All right, let's get started. Hey, everybody. Thanks for joining us. My name is Koji Ikeda. I'm one of the software analysts here at B of A on the enterprise software team. We are absolutely thrilled to have Alteryx here today. We have Kevin Rubin, CFO. Thank you. ... Paula Hansen, President and Chief Revenue Officer. I forgot you had two titles. No, thanks for doing this. I guess, you know, why not just level set the conversation here for those in the room, maybe not familiar with Alteryx, and those on the website or on the webcast that might not be familiar with you guys. Just real quickly, what do you guys do? Maybe just a minute or two on your background? Sure. Our mission at Alteryx is to put the power of data in the hands of knowledge workers, business users across enterprises. We have, for a couple of decades, built out a very rich platform to help them turn that data into insights to be able to make better decisions for their business. Our sort of hallmark differentiators with that platform are, first, it's ease of use. You do not need to be technically sophisticated to be able to use our platform. It's a very elegant drag-and-drop GUI user interface, low code, no code, as well as code-friendly, so we really welcome people very quickly onto the platform. Another differentiator is the broad base and breadth of analytic tools that we can apply to your dataset from, you know, of course, starting with the basics of prepping and blending that data for analytics. Once you're ready for analytics, applying over 300 different building blocks to that data, from geospatial capability that might help a marketing person better understand, you know, how to spend their dollars geographically to serve their TAM, to predictive analytic capability that can help various aspects of enterprises forecast their business. That's a second differentiator for us. The third is just the fact that data is very disparately available across enterprises and various types of data sources, both on-prem and in the cloud, as well as various types of data formats, structured, unstructured, and so forth. We have the capability through our data connectors, to be able to get data wherever it is, so we're not partial to where data is, to be able to bring it into, and pipeline it for analytic analysis. We're very excited about the future with generative AI as well, which I'm sure we're gonna talk about, and the way that that just opens up the analytic opportunity to an even broader population within the enterprise. We've made some really exciting announcements about that, which I'm sure we'll get into here. Got it. No, thank you for that. I'm asking everyone a couple of standardized questions, you know, kind of one on the macro and then one on AI. We'll get into that. On the macro front, could you characterize maybe how the demand environment feels today, June 2023, versus January 2023, versus maybe a year ago? You know, do things feel the same, different? Is the end market talking about Alteryx different? I'm just curious to how the end market's feeling to you. I think the demand market is very consistent over that horizon. you know, if you look at CIO surveys, from last month and CIO surveys from a year ago, data analytics has been in the top three to five investment categories consistently over that time. In fact, you know, I would say that right now, as people are looking for more answers in this uncertainty, they want to see around corners, they want to move with speed, analytics can provide them that capability, to be agile. Of course, we're seeing the macroeconomic environment in our sales cycles. We see, you know, more scrutiny on the deal. We see, you know, maybe a few extra approvals required. It's a dynamic environment, so we have to stay very attuned to that. I'll remind you that in our last, you know, Q1, we did hit our guidance in still dealing with the macro environment. What we've done in the go-to-market team to really pivot towards a value-oriented sales motion well before the macro changed, I think is serving us well. That means we're talking at the executive level to the people that have both the budgets as well as the insights into where money needs to be spent, and we are able to have conversations with them around not only what the technology does, but what's the value that it unlocks, what's the return on that investment, and that helps us navigate these conversations. Got it. Just thinking about the last quarter results, I think. Please correct me if I'm wrong, but I do remember some call-outs of maybe deals pushing out. Mm-hmm. Have those deals closed? You know, how do you feel about kind of the pipeline going forward? Yeah, we talked about last quarter that as we got into the middle of March timeframe, and as you all know all too well, with Silicon Valley Bank and First Republic Bank and just all this, you know, what's happening in the world of banking, we did see that cause some pausing in customer behavior. I think people were just trying to make sense of it, trying to get their arms around it, and so, you know, timing-wise, that was in the final weeks for us, so it did affect some deals for us. You know, there's nothing unusual right now about our pipeline, our pipeline coverage, and, you know, the normal maturation of deals within our business. Okay. Okay. Moving the topic over to AI, you just had your customer conference. Great conference. I attended. It was a good conference for sure. The big announcement, AiDIN. Can you talk a little bit about, I guess, you know, maybe for those on the here that are unfamiliar with AiDIN, what is it? You know, what is AiDIN? Why did you guys introduce it? From a broader picture perspective, what is the AiDIN plus, or maybe times, the Alteryx engine mean from a differentiation standpoint for analytics? Why was it differentiated from maybe just, you know, what I often hear is: Why can't I just use an LLM and just shove data into it? Why can't that just replace Alteryx, you know? Yeah. AiDIN was a big announcement. Yeah any sort of time you can spend on that is super helpful. Yeah. I'll get started. There's a lot here. Yeah ... I'm sure Kevin will wanna add. We announced AiDIN at our user conference a couple of weeks ago. AiDIN is our generative AI and machine learning engine that's available across the Alteryx cloud platform. This is, you know, not new to Alteryx, right? We've been in the analytics business for a long time. We've had AI and large language models in our platform for quite some time. I think we're bringing it more to the forefront now and continuing to, you know, accelerate the innovation here because of the significant interest, obviously, in the marketplace. Some examples of how that's coming to fruition in what we do, when you think about analytics, it's a very dynamic process that you have to go through when you're analyzing your data. For less sophisticated people, technically sophisticated people, they want things served up to them in an automated way, in a very easy-to-understand way. We've actually had a product for a couple of years called Auto Insights, which applies AI to your data analytics and then looks for anomalies, looks for trends, and sort of serves up in natural language, insights to the end user about what is happening, what's the data, what's the story within the data, what's being told. We're now extending that with generative AI, with something that we launched called Magic Documents, which takes those insights and then extends them into an email that can be automated and sent to a distribution list of people who want to get access to those insights, or into a PowerPoint, if you wanna see it displayed in some certain type of graphical format. Generative AI, in that case, just sort of extends the visibility of the insights and the analytics that we've been, you know, powering now for a couple of decades. Similarly, we, you know, run millions of workflows across our customer base, and people want to document those workflows. They wanna be sure that they can apply governance to those workflows and understand who's getting access to data, who's running different reporting. Mm-hmm ... what is it that can be replicated and automated further across the enterprise. We're using generative AI in that capacity as well. We have an Open AI connector, and we're just getting started on this. I think the next wave of this, we demonstrated at the conference, was something called multimodal analytics, which is across the enterprise. Again, if you think about this mission for analytics for all, some people will want that user interface to be very, you know, GUI-based. They wanna work on a whiteboard. They're very visual. There's other people that wanna work with SQL. There's still another persona that wants to work with, you know, Python and Jupyter Notebooks. Our strong belief is that if you think about business process and you think about analytics, it's going to cross all of these personas. They need to collaborate together on an analytics platform, and having that interface serve each one of them with the language and the way that they think about analytics really just increases the number of people that participate in the analytics opportunity. We're really excited about where this is going. Got it. Let me just add on a few points. If you think about the reason that we have been so valuable and successful in our large organizations is just the sheer complexity of those environments. Data is fragmented across these large organizations. They're in different formats. They're in, you know, they're sitting on-premise. They're sitting in clouds, warehouses. They're everywhere. As a traditional business user, you understand the context of your business as you're sitting in, you know, some domain-specific role, but you don't generally understand data structures and data environments. Your ability to go into a Snowflake and interact with an IDE is impossible. That's not your skill set or your understanding. We have allowed a level of abstraction in the product today to allow those users to go into incredibly complex environments, be able to analyze a very broad set of data and do so with the understanding and the context of the problem that they're solving. When you extend to large language models and AI, and how do large companies deploy that within their environments, you haven't done anything to satisfy or change the complexity of the environment and the ecosystem that these business users are dealing with. We think that as companies start to deploy these types of technologies, they are going to need a responsible way to manage, deploy, and understand what's happening within their data environments. That is something that has been core to our platform for a very long time, the ability to govern and provide transparency and understanding. You now talk about large language models. We don't believe our large customers are going to expose their sensitive proprietary data to public foundational models, right? They wanna be able to leverage that technology, but do so within their confines of their environment, behind the firewall, with their data and their data alone. They don't wanna be able to, you know, have their data used to improve a public model that's gonna then, you know, help a competitor with their model. They wanna contain it within their sphere. We think, you know, Paula referenced, we've got millions of workflows in the wild. We have the largest population of analytic processing in the world available to us, and we think we'll be able to take that data, build an Alteryx-specific LLM, and then benefit customers by training their own data within their environment. We'll provide a workbench that allows them to understand what the model is doing, how it's making its decisions, understand biases or any hallucinations that may be in that model, so they have confidence that the users internally that are using these models and the datasets are doing so responsibly. We, we previewed, in addition to the multimodal, this AI Workbench concept at our Inspire conference as well. Those will be two separately monetizable SKUs that we expect to have available for customers later this year, generally available early next year. Got it. Thank you. Just to follow up here, thinking about the way AI changes the pain points for your customers out there. Does AI and the rapid awareness of AI over the past six months, does it increase the pain points? Does it decrease the pain points, or does it change the pain points of the way your end market's looking at analytics? I think of it as changing the pain points, right? I think that, of course, the benefit of AI is clear in terms of just the way that it can rapidly open up insights for a broad set of people across an enterprise or an organization, which is, you know, what we're so passionate about in our business. I think the new pain point, frankly, that it introduces is around this concept of governance, right? You know, I've talked to, you know, dozens of companies, you know, probably nearing 100 since Generative AI really hit the world stage in the November, late November timeframe. Everyone gets the use cases and the benefits, but the technologists within our customers, the CTOs, the CIOs that we speak with, say, "I need to make sure that this does not introduce risk into my business. How, Alteryx, you know, can you help us with the governance piece of this, the audibility of it? You know, we're operating in highly regulated markets like the markets that you serve in financial services. This cannot be unleashed in a way that introduced risk into the business." You know, we have always been strong in the area of governance. We've invested heavily in that over the last couple of years, in particular, because we're so focused on the Global 2000. you know, roles-based access, integration into enterprise vault systems, integration into enterprise authentication systems, versioning of workflows, so it can be very easily traced and understood every step of the way, what's happening in the data pipeline. AI is going to accelerate that need for governance, and we feel very well equipped to be able to address both that opportunity that I mentioned, of the insights and the simplicity that it can drive, while at the same time making sure that the governance is in place. I think that, again, based on the conversations I have with customers, they're excited about the role that Alteryx can play in that, because we've been a trusted brand with them, with very important data for a really long time. Yep, yep. The last question here, kind of on AI, is the monetization aspect, and Kevin, maybe over to you, is, you know, you mentioned 2 SKUs. Mm-hmm. Where else are you able to monetize, I guess, specifically, AiDIN? You know, since you already have AI throughout the platform. Is AiDIN gonna be included within the platform? Is it gonna be in Designer? Can we find it in Server? Is it gonna be able to create new products as we go along or premium SKUs? I mean, how do we think about the monetization? It's gonna cross over that spectrum. You know, Paula mentioned 2 capabilities, 1 being Magic Documents, and 2 being the ability to annotate and attach metadata and information to workflows, so customers can better manage and understand what's been deployed and how it's being used. Those, we believe, are important for those respective products. Magic Documents will be part of intelligence, Auto Insights, and just improve the capabilities and that product, specifically. Designer, Server, Designer Cloud, this ability to leverage GenAI to go through and do the annotation, we think that that's important for all of our customers to be able to use. It just simply makes those products more valuable, given the price points that they sit at. The multimodal, the AI Workbench, we believe those are incrementally valuable on their own and demand or warrant individual SKUs. Then, as we go forward, there's gonna be capabilities that we find on both of those spectrums, additional capabilities that we can roll out to the broad population of users, that will just simply enhance those products as well as other SKUs that we can add over time. Got it. Got it. I'm gonna ask you guys one more question. Then open it up for Q&A for the others. If you have a question, just raise your hand. We'll get the microphone over to you, and you could ask your question. Oh, I lied. There's AI in this question. Right. On the competitive landscape. Yes. You know, with AI, analytics, I mean, has the competitive landscape from your lens changed over the past six months? How would you bucket the competition out there, maybe from, you know... How do you think about the enterprise competition? How do you think about citizen data scientist competition? Any sort of help there or color on the competitive front would be. Yeah. really helpful for us. Yeah, we have not seen a material change in the competitive landscape over the last several quarters. you know, the reality is that many enterprises are still running very, very manual processes, for aggregating the data across the various data sources that they have, applying, you know, cleansing capabilities to it, getting it in a mode where they can actually analyze it and then apply the analytics, and more importantly, the automation of the analytics to be able to scale it across the enterprise. you know, more often than not, we're sort of competing against that landscape of old that, you know, companies and organizations are just losing their patience with. It's error-prone, it's slow, and it's costly. As we think about the, you know, the buckets of categories, to be truthful, when you think of the citizen data scientist or, you know, the lines of business user, there really isn't a competitor that we feel can stack up against the breadth and depth of our portfolio. There are players that do visualization, of course, and we're not in the visualization space, and that today is still only, like, 20% of what we feed into at the visualization layer. We see niche players on the very sophisticated technical side of the continuum, right? Serving the data scientist community. That's a small population of users, and it's also, you know, a space that we also can participate in. That's probably a bucket of category of competitors that over the last year, we've seen some people sort of apply their attention to. Frankly, we feel really good about where we stack up competitively, and, you know, still see so much opportunity because of the ways that companies are operating today in these very, old sort of legacy ways of analyzing their business. Got it. Any questions from the audience? Please raise your hand. We got a question here in the front. Hi, Joe with Nuveen here. A couple questions. I guess one is on location intelligence. I know you guys announced it and put a press release out there, but have you talked about, all about kind of putting the numbers around that? I know, like, Esri, a lot of your users are using Esri right now and using Alteryx to kind of put data in, you know. Is there an opportunity, kind of hard dollars, of how you're thinking about that? I guess the second question is on ESG and reporting. Obviously, your Inspire event keynote speaker was with HelloFresh. You know, I was talking to an IR team the other day, won't say the name, obviously, but, you know, they were doing everything manually. Mm-hmm. Huge, huge opportunity there. You know, they have 2 people, and they were trying to do the entire ESG operation of, you know, a multi-billion dollar company. Mm-hmm. with two people, tons of data. Can you also talk about the opportunity there as well? Yeah. With Location Intelligence, the origins of Alteryx actually were in the geospatial area. This is an area that we're incredibly comfortable with. What Location Intelligence does is just make that much more accessible, right? That, again, going back to the fact that we wanna address, you know, less sophisticated, less technical people within the lines of business. With Location Intelligence, it's a guided experience. It's making that understanding of how to apply geospatial capabilities in an analytic pipeline much more in reach for people who may not be technically sophisticated. We do think it is an interesting opportunity and one that we have a lot of experience with, and I think, you know, it's not a standalone, you know, business. It's more compelling as a part of the broader, you know, analytic platform that we provide. The ESG piece is fascinating. We see a ton of opportunity there. This is where our partners are particularly gravitating towards. We talk a lot about our partner ecosystem with the global systems integrators, you know, the PwC, KPMG, Ernst & Young, the list goes on. Some of our largest customers, as well as some of our largest partners, they see the opportunity in ESG, and they're in the middle of that with their consulting services. We're actually in a couple of places, bundling up our platform, their consulting services for a, you know, off-the-shelf type of ESG engagement model that we're certain they're gonna be in the middle of a lot of those. It is a rich opportunity for us, and as you said, you heard from the HelloFresh customer at our conference in terms of the impact that that's had for them. Any other questions from the audience? I got lots. Okay. That's good. I wanted to ask you a question about the Analytics Cloud, the progression it's had over the past year and a half. Yeah ... specifically around Trifacta, the integration, and then the capabilities that the Analytics Cloud maybe has versus the legacy Designer product. You know, where is it at today? How much more there is to go? Yeah, just curious to hear your thoughts on that. ... we did an acquisition of a company called Trifacta in February, excuse me, of 2022, and their business was all around building the modern data analytics stack for companies that are leveraging cloud data warehouses. As companies are moving more and more data into a cloud data warehouse and modernizing that, it comes to reason that they would also look at their data pipelining capability and want to modernize that at the same time. When we bought Trifacta, that opened up another persona for us within the enterprise, the data engineer, who's usually in the middle of those sort of modern cloud migrations. That is a core component of the Analytics Cloud platform, but it's also the underlying platform as well. We, you know, the Trifacta multitenant SaaS platform is what is now the basis of all of Analytics Cloud going forward. On top of that, we have a machine learning capability in our cloud platform, which again, is meant to reduce the barriers to entry for a non-sophisticated user to start building machine learning models, and have seen really great interest in that from lines of business users. We have the Auto Insights capability, which I referenced a little bit earlier, which is really around, you know, time series data and understanding what's happening with that data from a anomaly and trends perspective, and serving up detailed, automated insights for the non-sophisticated business user to understand what's happening in the business. One thing I heard at the Inspire conference from your partners, and customers, too, was that a lot of Designer desktop users have complex workflows created, and you mentioned there's millions of them out there. Mm-hmm. One thing I heard was that they were super excited about being able to now port those workflows. Yeah. to the cloud. Yeah. You know, I guess I don't understand that feature all that well. Could you talk about that a little bit more? What does this mean for maybe as a driver of cloud adoption in the future? I think that if you look at our install base, which is largely on-prem users, we do see that some of them will want to maybe migrate to the cloud. A lot of them, frankly, wanna stay on-prem, too, right? There's this clear sort of hybrid environment that we foresee for certainly the foreseeable future. As those same users want to collaborate and share their workflows with maybe new users in the enterprise and those that come onto the cloud platform as a new user, this cloud-connected desktop, we call it, or Cloud Execution for Desktop, means that those on-prem users can continue building their workflows on-prem in the way that they're comfortable on their desktop, but they can, you know, publish them to the cloud and share them with cloud users. It just really opens up the opportunity more broadly for collaboration across the enterprise. Got it. One point of maybe just taking that a little bit step further. On-premise customers have deployed Server, often, by the way, that is deployed in cloud services within their environment, so there's a cloud component. When they deploy workflows that are effectively operational and analytic processes into Server, it becomes like dry cement in these organizations. It's incredibly sticky. It's very difficult to port to something else and replicate. What Cloud Desktop will allow for those users that aren't Server customers, that same benefit. They can deploy these to the cloud, and they can run in their environments without having to manage a Server deployment. From a durability and a renewability, it provides us with a lot of stickiness. Got it. I know we're running up a little bit close on time, so last question for you, Kevin, is: At the Analyst Day, you put a stake in the ground in your long-term targets. What was giving you the confidence to do that, and what are you seeing in the business? Have you seen it in the business the whole time, and now you just wanted to give more visibility? Just really curious of, you know, why 2028 all of a sudden? Yes, I think we've long, you know, described our long-term targets as being a 4-6 year horizon. Yeah. It was always calibrated to the growth opportunity in front of us and how we thought about the balance between growth and profitability. Guidance implies we cross $1 billion in ARR this year. Felt like an appropriate stage to be more prescriptive around scale and how that looks. The general, you know, sentiment and temperature in the environment around profitability is clearly different than it was 3 or 4 years ago. You know, I think we've addressed a lot of the points of confidence around the business as we think about achieving the long-term model. You know, just as a refresher, we have less than 1% penetration into a large market opportunity that we think just continues to expand with generative AI. We have a transformed and growing level of sophistication within our go-to-market that we think will serve us well over time. We've got roughly 47% of the Global 2000 that have 131% net expansion rate, which is up 3% year-over-year, 3 points over year-over-year. We have an increasingly growing partner network that we think will help drive significant efficiency and scale as we think about growing the business. We have a complete suite of products and innovation coming in front of us that we think will help continue to drive growth. Got it. We're all out of time. Great. Thank you so much for doing this. Thank you. Really, really appreciate it. Yeah, thank you. My pleasure. Thanks for the opportunity.