.
Matt LeBaron: Why would you want to hand over shopping therapy to a bot? I’m not going to hand over my doom scrolling to a bot. Why would I hand over my shopping, right? So I remain skeptical, but I do think that hype bubble is still expanding. And so I think we’ll hear more hype about it. How much of that’s going to be real, so to speak, I’m not sure.
Greg Kihlström: Got it. And last question for you. What do you do to stay agile in your role, and how do you find a way to do it consistently?
Matt LeBaron: I’m always talking to people. I think that’s the most useful thing for me in my role as CEO. I love talking to customers and partners, and folks like yourself who are immersed in the industry and are able to provide insights. Not everyone has every experience, and so I love talking to people who had different experiences. And I think it’s really, really smart to learn from your own experiences. I think it’s genius to learn from the experiences of others, especially the mistakes, right? So I’m constantly looking to talk to people who have done things and experienced things, and to learn from them. That’s how I keep sharp and agile.
Greg Kihlström: Love that. Well, again, I’d like to thank Matt LeBaron, CEO at Revvum, for joining the show. You can learn more about Matt and Revvum by following the links in the show notes. 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. The Agile Brand.
