So we're going to stay in the grocery store aisle for a little bit longer to talk about a performance marketing revolution in the consumer packaged goods segment. You probably know about this killer brand of water called Liquid Death. Have you all heard of it? Yes. Okay, yes. I am a fan of Liquid Death. Definitely all my concerts, I have a Liquid Death. So here to share some insights is the company's Chief Media Officer, Benoit, and also joined by Bryan, the CEO of Ibotta. Give it up for them, y'all. [Video Narrator] All right. Thanks, Dave. So appreciate being here after that. How's it going? Welcome to my hometown of Atlanta, Georgia. It's not a bad place. Hopefully, y'all get a chance to get out and check it out. We're in for a trip today because we're joined by one of the persons that's talking to me today. I think in certain sense of all the people I've worked with in the last five years, Benoit was one of the most kind of disruptive forward-thinking innovators in the first couple of months. The things that they've been doing in their brand's name really are leading and making changes. So today, we're going to talk about that in the context of digital rewards. And we're going to hear from Benoit while I'm talking about the experience they've had using performance marketing in a different way, especially in the context of a lot of product and services roles that we're facing. Okay, so earlier today, we announced or yesterday, we announced the first ever performance marketing platform in the CPG industry, which we call LiveLearn. This is a set of solutions that essentially allow our partners to measure the ROI of a digital reward. In the past, the challenge has been hard to know whether or not a digital promotion really drove the digital sales or just subsidized people who are actually buying the product and what the cost per incremental dollar is. So in the same way that you can do digital performance marketing to set targets and create rewarding measures, and then optimize what we're getting in place, that's what we are now bringing to market. Liquid Death is a company that I've been working with for the last six months, and they're one of the relatively new companies that's gotten a chance to try this out and has really provided me with an insight into what we're bringing to market. So today, I'm going to ask him some questions a little bit about his experience with the challenges of marketing, particularly in the CPG industry, the challenges of measurement, and what they've learned so far from this and other solutions like it. So welcome, Benoit. Thanks for being here. Thanks for having me. So I think I alluded to this briefly, but the big challenge is actually having credible measurement of ROI. In the digital world, you have a click, you have a pixel, you have some sort of SDK to check if there's an install of an app or conversion. But in the physical world, you don't necessarily have that. So maybe you could set the stage and just talk about what's challenging. You've come into this world, Liquid Death. You've got stuff in stores. How does that present unique challenges? Yeah, I mean, it's one of the biggest challenges. We do not have self-signal real time, right? We do a lot of work from upper to lower funnel. We do everything we can to drive people into the store and remember our brand and buy the brand. But how do we measure impact? And obviously, there is a lot of discussions when you buy media around performance in this famous world that I cannot stand, the ROAS, where that basically means nothing, as I can drive iOS all day long. But then incrementality comes in, and incrementality, you can really measure incrementality only if you get the self-signal from the retailer, which is another problem. And it's also very delayed. So how do you really build your campaigns, your full media mix to lead to performance? Because at the end of the day, we can talk all day about brand, which is key and which is really leading the upper funnel, but we all work to sell product. And it sells product at the aisle. And how do we do that? And now we measure that as close to real time as possible so we can decide to invest less or invest more is really the challenge. When I first talked to Mike, your Founder and CEO, about digital promotions, he was a little skeptical. His view was, "I don't want to tarnish our brand. A lot of people already know about our brand. They're already buying our product." And we sort of brought him along on this conversation about how you could use data to confirm or deny these suspicions and establish that you're driving profitable revenue growth. How do you think that conversation has evolved as sort of more real-time data has become available in this context, for example? Yeah, I mean, it's the center of a lot of discussions, especially around investment, because you don't want to obviously put TPR or promotions dollars in front of people that are already about to buy. It's just a waste of money, especially if you put media behind it. Then you spend dollars to get someone that was already going to buy, and you give a discount on your product. Then the return on that investment is very, very poor. But we know from us for our North Star around household penetration, we know that TPR, we know that promotion is a big driver for people to try the product. Now we need to get that promotions in front of the people that never bought us, not people that know about us already. And that's a big challenge. When you do that in store, it's a big challenge. You don't decide who walks by the end cap. So from a digital standpoint, you probably have a lot more flexibility in doing that. But again, you need to be able to measure that. And that's the challenge. So let's talk about the measurement. In the digital media world, you've had these lift studies, right? So if you run an ad on YouTube or on Google, you can look at two matched audiences that are statistically very similar. And you can say, well, this audience was exposed to the YouTube ad and this one wasn't in the same time period and kind of hold all other variables constant. And then you can pass the user ID to a company like Circana, and they can look at frequent shopper data to see whether or not there are any statistically significant differences in the purchase habits of people who saw the YouTube ad versus didn't see the YouTube ad. That basic principle has been at the heart of digital media, but it's never been brought to the promotion space. Never. So what we're trying to do is instead of looking at sales that occur in one period of time and then in a later period of time, and sort of the problem with that is there are a lot of variables at play there. We're trying to control for variables so you can make a claim about what caused what. How do you think that sort of statistics, when you compare that to something like a marketing mix model, which is six months later or 12 months later, when you have to use one or the other, which one do you prefer and why? I mean, obviously, real time is the name of the game, right? Because if I invest a good chunk of money and I don't know that return until three, six months later, even worse if you start talking and those MMMs that looks at like, okay, let's look at the last three years of data and maybe we can decide what we're going to do three years from now. I mean, especially as a brand like us, we need to move fast, right? So we need to make decisions fast. So in order to, I mean, this concept of testing against the control real time and getting signal real time is the only way that we can really operate. And we are trying to build on our end practices that do that through MMMs and kind of as close to real time as we can. But it's very difficult to ingest the signal of promotions, right? Because the promotions, we need to understand if the sales was attached to promotion and that there's not a lot of data set that can give you that. And once you ingest that, you don't really have it real time. So then you're still delayed. So we can do incrementality testing, but promotion signal is very difficult to ingest from an internal practice. If you have a more ongoing signal that's telling you the difference in sales between these two populations yesterday or last week or two weeks ago was this, and then you're able to adjust the promotions while the campaign is going on, what is the purpose of that? Why would you want to put a different promotion in front of a different person? You mentioned the TPR. That's like an end cap that everybody sees in the store. Everybody gets the same thing regardless of their prior propensity to buy your product. What's the advantage of being able to tailor the promotion based on an individual's propensity to buy your product? I mean, that's what I was talking about earlier is that I don't want to put a promotion in front of someone that was about to buy regardless, right? So the ability to decide first, is it an incremental buyer, shopper, household or not? If it is, then should I or not? And yes, I should get those people to trial. So I should put a promotion in front of those people if they are at that time of trial. But if they are already a repeat buyer that regardless was going to buy because they are buying every, let's say, three weeks and they are at the end of the three weeks, they are not going to lapse, they are going to buy again, why would I put an offer in front of them? And that happens a lot, by the way, if I don't have the tools to do that. And that's a really heavy investment for no return. So that's what I'm trying to solve. And I think we're solving right now with that tool that you built. And that makes me feel a lot better now when we put dollars to investment. And not just me, by the way, because I'm sure everybody in the room, we all talk to your CFO, right? And it's always challenging to talk to CFOs when it comes to invest media dollars. Well, I think that's the one where you can have a good discussion with your CFO because you can really show like, hey, we're investing promotion, we're investing media, but by the way, it's on an incrementality basis. So, discussion with the CFO has never been easier for that one. In the past, the way our industry has worked is you run a promotion and then some weeks or months later, you get a recap deck that talks about ROAS, right? And as I've gone around and talked to CMOs and CFOs, they've been pretty skeptical of those claims. Oh, look at that, another great ROAS outcome, right? Versus being able to see it on an every two-week basis closer to real time and be able to actually adjust while the campaign is going on. As you think about it, one of the things we did together was you sort of figure out what the profit margin is of your product, and then you set a constraint. I only want to pay this much in terms of a cost per incremental dollar because that way I know that every incremental dollar drops enough to the bottom line that it can cover that cost. So I can leave it on, in other words, because in the same way you would leave on a Google campaign, these are very familiar ideas in the world of digital performance marketing. I know my cost per conversion, my LTV, my CAC, and I set that up and I let it go while it's within these constraints. How is that mindset coming to the CPG industry with tools like this? I mean, yeah, let's first, I always say that, and I think all of us here in the room need to really work hard at this. We need to stop talking about ROAS. I feel like ROAS was made for media buyers and media agencies to look good in front of the brand. That's not how that should work, right? We need to really work towards performance because this is also where we're going to get the right media dollars to drive up our funnel. Because again, there's no lower funnel if you don't have any upper funnel to feed the lower funnel. But now we need to measure our lower funnel properly because if not, yes, we can show amazing ROAS and that's going to make everybody feel good during a meeting, but your top line and bottom line are not going to look good. So you need, it's not easy, but you need to hold the standard around measuring incremental sales. So with a tool like that, you can, right? And I'm sure a lot of you are doing already incrementality, not against promotion, but incrementality overall. And we need at least, let's add an eye in front of ROAS. Let's talk about incremental ROAS. But let's talk about those costs per incremental dollars. This is the way it should be done. And you have a tool here to do it. And that's changing the way we're thinking now when we're investing our dollars on the lower funnel. And that's, I think a lot of other platforms are going to have to really think that way. It's not going to be easy because they don't have the signal, but that's what we should all do and all think through. We talk about the outcomes era. This idea that in the age of AI, you can kind of define a clear and specific goal like, "I need $50 million of incremental sales," or, "I need to grow net new households by this amount," or, "I need to grow basket size or trip frequency by this amount," and then you allow machine learning and these algorithms to test thousands of different hypotheses, right? We give everybody in this room a different offer over time until we figure out the elasticity of demand of each individual and find the optimal price at which you can clear the market. I feel like this is a broader trend, whether it's digital upper funnel, whether it's anything else, rather than a single human with a single hypothesis running with that hypothesis for six months and then and checking that six months later, running all of these experiments kind of at the same time and then adjusting as you go. That seems to be how you guys have operated more than a lot of other CPG brands because you come from more of a digital background. Yeah, and that's the beauty of digital. You can do that, right? And the fact that sales in e-comm are growing substantially now, the signal we get from e-commerce sales are really important in validating what's happening. So yes, the ability to leverage AI tools to be able to have a lot of scenarios that you want to test, and those scenarios can be tested, and then you can get the results in return. It's why there's a lot of discussion around AI, but I think efficiency on how we do things is where AI is very strong today. And when it comes to media buying, this is where it can come into play because, yeah, you cannot have a team that will plan those thousand different tests, get to execute on them, get to report on them in a short period of time. There's no way. And so having tools that allow to do that is where it makes our media buying a lot more efficient and things that I think will make us a lot better down the road for sure. How do you think about sort of the challenge of your product being sold in lots of different environments, right? It's sold at Walmart, it's sold on Amazon, it's sold on Instacart, it's sold on DoorDash, it's sold in the dollar channel, it's sold in the grocery channel. You're trying to get a perspective, I would imagine, on kind of incremental sales across all these channels. So you need some data that looks at in-store purchases as well as online purchases for millions of consumers in order to sort of model what those incremental sales are. It's so challenging. I mean, that's why I love when you get pitched on incrementality and people are like, "No, that's super easy," and yes, they do it with D2C brand. When you have a D2C signal that comes real time from your Shopify store, of course, you measure incrementality, but we don't have that, and so we have to build. I mean, leveraging syndicated sales like Circana is definitely a strong way, but also working directly with the retailers if, I mean, most of them want. Now the price tag against it is pretty heavy, but share that sales at the UPC level, at the store level, and we have to ingest that in our own data lake in order to measure that against our dollars. So all of that has been a lot of work for us as an emerging brand to make that happen with limited resources. It's a lot of work and a lot of money, to be honest, not from an infrastructure standpoint, but from a data standpoint. The cost of data is crazy, right? So please, if there are any retailers, I don't think we have any retailers here, but lower your cost on data, damn it, because I cannot do that, right? But now with a tool like when you guys obviously get the signal from the sales on the Ibotta side, now I don't have to rely on Walmart telling me if a sale occurred, you know that sale occurred because you are in there. And so this is really, that's part of why you can get it real time and you cannot get it anywhere else. But that makes my life a lot easier because if I don't have that, then it's messy and it's expensive. Yeah, so your promotion's running simultaneously on Walmart, on Instacart, DoorDash, Dollar General, on the Ibotta app, all the publishers in our network, and we're providing all of the estimated incremental sales. And then if those are above the cost you're willing to tolerate, you titrate or optimize that program. 100%. Yeah, I mean, it's, you know, I'm sure there are performance media buyers here. It's the good old days of optimizing against the CPA on Facebook. Same approach and that's how it works, right? And I had a lot of discussions of retail media networks. It's like they optimize, most of their campaigns are optimized against ROAS. ROAS, as I said earlier, is most likely non-incremental. So then they are optimizing your dollars being spent against non-incremental sales. So it looks good, but then you waste your money. Now, I ask, at least optimize against YouTube brand because probably that's your best proxy into incrementality. Here, what you build is really against true incremental sales, not just YouTube brand, but also someone that was about to lapse, that now is not lapsing anymore and is buying. It's a true incremental sales. And the ability to now have the platform optimized towards that incrementality, yeah, it's a dream for a media buyer for sure. You just said something that I've come across a lot, which is that when I visit with the CMO of any CPG brand, when they think about incremental sales, they immediately think about net new households because that's kind of like, well, we didn't have distribution in this channel, we got it, and there's net new households. But in fact, you just mentioned you could increase the frequency with which someone buys your product or the amount they spend on your product in a given month. And you might find that one is a cheaper way to grow incremental sales than the other. It's not all about net new household acquisition. It's one of many pathways. Net new households, it's net-to-net. You have to get new and you have to churn less. So actually, you can grow household penetration by churning less. By churning less means that someone that already bought will buy again. And so I'm spending a lot of time thinking through the churn mechanics. And actually, this is what promotion can get people back in because I've talked a lot about that, but there's no loyalty in the CPG world. Yes, we have super fans, but it's a small group of people. And then you have the thing that will get you scale, which are those light buyers. So in order to get that, you need to get those people that are considering a lot of different brands when it comes to buying some sparkling water. They have us, they have a bunch of other brands I won't name on stage, but they are good, buy them. I need to get in front of them before they go buy the other one. So that's where the incrementality comes in. You don't churn them because you don't churn them and you also acquire new ones, then you grow your household penetration. So yes, it's not all about YouTube brand. YouTube brand is the easiest one to go after. It's the easiest one that makes the most sense. But I can show you, and I've run into that before, you can go have a YouTube brand and still not get any growth in household penetration because you churn so much. Yeah, you really think about your product the way a digital marketer would with a funnel. That's unusual, I think, in the CPG space. You mentioned earlier Circana. For the last 50 or 100 years, actually the first coupon was invented right here in Atlanta, Georgia by Coca-Cola in 1880 something. There's never been a third-party way of measuring the claims made by digital promotions companies, right? We recently announced that we're partnering with Circana, for example. To allow them to create a lift study that can look statistically with their own data to see if in fact they see that there's real lift using this matched audience approach. How important is it to you to have a third-party versus, say, a vendor making claims grading its own homework? I mean, yeah, this is a must. I'm big into that when it comes to anything. Like you cannot have anybody grading that on homework. You cannot have church and state have to be in a different place. The person that planned the media is not the person that buys the media. The person that drives performance is not the person that grades their own performance, right? So it is very important. So Circana is a big one because they syndicate all this data. So they are a true third-party. They are not the retailer. They are not the media buyer. So they definitely get you the signal and they are giving us all that sales data that you need to have a very granular level. So it is very important to have that. Sometimes Circana doesn't necessarily have the granularity or I cannot afford that granularity of data that I need for incrementality, but they are a really good proxy to grade that performance. When you think about the agility of marketing in CPG, today the way that works is there's kind of an annual planning process and then there's an annual measurement process or semi-annual, and then you kind of look at the results of your marketing mix model and you plan a bunch of executions for the following year, and then versus a world more like you described with the Facebook example where you're using PMAX and these kinds of tools and you're moving dollars around hour to hour, day to day, week to week, there's this kind of habit of agility where you go, "Oh wow, I'm getting really good cost per incremental dollar. Let me spend more until I'm no longer able to achieve that." I think there's a cultural shift, don't you, in terms of that type of ongoing brand management? Yeah, I mean, I hear a lot of people talking about agile incrementality testing, which I think is the right word. I'm sure there's going to be an acronym for that at some point. But that's how it has to be. And it's interesting in the media world when you look at your full funnel because there is a delay on the upper funnel, right, from your dollars that you have spent on upper funnel before they really impact the lower funnel. But also this delay is because you have to have a strong mid and lower funnel. So on the lower funnel, though, it's pretty instant signal that you get. So the ability to change your dollars, and we do that. Yes, I'm in 2026 planning right now. Yes, I'm deciding how much I'm going to spend on each platform for the year. But I've seen that through this year. Things change very quickly for us. I'm moving dollars around on a monthly basis. At the end of each month, I look at how much I spend on every single channel. I'm getting the MMMs, the signals that are coming in that tells me that we need driving incrementality, that didn't drive incrementality, then let's spend there. Now, I think there's things that should be built at some point is incrementality of a path to purchase because we should not look just at incrementality of a very given platform because it all has to work together. I don't think we're there yet. There are some platforms that do that. But yes, moving dollars on a monthly basis is part of our DNA. And I think a lot of people are going towards that. I agree. I think figuring out how to incorporate promotions into retail media and upper funnel media so that all become more efficient is clearly a next phase. So we've got some time to answer any questions. I think we have five minutes allocated by the good people here at Brandweek. Who has the first question, as I like to say in my company? I can't see a damn thing. So I assume somebody's going to hand you a microphone. We're going to start hearing words come out of that microphone. I see hands raised. Yeah. Yeah. I think there's one person over here. You can shout it out and I can repeat it. What do you find as far as your most effective media channel to drive incremental sales? Most effective media channel to drive incremental sales right now? Not at scale yet, but this guy is at the top right now for sure. Before I was able to do incrementality with Ibotta for in-store, I mean, it's been retail media and it's been for me actually the targeting offsite leveraging retailer in category data, so audience data, and to be able to bring that up to CTV. The incrementality of CTV against in category data is massive. Wow, interesting. All right, who's got the second question? Yes, right here. Shout it out. So far as blue right now, but so far as our point, how are you actually looking at customer retention for retail? Customer retention? Customer retention for retail. Yeah, how do you look at customer retention through different channels? So, for example, with us, you're looking at this cohort of people and the total sales that cohort generates. But how do you do that in context where you don't have purchase data? It's so difficult. Two ways. One, obviously you look at your average buy rate for a given retailer. That's not far from being perfect, but that gives you a sense of if things are moving in the right directions with buy rate being made up of the trial, the repeat, and the size of basket on each purchase. So that gives me a sense at the retail level if I'm moving the needle on that front. And then doing a lot of work on first-party data. So from a first-party data standpoint, we capture through our CDP PII, and from this PII, I'm able to map it in clean room against some purchase signals. So I understand the repeat cross retailer because that's the challenge, right? Is how can you go cross retailer because that happens a lot, right? There's someone that will order through DoorDash a tall can for us, and then those people are going to say, "Oh, I like it. Now I'm going to go on amazon.com and get on SNS, and I'm going to get a box at my door every two weeks." So I need to be able to cross the signal, which very few people can do. So I do that through first-party data. And then leveraging, of course, some of the panels that are out there that have some cross retailer data, but it's very difficult to attach the panel data to the media dollars. So that one is more holistic. But yeah, first-party data and buy rate at the retail level is really what I'm looking at. If you're willing to share some of the results from the pilot you did with us, I mean, we talked about looking at the purchase cycle and what happened. Did that shorten? Did that lengthen? We looked at incremental sales, so sales lift, and then also lift in daily units moved. What did you see? I see the whole of them moving. I saw incrementality. First, from an incremental standpoint, we were profitable. It was an incremental sales. True, the dollars that I spent were all incremental. So it was from a valuation standpoint, it was profitable, right? Now we reduced the sales cycle or the in-between trips also quite a bit. So for me, that was a double win there. It was fully incremental, but also talking about retention, it was a growth of the buy rate, which we're always looking for, obviously. Has anybody here tried the Fruity Pebbles version? This just came out. I ordered it off Amazon based on a LinkedIn post that he put in there, and it's now a staple in our family. I think we're out of time. I just want to join me in thanking Benoit Vatier for being here today. Thank you. Appreciate it.