Definitive Healthcare Corp. (DH) Earnings Call Transcript & Summary

Thanks, everyone. Welcome to the Credit Suisse 26th Annual Global Technology Conference. We're very excited to have everyone here this morning. My name is Maneesh Nisargand. I'm a Director on our technology investment banking team. And we're delighted today to have Jason Krantz join us from Definitive Healthcare. He is the founder and the Executive Chairman. Welcome to the conference.

Thank you. I appreciate the diehards getting up at 7:30 in the morning for this.

Excellent. Well, congratulations, first of all, on passing -- I guess, a couple of months ago, passing your first full year as a public company. For some of the folks who might be new to the name, I think it'd be great if you could go through a little bit about why you started the company back 10 years ago and how that's evolved over time, just to kind of set the stage of what Definitive Healthcare is all about.

Sure, absolutely. So when we started the company back in 2011, there was a vast changes taking place within health care that frankly continue today. But some of the things that really drove me into health care back then was electronic health records were really starting to take off. So there was this influx of new data at a level people have really never seen before, and nobody really know -- knew what to do about it. At the same time, the Affordable Care Act was coming out. So you have all of these different changes in how physicians are reimbursed and hospitals are reimbursed. And then finally, there was consolidation really starting to take off. So as health systems were figuring out, how do you make the economics of health care work and how do you create a distributed platform where you can bring patients into your health system from all different areas, whether it be clinics, rural areas, whatever it might be, into these main centers. So all these changes were taking place. And everybody I talked to said, we want to sell into health care. We know it's a big market. It's 1/5 of the entire U.S. GDP, but we don't really know how to do it. We don't understand the changes that are taking place, and we don't understand how this new context of a post merger and acquisition and consolidation world will look. So that's a problem we set out to solve. So we started creating a database of every single health care entity in the U.S. So we started with hospitals, but we quickly branched out into all the outpatient facilities, urgent care clinics, skilled nursing facilities, physician groups and physicians. We pulled all of this into a database and then started doing deep analytics and collecting proprietary information that helped us -- helped our clients understand how are all these organizations linked together. And how do patients flow through this very complex network? Who's providing the best quality of care? And how do you think about referral patterns that drive all of the business to hospitals and to skilled nursing facilities. So over the next 10 years, we started building up and we kept adding new data and new analytics and new data science to bring this all together. And at the end of it, we have this highly proprietary view of the entire health care ecosystem that doesn't exist anywhere else. We know every single provider, we understand the deep analytics on it, and we help our clients sell and market more effectively into this market.

Got it. So you mentioned the data set and sort of how that creates some of that differentiation. So when you take that differentiation and think about it in terms of the competitive landscape, so how do you think about your competitors? Who are the ones that are relevant to you in your main core sectors? And how you sort of keep yourself one step ahead when you have the chance to do that?

Sure. So maybe I'll start with where we get our data from. So there's really 4 key areas that we get our data. The first is we've built technology to pull information from about 200,000 different websites out in the public domain. So we're there gathering data from physician group websites in industry journals and research publications and pulling all of that into our database every single day. Second, we complement that with first-party research. So we make about 700,000 phone calls out to providers to get data specifically from them that's important to our clients. Third, we get data from government sources. So health care is highly regulated. There's about 25,000 different sources we get from government based on -- from municipalities, towns, Medicare and Medicaid. And then finally, in 2019, we started supplementing our data with third-party data, mostly claims data. So we now have claims data, medical and prescription drug claims and about 250 million patients across U.S. The key to all of this, though, is the data science that we apply to pull all this information together and make it useful to our clients. So data is a very messy business, especially when you're getting this data from 200,000 websites and 700,000 calls. But we figured out over time and iterated on algorithms to figure out how can you pull this together. And when data doesn't match? How do you think about what's the right data? And how do you fill in blanks and create proxies for data over time. So over the course of these 10 years, we've built up this view of the world that nobody else has. And frankly, you can't rebuild at this point. It's longitudinal. Everything that we've built upon requires the data before it in order to continue to get more sophisticated over time. So now we have this view of the ecosystem that really doesn't exist, and it creates a tremendous competitive moat for anybody to enter this.

Excellent. So with that moat, sort of how do you think about who your competitors tend to be in each of the sectors? Do they tend to focus on certain subsectors? Or how do they kind of come up in your sales? Maybe I'll ask the question differently.

Yes. So starting off in terms of who we sell to. So health care is a giant market. There's about 100,000 companies that we think are in our target market. They primarily fall into 4 key sectors. The first is life sciences. So this would be biotech companies as well as medtech and med device companies. That -- at our time of IPO, about a year ago, that made up about 50% of our ARR. The second is health care IT. So these are organizations that are building new technologies, AI to sell into the market. At IPO, that made up about 20% of our overall ARR. The third is the health care providers themselves. So hospitals, big physician groups. They're using our data to figure out how do they build the most important physician networks that they can to make sure that they're getting referrals into their market that meet all the needs that they have. They also use it for corporate development and M&A type of activity. That makes up about 10% of our ARR as of last year. And then finally, we have this whole group of company we call our diversified group. These are companies that sell to many different industries but understand that health care is a big important part of their market, and they need health care-specific intelligence to succeed. And that made up about 20% of our ARR as of last year. So we have these 4 different markets, and we compete with different people dependent on these markets. So within life sciences, we'll compete with companies like IQVIA and Symphony Health and DRG, which is now part of Clarivate. Those companies are primarily claims analytics providers. So they take all of the claims that we have on these 250 million patients and allow their clients to analyze and slice and dice those claims. Now what we do that's completely different is we have all this claims data. But when you tie it to the proprietary data that we have, our affiliations data, our quality data that only we have, you can now make far more sense out of this data. So you can start to roll up the claims analytics to the health care system level or the physician group level. And you can really understand when a patient is going in and out of network. And these are key insights that you don't get from claims analytics alone. Outside of life sciences, we compete with -- in our diversified area in health care and IT, we compete with companies like ZoomInfo. ZoomInfo, a great company, but they're a mile wide and an inch deep. If you really want to succeed in health care, and if you think about why I started the company in the first place, health care is a different animal. It's extremely complex, and it requires health care-specific intelligence to succeed. So when we compete with ZoomInfo, if a company is really focused on health care, we have a much better solution than them.

Excellent. Some of these customers you mentioned are obviously large, complex organizations. Can you talk a little bit about how you sort of grow with the customer? You don't have to necessarily name names, but I think just -- give the group just an example of how you've sort of built those relationships and sort of how that grows over time when people start using your data and seeing the -- what it can bring to their business?

Sure. So if you just think about how we grow overall to start and then I'll come back to the land and expand. We really grow organically in 3 different ways. The first is bringing on new logos. So there's about 100,000 companies, as I mentioned, that we think should be buyers of our product. We have about 3,000 today. So we have a tremendous opportunity to improve our sales and marketing, get ourselves up there, see more companies and continue to grow by just bringing on new logos. The second is innovation. We continue to build new data sources that our clients need in order to run their businesses, their sales and marketing more efficiently. And at the same time, we continue to build new ways of using that data to solve new problems for our clients. So an example of this could be a new analytical system to allow -- to use the same data that we've been using all along to help our clients think about how are health insurance companies reimbursing for different drugs across the country. We have the data we need to present it in a way that makes it useful for that use case. And the third is once we bring a client on -- once we bring on one of these 100,000 clients that we're targeting, how do we continue to expand with those clients and grow throughout the organization. And that's a really important part of our growth. So if you take a life sciences company, for example, we'll grow in 3 different ways with them. The first is we'll typically bring on -- bring them on with a single module that we have. So maybe they'll buy access to our hospital data, our physician group data. We actually have 16 different modules that we can sell them. So being able to sell and expand and get them skilled nursing facility data or if they want to add our prescription drug data or medical claims data. Every time that we sell one of those things, that's an upsell that continues to drive the growth within that client. The second is expanding to different parts of the life sciences organization. So the example that I just gave, we might be in a rare disease group, for example, and we'll send them all of the data that applies to the rare disease group. But we can expand within a Pfizer or Merck or whoever it might be, we can now expand to their other therapeutic areas. So maybe they have an oncology practice. That's a whole new opportunity for us to continue to sell that data over again and apply it to the different therapeutic areas. We can also expand to different functions. So we've started the business, we're were really commercially oriented, so helping them with sales and marketing and figuring out how to be more effective. Over time, though, we've expanded and now we're in medical affairs. So we bought a company called Monocl in 2020 that has data on 15 million experts across the entire globe. And that's sold into medical affairs groups to help them figure out who should run their clinical trials and who are the most influential physicians as they bring a drug to market. So we can not only expand within -- across therapeutic areas, but across functions within those therapeutic areas. And then finally, we expand by adding more users. So as we add use cases, as we expand through therapeutic areas, we're able to add more seats on overall as well to drive growth. So there's a tremendous amount of opportunity for us to continue to expand clients. Where you see this in our numbers is you'll see our enterprise clients continuing to grow at a faster rate than our regular clients. And this is due to 2 ways. We bring on new clients that are over $100,000 in size, which is how we define an enterprise client. And then we continue to bring on clients lower than that, but we grow them over time to be $100,000. Another good example is about 2 years ago, we didn't have a single million dollar client. Now we are up in the teens, and that continues to grow every quarter as we expand these relationships to become much more important part of their overall information solutions.

So you mentioned the -- just at the end there, how you think about acquiring new clients. One of the themes that obviously coming up a lot now and especially in this quarter are fears of recession and those sorts of things. So how do you think about your selling cycle and how that may change or may not change during this time? And how do you see clients reacting to that? On the one hand, I can see that people might be a little more hesitant and then the sales cycle lengthens, but on the other hand, your data obviously is very differentiated and may actually give potential customers an opportunity to really be more efficient and optimize at time. So curious how you've seen that change at all or if it's actually still pretty consistent in your view?

Yes. So if you think about what hasn't changed, the demand for our platform has not changed? We have a tremendous amount of top of the pipeline coming in, people coming to us and saying, we need this data in order to grow our business. At the same time, our win rate has not changed at all during this period. Now what has changed is sales cycles have lengthened. So what this means is stuff is just essentially staying in our pipeline for longer. People are trying to figure out budgeting, they're deferring it to the next month, quarter, whatever it might be. So that is a headwind that I think a lot of companies are seeing right now. But as we move through that, the pipeline that we've built and the demand that we see for our products, which are really about how do you drive growth, how do you continue to expand our clients' business. These are key things and frankly, the first dollar spent as growth continues to resume within the economy. So we think long term, we're going to continue to see that. We're going to start to accelerate our growth once again and get back to the growth areas that we're typically used to.

And then when you think a little more steady state from a growth and profitability perspective, obviously, your numbers are great. People talk about the Rule of 40. I think in your last earnings, it was closer to the Rule of 60, so obviously in great shape there. So how do you think about what that long-term combination of growth and profitability is? And how you think about balancing that as the business continues to evolve?

Yes. So from the very beginning of Definitive Healthcare, we've always balanced growth and profitability. So in the second year that I started, we were cash flow positive by about a year, 3 or 4, we were EBITDA positive. So it's a really important focus on how we drive the business. And what this means is we're measuring everything, and we're making sure that every dollar that we spend is spent in the ways that are going to optimize our product or drive high sales efficiency. Some of this shows up. We have an LTV to CAC of right around 10x. So really efficient sales model driven by this desire to have not only growth, but higher profitability as well. So that's really important. As we go forward, we'll continue to manage the business with that balance. So I think a rule of 60 type of company is sort of -- that's the type of thing that I think we can shoot for a longer term. We seem to be able to hit that right now. But what we have an advantage of right now is because we've been focused on profitability, we'll continue to invest through this environment. So we'll be very -- a little bit more selective about the areas that we think are going to be the highest impact, but there's real opportunity for us to increase our competitive moat during this time, continuing to invest in both high opportunity product investments that we're making as well as continuing to invest in the sales and marketing resources that we think will be most competitive and most effective and will drive continued sales efficiency that we've seen in the past.

And then related to the growth point you just mentioned in terms of product investment, sales investments, how do you think about balancing organic growth versus acquisitions? And are there -- even if it's not specific companies, obviously, but perhaps categories of areas where you're thinking about acquiring that could make a difference for you? And sort of how do you and the management team think about that?

Sure. Over time, the vast majority of our growth has been organic. So we've built fantastic products. We have a highly competitive moat that not only makes it hard for competitors to compete, but it creates an opportunity for us to continue to innovate on top of that data set to create new products. So what I mean by that is we have this huge data set that we can continue to reutilize through data science and through new applications on the front end to solve new use cases for our clients very, very quickly. So our flywheel of innovation moves extremely fast. That will continue to be a huge focus for us. At the same time, we've done 6 acquisitions since the beginning of the company. What we're focused on from an acquisition standpoint, and this will continue to be very important is how can we bring on new data sets or new capabilities things sort of tuck-in type acquisitions that can increase the power of our overall platform. So really, the sort of 1 and 1 makes 11 type of mentality. So as an example of that, we bought Monocl back in 2020. I mentioned Monocl has data on all of the experts across the globe to drive clinical trials and find influencers and key opinion leaders. What we were able to do is immediately combine that data set with what we have and instantly make their data set better. So we were able to push our affiliations data that only we have into Monocl. And we're able to push claims analytics into Monocl. So we took their product from being very good to being best in class within about 4 months for very little cost. That's the type of acquisition that we want to do. So there's really 2 key things is how do you make our product better and how do you make their product better. So we want those product synergies by linking our data. But then we also want to be able to sell that product through our very large commercial organization. So in the Monocl case, we had relationships with many life sciences companies. We're immediately able to accelerate their growth by pushing it through our existing sales force to our existing clients. So that's the type of acquisition that we're looking for. In terms of size, as I mentioned, we're looking kind of thing tuck-in. So $10 million to $25 million in revenue is sort of a nice size. We'll go a little bit below for a great asset, a little bit above if we find a good asset as well. But we want something that we can quickly tuck in and accelerate their growth. Profitability-wise, they don't have to be profitable day 1, but we want to see a path to similar economics that Definitive Healthcare has. So high gross margin products. We want to be able to achieve synergies to drive that EBITDA level to levels that we're typically used to seeing.

Okay. Excellent. Just looking at the time, I want to pause and see if anyone in the audience has questions. I can obviously keep going, but I want to make sure everyone gets a chance.

You mentioned new logos are an important part of your growth. I'm wondering if you can comment on how the current macro environment may have or may affect that level of growth in new logos in particular?

Sure. So what we've seen today that we've talked about on past earnings calls is new logos tend to get hit first in this type of thing. So we saw more weakness out of our new logo growth in Q2 and Q3. As we think about planning, over the next year, there's a lot of uncertainties, of course, on where the macro environment is going. But we're planning as if that new logo weakness that we've seen will continue over a period of time. Hopefully, things turn around quicker. But that is where we tend to see the weakness first. We've seen a little bit in upsells within Q3, but it is more pronounced within new logos overall.

Anyone else for now? Otherwise, we'll keep going then. I think related to that question actually about the macro and how clients are thinking about using your products. Have you seen -- you've obviously said the sales cycles lengthen a little bit, but the need for the data hasn't changed. Are the use cases at all changing in this environment? Are the -- are people looking for the data to accomplish different goals for them? Or how is that kind of evolving, if at all, during this time?

It hasn't changed a tremendous amount. If you think about sort of what's happening with data overall from a sales and marketing use case, it's all about how do you make these very expensive sales and marketing organizations much more efficient by using data. So at the core, you think about, we help our clients set their sales and marketing strategy. We help them figure out who to target, who are the most important people to spend their time on. And then we figure out -- help them figure out how to message. So as you think about when you go and have a conversation with a client and it's becoming more and more difficult, particularly in health care, as access to physicians has gone down, you need to make every one of those hits really important. So let me give you an example, I think, that really sums up how important our product and how differentiated it is. So we have a client within IT that has a mobile platform for helping patients after they've had a knee replacement. So the big thing within knee replacements is you've got to do your therapy, you've got to do all of this to make sure that you don't get readmitted. That's a big no-no within the industry. Medicare will actually penalize you if you have too high of a readmission rate. So this company wanted to -- came to us and they wanted to figure out how to target this market more effectively. So the first thing that they did is they wanted to figure out who are the physicians across the country that are driving the most volumes of knee replacements. So who's doing the most knee replacements. That was the starting point. But what's important with Definitive Healthcare is they don't -- those physicians don't actually buy products like this. It's bought by health systems and the physician groups that they're part of. So they used our affiliation data to roll it up to the buying unit level and figure out who are the units across the country that really control the amount of knee replacements that are done in the country. Now within that group, who are the most important people to target? So remember, they're trying to reduce knee replacement readmissions. So what they did is they used our data to figure out which of those buying units have the worst readmission rate problem today. Those are the first targets that they want to go after. So now they've got the biggest targets, and they've got the ones that actually have a problem. Then they want to figure out what's that messaging. You can actually quantify with our data. They were able to quantify how much that health system could save if they were able to get the readmission rate down to the level that their other clients have. And now you figure out who to target. We have the executive data at those companies to drive that messaging home. So it's really this end-to-end, a very thoughtful way of how do you make your sales and marketing extremely efficient and effective, and that hasn't changed. In fact, that's become more important in this environment.

Yes. I can imagine. That use case, in particular, is pretty important because of the reimbursement rates and how that directly affects profitability for a lot of your clients. So I think that's a great way to highlight the data. And related to the data, we've obviously talked about that a lot and how your clients really value that. At the beginning, I think you mentioned just how broad that data set was and how you're acquiring data over time, has the ability or the types of data you're able to acquire and then just changed over time? How are you sort of thinking about keeping that data set and that moat best-in-class and top of mind when the competition starts to increase and then obviously trying to keep that asset at its best?

Yes. So if you think about sort of broadly speaking, you've got kind of 2 chunks of data. You've got the claims data, which is sort of accessible. We want to keep building that up, be best-in-class there. But where we really differentiate ourselves is on all the proprietary data that nobody else has. And that's a big focus of ours. So how do we continue to add new -- bolt-on new data sets that nobody else has that when you link to our other proprietary data and claims data, you can now solve new problems that nobody else has before. And also on that, we continue to apply new data science to create insights out of our data so that our clients can go make a decision very quickly. So our clients don't want mounds of data, they want answers. So we're trying to use data science to help them get those answers. So I'll give you 2 examples of things that we're pretty focused on right now. The first is social influencers. So we're trying to figure out who are the digital influencers that are impacting and have influence over the doctors and the physicians and the equities. How can we bring that data into the system to help our clients figure out who is most important from a digital perspective based on the physicians that they're trying to reach? Really important data when you tie that to our claims data and our quality and everything else, that becomes amplified in terms of its value. The second is a data science application. So we've developed something called Rx Decision Insight. This is really important and interesting data. So the historical way to look at prescription drug claims is I just look at all of you in the room and I add up, all right, how many prescriptions of Lipitor have all of you done? Now what that's missing is maybe one of you actually prescribed Lipitor the first time, but then another person just keeps refilling it. So you're actually not making a decision when you just refill every single time. So what our data science team has done is they figured out a way to reapply all of the volume of Lipitor, in this particular example, to the person that actually influenced it from the beginning so that initial prescriber that drove that decision. And what that does is it completely changed the landscape of who are actually the most important physicians to talk to. And this allows our life sciences companies to focus their attention on who actually matters, who's driving the decisions not who's doing the refills and the maintenance afterwards. That's really important. Those are the types of things that we're continuing to work on that take our data and turn it into actual intelligence that our clients can use.

Excellent. That's another great example. Thanks for that. I see we have a few minutes left. Any final questions from the audience here before we wrap? Okay. Seeing none. Jason, I want to thank you so much for joining us, especially this early in the morning, and thanks to all of you for joining as well. Enjoy the rest of the conference, and we'll speak later today.

Great. Thanks for your time, everyone. Appreciate it.

Thank you so much.