Liftoff Mobile, Inc. (LFTO) Earnings Call Transcript & Summary

Yes, it's a great question. And my CFO is sitting in the first row, I have to take it. So when you think about the way in which we structure our model, not the machine learning model, but the financial model, what we've articulated is this is a very high-growth industry. The numbers that we subscribe to show something like an $80 billion TAM turning into approximately $130 billion TAM in 5 years. The CAGR is there about 13%, 14%. You've got a market that is just growing. And we certainly are benefiting from being early, building a really nice foundation in this market, but that's an underlying piece of our model. And then you have the ongoing benefits longitudinally over multiple quarters of recursive learning, the model just getting smarter on its own without intervention, without me or you touching it. What happens inside our building at Liftoff is everything that sits on top of that. So it's -- did we release a new column in our model, if you will, a new feature as we did in Q2, did we create a new derivative field. This is what machine learning engineers and data engineers work they exist to do, what we exist to do at our company. Everything from design dock to the it propagating through our system and finding more high-value users for our customers. Now for our business, I think when you look out multiple years, it's really important to think about the market we're after. And I think this helps distinguish us from really any other named business out there that does what we do. Our market is the entire app ecosystem. Gaming is one vertical for us. And gaming is important. It's a lot of the innovation that was born in this ecosystem, came from gaming. But you see these verticals emerging in the last few years, prediction markets, [ OSB ] is now coming to town. You have financial financially oriented apps that previously were really focused on durable contracts are now engaging in this, let's call it, the prediction market craze. You also see this happening in e-commerce, collectibles are coming to the market. And so when we think about what's possible in 5 years, I would really think about where can consumer taste go across the app experience as hardware evolves. There may be some announcements from some companies we know tomorrow around hardware. There's the software we just talked about is really evolving with experiences. And the experience is that we are willing to engage with our phone are certainly becoming more dynamic. And so our objective is to be there for that next business that catches the consumer craze to help that business acquire customers that are ROI positive and we'll acquire as many as our model allows.

Okay. Last one on this point. When you think about the application of either capital or data, how should we be thinking about the need to scale compute for model performance, data for model and performance. What are the variables you're the most focused on scaling to make sure the outputs that come from model performance over the next 3 years whether linear or nonlinear still get you to the end state that you think is a real possibility for the company?

This is a fun question because I think it helps us paint the brush to see where we sit in the ecosystem and gives you a little bit more context on our operating model and how diligent we are in terms of assessing how accretive a given innovation is. So our business, if you think about how many features we have in our model, just to keep it simple, think tens of millions. And we've heard LLM quote numbers in the many billions. We've heard Meta disclose where they are in terms of features. So why would you have a different count of features or different orders of magnitude the problem that we're solving is a very hard problem. It's actually impossible for it to be one out of one. We are trying to assess the probability of a user engaging with the business, installing an application and either purchasing or showing some sort of proxy for an ROI-positive user, and we're pricing that opportunity. So it's a narrow problem that we're solving. Because it's a narrow problem, still a hard problem, we need less features in order to respond to the prompt. Because of that, we don't need to communicate across boxes or across chips. So it's different than the photo that you took up something where you're asking an LLM to interpret a visual and then give you directions. Because of that, we can use chips that are not as competed for in terms of the pricing. The liquidity that's available to us in terms of our cost structure is advantageous. Now we also have the benefit of determinism on a proprietary level. When we release a model or I should say, when we make a decision to release a model change, we can see down to the bottom line, how accretive that usage of compute is. So if the usage of compute, that we see as short-term accretive, long-term destructive or the inverse, we can use that calculus to help determine if we should merge the update. And this is a real benefit in B2B recommendation engine focused business model in the types of chips that we can use and the determinism of our releases. And so certainly, if adding a 0 or an order of magnitude to our feature set, we see as accretive, we will do that. We will make the economically rational choice.

Okay. Moving on from the model in compute. You talked earlier about the mix between gaming and non-gaming. But when you think about your go-to-market strategy and emerging verticals that's obviously also relatively unique relative to the industry. Can you talk a little bit about standing up your go-to-market strategy, what it means for not only the non-gaming side to continue to grow, but how there could be increased verticalization of non-gaming that you gain exposure to over time?

Yes, absolutely. Our business having been founded on the back of this more heterogeneous mix of customers started establishing not only a machine learning approach that was optimized to heterogeneous outcomes, but also an operating model. And so as we aim as a business to be the first to be able to make a new vertical work, whether that be prediction markets or LLMs or collectibles or you name it. And we've seen that the empirical evidence over the last few years is quite powerful. There are different needs that are quite parameterized in terms of what types of needs they could be. But there are different needs that a given business has as a function of the way in which they engage with the consumer. For instance, as I mentioned earlier, if I'm a hotel booking app, I'd like to KPI against, did that user stay in the hotel? Well, that data signal is determined after the user actually completes the stay. And so our business, we have become really well attuned to the way that reporting structure may vary, the KPI structure may vary and the creative storytelling may vary based on a given creative or a given product, excuse me. Now that model scales in a nonlinear fashion. In fact, the way in which our workforce has evolved, leveraging technology, leveraging systems, adapters, as we've discussed in our materials. This is an operating model that is truly native to our business. That is very hard to repeat. It is very hard to go and build out an entirely new business that caters to a different type of advertiser and that's something that we pride ourselves on. I think that should the next vertical emerge, you should expect us to be part of the growth story of that business.

Okay. So when we pull this all together, we start the process of pulling it all together. When you've got the growth you're putting up, the market opportunity, the verticalization opportunity as well. How do you prioritize what the key investments are that need to be made in the business when you think over the next 12 to 24 months that are critical to capitalize on this opportunity set?

So with capital, I think the first question on a macro level is where does the money go? Reinvesting in the business is our first choice. Getting to the right amount of leverage is our second choice. We're rapidly delevering as our disclosures would show in our earnings call, we discussed our next -- the next bucket would be returning capital to shareholders, which is certainly a consideration given the high flow-through of our business given how profitable it is. And then your fourth bucket would be M&A, which we have empirical evidence of having done M&A, both tech and team and transformational. We are lift off with the capital is a function of a merger between to well-known businesses. Within the reinvest in the business area, the index towards technology is always very strong. Now I mentioned how we make decisions that are deterministic in nature as it relates to deploying an extra dollar of compute. We have systematized to a pretty detailed degree where we see dollars working for us to -- in our favor. As you know, Eric, we have had a private equity investment for the last 6 years. And so we learned to make responsible decisions as a business many, many years ago. This is our opportunity to show it to the public markets now. We're 90 days in.

Yes. Okay. Understood. And then in the investments you would make into the product, the platform and the growth talk to us just a little bit about what you think about some of the more higher yielding return investors might be? Is it platform investments at this point? Is it continuing to scale Cortex and seeing what the output of that is?

It's a fantastic question. I think that when I consider like what variable we're investing as the most scarce resource, it's time. Is the time of our team because the opportunity cost of that time is quite tremendous. And the primary area of focus is always going to be on developing a smarter recommendation engine. So what does that mean? Well, there was this step function paradigm shift from our old model, if you will, to Cortex. And now it's about taking intermediate steps within this paradigm of advancing Cortex. And so how does that investment get deployed, there's time. And there's also, we have a team that has tremendous tenure. We have some of the best experts in the world working on our platform and we need to retain and engage that team. It's a really, really strong group, solving a very specific problem. And that group will then work to build out that architecture of Cortex further. Now some examples could be are we going to optimize for an entirely new KPI for an advertiser? Well, that could mean that there's opportunity cost of a team being deployed against that project. We could be testing with customers. These are derivatives of strategic choices that we made at the highest altitude that we end up executing against.

Okay. Understood. Probably the last big picture topic I wanted to talk about, which is another output of a lot of investor conversations is I still think there's a lot of misunderstandings about the competitive landscape. So I think a lot of conversations with investors get bogged down into gaming versus non-gaming, demand versus supply, what's a differentiator here. What is your imprint you want to put on the conversation of what the competitive landscape looks like today and how you see the industry evolving against the market opportunity and all the inefficiency you see and adds today over the next couple of years?

That's a fantastic question. I would get the family viewed one. So I think I think that it's a very unintuitive structure of this market, but it's understood by looking at a few things. One, you have a vast supply ecosystem. So meaning that there are more impressions available for advertisers to buy than there are qualified advertising dollars. And I'll get back to that in a second. So what that means is just in Liftoff terms, our SDK is integrated in apps that see about 1.5 billion users a day. The SDKs in last time that we reported on it, I think, 167,000 apps. These are all different businesses across the ecosystem that have implemented ads in a specific way that's proprietary to them based on their modernization goals. You then have advertising dollars that are flowing in from businesses that are looking to acquire qualified customers. Well, first, that is a pretty scary big problem as an advertiser. It's not like TV. It's not like the web where there's a single surface. You're looking at millions of different applications that could be delivering your product proposition. And so that's really challenging. As a result of this, and then we talked about the $0.07 per user hour of monetization earlier, you have less demand flowing into the ecosystem than you have qualified supply. At the same time, you have very few businesses that have the qualifications to deliver users to you at scale. So what are those qualifications. I think there are 2. Does that marketing channel have a ubiquitous software footprint into the inventory where your ads will be shown. And does that marketing partner have a smart recommendation engine that can find users for you that are ROI positive. If you apply those 2 filters, you have very few businesses. But you also have across an equals about 3 different underlying data sets that are going into the recommendation models that create very different predictions and desires for the model. So you now have, as an advertiser, you have a few different channels that can go and find new users at scale in an economically effective way and your objective as that advertiser is to acquire as many of those as possible. And so this is a major distinction between, let's call it, [ Madison Avenue ] advertising and performance advertising. The customer here is attempting to buy as many consumers as possible that are ROI positive, not attempting to distribute budget across a certain amount of partners that's fixed. So the pie is not fixed. The supply side is vast. And because of that, you have the customer conversations sounding like "Jeremy, why aren't you able to give me more users? I want you to get me more users." There is no shifting of budget. There are effectively, there is a macro or assemble model that you're deploying as a user acquisition manager. And that ensemble model may be Liftoff and app, and you and others. Could be a subset of those, but you're going to be attempting to get as many users as you can, that's ROI-positive. And I'll get off my soap box with this last comment. Our business functions a lot like COGS in a P&L. The primary executive customer these days is the CFO. And so when you think about how that works, the CFO doesn't make a decision to go and use more cloud tomorrow. Cloud is the lifeblood of the business. You have to be using your technology in order to grow your business. It's just like user acquisition. And so this is why the winner take all or winner take most sort of paradigm doesn't apply this ecosystem. You also have this property of it being vastly underpopulated. Now gaming versus non-gaming, I think, is a different question. The -- what I would say is we, as consumers, we might play a game because in between sessions this afternoon or you might use a ride-hailing app, where that you're the same person in 2 different experiences. And because of that, I think it's not too big of a leap to expect that we could advertise different products to you that could be relevant to you as a consumer across those experiences. And so as a business, we do not aim to achieve any mix of gaming versus non-gaming. We want to be where consumers' attention is accumulating and provide high-quality users to those advertisers who are accumulating that intention.

Okay. So to bring it all together, we have a few minutes left. Over the next couple of years, where do you think the biggest growth opportunities sit inside the company where you're aligning your strategic priorities in those growth opportunities? And then the second part, which is kind of a follow-up to the last one is just, how do you think about where a competitive advantage for you as a company will accrue over time?

Awesome questions. The primary area of focus for our business from day 1 through day x will be enhancing the quality of the recommendation engine. In a business like ours, you're getting it wrong most of the time when you're predicting the propensity for that given user to then buy something in the given advertised app. And so there's just so much runway to improve. The question is really around sequencing the efforts and the tests and getting smarter and faster with testing. And so I expect there to be us, tremendous progress over the years to come with the quality of our recommendation engine. And I think the empirical evidence is in the public domain. When you look at the innovation curves of some of the businesses we're hearing about day to day that are on the cutting edge, some of the [ LOMs ], the metas of the world who have taken the [ neural net ] paradigm and advanced it many years into the future. It gives us a lot of -- as much determinism as you can have when you're thinking about where the model can go. So that's certainly very exciting. But I'm also really excited about the consumer side of this. But the amount of apps that we're seeing created far exceeds that of previous chapters of the app economy. The -- we're seeing the democratization of content creation innovation within the container itself, which I think means that the focus will become increasingly on the storytelling in the marketing of those businesses. Certainly, content creation is important. But when you can push a store update with software, it's quite frictionless especially now with access to LLM and some of the more advanced technology. So I think we'll see the content creation threshold continue to come down, and we'll see the focus on telling that story in an economically viable way increase on a relative basis. And I think what we'll also see is that we, as consumers, will have more specific experiences that are really interesting to us that we'll be able to consume. Hardware is evolving. We've just talked about software. So what does that mean for our business? Well, it's very clear to me that the direction of travel is a more heterogeneous experience across the app ecosystem, meaning you're not just talking on the phone and playing games. You're booking a hotel, you're booking a ride, you're banking, you might be engaging with the NFL in various ways tomorrow evening. Now there are all kinds of different experiences that we're engaging with, what business is best suited to help those businesses grow in the app ecosystem, I believe that Liftoff is.

Okay. Well, we hope you had a good enjoyable experience here. Come back next year, and thanks so much for the conversation.

Thank you.

Please join me in thanking Liftoff for being part of the conference. Thank you.