B (3:39)

Great, thanks for having me.

A (3:40)

Oh, it's really great to have you here. It says we, we close in on the end of the year. In the holidays, people are buying gifts and what have you. Their pockets are a little challenged as they're looking for ways to save money. And I think you've got a lot to share with us on how brand advertisers are reaching those consumers with powerful marketing messages. We're excited to get the conversation started. I want to, I want our audience to know that as they're listening to us, they should check the digital show notes of this episode where they're going to find a Hyperlink to your LinkedIn profile link. Ibotta's LinkedIn paid in Ibotta's corporate site so that they can multitask as they're often want to do. That's why we do it in podcast format. People can listen, they can things, it helps them get get things done. But without any further ado, I want to get to the questions because I think we're going to have a very interesting conversation today. As I think back to my original familiarity with ibotta, it started as a wildly successful cashback app, right? When did you first realize and what was kind of the genesis behind the company needing to evolve into more of a performance marketing platform that could power promotions for national brands and retailers, not just hey, here's a savings for consumers. What was, what was behind all of that?

B (4:58)

When I founded the company back in 2012, the need that was the most acute was an ability for CPG brands to have a direct connection to the millions of consumers who buy their products through a mobile app. The App Store was relatively young and that didn't exist. We also were the first to invent the idea of fee per redemption promotions. So everybody else was, and many of them people are still using fee per clip, fee per print. IBOTTA really pioneered the idea of total alignment of interests by having a fee per sale model. So we don't get paid unless our clients drive an actual verified sale. Those things were popular as was an app that gave people direct cash, not points. We got 50 million people to download our app and that was a profitable business. It was exciting. The clients that we worked with were there were hundreds and hundreds of companies, thousands of brands and they loved the fee per sale model, they loved the targeted offer capabilities, they loved the direct access. What they thought was challenging was just that even if we have 20 million people actively using our platform, that's still a relatively small amount of total addressable spend in a market that's over a trillion dollars in the US and so they really wanted, as I like to say, more cowbell. They wanted a lot more scale. And we knew that you needed to be able to intercept customers wherever they were considering what to buy. And that means as they're typing in their search for cereal or whatever it is on Walmart's website, we want to intersect that shopping journey, we want to intercept it with a digital promotion that steers them in a particular direction to drive trial or an incremental sale. And there were so many people going to these retail websites, Whether it be Dollar General, Walmart, whether it be DoorDash, Instacart, Family Dollar, we saw an opportunity to take everything we'd learned as the leaders and innovators for a 10 year period and build out a network. This is about five years ago, six years ago, right before the pandemic, we said, you know, let's approach Walmart about creating the first ever digital manufacturer offer program in their history. Now this company is obviously been around for 60 years and until they introduced the Walmart cash currency, this construct wasn't possible to do an item level digital promotion at Walmart. So we went and we partnered an exclusive basis with Walmart on a long term contract. They became a strategic investor in ibotta. We built out the capabilities to deliver all across the experience. Whether you're shopping in store, online, buy ahead, pick up in store, you can see these offers in in store mode, you can see them in the search results, you can see them in the offer gallery and now increasingly in store. And this was the beginning of what we call now the Ibotta performance network or the ipn.

A (7:49)

I want to get to the Walmart one because I really want to double click down on that. But I guess what was kind of like the, the aha moment when you said okay, we can, we can actually put together a performance network and told you the future wasn't necessarily direct to consumer incentives, but something that was probably more full funnel retailer connected ecosystem. What, what drove that?

B (8:15)

Well, I think this desire for scale and the fact that we were the most widely used app in a place like Walmart other than their own app. And so we had built a decade's worth of credibility and they were interested in us bringing incremental national dollar budgets into their ecosystem that they couldn't get in their trade and JBP conversations with their current clients. And so we brought thousand plus extra new offers that were not cannibalizing dollars they already had access to, but were additive and they were really responding well to that. And we realized, wow, this is something that could really increase the impact so that we want to get to a point where we can move a billion dollars or more in incremental sales, sales that would not have occurred otherwise for a brand partner of ours. If we want to move the needle to the tune of 1 to 5% of their overall US sales in a given year, we're going to need way more than just the most popular direct to consumer savings app.

A (9:12)

Yeah, I think what's interesting about this is the model that you talk about in terms of sourcing the investment for this is really fascinating because those who work in the industry understand that Walmart is a dead net low cost retailer. And so that's typically where the trade funds are going. What you're bringing to the equation are dollars that typically wouldn't have been allocated to the trade fund. They would have been more national promotion dollars that went to digital coupons or other things like that. And so you're able to now bring additional value into the Walmart ecosystem that had up until now not been accessible to Walmart. So was that kind of like the unlock for the further relationship that got Walmart started? What was really kind of like you had this idea that Walmart could be a very important strategic partner. What really got them to say, okay, you know, what we really need? Because you know, up until now that had not been a primary consideration. You know, your, your, your rebate program worked at Walmart but it was independent of Walmart. So what was kind of like the unlock there that really got them thinking this is, this is a partnership that.

B (10:23)

Could work a bike ride. It started with a bike ride. I went on a podcast called Business on Bikes. You can look it up on YouTube, just Google. Brian Leach, John Furner, the new global CEO of Walmart. Back then he was the CEO of Sam's Club and he invited me to go biking with him on his Business on Bikes podcast. So they're recording the podcast while you're suffering up various hills in Bentonville. And we became good friends. He invited me to their all hands presentation. I gave a presentation to, to all associates at Walmart on kind of entrepreneurial cultures. And once he became the US CEO of Walmart, I approached John and said, look, you guys need to think about driving your retail media business. Your alt revenue sources that are very high margin. To do that you're going to need digital engagement. So you're going to need people who are actually checking out with their phone number, people who are identified even though they're shopping in a store. You're not like Amazon where everybody's logged in when they check out. So in order to do that you're going to need some incentive, some reward structure so that people bother to raise the hand and say, I'm Brian, here's my phone number when they check out at a place like Walmart. Not only that you want to drive membership enrollment, you want to drive usage of your financial services product. These are all tailwinds to those things because you're creating a positive cash balance called Walmart Cash. Most foundationally, you're not providing discounts. So if you, if you provide, if you're the world's largest acceptor of paper coupons, not only are you slowing down the flow of your stores operationally, but all those dollars that people are not spending, well, next time they could choose to spend them on Amazon. Whereas if you create a positive reservoir of currency called Walmart cash, you drive genuine loyalty and people will come back more often into Walmart instead of shopping in the store in Walmart and then when they think to shop online, going to Amazon. So these are just some of the ways in which this was strategically aligned with the future of how Walmart unfolded. And now with John as the global CEO and he's sort of the, the architect of this partnership along with myself, we're really excited about the future of that.

A (12:28)

Yeah, it sounds like it. With John there, as you said, what a great partner to have as he was there from the beginning and that just strengthens the relationship you have. So that was two years ago. IPN has changed since then. You've Added big intermediaries like Instacart and DoorDash. And you've elevated your focus on measurement. How has that reshaped or enhanced IPN as you originally launched it?

B (12:55)

Yeah, so, I mean, I think the most, I like to say there are three or four major things that IBOTTA has contributed to the world of digital promotions that really challenged the conventional wisdom that existed when we arrived. So the first was the fee per redemption model. We were the first to do that. We talked about that. That was really a pioneering alignment of interest. The second was the creation of a single network that could reach over 200 million consumers. Nobody had ever had a way to drive that many sales. And that continues to grow. We added DoorDash. Yes. We also had Dollar General, Family Dollar Schnooks and others. The third innovation and the fourth most important innovation have both occurred in the last month. Right. Last six weeks. And the first of those is the ability to have ongoing measurement of your actual profitability of your promotion. So that's what we call live Lift and that is the way to actually look at while the campaign is happening, the statistical lift in incremental sales using the absolute best practices that all media lift studies employ. But instead of having to have a snapshot of how your media performs six or 12 months later, or instead of relying on an MMM, now you have that on a rolling measurement basis. So you can perform your marketing the way that a digitally native company would, where you set a target of a certain amount of cost per incremental dollar that is a constraint for you given the margin structure of your product, a certain target incremental sales goal. And then you can let our system cook as you as it were and deliver against those goals. And you can optimize those campaigns while they're still in flight. So that's a major innovation that is changing the conventional wisdom and showing people that promotions can drive profitable revenue. The other major invention is the idea of third party measurement. So for over a century, digital promotions companies have been grading their own homework. And we're among them, Right? But not anymore, because we were the first to introduce a partnership with Circana, which allows our partners to buy a lift study from a third party that independently corroborates the statistically significant difference in the purchases of the group that is exposed to the offer versus a matched audience. That is all other characteristics are the same, except they were not exposed to the offer and we look at both of their frequent shopper data over time. This is what Facebook and Google and others do to measure their media. We're able to get a independent benchmark against that media to see how we perform relative to other media. And we also expanded that recently by adding an ABCs, another measurement company that's very well regarded. So for the first time, you can measure promotions on a rolling basis and optimize those, leveraging our recommendations and increasingly machine learning and AI. And for the first time, you can check the results with a third party. And it's exciting that two of the four most interesting and impactful innovations we've ever brought to market were in the last six weeks.

A (16:01)

That's great. You know, when I think about marketing mix modeling, the fact that you get it six months to 12 months later doesn't allow you any opportunity to, to optimize the campaign. And what you're telling our audience is no, no, this, this information is coming to you mid flight and you have an opportunity to course correct, make adjustments and ensure that your campaigns are hitting their targets. You don't have to look back half a year later and say would have, could have, should have. No, no, you can actually do it in real time. Am I, am I on track with that assessment?

B (16:36)

That's right. I mean, if you think about the, the, there are two problems. One is that almost every CMO and chief Commercial officer that I've talked to about promotions prior to the launch of this product will tell you in their unguarded moments, promotions are not a high ROI alternative because they subsidize so many different people who would have bought the product anyway. And it's therefore something you do as much as you feel you have to do, not as much as you could do. Right. And that's because you want to placate your merchant. You want to get favorable positioning in terms of end caps and such. You want to support innovation products. But what this demonstrates is that with this statistical technique of two identical matched audiences, now you're controlling for exposure to the same price, the same super bowl ad, the same radio ad. Everything's the same. Except this group, Group A is exposed to the offer on Walmart or on Ibotta or wherever in our network. This group's not. And all we talk about and take credit for is the delta, the statistically significant delta between those. Now you can actually look at, okay, well how many incremental sales are there on a rolling basis and what am I paying fully loaded on a cost per incremental dollar basis? And how does that compare to the contribution margin of each SKU of my what I sell? So this notion that it's not profitable to run promotions is dead wrong. Every promotion can be profitable or unprofitable. You can set the parameter of a promotion. You can say you get dollar, get $2, get $3.50, buy one, buy three. You can, you can configure who sees the offer, how often they see the offer and through manipulating those parameters, you can land the offer that's highly, highly efficient and profitable. Only problem is you're going to get fewer, you know, incremental sales if you bid a lower cpaid cost per incremental dollar. Or you can be maximizing volume at the expense of efficiency. And it is a continuum that we are now giving our partners a chance to manipulate while it's still in flight.

A (18:41)

Yeah, because if you're a brand manager and you're launching a new product, the profitability of that product is not top of mind. It's reach. It's about getting exposure and driving trial of a product. If you're trying to drive an IRO as on a new product launch, you're probably not using the right KPI. So I think that's great. You talked a lot about when I talk to brand advertisers, the two things they crave more than anything. You touched on measurement you've got to have. And having independent measurement particularly is highly desirable. But the other one is scale. So now you are reaching according to the metrics you share 200 million consumers. What does personalization really look like at this scale? And what have you learned about delivering the right promotion to the right shopper at the right moment that is manageable. And is AI helping us get closer to one to one because, you know, one person sitting in a keyboard and trying to create iterations of campaigns for different consumer groups is, is challenging to do and it's, it's almost impossible to maintain. What, what, what, what is going on now with personalization in this world of massive scale that you've achieved?

B (19:52)

Yeah, I think there's two questions in there. One is what's the role of, you know, one to one promotions? And the other one is how are we changing the culture of how we operate from a kind of single person with a single hypothesis to what we call the outcomes era. So let me just briefly touch on both of those. You know, with regard to personalization, I like to distinguish between what people call targeting or segmentation or personalization and optimization. So targeting or personalization, these are related ideas. The idea that you're going to see a particular offer that's different than the one I see because we have different starting propensities to buy a given sku and we can look at our longitudinal purchase data, look back to all the full basket purchase data we see for millions of consumers across 80 different retailers tied to our customer ID. We can say, well, Brian's propensity to buy hole in spring water is here and therefore we need to stretch him to buy two. And we're only going to take credit for the delta between, you know, the one and the two that is different than the. And that means, you know, different offers to different people at different times. There's also this idea of optimization which is even if for example, the entire ecosystem had one offer, you could run that offer and then you could run a different offer and then a different one and then a different one in a time series and you could learn how to optimize those offers based on what season it is, for example, or what retailer environment it's in. And that would be another form of using AI because you're using machine learning to look at the effective cost per incremental dollar under each of those different experiments. And you're running these behavioral economics experiments to find the most Pareto optimal or efficient curve on which to promote every product that you sell. And that is a D averaged approach. It's not a single price reduction across the board for everyone. Well, you're going to have a very high cost per incremental dollar if you do that. Because I was going to come in and buy a lay's potato chips during the super bowl and you gave me a, you know, a two dollar off when, when I use my loyalty card. Well, guess what? I did exactly what I was going to do. Zero incremental dollars, very high cost, infinite cpid. Right. So that's, that's personalization in the age of a, of AI. I'm happy to stay on that, but I do want to address this point about kind of the way CPG companies culturally allocate resources and the way they think differently than digitally native companies and why that is changing. So look, I mean fundamentally we call it the outcomes era. So in the world of digital commerce or digital media, let's say you work at a video game, mobile game developer, you know exactly the value of a, of an install of your app, the registration of your app, a first time, first level, tenth level, hundredth level, and you can assign that, and you can create a LTV of each of those lifetime value of each of customers at different depths of that funnel and figure out what you're willing to bid at the Top of that and for a cost per install all the way down to your cost per ongoing user and then figure out the payback period or the ratio of lifetime value to cost of acquisition. And once you dial that math experiment, you know, just like you would for a Google campaign, what you're willing to bid and not bid on every single type of net new consumer because you've got that real time feedback loop. You've got a SDK, you've got a pixel, you've got something telling you conversion, right? And so then you can use that data and you can educate through your SDK. You're passing that information back to Facebook, to Google and they're honing that model so that they send more of the ads that work and fewer of the ads that don't work until your cost per whatever event you care about is optimized. Okay? You're running in effect tens of thousands of experiments Facebook. You're not seeing all those rules behind the covers, but they're, they're testing out who to show what creative to when. And you get as interface that shows you your cost per install, your cost per ongoing user. Now, in the CPG world, Brian and Peter show up and it's time to plan for next year. So we come up with a single hypothesis. Let's say we think, all right, we're going to promote this new format of Mini Can Cola to Latino audiences in Miami. And then we think, okay, they're going to like it because ABC piece of research suggests that they're going to like this new flavor. Now where should we, let's do a tie in with mls. And then we, we pitch this whole execution with the help of a media agency and we get it in a, in a budget and that's then in the budget for the next year. We then run our execution six months later and then six to 12 months after that we get MMM or somebody to tell us whether under some imperfect econometric model it worked or didn't work. It always works, right? And then basically you go and get more, you pitch for more dollars the following year. This is deeply unagile, it is not really measurable and ultimately it's, there's just only so much time that Peter and Brian can sit there and devise one hypothesis and run it to ground. The way the whole industry is moving is toward the outcomes era. That means you define the outcome you care about. I want to maximize incremental sales subject to a constraint of lesser than or equal to 30 cent cost per incremental dollar. Because that's my contribution margin on each sold incremental sale. Right? Go. That is now a rule that is pre authorized to spend up to whatever money is going to deliver top line and bottom line growth. That rule is, is automatically blessed. I don't have to go back and get a discrete allocation of resources. It's running and then instead of one hypothesis, there's hundreds of different tests happening, different price points, different offer parameters and we're collapsing down to the efficient model that drives maximum incremental sales at the lowest possible cost. And in order to operate that way culturally, you have to shift the way you think about resource allocation to the way that companies do with Applovin or the trade desk or Facebook or Google. You can't have a culture that doesn't have that agility. You have to have a culture that is comfortable defining rules that can be ongoing guideposts for resource allocation. On the understanding that you can turn them off anytime you want. Right. There's no penalty if you don't achieve the cost per incremental dollar that you want. On iBotta. You can pause it or turn it off with no penalty any time you want. But what we can do is allow you to say I need to close a gap of $65 million this quarter of $125 million this quarter. And these are the SKUs I need to close it on. Tell me what the cost per incremental dollar would be to close that gap. I only have 10 weeks left in the quarter. I only have three weeks left in the quarter. We'll then be able to help you do that through all these experiments that Peter and Brian couldn't dream up.

A (26:35)

Right.

B (26:35)

It is a low or no cost Peter and Brian, right. That is generating the right soup of experiments that ultimately yields the outcome that CPG wants. And we think this is going to be true across every facet of cpg, not just price and promotion, but we're the leaders of that in our, in our neck of the woods, which is finding the optimal price and the optimal promotion.

A (26:57)

Brian, first I'll say that we have come a long way from a brand manager planning out four FSIs and pretty much calling it a day for the rotation before they moved on to their next brand. But what I will, what I really want to hone in on is the outcome era. Because what I hear you saying is it's not about running a campaign, it's deciding what your outcome is. Understanding the customer journey and using these experiments to figure out how to bring Brian and Peter on the customer journey to get the best possible outcome at the lowest cost price and ultimately generate lifetime value and loyalty for brands. And that's what the IPN is delivering. Let me remind our audience, I'm speaking with Brian Leach, he's the founder and CEO of iBotta. So when you talk to brand partners today, what are their top priorities that they think they're needing solving for? And I've got to imagine that ROAS is, is pretty much a term that they, they are, that they know what it is, but it's, it's not in their, in their area of focus these days. What, what are your thoughts on this?

B (28:00)

Well, first of all, you have to establish that, that promotions can be the single most profitable form of media investment or trade investment, however you want to look at it. And that gets them into the mindset of it's not just something I do to spike volume for a short period of time to make sure I maintain shelf distribution and oh well, tell me more then. It's what is the appropriate measurement? We believe ROAS is not at all appropriate. Right. It is, it is incomplete. We talk about incremental sales, total incremental sales and the cost per incremental dollar, which some people want to say IRO as that's really like one over cost per incremental dollar.

A (28:36)

Right.

B (28:37)

The reason why we talk about CPID or cost per incremental dollar is you can easily compare that to your, your contribution margin of a sale. Right. So if you drop 20 cents to the bottom line or 40 or 60 cents to the bottom line depending on your margin structure, that then you can compare to the cost of delivering that dollar that you wouldn't have had otherwise. And you can say, well, let it stay on up until it exceeds that number and then have it turn off as soon as it exceeds that number. And you have that, that sort of guidepost. Right now what our brands want is two things. They want top line growth and bottom line growth. So we're in a moment where most of the large CPGs have had a difficult two years. They're missing on top line. They need to re, accelerate, regrow their businesses. There are a lot of reasons for that having to do with moves they made after the pandemic. But they face a lot of different headwinds. It might be private label, it might be RFK and the MAHA movement, it might be just a lack of innovation, it might be that the pricing was not in the right place for a while. It might be consumer sentiment. But all of that means there's enormous Pressure to regrow the top line. And if they can do that while breaking even without eroding their margins, that's a win unto itself. And that's probably priority number one. Priority number two is the holy grail. How could I also grow bottom line? I want to grow my actual margin. I want to grow my actual ebitda, my profit, my net income. And if you set a promotion the right way and you great baselines and leverage that data to build a model, you can absolutely deliver profitable revenue growth. And that is something that you can feed all the way up to a scale of something that's reaching, you know, 200 million consumers. Now, we have to go out and see if that's true. We'll run those experiments based on our predictive models. Now, that's a really large amount of sales, even for the biggest companies in the US and then you're gaining these learnings along the way about effectively the elasticity of different types of consumers for different UPCs in different seasons. That entire soup of data is so incredibly valuable. The companies that lean in to this AI era where you're really thinking about continuous optimization and you're authorizing people to spend money up to certain acceptable thresholds, are going to run circles around the companies that maintain a focus on discrete annual, you know, rigid budget allocation and who are still relying on things like mmm. Now you have to have mmm because, you know, I can't otherwise measure the value of being on the Denver Nuggets jersey patch because I can't create two statistically identical populations of people who saw and didn't see the jersey patch. However, it is deeply inadequate and it is always lagged. Right. And so you should prioritize the tactics that give you that real time or near real time, at least while the campaign is ongoing type of signal. And then you should run pilots, gain baselines, build conviction, and then go all in. And that's what our partners are doing in 2026.

A (31:35)

Yeah. I'll tell you, you may not want to disparage mmm as much as I do. I tend to refer to it as alchemy in the age of performance marketing. But I do agree with you. It serves a particular purpose for advertising mechanisms that are not easily measurable the way performance marketing networks are. So I want you to clearly articulate for our audience, what gap do you see live lift filling for in the world of brand advertisers and really emphasizing why this is so pivotal rather for ibotta in terms of the offering you're taking to market?

B (32:13)

Yeah. In My opinion, there has never been, never been an at scale marketing platform in CPG that allows you to measure incremental sales on a rolling basis in the offline world, right? And then take that signal that's coming in on a rolling basis and build machine learning models to optimize the way you run your business. If you want to win in the age of AI, you have to use systems that have the kind of data that we uniquely have access to because they allow you to create an infinite number of experiments and that ultimately hone in on the best way to grow your top and bottom line. It's using math, it's using science, right? I like, I think of the Martian, right? How are we going to get off this planet and back to a planet full of growth and prosperity? I got to solve one problem, then I get to solve another problem, then I get to solve another problem. You got to use science. You got to science the shit out of that thing, right? That's what the IP is now. Not, you know, alchemical science where you have a single coefficient of causation that you arbitrarily assign in your econometric model. No, no, there is not one coefficient. You have to run these behavioral experiments and see, it all depends what are the inherent properties of your product? How much repeat purchase do people want to make when they try your product the first time? If it's a really great product, they might want to try it a bunch without a promotion and that affects your overall incremental sales and your cpit. How much baseline trust do they have in your brand? If you're Coca Cola, you have more brand equity than if you're liquid death. So you don't know one size fits all what the coefficient of causation or incremental elements are. You have to run a truly robust statistically matched audience approach that has never been done, ever, on an individual campaign basis. Now, there are companies out there that'll claim, oh, we work with Nielsen, et cetera. That's true, they're part of the mmm, okay? But to be able to provide somebody a free rolling lift study that is just as good as what Google and Facebook does. That disproves the conventional wisdom that promotions aren't profitable is a giant paradigm shift. Right? And the companies that never did promotions before, right, are now out there running promotions on our network. You can see in our network a lot of offers from companies that are over a century old, some of which were part of creating coupons that are now leaning in a really big way when they were allergic to them before. So we think this is going to become a complete rebranding of the category. Right. Ibotta was already the leader in the category, but the category needed to be thought of in a different light. And that's what third party measurement and rolling measurements, setting targets optimization is allowing us to do.

A (34:57)

All right, so let's try to demystify any black box impressions people have around Live Lift. How is it helping to quantify the true incremental impact of promotions? Whether it's around velocity basket expansion, shortening the repurchase cycle. Help people understand. Now this is is not magical. This is the real deal.

B (35:18)

Yeah, look, the gold standard is statistics, the scientific method. You have two populations of people that are identical across 10, 15 variables, right? Where they live, what they spend on their purchase, frequency, demographics, geographics, etc. And except one is exposed and the other is not exposed to a particular stimulus, in this case our promotion. You then look at their longitudinal purchases from that moment forward and if there is no difference between those, you know that it was pure subsidization and there was no incremental lift. If on the other hand, there's a Statistically significant, say 99% confidence interval stat sig difference, you look to that difference and you only talk only talk about that difference as the value delivered. That's why the word incrementality is dumb. Because incrementality is the idea of how many sales were incremental in the numerator divided by total sales. Who gives a crap about total sales? No one cares about sales. That would have happened anyway. I only care about incremental sales. 100% incremental sales is all we take credit for. Right? And then what is the fully loaded cost of that incremental sale?

A (36:28)

The best way to measure it.

B (36:29)

Now, what you've seen in the past is time period A, someone says there's this many sales, then we run a rollback or a promotion. There's this many sales and so the delta between them is incremental sales. Sorry, but no. Because a lot of things change between time period A and time period B. Right? Lurking variables. Now there's a radio ad. Oh, the price change. Oh my competitors. Price change. Oh, private label price change. You can't either take the credit or the blame for what happens between time period A and B unless you hold all other variables constant. That is why you end up in the morass of mmm, because I can't disentangle whether it's the Nuggets jersey patch or our billboard or our radio ad that drove, you know, download of the Ibotta app. And so what we're doing is what has been going on for 20 years with Circana and YouTube. With Circana and Google and Facebook, they take exposure log data from a YouTube video and a matched audience that wasn't exposed. They take a hashed email or hash mobile device id, they look at the frequent shopper universe that they have and they answer the question right, how many net new household penetration is statistically significant? What's the basket increase? What's the frequency increase? That's what we're doing. Only instead of charging forty grand once a year for a list study, we're providing it on a free rolling basis for the first time ever.

A (37:46)

Wow, that is awesome. All right, now you brought up Liquid Death. I've got to give. First of all, I'm going to give props. I always love to give props to them because they have managed to create a brand out of the single most commoditized product that you can buy in a grocery store water. And they've done it famously. So you guys have done a case study that really highlights the, the effect of your, your platform on their brand. Can you talk about that in and why you think it was so eye opening for them and should be for the industry?

B (38:23)

I mean, first of all, shout out to Mike Cesario and Benoit, Amazing, amazing marketers.

A (38:29)

Yeah.

B (38:30)

Full disclosure, I'm a personal investor in the, in the brand and the company. So I've been a believer in theirs for a while. What's so interesting is that at Brand week, Benoit got up on stage and said, we have no other tool like the Ibotta Performance Network and Live Lift because we, even though we're great at brand and you know, I got Travis Barker sending me an enemy Enema kit and you know, I got God knows what other collabs they have. Right. They still have got to figure how to pull through consumers to a confirmed item level purchase of Liquid Death at Walmart or at Instacart or at Dollar General. And they rely on us to do that in a way that works with some pretty tight margin constraints on that product. People think, oh, they sell water, they must have crazy margins. It's the opposite. They have shipping a lot of different.

A (39:14)

Intermediaries, profitability out of it.

B (39:17)

Right. And so, so it's a challenging business to make money. And all the more reason you have to be really lean forward and be innovative. And they've done that and figured out, okay, at this level we will leave this always on and running. Because we know it's driving top and bottom line growth. Their CFO has signed off on that and away we go. Now we have a rule based resource allocation. We do not have a discrete annual, deeply non agile system for allocating resources. That's how they're going to be a challenger brand that wins because they're going to get into those things earlier. We now have 18 companies, 18 companies who are piloted Live Lift. And of the ones that have finished their pilot so far, already 83% of them have come back in to invest again on the IPM with Live Lift. The other one is coming soon, right? So we believe it's going to be 100% follow on participation in this product. And people are saying this is a complete game changer. How much money, how much capacity do you have to give me profitable revenue growth next year? I want to use this for all my brands. I want to replace my least efficient one size fits all promotions. And it's really a major turning point in our industry, not just for ibotta.

A (40:29)

Okay, so my last question to you is going to look towards the future. Obviously there's been an enormous amount of transformation at ibotta and by extension the promotion landscape in the last year or two. Looking out towards one to three years, what's next? What do you think is going to separate the brands that win from those that lag behind?

B (40:54)

I mean, first of all, the outcomes era, you have to understand that you got to think like digitally native companies. If every CPG product was sold online, everybody would already be world class at this, right? They would know their conversion rate on Google, their conversion rate on this retailer website and that one, and they would have dialed every single channel with appropriate payback. You got to think that way for the 85% of where your products are sold in store, right? And so outcomes era, companies that change their culture to capitalize on pockets of potential revenue growth instead of just saying, all right, tick that box a little bit of, a little bit of promotions and done right, you've missed the entire point of profitable revenue growth. So that's the biggest thing. But there's other major behavioral change coming, right? Agentic shopping. Recent study by Morgan Stanley showed that 49% of agentic shopping is in, is going to be in grocery. Whoa. Robots do not respond and decide what to put in a basket based on your search result, based on your ad, your display ad. Right? Robots are looking at the inherent quality and the metadata of the product, including its price. So if you figure out using math and science, what the Appropriate incentive should be for each individual to stretch them to take an action they wouldn't have otherwise that will influence the algorithm that puts your product in the basket. If you're not leaning in on those things now and you're still spending money on things that don't influence robots, you're going to be in for a very rude awakening. Right. And so taking into account how shopping is changing is really, really important. So that's, that's another trend I think you're going to continue to see online be where sales growth occurs. The hundred percent of a lot of brands, net new sales, are digitally originated sales. Right. And so you can spend a ton of money on things that don't really change management, don't change the outcome. But where you can get in front of consumers digitally and drive growth, companies are already aware of that. But that's going to continue, that's going to accelerate. You're going to continue to have more and more and more companies that understand that that front door is where you're going to win with Gen Z. And that's actually great for a tool like ibotta because you can intercept those people all throughout that environment, whether it's in the product detail page, on the checkout, in the search environment, when they're searching for your competitor, when you're in in store mode, when you're in a digital shelf tag. I mean those types of in aisle technologies that are tied into your promotion. So in Schnooks for example, you can see that there's an ibotta promotion right on the digital shelf tag. You can scan to clip it. These are all ways of kind of understanding how consumers are either learning about or selecting the product they want to buy or robots are and making sure that your product is positioned the right way.

A (43:43)

Brilliant. To our audience, we want to thank you for joining us thrice each week for our podcast conversations, our weekly commerce rift. We we greatly appreciate the now over 40,000 followers on LinkedIn. I still shock myself when I say that who like and share our content. Please do follow us on your profile preferred listening platform and if you can give us a rating. The reason that's important is it it feeds the algorithm and makes our podcast more findable by industry contemporaries of yours. And while you're at it, make sure you go to our LinkedIn page. Just this week I published a list of our favorite podcasts to listen to because we're not about just listening to the CPG guys. We're about creating a culture of people who get educated and entertained through podcasts and there are a lot of other great podcasts if you're over in Europe. Our sister podcast, the FMCG Guys, there's a great new podcast by Christina Marinucci and Jackie Danowski, she Commerce and lots of others. So we strongly encourage you to listen to them. You can find a list on our LinkedIn page. We look forward to seeing you at person at one of the many conferences. Now we're on a little bit of a hiatus during the break but first week of January we're out to CES and at all starts up again the crazy dash towards June before I think we take another break. Listen, my big takeaway from this is ibotta's built Performance Network that is taking advantage of all the changes around AI. The ability to focus not on the inputs but the outcomes that doing so and being able to do so at scale is the only way brands are going to win in this world. It's the only way you're going to drive down costs that doing so is going to require that you show your homework. And that's why they've partnered with Circana as a third party entity that can say no. It's not just us saying it, it's somebody who's using the best in class for measurement. So Brian, I want to thank you for taking time out of your break between Thanksgiving and Christmas to join us on the podcast. This is an absolutely fascinating conversation and the kind of stuff that our audience is asking for constantly. So you, you over delivered on the expectation, at least from my eyes. I'm pretty sure from our audience too. So thank you.

B (46:03)

Well, thank you so much for doing this podcast. It means a lot to our industry. It has meant a lot for a long time and I hope that folks who are interested in learning more about where we're taking the industry from our perspective will reach out to us.

A (46:16)

Yeah. So I would again encourage our audience go to the digital show notes of this episode. Brian's LinkedIn profile link is there, as is a link to Ibotta and their corporate site where you can learn about Live Lift and all the incredible innovations that they've been making to the ibotta Performance Network. To our audience, thanks for choosing us to educate and entertain you. We look forward to speaking with you on the next episode of Wait for it. The CPGuys podcast.

B (46:43)

Goodbye.

A (46:49)

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