Altair Engineering Inc. (ALTR) Earnings Call Transcript & Summary

Sure. So there's a history to the business model. I got a lot of patents around it. There's a couple of companies who have embraced it. But essentially, we had one product with great success that went back. That was HyperMesh that I mentioned earlier. And then we had created a second very, very exciting product called OptiStruct, which was a commercialization of a technology from the University of Michigan for topology optimization, creating these very organic shapes for structures. It's a beautiful technology that's used throughout industry now. And we had a third product as well that was quite nice. But selling those additional products was very arduous going to an account that's already using HyperMesh and you have to go through a very arduous sale cycle to look at a very innovative product like OptiStruct. And so I created this concept where we converted all the customers to what we call at the time, HyperWorks units, but today are called Altair units. And every product in our portfolio, we only had three at the time, drew units from the pool. And while you were using the product and then put the units back in the pool when you were done running, and so suddenly, I made OptiStruct and at that time, MotionView available to all my customers. And that created a very quick take-up of OptiStruct, which was a fantastic technology. And then through the years, we have grown out that portfolio. We have almost 100 products of our own, which are available to customers, including all the data science products. And I also added this concept patents, again, where you could bring in third-party software and that third-party software could be licensed with our units model and available to those customers as well. So we have, I don't know, over 200 applications now. So every fatigue analysis software -- I mean, there -- we live in a fractured world of engineering. But suddenly, all those products are available to customers and it's very sticky. We have -- I don't even know what the average is today, but it's probably approaching 20 applications on average per customer that are used. And it's a kind of Netflix model, right, where you may use this product, but your wife may use those and your kids might use something different. So across the enterprise. And now as we're bringing some really top-class data analytics and data science tools, we are beginning to see take-up even in our traditional customers. And we're moving the data analytics, traditional customers like banking customers to the units model. And as we've grown out the portfolio there, it's beginning to get some traction. Initially, the salespeople that came in from sort of this foreign world of data analytics, which was a very different mentality and culture, I'll say, than our world. Today, they bleed Altair, all those guys, and they love the model, and they completely get it and the customers are getting it now, too. So it's quite powerful.

Got you. Very good. I think naturally lends itself to lots of resiliency and ease of expansion with customers. And I guess maybe to flow on to a little bit on the financial side. You had a nice Q1, above expectations, even with the macro backdrop. How do you -- and investors are clearly interested in profitability nowadays. How do you think about, number one, the macro environment? And number two, the balance of investing for innovation and growth, when does it make sense for that versus driving the bottom line?

Okay. Very different questions. But I mean, we are very, very committed to continuing to grow the bottom line, if you will. And mostly, we see that, for us, it's all about execution at this point. And so we're very focused on very strong execution. And we feel we had a commitment to 2023 that we made a few years ago up to 20% on a very good path there. And we don't see ourselves stopping. We see ourselves continuing to grow that adjusted EBITDA line, and I don't see anything in our way coming to the economy. And so we're -- let me just say that we see the importance of a balance between growth and growth of profitability. And I could hire another 100 sales guys, and I can grow faster, which you see in certain companies. But that's not our mode of operating. We're sort of steady as you go and have always been in an annual license mode as well in terms of how we do our sales. Coming to the macro, I mean, this is not the greatest year. I've been around 38 years. So I have been through a lot, and I've been in Detroit. A lot of those years -- I live in the Bay now, but I spent most of the years in Detroit. So I've seen a lot of ups and downs. This is not the worst year. It's not the greatest year. It's a year which requires execution and basically fighting. I think our business model in many ways really starts to shine in these kind of moments when customers are looking at cost savings. And we're not seeing -- it surprises me because I tend to be the more pessimistic. It surprises me, but our business continues to be relatively solid. All of the renewals are -- there's just no risk around renewals as far as we see. There's no delays. But customers are certainly concerned. I see the automotive industry, which I get asked a lot of questions about. There's still really good demand for cars. Their pricing is holding up. They have their challenges. They all seem to know how to manage their own budget. So you see them trimming and cutting costs and all that. But that is not really significantly affecting us. Engineering is a relatively small percentage of their investment, and it's so critical to be innovative, especially in a moment where they have a lot of competition. So that's just not a big concern. Aerospace and defense is a very, very strong market right now because of concerns, geopolitical concerns. So you're seeing more investments than ever before and so that's good. For us, the banking market is new for me. But I think as we're changing business model in there, the units model, we have a lot of opportunity. The RapidMiner product really was kind of transformational for us in terms of positioning. We had all these other important elements. We had success as point solutions in a lot of these accounts, but we're really converting them to more enterprise level sales and building relationships. So I mean, it's not the greatest economy, but it's not 2008 or '09, at least not at this moment. Anything can happen, yes, as you well know.

Yes, yes. And what other factors you kind of keep an eye on from a macro perspective, whether that's FX or other elements like that, given global business?

So FX has certainly been a big factor in the last couple of years for us. That's definitely much more of a math question, my CFO. But it has been not great for us in the last couple of years. It's been good for us in other years as well. So I don't see that. I'm not paying a lot of attention there, although perhaps I should pay more. Yes, what else do you want me to cover there, sorry?

No, no, no. That's all right. Maybe you might just pause there for any questions from the audience. And if -- okay, hand up and we'll have a microphone that will be coming around as well.

Can you introduce yourself, too? Thank you.

I'm Rob Lutts from Cabot Wealth Management and Sale. Would you share with us how the automotive industries using your products, particularly in light of the transition that they're going through changing to a whole new platform? Is that creating a lot of new business for you? Or what does it do into your business?

Sure. Thanks for the question. So historically, Altair's solution set into the automotive industry was not heavily into the powertrain area, quite honestly. We did have one product, which was used for modeling powertrains, but it's still used for modeling electronic powertrains actually. So for us, there's just been a lot of -- I went through a transition of acquiring and building out an electronics portfolio. And so we have very strong tools for electric motor design, battery design, all of the elements that are different in electric. And if anything, I think it's expanded our opportunities because we really were not doing as much in traditional IC engine design. And I think that whole transformation, in general, has been a good thing for us because it's created a kind of very competitive atmosphere. First of all, you have a lot of new players that are all my customers, too. And then the traditional players have been playing some catch up. And they've had a tough situation because they have to support their legacy platforms and make this migration and a lot of tough management decisions there. I think today, when I look at that market, the new players are starting to feel some strain. And the traditional guys are starting to get their act together. But it doesn't really change our business that much. We're -- if anything, it's been a little bit expanded, I would say. But yes, I mean, we work with all of them and across their enterprises. But we see more and more system-level modeling becoming important. And it's another area we have poured investment in with no one noticing. So I have probably 20 years of investing in a solution for math and systems modeling. It's all heavily used for digital twin, and it's really coming to the fore now. Everywhere we look, there are digital twin opportunities that are opening up, which are really exciting and interesting and they kind of sync together with all the data analytics side of the business as well. So a really good moment. Yes, thank you.

Anything else from the audience at this stage? No. All right. I guess I'm thinking through data analytics and engineering simulation, all of those being clearly very compute-intensive functions and you mentioned earlier, Altair clearly has got capabilities around HPC. Have you seen an increase in that area of the business as people get focused on AI analytics and those elements? Or given the strength in simulation, is that something that's been just consistently strong throughout?

So we are, I guess, arguably the leading player for HPC scheduling and such. And the -- part of the reason I started investing there is I was going to supercomputing conferences, and we were seeing a lot more data science and data analytics playing out there. For us, we've made several acquisitions in that space. So we started in the early 2000s. We spun a team of nine people out of NASA Ames. It was 0 revenue, but computing was changing from very specialized hardware to commodity clusters, what we see today in these server farms. And where and when all the jobs run -- for people in HPC, we used to have something called NQE and NQS that would sort of drive the jobs running. But mostly in the old days, you would say, I've got my job to run. It's a crash simulation. It will run on that computer and the computers were all named after Star Wars characters and whatever. And you didn't really know. There's a big queue over here, but this computer's empty. And you would still stupidly put your job over there. Well, with the scheduling software, you really maximize the utilization. It's a very clever technology actually. It's quite deep in terms of how it fits jobs in and all that. So with data science and data analytics -- coming back to your question, sorry, I thought I'd give some background. It is more and more important, when we acquired -- I tell the story, when I acquired RapidMiner, the largest customer, which I think is going to be this amazing lighthouse customer for us. They've implemented something like 2,000 machine learning models. And they want to scale it out. And the big boss there wanted to speak with me, he was worried, oh, Altair is acquiring this company, let me talk to him. And their big concern is scaling, scaling all this out. The technology is used across all their departments, sales, finance, engineering. There were seven departments he listed for me, and they were worried about scaling. And scaling is really actually all about HPC scheduling actually. And within RapidMiner, they have developed their own scheduling technology. So I kind of have another scheduling technology. But it's very apparent that the depth of our expertise in that space is -- and it's clear to them now too, really brings a unique power. And I talk a lot about convergence in this kind of a marketing message. But it's real as well. I mean there really is a convergence between all these things, simulation, HPC and data science. Yes, so in fact, last night, there was a question. I was in a different dinner in the -- I don't know what his title is, the Chief of Strategy for Microsoft was attending, and he was asked about AI and aren't you running out of data? And I thought that his answer would be no one asked me, and I just stayed quiet. But I thought his answer would be -- and it was a little. He talked a bit about that. But to us, we see the data coming not just from a lot of sensors, which is a really important element and part of why our real-time data technology is relevant not just in banking, on trader desks, but in engineering. But it's going to be synthetic data as well. So running simulations, generating synthetic data is going to be super relevant in that space as well.

Yes. And I'll just see if there's one more question.

No, no more questions.

Right. I think we're just about out of time.

All right. Well, thank you very much, Mark. Appreciate it. Thank you all.