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

To the management team of Altair, real pleasure to have James Scapa and Matt Brown, CEO and CFO, respectively. How are you liking the conference so far?

Good.

Amazing, right? I mean it's day 3 and I did not kid and I said day 3 is the best day. We got you guys. And we got Alphabet, Microsoft, you're a great company by the way.

Thank you.

And a whole bunch of other companies. And so real delight to host you guys. I'm also going to be joined by Matt Martino and my team. We have 2 Matts here. So the question -- I don't know how -- Martino, I'll just say, Martino, go ahead the question. So quick introduction to the Altair story, if you don't mind giving us a good thumbnail sketch, and then we jump into some questions, that will be a good way to get with people that may not be completely familiar with the Altair.

I like how you interact with the audience. Great skill. So I'm Jim Scapa. I'm the founder and CEO of Altair. Company was founded 38 years ago in my 20s, and I've been running the company since, based in Detroit, started initially as a consulting company in the area of simulation, very quickly began developing our own software, had a very big hit with a product called HyperMesh, and then continue to reinvest through the years in a number of different software applications. I've been very, very acquisitive and acquired 51, primarily founder-led technology companies, have built a portfolio both organically and through this M&A activity, initially in mechanical simulation like structures, fluid mechanics, motion simulation, systems modeling and then move to electronics, simulation for printed circuit board, power integrity, signal integrity, electromagnetics, high frequency, low frequency. In the early 2000s, we began investing in high-performance computing, scheduling and workflow management. And there the customer base unlike simulation, which is primarily manufacturing, the customer base is manufacturing, again, we're running, for example, crash simulations on massively parallel computing architectures. But we also cater to pharma and life science to financial services to energy and basically a lot of other vertical markets through the HPC, so a lot of government agencies as well that are using computing and need to really manage where and when the jobs run and also dependency management. And then over the last 10 to 15 years, we have invested in data science and data analytics. So we have a portfolio of those 3 main elements, and we really see a big convergence in the market between simulation, HPC and data science, and it's quite a real trend for the business. Let me say a couple of business oriented things. So we are around $600 million, around 3,000 people. We're a very global company. We're in 27 countries. Revenues are very evenly distributed about 1/3, 1/3, 1/3 between the 3 major zones. Great culture in the company, actually a very technical company and probably 15,000 to 20,000 customers.

So Jim, you talked about the conversions of simulation, data and analytics and high-performance computing. So can you just kind of help frame for your audience for the audience, your vision here, how these businesses ultimately tie together?

How they tie together? So initially, I was really moving towards this area of data science, machine learning, data science in general, because I saw the applicability of that technology in the world of engineering, quite frankly. We began developing some technology on our own. We made a small acquisition out of Italy actually in 2008, had a lot of failure actually with that, but we learned a great deal. As we got going farther, I realized I needed more scale to be very honest. And so I made an acquisition after we IPO-ed of a company called Datawatch, which had 3 really premier piece of technology, thousands of customers, but most of those customers were in the banking financial services market, a lot of expertise in the area of analytics and data science. With that acquisition, I began to sort of cross-pollinate bring that technology in more and more to my engineering applications but we continue to keep the team oriented towards banking financial services where we also sell our HPC technology and began to focus it more and more in that direction. And then over the succeeding -- the last few years we made an acquisition in -- out of the U.K. of a company that had a SAS, S-A-S language compiler and execution environment, about 600 customers, many, many major banks and insurance companies. And then we acquired RapidMiner which is a very premier data science platform and bringing all that together has been really, really strong for us. We reorganized going into this year and cross-pollinated some of the people so that we have some of the historically selling to the banking and financial services market but data science experts in our auto vertical, in our aerospace vertical, in our technology vertical and it's really going well. We have a huge number of applications really beginning to take hold.

That's really interesting. And maybe for Matt and Jim, can you talk a little bit about -- you talked about how acquisitive Altair has historically been. And I think that feeds in nicely to kind of your broader strategy around the Altair units. So maybe you can just talk a little bit about kind of the uniqueness of that model and just kind of frame that?

We're going to let Matt handle that question.

Sure, yes. So Altair units is a great way for our customers to go across our portfolio of products and also across their user base. So it really allows for -- in the case of acquisitions, Matt, like you mentioned. When we acquire a company, our typical company that we're acquiring is we're trying to find a really nice piece of technology where we can tuck in to our portfolio and then expand usage within our user base. Altair units allows for that to be very frictionless because we can roll that technology out into our suites and our customers don't have to go and sign up new users because we're not on a named user basis. They don't have to go through procurement or legal. They can just immediately begin using it. And for us, that's important because we want to encourage usage, which then helps drive expansion within our customers.

Units model was -- I created the units model in 2000, and all of our engineering or manufacturing customers are acquiring these units allow them to run any of the applications in the portfolio. As we went into the data science and data analytics world, there, they were selling more traditionally. Actually, in some cases, paid up and named user. And we've been converting, especially the enterprise-level accounts into the units model that they were running a product like Panopticon, which is our real-time or streamed analytics type application or they were running Monarch for data prep or they were running something different. Now we can bring them together. They can run those couple of applications plus now they can run RapidMiner or if they're running RapidMiner now they have access to Monarch or Panop. And so we're bringing that same model, which has been enormously successful on the engineering side into sort of this traditionally, I'll say, old-fashioned oriented business model world. At the same time, our engineering customers who are running out as many as 20 or 25 applications in our engineering now have access to RapidMiner and are beginning to do really interesting applications.

Jim, how do you price something like this -- how do you price a unit when that unit is fundamentally different in terms of the value that it offers across your engineering domain versus the data science domain versus the...

Because we price each product in some number of units that it's drawing from the pool. So the price per unit is, say, $650. We have difference. It's a little more complicated, but just assume it's $500 per unit and HyperMesh draws 20 units from the pool, RapidMiner might draw 15, very sophisticated product might draw 50, and that's how we do it.

Yes. And the suite is something else that we should touch on too because the suites are important. At the highest end suite, you have access to all of our different products. But what we found a little over a year ago, I think, now is in order to get more value at the highest end and to give us some flexibility at the low end for customers that maybe don't need access to all of our products. We broke into about 9 different suites now. So -- and those suites are tailored to specific...

Personas.

That's right.

And the price per unit for suite is higher as you have access to more and more [indiscernible]. So the enterprise level is like $850. The very, very base level is like $100. But you only have access to 3 fairly basic products.

Since the persona is a difference, so you can properly tailor your go to market to the right persona. So you're trying to sell uncut populations with a connected suite?

Yes, that's right.

Just one thing to add to that. What we were finding is that we were probably undervaluing the unit in our largest enterprise customers that were running the entire portfolio. So we raised prices if you were using all the applications at the enterprise but it was also too expensive to sell into the mid- to low-end market. And so by creating that low-end suite with a smaller set of applications, that are more appropriate for that customer set that might be more indirect, for example. We now have a more competitive price point for that market as well.

As you take a step back, you've gone through a period of acceleration, things are working out really, really well for the company. What are the drivers that are propelling the growth in new business that are at a macro level, micro level? What are the big trends that have changed in your favor?

I mean our business is all about helping customers make decisions based on mathematic algorithms, optimization technologies, AI or machine learning technologies. So it's all algorithm-based mathematics, numerical methods on the computer to drive decisions, basically. And I think the world is moving more and more to relying on this sort of technology to design better products, design better marketing campaigns to design better pricing models. And so we're really well situated to help our customers across almost everything they want to do.

But all of data and simulation using algorithms straight. Where else could you go with these capabilities? You said marketing, I've never heard of Altair in the marketing domain, so what else could you do...

Customers that are applying it in marketing as well. It's not our primary -- so we've tried to stay relatively focused by vertical. And so the verticals for us are mostly the historical verticals like auto, echo, ship design, rail, heavy machinery, those technology, consumer electronics and mostly in the design world, but we have such a large installed base now of banking and financial services, insurance companies that, that is also a vertical for us, and it's very substantial at this point and growing. And so do we have pharma. We have a lot of pharma actually.

How does pharma use your product?

So pharma is heavily using the HPC products to do drug discovery. And we are, without question, the leading player for the HPC scheduling and workflow management, but they also use our products to do modeling, mixing of drugs in a pill because you want to have a very evenly distributed formulation. We're filling a pill bottle on the manufacturing line. And so we have a lot of simulation, believe it or not, in the pharma world, and we see a lot of opportunity. We already have a lot of applications in data science as well. As we move more and more with the data science applications, even into manufacturing in general, we were not really playing, for example, on the manufacturing floor. But this last year, there's a tremendous number of applications where we're taking sensor data and then visualizing that sensor data with Panop and taking that data into RapidMiner and building models to predict downtime or scrap rates or all sorts of things.

So Matt, I just wanted to quickly pivot gears to just kind of the fiscal '23 outlook. So you guys have put up pretty strong performance despite the weaker backdrop, but even so the guide does call for some deceleration relative to last year. So just kind of thinking through the key aspects of your growth algorithm, where are you observing the most pressure within the business today?

Yes, we knew coming into 2023, if you think back to 6 or 8 months ago and more, most folks were calling for a very difficult 2023, and there's a lot of macro headwinds in fear of a recession. And at the time, it was well -- the debate was well -- are we going to have a soft landing or a hard landing. Now I don't think anybody knows what's going on. But when we were coming into the year, we actually just continued to feel very strongly about where we were positioned, how our customers are feeling about our products, renewal opportunities expansion opportunities, the pipeline looks strong. And so we felt very good about 2023, but still, of course, had some conservatism around how the year would play out knowing that 2023 was not going to be like 2022, for example. So 2022, we were coming off of a year where our software revenue growth in constant currency was above 17% growth. And in 2023, we looked and saw what was out on the pipeline and called for about 10% growth at the midpoint in constant currency. And our feeling then is, yes, 2023 is not quite what 2022 was. As the year has -- we've made our way through the year, we've just reaffirmed that viewpoint and continue to see that. So 10% growth, not so bad in a tough year but not quite as good as 2022. I think the important thing for us is we use this year as a really important building year. So understanding that this is not going to be the most spectacular year for us. We took that opportunity to do some important organizational changes. So we had some changes that we made around go-to-market, where we organized in the verticals specifically auto and aero technology, BFSI to help with cross-sell opportunity. Those are things that generally take a little bit of time. So we've been able to do that while still you're sort of changing the belt while the engine is still moving. That's now well behind us, and we feel very good about how those verticals are now positioned. I mean, at the same time, we've been making really, really important product updates, which we'll be announcing in the next couple of months in more detail. But all of that, we feel like sets us up in a just a really good position or when the economy takes a slightly better turn to really come out of the gate with a lot of force and feeling really good about where we are.

That makes a lot of sense. Matt, you said...

This is a really mature company. The team that I have across the world has been with me typically over 20 years, every country manager, every sales manager. The team is really mature.

Probably know their families too.

We know their families as well because I hired all the initial country manager. In many cases, we're on the next generation. We've had retirees. There's a lot of growth from within the company, but it's a very, very strong, solid base. And so this sort of regrouping that we do, the economy sort of breathes, right? It's an organ almost. And so this is the sort of regrouping that we've done.

You're in Ohana.

Right.

So Matt, you're asking, we don't know what kind of a landing this is hard landing, whatever. Do you know what landing this is? Jacob, what is it -- what do you call this?