CCC Intelligent Solutions Holdings Inc. (CCC) Earnings Call Transcript & Summary

Awesome. I think we're at a capacity here to a certain extent, hopefully, maybe 1 or 2 others straggle in. But from a high level, I'm Dylan Becker. For those of you who don't know me, I am the vertical software analyst here at William Blair that covers CCC Intelligent Solutions. For all the necessary disclosures for today's presentation and conversation, you can find those on williamblair.com. Joined today here with CCC's CEO, Githesh Ramamurthy; and CFO, Brian Herb.

So gentlemen, thank you for joining us today. Maybe, as a place to start the conversation Githesh, there's probably some people are familiar with the business, maybe some that aren't as familiar. Could you walk through kind of where the business has evolved, over the last kind of few years? What you guys deliver and how you service kind of the automotive claims ecosystem and each of those stakeholders with in that?

Okay, sure. I'm assuming many of you have cars, show hands. All right. Hopefully, you have auto insurance as well for your cars. So, if you are in any way, shape or form, what CCC does is makes the process of getting back into your car after an auto claim much easier. So, when you think about what happens after an auto claim happens is that many different parties are involved. You've got insurance companies, repair facilities, towards all kinds of players. So, it's a pretty large ecosystem. And from a scale standpoint, $275 billion are collected in premiums every year, and well over $220 billion, $250 billion, somewhere in the ZIP Code, our spend in settling auto claims. So, very large in dollar spend that takes place. So what CCC does is we provide software databases and increasingly those have been over the last decade, we've invested very heavily in artificial intelligence. So those products have been released over the last several years, so we provide capabilities to insurance companies that allows them to handle interactions with their consumers, managed anywhere from 1,000 to 30,000 claims staff, routing information across helping schedule repairs and the like. We also provide software tools to a large number of repair facilities, 28,000-plus repair facilities around the country. We also connect them to banks, solvers and other providers, parts providers and others in the space. So that in a nutshell is what CCC does.

And I want to get into the data piece. There's a lot obviously to dig in within that. But maybe, Brian, switching over to you really fast, too. As we think about the scale of the business, kind of some of the financial metrics, obviously, a healthy combination of growth and profitability here. How do you think about the opportunity set relative to where the business sits today? And from your chair, what are some of the key kind of financial metrics that you think investors should focus in on?

Yes. Sounds good. So we operate in a large TAM. So the TAM, we think about is about $9 billion for U.S. auto. We're about $800 million. So, we're under 10% of the TAM that we're operating in. We've seen long growth cycles and we think about the business in both a predictable model. So, we have gross dollar retention of 99%. We have software recurring revenue, about 96% of our revenue comes from software. It's durable revenue model. So we've seen -- 20 years of consecutive revenue growth and growing across all cycles. And, we have a very strong margin so we see margins last year were at 39%. We've seen 900 basis points of margin improvement over the last 4 years, and we see margins move into the mid-40s. So it's a durable, predictable revenue model with really strong margins and the ability to expand those margins and doing that over time.

And is there any really quick way to think about kind of -- you touched on kind of the core customer segments and stakeholders you're servicing today, kind of how the mix shifts within each of those from a revenue contribution standpoint?

Yes. So the way we think about the revenue model is about 20% will come from new logos, and then, about 80% of the growth will come from cross-sell and upsell of our existing clients.

So -- and I'm sure that this is going to be a function of the data question that we're about to ask. But, within that cross-sell dynamic, you see about $1 trillion or you've seen $1 trillion in historical claims volume. You see about 70 or so percent in a given year and that gives you a lot of optionality across this ecosystem. How do you think about utilizing that data asset? I think that there's unparalleled insights there, but drive incremental innovation and delivering value to the ecosystem off of that.

Sure. So we've -- over the last many years, we've done really three things. So every product, every solution we have has a very distinct ROI associated with it. And -- and we have also collected, and we have processed over $1 trillion of historical data. We also collect, for example, billions of photos of accidents and the like. So what this has allowed us to do is to generate and deliver a whole new series of solutions. So, about a decade ago, we started putting a lot of effort on artificial intelligence -- and about in November of '21, we delivered a groundbreaking world-first product, where you could go directly from taking pictures, guided pictures around your cell phone to actually producing a line level estimate. First of its kind in the world. And, we announced that this March, we had -- we were at a $1 billion run rate claims processed through that particular product. It's called Estimate-STP. Last March, that same solution processing $100 million. This March, the same solutions processing $1 billion. With that said, we also said this is still less than 1% of the total claim volume processed by our platform. So the -- so that's one ability is massive amounts of data, a lot of AI and models we've built. And the beauty is we can deliver the AI in line with existing workflows. The other thing you have to also think about is the insight we are able to provide carriers, parts providers, car companies, repair facilities, about what is happening in the marketplace. It's a very unique insight given the breadth of our data set. We're able to provide insights to a lot of different providers, so they can understand strategically what they need to do to improve operationally. So that's kind of a second bucket. I would say just the third bucket is that a lot of our innovation is really driven off of the fact that we have a very broad ecosystem we participate in.

Right. And as we think about the value proposition across each of those core stakeholders, right? I'm sure that there's kind of nuances within each particular one. Obviously, growing automotive complexity, cost of repairs for the repair facilities, how would you force rank or what are you hearing most from customers relative to the existing ecosystem today of what's really driving to make an emphasis towards your software?

So, I would say there are two very large drivers from a customer standpoint. First, and foremost is vehicle complexity. As cars are getting more complicated, there are more sensors, more cameras and the like. So the cognitive load on actually how do you repair a car, why do you -- what you should you be paying for this repair, et cetera, becomes very complex. So, customers are relying increasingly on software and AI to be able to process that. I would say the second macro trend is that across all our customers, collision repair facilities, insurers is a tremendous shortage of people, people with experience. So, there's a huge amount of shortage and our customers are saying, how can we find more efficient ways of winning these things. And that's -- and how can you provide tools that make -- help improve the productivity of our people. So those two trends have been very critical. And the third piece of that triangle has been that if you look over the last 36 months, inflation has been a huge issue. So the cost of repair, cost of parts across the board, inflation has actually caused claims cost to go from $3,000 repair to $4,500 plus repair. Total losses going from $10,000 to $15,000. So we've seen significant increases. So the question is, how do you manage the first two pieces of complexity, vehicle complexity labor -- tight on labor with increasing inflation.

Right. And obviously, there's kind of the three core tenets and stakeholders across this ecosystem today. But given, you have the scale and depth that you do, I would assume that there's a lot of optionality to service and deliver value to more constituents of this ecosystem over time. And as you grow that customer segmentation, it kind of begets further traction and momentum from that ecosystem coming towards you guys.

In fact, if you look at our history, right, as Brian said, we've operated for 25-plus years, continuously growing. So we start out delivering 1 or 2 solutions to our insurance customer base. Expanded that suite pretty broadly. We then added -- we started providing services to repair facilities that has gone from zero to about 28,000 repair facilities. And now, we've added parts providers, car companies, and we're adding banks and other providers. So our strategy has been to deliver great value to every one of our constituents, but solutions that are unique for their needs. For example, to deal with vehicle complexity for a collision repair, we've introduced a solution called Diagnostics. That allows you to scan the vehicle multiple times, integrate that, provide transparency, for more of the carriers, more of the AI has been super helpful. So, there are different solutions for different parts of the ecosystem that we've been providing. And we're also adding other participants.

And maybe, Brian, how do you think about -- and obviously, Githesh, this is a question for you as well, but the evolution of the business, right? You guys have been at this for a while. Obviously, that data asset has continued to grow -- you have a lot of core kind of platform capabilities. You've got some newer capabilities as well. I would think that there's opportunity within the existing tool set. There's also a lot of exciting initiatives in kind of some of these newer iterations as well, too.

Yes. I mean the way we think about the growth is especially I talked about kind of the build, 20% coming from new logos, 80% of the growth coming from cross-sell and upsell. And within the 80% cross-sell, upsell, we see about half of that coming from solutions that have been in the market for a while, such as casualty or the repair package upgrades and then half of the growth coming from these new cohort of products. So Estimate-STP, Diagnostics, Subrogation. So we see very much of a portfolio approach and seeing opportunities both in solutions that have been in market and newer solutions that we've recently launched. So, a lot of ways to win.

Is there a way to think about -- as you go back to kind of the growth -- the broader high-level growth equation, what are these kind of new opportunities and solution sets can look like? And maybe the monetization piece of tying that to the value that you're actually delivering to a carrier or a payer facility.

Yes. I mean we price the solutions very much on an ROI basis. So ultimately, our solutions are about driving efficiency for our clients and also driving accuracy. So the way, we set the price as we look at the benefit clients have and then we price accordingly. So it's an ROI pricing model. When we look at these new solutions, as we talk about Estimate-STP or we talk about Diagnostics, we talk about Subrogation. We see each of these being $50 million plus run rate opportunities, when we get to start to get to scale against those. So they become a meaningful part of their growth going forward.

And maybe Githesh, as well from the carrier perspective, you mentioned kind of some of the adjusting expenses, kind of some of the claims leakage expenses. I would think that this data set can help refine the waste, if you want to call it that, on that side of the equation. How do you think about that enabling the AI opportunity to drive more automation and efficiency from their back end or their operational [indiscernible]?

So our broad term for that is straight through processing or STP. The first solution we delivered in that space was to go directly from a photo of a car, to actual repair costs of the car. So, we've been working a number of carrier customers to really look at straight-through processing on a whole variety of areas. So one of the hardest problems to solve was to go from using our image algorithms, which is a lot of basically AI models that we've built, and that have gone, and that's given us a lot of credibility, but we've been working across a whole host of other areas. So STP is our broader term for applying artificial intelligence in existing workflows that we are in, and we are working in parallel on about 4, 5 different solutions. So we think, this will be a game changer for the industry over the next several years. And our customers are pretty excited about it. And same thing for our repair facilities, there's a whole set of areas where our repair facilities can interact with consumers and the AI will be applied to a lot of different areas, including how do I repair the vehicle and the like. So a lot of our deep learning models that we built and deployed are now seeing a lot of real positive reception.

And I think it's valuable, right? So we were at the conference with you guys last month or so to give maybe an illustrative example of what that end-to-end workflow could look like with STP, Estimate-STP just kind of scratching the surface within that. Because I would think that, again, there's a lot of room for operational efficiency here, and it's the #1 emphasis for carriers that we talk to. So maybe can I give an illustration of example, yes.

Sure. So, if you look at today, what happens is the moment claim is reported, it creates about 7 different streams. So what we can do is to -- with our capabilities, execute about 7 or 8 different parallel work streams. You could start the process of saying, "Should I total this car? Should I repairs this car? Is there a medical claim associated with it? How do I schedule this person?" We have the -- in our cloud, we have the real-time schedules, all of the repair facility. That's another example. And if the car needs to be routed to for salvage, if it is a car that's total, that can be done in parallel. A payment can be executed in parallel. So, we can kick off a whole series. And for example, if on policyholder hit another carrier's policyholder, we can kick off the Subrogation process. Subrogation means connecting -- that means one carrier collecting from another. So that effort can be started in parallel as well. And then, part of our software and the physics of the accident that we have evolved allows you to look at the physical damage of the impact forces in the body and impute medical claims and all of that from the physics models of the vehicle. So there's about 7 or 8 or 10 different things we can do in parallel, and we're stitching all of that together and calling that STP. And Estimate-STP, our first solution in the space, which we delivered in November of 2021. It's now been adopted by 8 of the top 10 carriers in the country. And I think, we have announced publicly at least 15-plus carriers using it. And that's given us incredible amount of credibility about solving this problem. So there's all these things we can do. And then, if you look at it from a customer standpoint, if you're an insurance company, having a large network of repair facilities, parts providers, car companies is very valuable. They're repair facility being on a large network where you have so many different participants is also of a huge value. Sorry. So having wired and connected all of this ecosystem actually is a precursor to be able to deliver STP and AI without -- 20 years of work, I call it overnight success, after 20 years worth of work, that's what it's taken to really put these pieces together.

And that's a great segue to my next question, as I was thinking about, okay. So why hasn't this been done in the past, right? It's not something that you can just flip on a switch overnight. You have to have this integrated data set across all of these core stakeholders to even enable that. So you're just scratching the surface on what innovation can look like in introducing new capabilities and functionality.

Exactly. I mean the -- and building trust, right? A huge part of this involves building trust across a very wide network of participants. And that trust is very core and very central to how we operate. So in every solution we do, given the diagnostic solution we talked about. We increased the level of trust between the repair facility, the carrier, the car company, and they have different needs for -- one wants to verify it's done the right way. One wants to make sure all the right procedures have followed. So, it creates a high level of trust. And the other thing that's also been central to this is that the average software company Net Promoter Score is 14, 1-4, our Net Promoter Score is 82. So having an 82 NPS, as you saw at the conference, is a dramatic difference in that level of usability and people trusting that you will deliver the next solution that's going to work.

Right. And it's interesting because I wonder -- and it's readily apparent in the customer conversations. But how does that obviously help fuel the flywheel to, from an innovation perspective because you've delivered value in the past, they're more than willing to be those early adopters of the new products and iterate because of the applicability across the system.

In fact, many of our customers say, "Hey, we would like to go first on your new solutions". So our customers -- we like to partner with our customers. I always say that it takes the same amount of money to build a great product, has to build a bad product, but the difference when you have great customer input and you're deeply emerging your customers' problems, you deliver solutions that are dramatically better when they come out -- and that's a huge difference in terms of having that NPS. And the relationships, we have enables that flight we have.

Right. And even as we think about going to maybe even the casualty side or kind of some of these other newer areas around crash detection, fraud analysis capabilities as well. I mean -- and I hate to believe the point of the value of this ecosystem, but I think it's worth highlighting, right? So, how do you think about the capabilities of embedding fraud detection within -- like again, the number of use cases and applicable areas, I would think, for insurance carriers are massive. And it's almost a challenge of how do you guys allocate and prioritize your R&D investments versus everyone saying sell all of this for us today.

I think, one of the ways we have figured out how to do that is actually have advisory councils. So we actually have a number of advisory councils for each of our customer segments, and we meet on a very regular basis. So, we're actually laying out our product road maps. We're sharing with them. And we're also -- they're also helping us prioritize. This is a really big problem. This is not a problem. Don't waste your time on this. And that is having those relationships with our customers has been incredibly valuable. So that allows us to prioritize our R&D spend and our products and our road maps. So, in terms of having those deep relationships and also are being immersed in the problems our customers have also allows us to think about how we bring technology to solve those problems.

And it's a great point, and maybe a good segue to Brian, of the efficiency of this R&D motion and your funnel of product innovation, leading you to a highly profitable financial profile. There's a lot of irons and stakes in the fire here. But how do you think about kind of the core tenet of the reference ability, obviously, the targeted R&D fueling the 40% roughly kind of margin cadence in the business today?

Yes. No, we spend a lot on R&D. I mean, if you look back over the last 10 years, we spent over $1 billion on R&D and really driving this innovation. And it's both a culture but it's prioritization. And really, as Githesh said, the councils and getting the feedback is really important. But the investment that we're driving in R&D, we're able to do that at the same time as progressing margins. I mentioned earlier, if you look back 4 years ago, we were at 30 basis point or 30% margin. We ended last year at 39% margin. We're going to -- we're guiding to 40%. But we also see a pathway as we continue to look at the durable revenue growth but to expand margins, both from a gross profit moving from mid-70s to 80. And then over time, moving our EBITDA margins from the 40% to mid-40s. In mid-40s is not a ceiling by any means. But we -- it's a target that we feel good about reaching over the next 5 years or so, 5 to 7 years and then going beyond that. So, we feel like we have the infrastructure. It's also a highly efficient business model, a lot of areas for operating leverage where we can continue to invest and drive the innovation, but at the same time, progress margins.

One only thing I'd add to what Brian is saying is that last year, for example, we increased our development capacity product development, engineering capacity by over 20%. And that was -- that's very important, right, in the context of additional products and solutions we're building. So the margin improvements did not come at the expense of R&D and continue to invest and continue to build. We've been successful over a very long period of time. We've architected the business, so that we can be successful over the long haul. That means real emphasis on service, delivery, quality and R&D.

And you kind of stole the thunder but of the next question. But Brian, relative to some of these newer initiatives, you kind of talked about the high-level growth equation, but what is kind of that not steady-state even, but what is the longer-term kind of model framework? And how do we get there thinking of it relative to corset today, some of these newer initiatives and driving that margin capabilities.

Yes. So, we think about the organic revenue targets at 7% to 10%. We talked about it earlier, but just 20% coming from new logos, 80% from cross-sell, upsell. And then, out of that, about half coming from these newer emerging solutions. And we think about doing that, delivering that at scale over time and feeling confident that, that's a target that we can operate. And again, that's an organic position that we feel very confident to deliver against.

And maybe going back to Gitesh, one area or one question that we always get and I'm sure you get the question just as much, if not more frequently. But around kind of where you could see the number of claims going forward, as we shift more to electronic -- electric vehicles, autonomous kind of type of driving framework. There's probably some puts and takes, and maybe the pie in the sky 2050 kind of type of framework. But how do you think about that relative to a risk in the business versus incremental parts and repair complexity, as kind of a near-term tailwind?

So about 2 or 3 different data points, right? So first and foremost, there's 300 million vehicles in operation in the United States. So the turnover of those vehicles to electric and like is going to take a long period of time. There are 20-plus million auto claims every year. And that number has been slowly increasing or flat for a very long period of time. So that's our view over a long period of time. So, we are also today seeing a large number of electric vehicles go through -- through the CCC platform. And the cost on electric tends to be higher in terms of repair costs, et cetera. Hopefully, that will stabilize over time. So, the additional complexity of the electric vehicles is actually providing more -- providing the need for -- increasing the need for more solutions. So we are -- so all of these trends of complexity, and we see that in terms of autonomous, either it's Level 1 or Level 2 and the like, we are seeing a lot of sensors being added to cars that are again increasing complexity and cost. Whereas, we have not seen any material trend, and we think it's going to be a long period of time before the car part turns over. Meanwhile, what we're seeing in our China data is that claim volume is picking up. In the U.S. last year, we saw that 6% of cars sold for electric vehicles, but only 1% of cars in a collision where electric vehicles because it's still ramping up. So we think, it will take a fair amount of time to go through.

And to your point, with more sensors requiring more diagnostic scans, et cetera, kind of incremental...

More complexity. There's a lot more solutions. And the macro point around the whole industry is that there are so many manual processes that need to be automated. So, if you look at a very macro level, this ability to digitize a lot of this industry with artificial intelligence in existing workflows is going to be -- is a far bigger.

And it doesn't hurt that auto insurance to your point at the beginning is mandated as well, too.

Is a mandated industry.

Brian, you touched on the idea of kind of potential M&A. We've seen safe keep coming on the Subrogation side. You've got a healthy balance sheet here. How do we think about the optionality of, again, leveraging the existing data set, maybe moving into newer P&C lines. International as a business is kind of largely U.S.-based today. How do you think about kind of capital allocation here?

Yes, that's Good question. So we're about 1.5x levered. So thinking about the overall ratio, it's an efficient balance sheet. We do think about M&A as a primary use of that balance sheet in the leverage would then go up and then over time, it would come back down, as we pay it down. I think Safekeep is a really good example of the type of M&A that we look at. So it was buying functionality that was very complementary to what we do today, as Githesh explained, Subrogation. So those are the type of solutions that our clients are interested in. And so we think about bringing that to our carriers -- and it was something Subrogation is a good example of something that was on our road map as an opportunity to go after, but it was a buy instead of build. And so, we think about that as speed of revenue, and getting product into the market. So product tuck-ins are a really key area for us, as we think about M&A. As we talk about international, I know Githesh referenced China, but as we think about broader international opportunities, those would likely come through M&A as well. We're not looking at organic builds internationally, so it would either come through partnership or M&A. And then, the third area that we think about is just customer adjacency, so even in our -- the ecosystem that we support, as we think about getting into new customer groups within that ecosystem, M&A would be a way into those as well. So those are the threeareas that we think about from an M&A perspective.

Got it. And I know we've got about a minute left or so here. So maybe Githesh, wrapping up with you. Obviously, a lot of new investment. You've got Estimate-STP, Subrogation, Diagnostics, Casualty, a number of new initiatives, maybe what gets you the most excited about the opportunity in the business? Because it seems like it's something you've been building forward towards the last I don't know, a decade plus and now you're actually kind of able to capitalize on it.

So I'd just say having done this for a very long period of time, right, helping the industry go to cloud 20 years ago, helping the industry go to mobile capabilities about 13, 14 years ago. So, each of these waves of technology has really helped solve a whole different set of problems. And what we're super excited right now is really about two things. One, the fact that we have a very large ecosystem that we support, where we have already got connections and are wired. And frankly, the investment we've made in artificial intelligence over the last decade. That -- the combination of those is really what I'm excited about, in terms of continuing to solve problems for our customers and keep growing.

Wonderful. I know we've got a flashing red screen here, but looking forward to carrying on the conversation. We'll have a breakout session upstairs in the Richardson room. Githesh, Brian, thank you both very much.

Appreciate it.

Appreciate it. Thank you.

Thank you.

Appreciate it.