Thank you to everyone for joining us with Needham's Tech Week. I'm Mike Cikos, and I'm pleased to announce that with us today, we have the management team from Alteryx- the CFO, Kevin Rubin, and the CPO, Suresh Vittal. Just for some quick logistics before we jump into it, but this is a fireside-chat format. I've drafted up some questions on my side, but would like to make this as interactive as possible. Wanna make sure we're maximizing your time here. So if the audience has any questions first, there should be a chat box on your interface. Feel free to lob in your questions in that format. If not, feel free to email me at mcikos@needhamco.com. I will do my best to make sure that we're getting to that while we have the team here. With that out of the way, Kevin and Suresh, thank you very much for the time and for joining us today. We really do appreciate it. Our pleasure. Thank you. Thank you for having us. Absolutely. So quick question here, just in the interest of an intro or an overview. I know there's probably some newer folks here to the name, some others who are probably dusting it off for the first time in a while. So at just a very high level, can we start with maybe discussing Alteryx, its position in the market as providing value to customers? Who are primary incumbents you're going up against, and who do you compete with when customers are thinking through the competitive landscape? Yeah. Thank you, Mike. I'll probably get started. Alteryx, we serve as a orchestration and automation layer for analytics across an enterprise. As we all know, our customers are wrestling with the problems of data fragmentation, data governance, being able to apply analytics for the right use cases across every different functional area. And so we are very unique in what we do, not just in what we do, but how we do it and who we empower on the journey. We're unique in what we do because the unparalleled nature of the Alteryx analytics automation toolkit means an analyst, a data engineer, a data scientist, a line of business user can do analytics of any shape, size, form, and advancement, as they choose to. We're unique in who we empower, in that we kind of cut our teeth in really helping the business users get the most out of their data and their analytics. And over time, we kind of start to serve the data engineers, and the data scientists, and the IT teams as well. We know analytics is a team sport, and that means being able to enable every part of the organization to do the most with the data they have to create the insights, and most importantly, operationalize those insights. We do that in an easy-to-use self-serve platform for our customers. The other place where we're unique is we have customers across the entire spectrum of deployment models. We have customers who have a lot of their data on premises and need Alteryx to run on-premise, and we support that. We have customers who are on their journey to the cloud and completely embrace the cloud, and so we support that. Regardless of which cloud you're in, you get to run Alteryx in that cloud, whether it's Azure, AWS, or GCP. Then, more often than not, most of our customers are in some kind of a hybrid format, where they've got data on-premise, they've got data in the cloud, they've got cloud applications that they're dealing with, and they kind of need a analytics, automation, and orchestration capability across all of those environments, and we help them do that better than anybody else. You asked a little about the competitive landscape. The... Our biggest kind of competitive topic would be customers would have built a lot of stuff by writing code, and they do want to replace that with packaged software. Other competitors who show up in this space, certainly Tableau has a prep product, and an analytics product in the space. Dataiku is one of the smaller emerging competitors in this category. And then there are other tools who kind of provide bespoke capabilities to functional areas, for example. Got it. Thank you for that. And I know we've also been asking a number of our companies today around GenAI. It was super hot earlier this year. I think it's still front and center for folks, even though I think generally people are taking a more considerate approach for the on-ramp of what that technology means, right? And so would love to get your sense, but with GenAI, how does that in any way change the market opportunity and impact Alteryx's, I guess, ability to serve up that value on behalf of its customers? And we can go into some different facets from there, but would really like to kick it off with that. Yeah. You know, the way I think about, generative AI, and certainly AI more broadly, and generative AI more particularly, well, what it'll do is it'll lower the barriers to adoption for capabilities like Alteryx. It's been our mission to empower analytics for all, for the longest time, and what technologies like generative AI do is they really help us accelerate that mission. We think about GenAI, which kind of gives us opportunity in really, broadly in two distinct ways. One, certainly it enhances our existing offering, so makes it easier for us to help our customers do some of the work that they embark on already. Faster to get data, faster to clean that data, faster to create analytical models. What could be better than to use English as your programming language, for example, and create these complex workflows just by dictating a set of instructions in English? And so we think that lowers the barriers to entry and really enhances our current offerings. What generative AI will also do for us, and has already started to show some signs of that, is it enables us to address multiple personas. I kind of mentioned earlier that we very much view analytics as a team sport inside the enterprise. Data pipelines are complex. There's data everywhere. There's metadata that needs to be recognized and rationalized. You have to kind of create analytical applications. So you have inevitably a business user, let's call them an analyst, involved in the process. You have a data engineer, and you have a data scientist. The challenge has always been that these individuals work in environments of their choice. For example, a business user would work in Alteryx and use a low-code, no-code way of creating an analytical operation. A data scientist would probably be in a Jupyter Notebook or be writing Python code, and the data engineer would be in DBT or writing SQL statements. And because they all operate in their own environments, the mode of collaboration was asynchronous. It would be Slack, or it would be email, or it would be a variety of other collaboration sources, and that's not really optimal. It's time-consuming, it's expensive, and it's error-prone. We believe with generative AI, we can enable unprecedented levels of collaboration between these people. We call that multimodal. And by helping our companies, our customers embrace Generative AI, we are allowing them to write instruction sets in English to create analytical operations. That could then get handed over to a data scientist, who would open that same instruction set in a Jupyter Notebook, for example, and start to work against that. And he or she could make modifications in Python, which would then kinda get picked up by the organizing workflow, and that could get handed off to a data engineer, who would work in their tool of choice in SQL and make changes. So that back and forth between these personas, we think that unlocks a massive amounts of innovation and really simplifies the lives of everybody involved. We're also helping our customers embrace generative AI more responsibly. So we've been training large language models on analytical operations. You know, our core strength is the millions of analytical operations that we've enabled over the course of the past couple of decades. So being able to train large language models on those analytical operations means we can allow every customer to customize generative AI for their analytical property and their needs. We call that our AI Studio product. And both of these things that I was talking about, the ability to write instruction in English for workflows and the ability to customize large language models for analytical operations, are gonna be available for our customers later this year. Awesome, awesome. And I think another thing I'd like to highlight here, too, we've actually heard from a couple of companies earlier today, but just with respect to GenAI, there's organizations that are putting in place, let's say, I think the, the quote was a star chamber, right? If you think about, like, there's a new management piece to the procurement process that's evaluating this from a security standpoint. We're making sure that everything is being properly implemented. We can explain why the, the model is giving the answer it's giving. So in that capacity, is that something that you would agree with, that you're seeing? And then I guess the derivative is, is that in any capacity, lengthening sales cycles as a result? Yeah. So we're certainly seeing customers being thoughtful and progressive about how they embrace generative AI technologies. You know, customers differ, so it's kinda hard to say one size fits all. But what we've seen from our customer base is a lot of interest in piloting some of this capabilities and really a lot of receptivity to talking to us about it. Because guess what? Generative AI systems and applications need well-prepared, well-formed data with millions, in some cases, billions of variables available to train, right? That means this whole journey of data preparation, blending, transformation, advanced analytics, is something that they already trusted Alteryx to do, so they're receptive to having those conversations with us. And then you also kind of hear customers kind of struggling between these variety of ecosystems that they're dealing with. So, would that slow things down? I certainly think it behooves every customer to be thoughtful about what technologies they embrace. What we do at Alteryx is kind of give them that level of insurance that regardless of which foundational model or which hyperscaler they choose to work with, Alteryx is compatible with that. Awesome. Okay. I think you kinda touched on this a little bit in your, your response just now as well, but, like, again, it seems like the market is still very much in an exploratory phase. You were talking about pilots, right? Versus some of these moving into production. And so the question is, like, what are you seeing as far as workloads moving into production on this front? Is it still very early, or is there increased appetite or more workloads starting to move towards the finish line? I guess, if we were looking for a status update, how would you rank that? Yeah, I think we're still in the, I'd say, the first inning, or you could even- Yeah Argue if you're using the baseball analogy, this is, they're still warming up, if you will, they're throwing in the bullpen. Mm-hmm. I think that'll continue for a little while longer because the ramifications are pretty enormous of getting it wrong, right? And so, I think they want to rightly, our customers want to do their diligence, they want to run pilots, they want to run hackathons, they start to kind of identify use cases where the application is relatively lower from a risk standpoint, and they start running these things. So I expect it's gonna be that way for a big part of 2024 as well before you start to see kind of mass adoption. So that's kind of how I view this evolving. And not dissimilar, Mike, as you'll recall, from how cloud made its way into the enterprise- Yep ... or how mobile made its way into the enterprise, or how social made its way into the enterprise. You know, rightly, CIOs, chief information officers, are cautious because company data is their prized possession, and they wanna make sure they're deploying these models the right way. Got it. If I shift gears for a second over to ELAs, right? So a lot has been made about this contract vehicle, what it permits as far as burst capacity, ability for customers to streamline adoption and really spread those analytics use cases throughout the organization, and especially since it is a strategic initiative for a lot of organizations. The question I have with respect to ELAs is, can you give us an idea how much of the customer base at this point has adopted? Like, where are we in that ELA penetration? And then secondly, I know that we've gotten data points in the past, but can you talk to how many of your customers who are on ELAs are actually, they're actively using that burst feature that you guys promote? Yeah, thanks, Mike. So maybe just, backing up a little bit, Mm-hmm ... for those that aren't as familiar. Fair. So ELA is for us, is a pricing and packaging vehicle. It allows customers, in a very frictionless, elegant way, to purchase a large population, of our technology, right? We have on-prem ELAs, and we have cloud-based ELAs. The context being, we want to give our customers the opportunity, to consume all of the technology that we make available, make it easy to purchase. They are bound by quantities of licenses, they're bound by price, and they're bound by time. So, they're not unlimited in, in use, they're not forever and always, they're just simply a packaging mechanism, that we think is attractive to the customers, and certainly, it's an easier, method for us. Oh, I'd also like to mention they include customer success. So we ensure that we're giving white- glove treatment to our customers to help them in their analytic journeys and maximizing the use of Alteryx technology. The other feature that are included in ELAs that you were alluding to is what we call burst licensing, which is in effect we build these ELA bundles around numbers of designers. And so let's say you're buying a, you know, a 300-seat ELA, right? That's gonna have some Designer licenses, it's gonna have, it's gonna have 300 Designer licenses, it's gonna have a concentration of Servers, it's gonna have Intelligence Suite, you know, Customer Success, as I mentioned, and really designed to optimize for 300 seats. We will then also allow that customer to get another up to 150 Designer licenses, that is no cost to them, that they can use for the very first year of their ELA. If it's a one-year ELA, then it just expires with the expiration of the ELA contract. If it's a three-year ELA, they get it for the first year. It's designed to allow them to really explore other areas within their organization where Alteryx can be impactful. It's proof of concepts, it's use case, proving out a use case, it's going into other departments and, and identifying opportunities, for customers to use it. We rolled out this particular version of ELAs, exactly two years ago, so Q3 2021 was the initial rollout. We've seen continued momentum in customers electing ELAs. Not every customer is interested in a prepackaged set of technologies. Some do like to very bespoke select, "This is how many, you know, designers I want, servers," et cetera. So it's not the majority of the organization of the customer base, it still is a minority, but it is a much more frictionless experience for the customer. They don't have to pick off a pick list and say, "These are the, you know, 27 different things I want." They have access to it all. They're attractively priced, so when you think about those conversations around price per user, it tends to get, you know, quite attractive, given everything that's in the bundle. And we've been really pleased with the performance. So Q3 was a significant quarter in terms of ELAs. We more than doubled the number we sold on a year-over-year basis. Those may recall that Q4 of last year was the largest number of ELAs that we had sold in a given quarter. And so those are coming due and renewing in this Q4. So certainly there's you know some optimism and encouragement that we have this large population of ELAs expiring and coming due. We do see customers using the burst, and that's a great indication and you know future opportunity for expansion. You know it is a different environment today, and customers are cautious in terms of where and how they use software, and this is just not an easy macro. that being said, I think the ELAs have provided a nice vehicle for customers to, to consume our technology and, and do so in a, in an affordable way. Awesome. Awesome. And I did want to pause it, like we've—I'll come back to that other question. Sorry, I'll just put it in the bank. But first, it makes intuitive sense to me why someone would adopt an ELA, right? It does. So if I wanted to take the other side of the coin, and I know you were talking about customers might prefer, like, a more bespoke approach, but beyond that, like, why would you not want to adopt an ELA? Can you help me think through that? Yeah. So it could sit on either side of the pendulum, right? You could have a smaller customer with an estate of technology, and the concentrations of the different products they have is what it is. And their willingness to take the ELA in the prepackaged bundle to them may be perceived as buying more than they need. And I'll give you an example, right? They may have a large estate of designers and a small estate of servers, right? Or by the way, it could be the other way around, right? Depending on the use cases, we've seen environments where we've seen a heavier component of servers to designers, depending on the use cases, and it may just not fit the ELA, right? The whole purpose of the ELA is it's prepackaged, and it's a generalist package, if you will. It's not intended to be customized. So you could be sitting there. You could also be on the other side of the fence, where you have a massive estate of Designer licenses and different concentrations of some of those other products. And, you know, there's a sense that I'm either not buying enough, and I've got to end up adding on anyway, or I'm getting something that is in a different configuration than I would have naturally selected myself, and they just opt to, you know, to put that together. So, there I mean, there are natural reasons why, you know, customers are doing it. In this environment, I think there is sensitivity from you know, CIOs and budget owners to make sure that what they're buying fits their particular use, and that they're not being perceived as buying more or two or different concentrations of technology than what is needed for their business. Got it. So I guess to build on that, I, I'd be interested to hear... It doesn't sound like the ELA is gonna be built for everyone, right? So over the long term, and you could choose, like, let's say, it's a five-year time horizon as an example or, or even longer, if you will, but, like, that ELA penetration of your customer base, is that expected to get to 50%? Is it, is it closer to 70%? Like, where is, have you guys gone through that process as far as thinking about what that ultimate penetration could be? Well, look, if Paula was joining us and you asked her, she would say- Yeah ... the customer to buy an ELA, right? It's- Yeah ... it's the least friction path to getting customers to adopt and consume the largest quantity of our technology. So it is not inconceivable to believe that it becomes more and more appealing and part of the motion, right? Part of it is also, you know, making sure that the sellers understand you know, how to properly position the ELA for success as well, and we've seen, you know, continued success in that regard as well. So I do think over time, you know, customers that have purchased them, I think, really appreciate the ease of purchasing and consuming. The sellers that are selling it you know, certainly appreciate it. So it is a heavy focus of us going forward. You know, that being said, we are very customer first, and to the extent that, you know, a particular customer wants you know, a bespoke set of, of, technology, we're not gonna you know, we're not gonna at the risk of, of, you know, customers' decision-making you know, offer them something that's inconsistent with what they're asking. Got it. And this is the other question that I was kinda holding in the wings, but it kinda builds on exactly what you were talking about. So one of the criticisms that I've heard from people, if they wanna put together, like, maybe a bear thesis around the ELAs, is like, "Hey, in year one, I'm gonna buy..." Let's use the numbers that you had earlier. So it was 300 Designer seats under the ELA, I'll get 150 to burst. And if you find success with that ELA, the next year when you renew, you're not looking to go to 450, you're probably looking to go to, let's say, 600. Mm. So you go to 600, and you'll get 50% burst on that, it'll be 300 incremental, and now you're at 900 in aggregate. And if you play that out over the next year or something like that, you might find, "Hey, we were at 600. From the 450, we realized maybe we only actually need more like 500 in total." And so there is a concern that customers might have over-indexed or over-adopted, which I know is not what your job is. Your job is to make sure the customers are finding value. But I'm just curious how you would refute that if someone was posing it to you as far as concerns on over-adoption from customers with respect to the ELA vehicle. Well, there, there's a few things I would, I would point out. First of all, we generally don't sell ELAs to new customers, so they're generally positioned to an existing customer who is looking to accelerate their use of Alteryx. And I say that because it's important. At the time we actually sell the ELA, we tend to see a nice increase in their estate, right? And from an ACV perspective, you just looked at ARR, pre-ELA, post-ELA. The ELA is a nice step up in terms of the ARR of that particular customer. So you get a benefit by moving them, generally speaking, to the ELA, because they're buying more technology, they're consuming more. Mm-hmm. With the annual cycle of the burst, if we really wanna play this out, and a customer goes from 300 you know, to 600 to 900, however that journey looks like, you're talking about two or three cycles before a customer may start in this hypothetical example optimizing use. So we've taken a customer... Let's assume that these numbers are accurate. We probably took them from 20 to 25 or 50 licenses- hundreds of licenses to nearly 1,000 licenses over a two- or three-year period, to find out that maybe they need something less than 900 licenses. That still t o me, feels like an incredible journey and a expansion opportunity with that particular customer. The other thing, just to keep in mind when we think about, serviceable or addressable market in these, in these larger organizations, we're talking about tens of thousands of employees in these large organizations that generally, are data savvy and could be potential users. We're not talking about hundreds. And so, I would like to believe that as you get into those conversations, there is so much opportunity and white space in these accounts. If a particular department determines you know, "I only need a certain number of licenses," there are another dozen departments that you know, have yet to consume and/or enjoy, and that's what our job is, right? We've got customer success resources, we've got value engineering resources, we've got sales resources, and their job. We've got SMEs. Their jobs are to go and make sure that those organizations and those departments we've yet to expand into are fully aware of what the offering is. So while I can conceptually acknowledge that, you know, there may be a point at which a customer continues up this journey and decides they want to stop. If you look at where they started, I mean, that's a tremendous opportunity path for us nonetheless. And then it's our job to identify those new people and those new use cases, and, you know, Suresh's job to put out amazing technology as he has to attract those folks, and so, I don't know. Yeah. Okay. Okay. It seems like, you know, it's still a net positive journey, and we're still meaningfully growing that hypothetical, you know, customer account. Agreed. And also would argue that in this environment where everyone is more budget constrained, people are very much focused on what they're adopting in the first place, right? So given the amount of scar tissue that's been built up with these organizations and their management teams, the hope is that we don't get into that for quite some time, especially as it seems like, at the very least, it would be several burst or renewal cycles away from where we are today. I would- Uh- I would imagine that to be the case. Yeah. Question for you, just because I know the renewals base comes up frequently with, with investors as well. So calendar 2023, larger renewal base versus calendar 2022, is there any way we should think about the calendar 2024 renewal base? Again, it feels like with contract duration kind of stabilizing around a year and a half, that the, the size of the renewal base continues to, to grow in some capacity. But interested if you could shed any light on that. Yeah, we obviously haven't provided any 2024 guidance per se. I think directionally speaking, you're thinking about it correctly. You know, we have had stable contract duration now going on almost three years. I would expect that from a, you know, just from a general proportional basis, that we're gonna see similar dynamics, as we go forward as well. You know, but a lot of that will be dependent on, you know, Q4 as well. I mean, Q4 is our seasonally strongest, largest population of renewals and strongest renewal quarter. Got it. And probably time for maybe one or two more questions as we're bumping up against time here. But, the first, what's interesting is, I know that you guys have demonstrated this, this remarkably consistent level when thinking about the NRR for total customers north of $250K in ARR, right? And it's, I'd just love to get a sense for how it's been so consistent. Is it a function of, hey, these organizations are that large, and there's that much opportunity to expand in their estate? Is it maybe, that white glove service that we were talking about with the ELAs and probably focus from Alteryx on those large organizations have? But what are some of the puts and takes we should think about in the context of that very strong NRR at those levels? I think it's a variety of things. So first of all, in most cases, I mean, I can't think of a customer, and Suresh, welcome your input here either, where we would say we're saturated, right? I mean, we're very early in many of our largest customers from a penetration perspective. We have very large populations of users, but on a relative basis, these are very large organizations with large populations of potential users. So, your comment about the white space opportunity in these accounts, I think is important. We also continue to expand the suite of technology and the products that are available that we can sell into these organizations, which gives us a cross-sell, upsell opportunity that candidly didn't exist two years ago, right? It was really just: We gotta sell more seats, and then we gotta drop in a server. And today, we have an entire suite of the Alteryx Analytics Cloud platform that continues to grow on a week-by-week, month-by-month basis, providing more and more opportunities. And then lastly, I would call out customer success. I think in these largest accounts, having dedicated white glove support to ensure that our customers understand everything that's available to them, that they've purchased, as well as to what hasn't been purchased. And working with them closely in their analytics strategies, I think has proven to be a very successful formula. Got it. And maybe the last one I know we're really up against it here, but would be on the go-to-market, right? I think management has done a good job on this most recent quarter, talking about several initiatives that they use to increase visibility to the business model. And just wanted to understand, could you walk us through these developments, where we are on driving these improvements on a more consistent basis, or at least on a go-forward basis? Yeah. It's a lot to pack in here, but look, here's how I would, I guess, maybe synthesize it. We knew coming out of Q2 that there were elements of the execution that we could do better, recognized that, and took a number of steps in Q3 to address it. You know, things like enablement, coaching, linearity, you know, making sure that the sales organization understands how to drive a value conversation, especially as budgets have continued to tighten. Making sure that we're really aligned with our customers on their project needs and their project cadence, right? So we're not getting ahead of ourselves. They're not getting ahead of us. Really understanding the quality of the pipeline, the deal progression, those conversations we understand. Many of the reps... I mean, you can imagine if you're a sales rep with 13 years experience, you've never worked in a challenging macro environment, right? Now, our more seasoned reps certainly have, but they haven't done so at Alteryx. And so, there was some, you know, foundational execution issues that we've addressed. And then lastly, we've made two meaningful leadership changes that, you know, that we talked about on the call. We changed out our head of Americas. I'm not representing that these two individuals impacted Q3 because they're recent adds, but nonetheless, as we think about going forward and continuing to refine on our execution, new head of Americas, new head of partners and channel, and I think that those two bring a very unique stage-appropriate skill sets to the business that will help us iterate and evolve going forward as well. Awesome. Awesome. I know we are at time. That was a, that was a great way to just slim it down, but Kevin and Suresh, and to the audience, thank you very much. I really do appreciate the time today. Our pleasure. Thanks, Mike. Thank you. Bye-bye.