Good afternoon. My name is Brent Bracelin, the co-head of Technology Research here at Piper Sandler. Really pleased to have Kevin, the CFO of Alteryx, with us. We're gonna do a fireside chat here. If you have questions, raise your hand. I have a bunch of questions. Kevin, a big part of our conversation this last two days have been around, you know, generative AI. I'd love to maybe double-click into Alteryx strategy around generative AI and some of the early feedback from some of the features that you've added. Yeah, so we announced in May, as part of our user conference, AiDIN, which is our brand of AI ML technologies, that will wind up as part of our Alteryx Analytics Cloud platform, as well as features and functionality that sits within our breadth of products. So today, we have a handful of capabilities that are in product being enjoyed by customers, and we've got some bigger efforts around broader aspects of Gen AI that are to come. I think at the highest of levels, we certainly see a pretty significant opportunity with Gen AI to, you know, be quite disruptive. If we look at what has distinguished or differentiated Alteryx over, you know, the 15 years that our Designer product has been in market, by way of example, it is really the abstraction of code writing and a level of sophistication, that a line of business user, which is who we tend to serve, the skills that they were required to have, we've significantly abstracted that level of complexity and allowed a less sophisticated user to be able to work with pretty sophisticated analytic modeling. And if you just extend that through Gen AI, we think that will open up the opportunity for a much broader set of individuals within an organization to be able to interact with models and analytics. So for us, we think it's a really exciting opportunity to further, you know, this notion of analytics for all, right? We believe, and many of our customers would agree and attest to, the more people inside an organization that are comfortable with analyzing data, playing with data, making data-driven decisions, just creates better outcomes. And so, we think that that's an opportunity to accelerate, you know, that concept. So on the bigger side of Gen AI, two of the areas that we're focused on at the moment, one is what we call multimodal. So the opportunity to allow different levels of sophistication to interact with the model and the model-building process. So anything from a data scientist who's sitting in Jupyter Notebook, who's writing Python code, to a complete novice who wants to interact in natural language. Being able to represent a model in the paradigms that work best for those different users, we think is incredibly powerful. Getting input from more sophisticated coders, as well as others who have more contextualized experience with the business, will result in a better, ultimately, a better model, if you will. And then on the other side, how do organizations responsibly and ethically deploy large language models in their organization? So we have a workbench product that we're working to bring to market, that will really allow a level of governance, control, insight into models that we think our customers will benefit from. Being able to understand how a model is making decisions, where... What data is being used in the decision process? Where is data going? And being able to then manage and deploy those models with a sense of confidence is kinda two of the big areas that we see- Interesting - more near-term opportunity. As we think about the product and use case, today, you're supporting prepackaged algorithms, you know, in that Workflow Designer product. How hard is it, or is there a change in that workflow as you start to think about adding large language models to that same package? And could there be a breadth, I think, on Hugging Face? I checked earlier this week, there was, like, 350,000 models. So I think there's more models coming out every week. But how do we think about large language models inside of a Designer workflow? Is there an opportunity to do more there? We certainly think so, and part of the accuracy and the impact of the large language models is the quality of data that's being fed in. So if you think about... So let's just do—let's use an ML use case, maybe not specific to Gen AI. We've got a significant amount of workflow experience in data transformation, analytic process, send that resulting analytics up to Azure ML or some other specific ML model that you want to run your analytics against, and then ingest that answer back into your model and allow the workflow to continue on. I would look at a large language model no different than that. You transform your data ...you get it into a contextualized place, feed it into a large language model, have an understanding of what's being done to the data, what the decision process looks like, what the output looks like, and then pull it back into your model and keep going. One of the questions was raised in a conversation earlier this week was around data prep, and there doesn't exist a data prep for large language models yet. Is that an opportunity? Can you do data labeling inside of the workflow or not? I'm just trying to think through this, obviously, data prep was a- Yeah ... big growth vector for the company back in the, uh, the Tableau days. Early adoption days. We're in the early adoption days now of now these large language models. Is there more opportunity on data prep for LLMs, or not yet? That's probably beyond my pay grade. But what I would say is, we do have the ability to append. We have the ability to do feature engineering, which is an important aspect. Okay. We obviously have a pedigree in data transformation, and then the advantage we offer is automation and the ability to deploy these workflows into, you know, runtime experiences that are automated. I don't see any reason that we couldn't be a leader there either. Perfect. So talk a little bit about the kind of external opportunity for, for AI and, and leveraging your tools, traditional tools, in kind of new ways. Internally, how do you think about AI, automation? Is that going to help lower costs for you as you think about kind of driving to this goal of returning to profitability? Walk me through the early learnings so far. ... using internal tools. So we have been pretty vocal that we think there's as much of an opportunity for us to help others in their GenAI journey as for us, and being able to deploy, you know, GenAI internally for us, whether it's within the engineering process, whether it's back office, you know, things like contract reviews and IT ticket handling and things of that nature. I think there is a large opportunity. Software companies are largely people shops at the end of the day, so when we think about cost and driving efficiency to the extent that we can offload... You know, 70% of our expense as a company, plus or minus, is people. So if there's ways to be more efficient in doing that, we're super excited. So I think it'll be as transformative to companies like us as it will be, to the customers we serve. Helpful. So, you know, obviously, a little unfair to ask CFO tech questions. So we'll talk about finance questions now. Whatever you'd like. Let's just think about the outlook of the business. One of the concerns that we have in the software space overall is this feature flood. We're in this kind of AI great experiment. In our latest CIO survey, we saw a downtick in spend intentions in all categories except for that AI category. Yeah. Is there any risk, as you think about your slowdown, that maybe it... that slowdown was indirectly tied to some pauses, a potential air pocket, as people evaluate these, this great experiment? ... that we're in, under for the next, let's call it, 6-9 months? There was nothing evident in our conversations with customers and our deal flow that suggested that some of the challenges we had in June, in particular, were the result of customers pausing to evaluate other things. And what gives me confidence in that is the conversations that we were having with customers were all large, deployed estates of technology with Alteryx. So these aren't customers that didn't have experience with Alteryx. They all had meaningful deployments. We were having conversations around expanding those relationships. In some cases, we did just expanded it at lesser amounts than we would've expected, or we just simply pushed those conversations further. And so, there's nothing that would give me an indication that there was this groundswell of, you know, "Let's pause and evaluate other things." That's just not the nature of the conversations that existed, so I don't think so, but- Okay. Maybe compare and contrast what you saw in March versus- ... kind of what you saw in June. You saw a little bit of hesitation in March. Sounds like there's a lot more in June. So maybe just compare what you saw at the end of Q1 versus what you saw at the end of Q2. Yeah. So Q1 seemed to be very specifically isolated to the regional banking crisis. We saw a pause, if you will, in customer activity, kind of in that week, in and around SVB and what was transpiring. And some of it was customers were directly impacted, and therefore, they were dealing with their situations. Others, I think, were just on their toes and trying to figure out, "Is this going to be the start of something bigger? I would say we largely worked through that as we got into Q2. The effect that we saw in Q2 was different in that it was isolated to, as I mentioned, largely to large customers, where we were having expansion discussions, and there was no compelling renewal event in the quarter. So the discussions were standalone expansion, somewhat opportunistically to those customers. And we do have linearity that generally backs up our quarters to the last month of the quarter. That puts pressure on deal flow. And then, I think just, you know, we've increasingly seen customers say, "Budgets are tightening. You know, we're not expanding budget for projects and other things. We wanna see what 2023 looks like." And so I think all of those dynamics, plus the fact that we know we can execute better in certain areas, is what we saw in Q2. Makes sense. So let's maybe take a step back and talk about ELAs. ELAs has been a really important part of the growth picture over the last couple of years. We continue to get questions around price... and ELA is a great vehicle to reduce friction around price. Maybe frame the ELA funnel today versus, let's say, a year ago. Is it different? When you talk about those large enterprise customers being hesitant, are they thinking about smaller ELAs, or are there still some big ELAs in the funnel? Just trying to compare, contrast- Yep today versus what you saw a year ago on the ELA front. Yeah, maybe just to level set for those that aren't super familiar. We have a pricing and packaging offer, which are different tiers based on quantity of Designer users. We've packaged a lot of software into these different tiers. So you start with how many Designer desktop users do you have, and then that determines how many servers, and cores, and other pieces of technology that we bundle into the ELA. Subscription-based, time-based, price-based. So it, it's an easy way for a customer to buy a large quantum of software. They're attractively priced, and then they do come with a feature we call Burst, which allows the users of these ELAs to flexibly use up to 50% more Designer seats to trial, prove out a use case, really explore where there are other opportunities within an organization to benefit from Alteryx. So sorry. We introduced these two years ago. Q4 of 2022 was the largest dollar amount sold to date at that point, and so we have a large volume of those coming up for renewal in Q4 of this year. Q2 that we just closed was the second-largest quarter of ELAs sold. So they are an attractive offer, easy for customers to consume. It's easy for us to sell in terms of reducing friction, better pricing, et cetera. So we're encouraged with the response that we've gotten. They still are, you know, they're not the majority of what we sell to customers in any given quarter, becoming more and more significant. We're getting more customers comfortable in seeking to deploy them for all the reasons I mentioned. They've got Burst and attractive pricing. So they are becoming more and more important, and we continue to position them where it's appropriate in customer environments. Are you seeing hesitation on some of the larger ELAs as well, just given the environment, or, or it sounds like it's a smaller part of it? We haven't seen any packaging-related hesitation, so it wasn't the case that a customer said, "Gosh, you know, I don't want a, you know, very large ELA. Let me take a smaller ELA." In fact, I don't believe any of the deals that we struggled with in June were actually ELAs to begin with. Okay. It's not. There's no commonality from a product perspective. The commonality really was, there wasn't a compelling event on the customer's side to make a deal happen. We could have executed better through that process as well, and that's where we saw softness. Totally makes sense. Let's talk a little bit about that, that path to profitability. This is a business model, high gross margins. You've been profitable in the past, but there is ASC 606 accounting, and, one of my fears is that if we do get another pause ... in any sort of large deals towards the end of a quarter, we won't know probably till the end of September or the end of December, you know, does that push out or temporarily delay the path to profitability if you, if you see further softness? I guess conceptually, the top line is a direct driver, to some extent, of near-term profitability. We've been pretty proactive this year in calibrating the expense structure to what we're seeing from a top-line perspective. We put up 2% op income for last year. We've guided to 8%. This year, we've signaled a 5-point + opportunity or leverage going into next year relative to the 8%. So we are definitely committed to driving increased level of profitability, and we will calibrate, you know, each year based on what we're seeing in terms of top-line performance. Perfect. Any questions from the audience here before I continue down the path here? Okay. Let's talk a little bit about the sales team ... and some changes we made there on the go-to-market side. Obviously, we've had a lot of go-to-market changes in the last two years. What kind of drove the decision to make more recent changes, and what specific changes were made? Yeah. So I think the point that we have made a lot of change over the last couple of years is important. We significantly transformed how we go to market, basically going into 2021. We hired extensively in 2021 and early 2022, largely expecting a different macro- ... as many in software did. We have trimmed our hiring activity. We've made some cost-related adjustments as well as a result of that. What I would say is, we got a lot right. Right, the orientation around enterprise, large customer focus, doubling down with customer success and really making sure that we are surrounding large organizations with the level of support and direct touch that they would expect from an organization like ours, I think was all the right things. We've seen over the last 2 years that there's opportunities for us to just do a little better, right? We've made some leadership changes in the business that we think will have a positive output. Doubling down on enablement, making sure that- ... the sales organization understands... the plays to run in a more challenging environment. So I think we did a good job bringing people into the organization, enabling them around things like ELAs and other sales motions, but really making sure they understand how to navigate in the environment we're in today, is something that we're really heavily focused on. Making sure we have appropriate deal scrutiny, deal progression. And we're just seeing the intent across all of the levels of the sales organization, around what we intend to happen, when we intend to happen, in the order that it should happen, so that we do have the level of consistency and predictability that we would expect. Yep. A little bit of a bump in the road here this year. Clearly making some changes, you know, obviously focused on profitability. Let's talk about next year. As you think about what's in your control to try to drive a re-acceleration of the business, what are the things, either go-to-market or product, opportunities, that you think investors should pay attention to, that potentially could have some dividends next year? Sure. So one is just execution, right? Being able to, you know, continue to refine the go-to-market and ensure that we're executing well. I would point to that as an opportunity as we go into 2024. I think the other areas of opportunity is really from a product perspective. So, GenAI will have real product in market, in customer hands in 2024, and we think that that is a pretty exciting opportunity. The cloud, the Analytics Cloud platform, will become more feature-rich. You know, you're gonna see a greater level of integration and collaboration between all of the products that we offer that will, you know, really start to make a lot of sense in terms of how customers think about engaging with the Alteryx platform. Being able to leverage the cloud for runtime and workflow automation, being able to have flexibility and choice around where models are being produced, and who's interacting with those models. So, I think as we go into next year, the things we can't control are macro, obviously. Like to believe that we'll start seeing improved macro as we get into next year, and customer budgets being a little bit more robust. But the things we can control, execute as well as we can, release great product as frequently as we can. Makes sense. My last question is one that we've had on several occasions, which is around price. What we've seen, other vendors do in the space, they've kind of changed pricing to persona-based pricing. That seemed to have worked really well for Tableau a few years ago. Where do we stand on price and how you think about that? Clearly, ELAs- ... have been a great way to- ... to address some of the friction around price. But are there other things you're thinking through, relative to moving more towards persona pricing, or for these new, some of the newer products, or do you feel good about where you're at on the, on the price front? I guess I would say this: Our pricing has been, and continues to be, very elastic. The larger the estate, the better pricing that our customers enjoy. It is the case that our on-premise technology is still per user, per core, kind of base pricing, which, for our largest customers, has probably proven out pretty well, just given the elasticity and price in these large deployments. As we move to cloud, we have introduced consumption-based pricing based on workflows and some other flexibility. So I do believe that will give us an opportunity to think about how we engage with different personas at different price point And it will start to become more significant to our business in 2024 and beyond. So I think pricing will be an opportunity for us as we go forward. I would just say that, generally speaking, I think pricing conversations happen more with investors than with customers. I think customers understand the value that we deliver to them. The ROI is incredibly powerful. Again, the more you have, the better your pricing ultimately is. Unless you're deploying 3 licenses at $4,800, you know, pricing is pretty elastic. Well, thank you for your insights, Kevin. We really appreciate your support here, and enjoy Nashville. Terrific. Thanks for having us. Thank you.