Root, Inc. (ROOT) Earnings Call Transcript & Summary

Welcome to the next company. We're going to speak today to Alex Timm, Co-Founder and Chief Executive Officer of Root. Alex is a veteran of Nationwide. He cofounded Root and is an actuarial by trade. Root has demonstrated a great ability to execute and perform, but importantly, they are early in their growth trajectory and the best is certainly ahead of the company. Root was founded in 2015 by Alex and his co-founders based on the idea that insurance rates should be based primarily on driving behaviors, not demographics. They utilize mobile technology and data science to offer fair, personalized rates and to good drivers. Sorry about that. Root went public in 2020. And today, it's the largest auto insurtech in the country in both one of the best underwriting track records in the industry. With that, let me introduce Alex.

And Alex, let me start with the first question. Can you share a brief history of how you got to where you are today, and how did you create Root?

I mean, first, right, it starts with the customer problem. And like I always say, I have never met anybody who, when you talk to them about car insurance, they're like, oh, what I get to do this weekend, I get to go shop for car insurance, right? Like nobody likes this industry. No consumers really enjoy the product. And you ask them why, right? And they're like, well, if I buy the red car, then I'm going to pay more or the black car. And no matter what I do, my prices just keep going up. I don't know how this thing works. It's a racket. It's -- I mean it's an industry that is generally pretty despised by consumers, and it's getting worse, obviously. And I -- to Brad's point on my background, I had a front seat to that. I'm an actuary by training. My father was an insurance entrepreneur. And I was working in corporate strategy at Nationwide Insurance, and it was my job to look at -- what do we do about this, right? And two big things were happening. The first is really the advent of data science and big data. And so as an actuary, I used to be sort of a hunter and gatherer of data. I used to say, oh, what's your credit score, can I get this credit report, and then put it in a linear model to try to figure out whether or not you're going to get to a car accident. And that was fundamentally changing, right? Now suddenly, we're all walking around with supercomputers in our pockets, called your smartphone that you're sleeping next to, and it's just generating data like crazy. And so the data problem totally changed. It was now I get to plug into these sensors and I'm just awash in data. And I have to figure out what data makes sense and what doesn't. And it turns out this thing called machine learning and -- or artificial intelligence was super helpful. And so this was 10 years ago, and this was sort of when these algorithms really were starting to be developed. And the second thing that was occurring was consumers are moving, right? Consumers are moving to the smartphone. It's the fastest-growing retail channel in the country, and there wasn't really a single large market incumbent that was leveraging mobile technology to meet consumers where they are. And so the core idea of Root was let's build an insurance company that meets consumers where they are on the smartphone. And by doing business this way, let's leverage all of the rich behavioral data that we can get off the phone to better and more accurately price risk. So insurance is all about matching price to risk, right? And by the way, by doing business this way, we can increase transparency. We can create a much better customer experience. We can offer 3-click purchases. We can -- now we're embedding with vehicles directly, so whether it's an OEM or a vehicle sale to make the consumer much happier to create a frictionless product, so it doesn't take 15 minutes to purchase a product online that you never wanted to shop for, frankly, in the first place. And so that's really how Root was born. It's been a long time. We launched the first product in 2016, and it's certainly been a journey since then.

That's very interesting. Can you -- love to -- we have a lot of new investors here who haven't known the story. Talk about the technology. What's behind that? And talk about the distribution of the model.

So the technology, I said it a bit earlier, one, there's obviously our mobile app and our mobile phone, and we were really the first to come out and say, hey, you can use mobile cell phone data to sort of predict auto insurance claims. At the time, all of the sort of most of the telematics state in the industry is based on these things they call dongles. And so the model was, hey, sign up for insurance. I'm going to ship you a device, you're going to plug it into your car, you're going to drive around with it for 6 months, take it out and send it back to me if you forget them, I'm going to charge you $200 for the device, all in the hopes of like a renewal discount. And obviously, consumer adoption was very, very low. One of the pieces of technology that we built was how do you actually do that using a smartphone? And that's super hard. That technology problem is very different than using an OBD2 device. If I showed OBD2 device, the data would look super -- you'd know what the car was doing. On a smartphone, you plug into these devices, and it's really built for video gaming. So we hit the device multiple times, 16 times a second, either super noisy sensors, and we have to take all of that sensor data to figure out. Were you the driver? Were you a passenger? Was that a bus trip or not, right? You've got to clean all of this data to actually figure out what the physical events are on the car. And by the way, you have to do it also in a manner that doesn't kill the smartphone battery. And so we really are one of the only ones that have developed internally as an insurance company a closed loop on data science and machine learning that is involved with the -- with that smartphone technology. So we control the way that we collect that data off of smartphones, which is very difficult, how you ultimately clean that data and then ultimately, how that actually relates to risk. And so we've had this now validated by Milliman as an independent third party that sort of said our telematics is just far more predictive than anybody else because of that platform, that closed platform we've built. We've also -- I mean the entire company is a technology company at its core, and so we've really created an insurance company as a platform. So one of the things, too, that we leverage in terms of distribution is not just our direct channel and our mobile phone -- our mobile app, but also, we have an embedded in partnerships channel. So if you go to carvana.com and you purchase a car, and you want to include insurance with it, that Carvana insurance, it's actually Root. And the reason we're able to do that is because we've built these really flexible and dynamic underwriting models and systems that allow for us to ingest all of the data during that vehicle purchase process, actually inject data -- ingest data also off of the vehicle if it's a connected vehicle, and then offer just a 3-click purchase experience for the consumer. So when we launch that product, for example, on Carvana, we, by many multiples, increase their adoption of car insurance. So that's been growing. We're adding more automotive partners to our platform right now, which we like. It lets us get closer to the data too that's coming off of vehicles as vehicle technology is changing. So really every part of Root, we've really rebuilt the entire insurance value chain based on modern technology, which then has allowed us to really put these really rich data science algorithms that sort of are self-tuning on top of that. So it's really -- the technology is pervasive. It's across everything.

It's interesting. There are these giant insurance companies, legacy guys. How do you compete though against them? Like can't they just do this?

Well, you'd think that they -- I mean they've -- listen, they have competitive advantages, and we don't sit here arrogantly saying, oh, we're just going to blow right by those guys. We've got a lot of money and a lot of brand, and they really compete on marketing, right, billions. It's actually -- auto insurance is the #1 advertised single product category in the United States. By the way, it's a mandated product to buy. So it's not like they're creating a bigger pie. That's how they're competing. The way we're competing is with product and technology. And the reason these guys can't do it, most of our competitors are over 100 years old. GEICO, as Jain just said at Berkshire Hathaway that they have 600 back-end systems, and they're trying to get down to like 600 or 700. A lot of these guys are still on main frames. Most of them actually within the last 12 months, Progressive included actually have had COBOL programmers posted on their websites or to hire. So when you think about trying to do enterprise modernization, and I saw this working at a big insurance carrier, they're trying to just get the basics right. And they've outsourced almost all of their technical capabilities, even like policy management and claims management, those aren't even their systems. So when you talk about creating a new closed-loop system based on modern technology that's leveraging machine learning. I mean -- they're not even close.

It's very interesting. So talk to us a little bit about -- I think you're the first insurtech company get to GAAP profitability, and you did that in the third quarter of '24. Talk about this and how it validates your business model. It took you a while to get there. And how are you guys thinking about that?

Yes. One of the hardest things about starting an insurance company from scratch is you have no data and loss data. And so one of the things -- if you look over the long history of the company, since we launched, we've been getting like 3 points out of the loss ratio every quarter, which is basically 3 points of margin every quarter that we've been building in the business. And there was COVID and hyperinflation, which was kind of a challenging period for us. It was a very challenging period when we saw used car prices go up. But actually, if you trend right through that, you kind of end up where we are today, which is best-in-class margins. And the way we're able to do that is because of the technology. Being able to segment risk better means we're able to pick up the most profitable customers. Being able to have differentiated access to customers through our distribution channels has also created really a moat around our business. And as we've scaled, what that has resulted in is really margins that are not just better than all the insurtechs -- by the way, they're also better than, I think, most all the incumbents. So we're very profitable. We've also grown our customer base. I think year-over-year as of last quarter, it was close to 100%. So the business is growing and is -- like I said, in terms of margin on the business, it's maybe the best or one of -- if not one of the best in the auto insurance industry.

Let's step back and assume this is not an insurance conference. Talk about loss ratio, explain that to them, how you look and think about loss ratio and what that means.

Yes. So it's the most important financial metric in any -- in an insurance company. And really, it's your losses divided by your premium. So how -- what percentage of your revenue are you giving back into pay claims? And that's so important because, one, it's 60%, 70% or so of all revenue, if you just look industry-wide, right? And so you might be able to innovate and do some interesting things on distribution costs or fixed expenses through automation. But if you want to be serious about disruption and taking cost out of the system, you've got to be serious about how do you get loss ratios down. And that game is all about who's going to get into an accident and who's not. And when you look -- take a step back, really what that game is about is how do you identify the 5% to 10% of people who are causing basically all of the accidents, right? There's like a few -- this data is incredibly skewed, right? Like when you're driving down the highway and you see somebody weaving in and out and go to that, guy is going to cause an accident, you're right. That's like 10% -- 5% to 10% of the population. And so what's really important is how do you identify that percent of the population and get them out of your book as soon as possible. And we've been able to do that both through our telematics advantage, and that's really what's led to that loss ratio being one of the best in the industry, again, if not the best.

That's very interesting. And then let's talk about you reach profitability, but you have to grow, right? You haven't gotten to scale. How are you going to balance that? Talk to us about balancing scale, investment in the business versus profitability. How do we think about that?

Yes. And this is always an interesting question to me because I never thought this would be an interesting point of view because you kind of go to school and you learn, hey, we're all supposed to deploy capital at our -- above our IRRs and then stop, and that's how you sort of optimize. But what we do at Root is we basically -- like I say, we worship at the altar of the unit economic gods. We continue to grow provided that we are returning with a high degree of confidence our cost of capital. That sounds obvious. That's a textbook answer, right? It's remarkable because when you look at our competitors, what we tend to see is that they're very focused on a 12-month period, and what that's resulted in sometimes is suboptimal decision making by them. And so for example, if I need to hit my bonus metrics for the year, and I'm ahead on profitability but behind on growth, I might just push a whole bunch of marketing capital into the system even if it's not accretive. Or conversely, if I had a bad Q1, and I'm not profitable and now I'm in Q4, maybe I'm going to pull way back even though my current marketing would be accretive. And what that does is it creates these cycles in the industry. And so when we talk about growth, what you'll see from us and what we've shown is we are almost countercyclical to that because we don't operate that way. When they pull out, we'll push in heavily. When they push in, you'll actually see us push out. And so on our direct channel, you actually see sort of this countercyclical growth sort of up into the right. But again, it can come down and up, and it can ebb and flow. On our partnerships platform, we actually -- that's been very consistent. That's been a balance of growth. We continue to add more automotive folks to our funnel. That funnel is extremely healthy. Given what we achieved with Carvana and then subsequent partners, we -- that's just coming through and is -- if anything, that's more about can we service all those partners as they come through. So we're very -- on the capital side and profit side, we're very, very healthy. And -- but at the same time, if we see the opportunity to push growth harder, we'll certainly do that, provided we hit our returns. So it's not we're going to make this amount of money every single quarter. We don't think that's the right way to run the business. It's we're going to deploy capital at our target returns. And that's how we optimize the long-term value of the company.

So it sounds like you're taking a long-term approach to growing this business and...

Yes. Yes. And it's provided long term -- yes, yes. I mean...

Versus the quarter-to-quarter.

Yes. Oh, yes, absolutely, not a quarter-to-quarter. We take a multiyear approach.

That's great. And then talk about these growth. What levers can you pull? How do you make this business grow? What growth leverage do you have?