All right, well, why don't we get started here? My name is Rich Hilliker, and I'm a software analyst at UBS. We're thrilled to have Pegasystems here today, Ken Stillwell, CFO, and we've got Peter Welburn here. Great to see you guys again. I know you've been patrons of the conference for a long time, so we appreciate you coming down year after year. Good to see you guys again.
Good to see you, Rich.
Why don't we dive in here and maybe level set? For those tuning in, you know, maybe not familiar with the story, could you tell us a little bit about the company, about the products that you sell, kind of how that's evolved over time, and maybe the key value proposition you're offering?
Sure. Pega, Pega's, you know, been around, been a brand supporting our clients for a number of decades, and we really started as an alternative to writing code. You know, back in the eighties and the early nineties, where, you know, when you wanted to build an enterprise-grade application, you really had to write software.
Right.
You just... You start, you know, by literally lines of code. And so we created this concept of having a, you know, a kind of a alternative to that by having some of the common actions that happen when you write code to be native to the platform so that you could actually configure right on the platform.
Right.
That then evolved to be becoming kind of an orchestration engine, where our platform would be a connection point to other applications that might kind of one-to-one talk to each other, but, but really was disconnected in the context of a workflow, of a series of steps that you might engage in 2, 3, 5, 10, 20 different applications as part of that process.
And then, and that's kind of where we kind of sat as this, almost this, kind of this middle layer, this middle tier of orchestration engine. Over time, as our product became kind of more feature-rich and our clients were using us for some of our core use cases, they started to look at us and say, "Hey, if I'm going to go out and buy a, you know, a solution like a customer service solution or a sales automation solution or a marketing automation solution, I'm going to really take a look at what I might be able to configure on Pega."
You know, we had embedded robotics. We had decisioning, which was our... You know, we've had AI in the platform since 2010. It's not something, you know, AI is not something new. It's really just the context of how AI has evolved. We had, you know, we always had the concept of a case and workflow. But as we are started to make it more feature-rich, clients looked at us as a way to build enterprise-grade applications-
Right
... where they might be able to be a little bit more flexible and kind of evolve over time in a less costly way. And so that's kind of our evolution was replacement for code to really becoming that orchestration process engine, to now we, you know, we're cust-- you know, we're a, you know, we're a digital contact center for many of our clients.
We're doing, you know, inbound, you know, lead routing and decisioning and, you know, in a kind of a kind of a headless environment with consumers when they come to websites. We're doing loan origination software.
Like, it's just really evolved into something where we have kind of certain types of activities where there's a lot of volume, and there may be multiple systems that need to be connected, and you want to automate them. Pega is really a perfect fit.
Got it. That's really helpful. Evolution, broad platform. We hit on a lot there.
Yeah.
Maybe we can take a step back and just kind of characterize the broader environment right now. Just I would love your thoughts just on what you're seeing and hearing and talking with customers, what you're feeling in the market in Q4 here, and how that maybe compares to the first couple quarters of the year.
Sure. I would, if you kind of go through maybe the last 2.5 years, I think we went from being a little bit kind of. And I'm saying this as a general statement on the economy. I think we were living a little bit in a fantasy land in 2021, thinking that, you know, the impact of the pandemic was going to have no long-lasting impact, and that things would just, you know, kind of like we'd just figure it out, and, you know-
Yeah
... people would continue to buy. And I think a lot of trends changed. And in 2022, I think you started to see some recognition in the market, that, you know, interest rate, you know, that the supply chain had been permanently disrupted and that interest rates were higher and that, you know-
Right
... all these, all these kind of, you know, these, these, things that we maybe didn't pay as much, much attention to for a few years.
Right.
As we entered into 2023, I think that the Q2 of 2023, to me, felt like a very confusing quarter, and I think that's because that's really when Gen AI hit, right?
Right.
I mean, that's really when people started to talk about ChatGPT. And, and then I think, I think the investment community, more than the buying community, really jumped to being like, "Oh my gosh," like, "you know, every company's going to be out of business, and this is going to change the business model.
Yeah.
I think, I think the actual companies were saying, "Well, we're going to try to figure this out," right?
Right.
So I think now where we are is, there's a much more clear path of what AI is and what AI isn't. And I think where you're seeing a lot of the spend around AI is where you would expect to see it. You see it in the Microsoft and NVIDIA and, like, the people that are selling the infrastructure to support running the AI models.
When you look at the actual use cases of AI, I think they're still early in the evolution. I think, I think clients, there's some obvious ones like, you know, can I take a bunch of library text and create, you know, coherent answers to questions? Yeah, I think that's a, that's a... I mean, and by the way, that's actually very powerful.
I don't think it's that sophisticated, but I do think it's a use case that's out there already. You know, our clients are... You know, we've been, you know, kind of, you know, showing that use case to some of our clients already. When you take that next step to say, "Well, what might AI be, and how might this change the way we work?" I do think we're in the early innings for that.
So, I think that AI is not going to necessarily steal dollars from the other enterprise applications. I think it will steal mind share, but I think the dollars are coming, but I don't know exactly where it's gonna come from. I don't think it's gonna come from things that are foundational for the businesses.
I think it will probably come from, you know, different ideation opportunities that clients have, where they kinda shift that to AI. We see our clients really trying to figure out how they can augment existing applications and systems that they have with AI, to be able to, you know, speed up the time it takes to process something, get better results, reduce the amount of time that a customer service rep needs to interact.
So I think the economy has kinda went from like, you know, a little bit we were living a little bit, like, kind of outside reality, to then thinking the sky was falling, and then AI hit, and I think we've kinda settled down. I... It feels like the buying patterns in Q4, certainly it's not a strong buying environment, but I also wouldn't characterize it as weak either.
I would say it's more kind of a more of a normal environment that I've been seeing in the last, you know, quarter or two, compared to earlier in the year when it was a little bit uncertain. Okay. Well, that's encouraging.
Yep.
Maybe we could dive in a little bit to earnings and recent guidance.
Sure.
I think earlier this year, you know, your cash flow guidance started at $150 million. You raised that to $180 million. And I think on your most recent call, I don't think you guys typically update quarterly, you know, your annual guidance, but I think you did mention that, you know, cash flow for 2023, you might be able to be in the $200 million range.
So I guess, can you kinda help us unpack that and understand, you know, the momentum here on the cash flow generation side?
Sure
... and kind of what's causing that?
Sure. So this was the first year that we really kind of guided cash flow, and I will admit that I was unsure, how that would all play out, given that I didn't have a, you know, a series of quarters or years to really kind of rely on the pattern of the business, and there was a lot of changes happening in the business, including us doing some cost optimization, earlier, and, you know, a couple of times in the last 18 months.
So I was, as I thought about guiding, I said: "You know, if we guide $150..." And that $150 that we guided was actually, it even had, we added to that some one-time items that we actually are kind of non-recurring items.
As we got through the first couple of quarters, it was obvious we were gonna beat that. It was also obvious that we were gonna beat that by a large margin. When we actually talked middle of the year, we kind of upped that number to $180. That was really because I knew that I had a shot at $200, but I didn't wanna go there yet. I just wanted to kinda see how it played out.
Now, where we sit is that number, that $150, the equivalent of the $150, is gonna be much more than $150. In fact, much more than $200, because the $200 that I mentioned is a number we're trying to get to of actual cash flow, right?
And that's even not giving us credit for some of the one-time items that would be non-recurring. So, I think if you kinda get to the end of the year and we're able to achieve those numbers, the difference in our free cash flow generation is so noticeable from last year, but even from what we guided for the year. And I think that just shows the power of a subscription model.
Right.
Actually, also, maybe, you know, we can take a small amount of credit for our commitment of running the business and trying to optimize it and trying to make sure we are responsible with our spending.
But I think, you know, it really just shows the power of a business that has, you know, over $1 billion of recurring ACV, highly sticky, and our gross margins really accelerating, you know, from what was just over 50% just a few years ago, to now approaching 75% on our way to 80%.
Wow! Okay.
Yeah.
Well, that's definitely helpful. So, I guess, is it fair to kind of think of... And, you've been messaging the subscription transition, I think, since probably 2017.
Yes.
Is it fair to think that we're kind of in the later stages of that, we're starting to see the impact on the model, as well as some of the, you know, the actual core business performing and helping you with that cash flow generation?
Yeah, I would, I would frame the subscription transition as in the rearview mirror.
Great. Okay.
I would say that, you know, we thought we would finish it at the end of 2022. It kinda drug out probably a couple quarters past there, so I would say the last quarter, kind of, you know, we're there. Now, one caveat is that because we don't have all of our revenue in a SaaS model, we do have some variability because of the accounting of the ASC 606, where term license revenue comes in a little bit more lumpy.
But if you forget about revenue and you look at ACV and billings and cash flow, those are both normalized, right? So I think what you really just have to, what we really have to watch is this, you know, revenue won't be as linear as ACV is, because of that, that, kind of almost awkward timing of how revenue-
Right
... is recorded. The good thing is, billings are unaffected by that. We bill typically a year in advance, or at worst case, a quarter in advance with our clients. And so there is a level of consistency there with billing and collections.
Right. Right, and that makes sense why you incorporate ACV growth in your Rule of Forty metric.
Yeah.
Right?
Yeah. Yeah, and that we use free cash flow as opposed to EBITDA.
Right
... because revenue could be higher or lower because of that weird accounting, but free cash flow is what it is.
Yeah.
Yeah.
Well, speaking of free cash flow, I think recently you guys changed the definition, and I know there's-- We talked about the one-time items here and there.
Yep.
Can you help us understand why?
So we were showing a disclosure that showed both our operating free cash flow, our operating free cash flow minus capital expenditures, but then we also added in the one-time items, like restructuring, one-time, what we believe are one-time legal expenses, things that the...
You know, we're trying to get to an unlevered free cash. And, you know, us as well as I'm sure a lot of other companies, you know, the regulators don't love when you come up with-
Mm-hmm
... unique ways to measure free cash flow, and we respect that. So we said, "Listen, the best way to do this is to show the free cash flow, kind of the way that the standard way, which is operating free cash flow minus CapEx.
But then we'll still show the other disclosures so that investors could be thoughtful around understanding what might not recur in the next year," which was really the intent of why we actually did it that way. So that was the change we made. That does create a little bit of a difference between the $200 that I'm talking about and the $150 that we originally guided.
Right.
But it's all in our disclosure, so I think, you know, our investors are. It's pretty transparent. But our purpose was really, the reason why we originally did that was to try to show what we felt like operators would wanna see. But, but like I said, this has been a big hot button for the SEC around, free cash flow and just being consistent.
And actually, I can really appreciate and respect why they would want that, and so we just changed our disclosures to just be the more, the more traditional approach.
Got it. Clear, traditional, and transparent.
Yep.
Okay. Sticking with cash flow, maybe one more question here. In June, you talked about your ambition to generate, like, $500 million in free cash flow. I think that's over the, maybe the 3-to-5-year time horizon.
Yep.
Can you help us with the linearity as we get there? Should we be thinking of this as, you know, pretty straight line? In other words, like 375 next year? Or is it gonna be lumpy? Can you just kinda... Any high-level comments you can offer?
So, I've often explained that the first year after finishing the cloud transition is when free cash flow kind of really looked normal, which would be 2024.
Right.
So when you look at 2024, and you start thinking about a free cash flow of, like, say, call it 25%, just picking a round number, that kind of. But to get to $300, excuse me, to get to $500, you know, you'd kind of probably have to be more, like, in the high 20s, and naturally, a revenue number that's, you know, or billings number that's bigger than what we have right now.
So you kind of are gonna see a pretty big uplift in 2023, probably a reasonable uplift in 2024, and then I think things start to look more normal in terms of the annual additive amount of cash flow until, you know, kind of getting, you know, a couple points maybe of accretion in those out years.
But if you're going from... If you go back to, like, 2021 to 2022 to 2023, you go from, like, you know, -7% to 4% to probably approaching 20%, right? In terms of that trajectory-
Right
... if you had, if you accounted for one-time items. So that's where the real curve was. I think you'll still see a reasonable uptick in 2024, and then it'll normalize.
Like building that recurring cash flow layer cake.
Yep.
Got it.
Yeah, exactly.
Yeah. Yeah. The best kind of cake, right? Free cash flow. Cool, so maybe on go-to-market product expansion. I think I noted that you guys did two reductions of force-
Yes
... in 2023. Can you give us a little bit of thought behind, or can you give us some color on the thought behind those reductions, and, where did those fall? I think it was sales and marketing, but can you give us a little bit more color there?
The majority was in our go-to-market area. The first one that we did was we announced in December, we executed largely in Q1 of 2023, and that was around trying to reduce what we viewed were roles or functions that may be redundant.
Yeah
... they may be unnecessary as we move towards selling, having a more cohesive selling team, selling to a targeted number of orgs, where we actually may have had more specialists and more kind of maybe horizontal resources when we're selling. So that we really wanted to attack that problem that we had really done to ourselves through from 2020 and 2021 and into the beginning of 2022.
The second one that we did was different but related to the first one. This, we really wanted to anchor on, we're going to target, you know, less than 1,000 organizations, and more likely, 200-500 organizations. That would be our primary targets. When we actually did the second one in Q3, it was around looking at the resources as they remained and saying, "Is this the right way to organize these resources?
Okay.
Do we wanna actually put some of the teams together and closer to the customer?" And when that happened, there would be redundant managers that may exist. Like, if you took different types of selling resources but put them underneath a selling manager, unfortunately, there may not be a manager on the other side that may have a role in the company, and so that was really how we the two phases.
So we're now done with that. We feel like we have plenty of selling resources, and quite frankly, through either of these actions, we didn't take out really many sellers. They were more like the selling support resources.
Got it.
So we feel like we have a tremendous amount of selling capacity, you know, and we, and we think we can, you know, manage a growth rate that's, you know, easily in the double digits, without needing a lot more resources in the near future. Now, when we get to 2024 and into 2025, naturally, if we continue to grow at the pace that we want to, at some point, we will need to add resources, right? But they will be under the model that we've actually established.
Right... Okay. As we think about the actual motion of that capacity that you just kind of discussed, I believe that you started focusing on existing customers as opposed to brand-new customers to the platform. So instead of pursuing new logos, you're kind of focused on the existing base. Could you give us a sense of why and how that sort of played out, the reasoning behind it and kind of how it's been so far?
Sure. So if you think about our business, we've got, you know, call it 750 clients. Two hundred of those clients are about spend $1 million or more a year. So we've got, we've got a, you know, reasonable concentration of our ACV with 200 clients. We know that there's a few hundred more clients that are under $1 million, that probably should be well above $1 million.
So that gives us line of sight to existing clients, that we're already penetrated a reasonable amount, the next cohort of clients where we feel like we have some business with them, but not as much as we should. And then we went out and targeted some specific new logos.
Really like, kind of less than 50 companies that we currently don't do business with, but they look like some of the clients that we already have. And so, so that would be. An example would be if we had State Farm and AIG as clients, but we didn't have Progressive. That would be the type of.
That's an example of why we might want to cover a company like Progressive. So those are, that was so we have some new logos, very managed, but we're not going broadly in at, like, almost in a territory selling model, like we had done with our corporate market strategy, back a few years ago.
Okay. Very pointed, strategic, focusing on growing the existing-
Growing the existing with very selected net new logos. And then we have our product called Launchpad, which is... Launchpad, essentially what we did was, we realized that if we were gonna go after new logos in more of a mid-market area, that we shouldn't do it with Infinity.
We should actually do it with a multi-tenant solution that was built more for kind of a one application sell to many clients, versus Infinity is more one solution sell to many use cases, right? So you might go to a client and have one solution or one platform where you sell multiple use cases.
We wanted to flip that and say, one solution that you sell to multiple clients, but that isn't our selling model. So we decided to go through partners or through independent software vendors or partners, and that... We're in the early stages of that, but that gives us kind of an opportunity to actually, over time, go after some new logos, but through a partner-
Got it.
right? With a solution that's more fit for that, versus Infinity, which is enterprise grade, which is more for the 1,000 or so targets that either clients that we have or future targets.
That makes sense. Got it. Well, maybe we can shift back. I think we hit earlier on, on generative AI, but maybe we can kind of return here, given it's such a big topic this year. You talked about AI being on the platform since 2010, 2011-
Yep.
So nothing new to Pega, necessarily. I guess, how do you contextualize, like, the evolution of, of AI? Maybe not as broadly as over that timeframe, but like, how does, how does this next kind of chapter of the AI story play out for Pega here?
Do you see it as, like, a threat or an opportunity, for, for model-driven software development platforms like yours? Or how do you see this sort of evolving? How does it become complementary, or how do you kind of evolve to, to make sure that you're, you know-
So maybe I'll start with our view of what our AI did before the GenAI kind of evolution. So, we bought a company called Chordiant. They had a product called Customer Decision Hub, CDH.
And I'm gonna be, I'm gonna be very kind of general in describing this, but really what it did was it allowed you to create kind of a library of rule sets, and those rules were like if/then statements. They were kind of directives on-
Right
... how to take certain actions based on. And then what you could do is you could watch the activity of workflow, and you could learn from that. You could auto-update the rules to say, "Hey, originally, when someone came to a website, and they made the following three clicks, I typically would put up a call to action, but that doesn't seem to be working. So maybe that's not the actual right next step. So maybe when they hit those three clicks, I need to ask...
I need to show them a video, or I need to give them a certain set of selective, 'What would you like to do next?'" You're kind of almost letting the system explore, typically in a non-customer service driven or non-sales driven environment, consumer on a website, and you, you know, they're moving quickly, and you don't really have the ability to have a human decide what to do.
That was really our version of AI. What that allowed you to do is you could apply that to workflow, you could apply that to URLs and web applications. What GenAI does is allows you, it allows you to do a different and very powerful kind of dimension on that, which is it allows you to take text, it allows you to take information, and it allows you to put that information in the context with other information in really almost a free-form way that, you know, traditional AI models couldn't actually handle.
And so not only can you create rules AI, where you use robotics and rules and kind of almost trial and error to create kind of an AI engine, you can actually use voice AI, layered in with the rules, with the text and the libraries, not only what you have, but what you're actually capturing, to create quite an impressive AI kind of revolution around what the system can do.
When we look at it, we view it as the furthest thing from a threat as you could imagine.
Right.
We view it as a reason why clients would want to deploy enterprise-grade applications, but still be nimble, but still be agile. And so what the two biggest, two of the biggest, factors that we think are where AI can really drive change is- how fast you can actually get an enterprise-grade application live, right?
If you think about the traditional non-IA based, AI-based way, is you'd deploy the product, you'd go through a series of design changes, you'd plan it out, you'd start to configure the application. In today's world, with AI, you don't have to do any of that. The system, if you say, "I'm an insurance company," the, the, you know, the ChatGPT equivalent should be able to say, "Well, this should be your three use cases you're thinking about building. Which one is it?" You click one. "Great. Give me three seconds.
Here's a starting application based on what I know from everything, either in your libraries, in the public domain, and whatever, whatever you want to connect it to." And so now you've got basically a design, almost set of choices, that might have taken you 90 days in the traditional, and that's aggressive.
It might have taken you a year. So you now have companies that would say, "All right, I'm interested in using Pega," where I wouldn't have otherwise maybe thought of Pega as a vendor, because now the speed and time to market is so much faster. The other thing it can do is once you're actually live, it can actually learn what changes need to be done to the application. Not only recommend them, it can actually execute those changes.
And you can come in in the morning and say, "Here's all the changes I made to your application. Would you like to accept them or not?" I mean, that would, that's like, you know, revolutionary in terms of how software is typically evolving. And those are just some of the early stage use cases.
I can only imagine how we're going to come up with, as an industry, so many more interesting ways to leverage it. So we're really excited about AI and, you know, and, Pega, Pega GenAI is available in Pega '23. We've got clients, using it as we speak. I mean, it's pretty exciting. I think what we don't know is we don't know where this is going to go.
Like, in terms of, like, which use cases are really going to be the most powerful and what new things that we haven't even thought about, you know, are we going to use it for? But right now, it's just a very exciting time to think about how it applies to helping our clients.
Absolutely. Very powerful and exciting.
Yes.
How do you think about... So, with how do you think about monetizing it? Or how do you model out the potential monetization of that over time, given it's so new and it-
Yeah
... could change so much? How do you think through that?
So, you know, it's interesting. Commercial models, although seem on the surface, relatively, when you think about building technology, you don't typically think of the commercial model being a complicated piece of it, right? Like, you build good tech, and then, of course, people want to pay for it.
But just trying to think about the right way to monetize it, I mean, we have a couple obvious ways to monetize it. One obvious way we have is we typically contract with our clients on a consumption basis, and so as AI drives more decisions, more activity, more... That's one way that's already embedded in the contracts. It's really a volume-based. That might work.
That's certainly an option that we've leveraged. Another option might be to actually pay for the actual AI engine itself, right? Some clients are uplifting, like, user licenses, as an example. We don't.
That's not as relevant for us because we don't have as much user-based licensing, and it's a little bit counterintuitive to think that I'm going to make money selling AI that should reduce the number of people that have to support your clients. And so how would that work? So you're going to actually need less, but I'm going to charge you more. It's a little bit kind of disconnected to the value prop.
However, if you have user-based contracts right now, that may be your only mechanism that you could use to-
Right
... to drive value. So for us, it's really around the volume and the activity and really around the actual processing that happens, you know, in the AI kind of engine or cloud.
Okay. That's really helpful. Maybe another, if I can tweak a numbers question in here as we're-
Sure
... kind of winding down on time. We've talked about how ACV is a really important metric for, for Pega. I think that this year you guided 11%-13% ACV growth.
Yep.
I think in Q3, in constant currency, you grew about 10%, so just slightly below that range. Wondering how you think about growth for the full year at this point. Is it at risk? Is there a, you know, an uptick in Q4 that kind of gives you, you know, a better, you know, comfortable feeling about that initial 11%-13% ACV growth guidance?
So certainly, I think where we are at the year being on the low or even slightly below the low end of the range is not a desired outcome, right? I mean, I think there's some very real things that we dealt with in the year. Quite frankly, you know, some of the changes that we made to our sales structure probably, you know, weren't helpful in the short term.
We've also had, you know, some kind of, you know, disruption in engagements with our clients around things like AI and other things. So it has been. There's definitely been some... It's been an interesting year for a lot of companies.
But I think us landing toward the low end of that range is definitely feasible, and I think, you know, we're not, you know, we're not thinking that our growth rate is, you know, continuing to decline. I also understand how hard it is to have the growth rate just immediately rebound as well. But we have a lot of activity in Q4.
Q4 is our biggest quarter, as you know, and there's, you know, a lot of pipeline to go after and a lot of, quite frankly, renewals that happen in Q4 that you can sell on top of. So I think, you know, we're feeling pretty good about, you know, how, you know, how we're going to work to finish the year.
I think our you know, our range of where we land, quite frankly, isn't going to probably vary materially from where we are right now, right? I mean, just because it can only move so much in a quarter.
In a quarter, yeah.
Yeah.
Well, very helpful. I think we're just out of time. I feel like we could have gone for quite a while here. Always fun to be up on stage, and again, we're really grateful you guys made it back to the conference this year. Thanks, everyone, for joining in, and thanks to Pega. Ken and Peter, thanks for being here, guys.
Awesome. Thanks, Rich.