Good afternoon. Please welcome Ryan Goodman, Vice President, Investor Relations. All right. Hey, Everyone. Thank you for joining us today. Welcome to the 2023 Alteryx Investors Day. My name's Ryan Goodman. I am the VP of Investor Relations and Finance. Welcome. Before we begin, I do have some housekeeping. We will make forward-looking statements in today's program regarding Alteryx's business, financial expectations, and other future events. I'd like to refer you to our SEC filings and our safe harbor statement for a description of our business and the risks and other important factors that could cause actual results to differ materially from these forward-looking statements. These materials, including reconciliation tables for non-GAAP amounts, will be posted to the investor relations page of our website. We have a great lineup today. We're gonna begin with our Chief Executive Officer, Mark Anderson, who will give an update on the market and an overall update on the company. We'll have Suresh Vittal, our Chief Product Officer, speak about the product innovation. Next will be Paula Hansen, our President. Have Kevin Rubin, our Chief Financial Officer, go into the long-term financial model, as well as some of the financial drivers. For those who are joining us in person, we're going to have an interactive customer panel that will be moderated by our Co-founder and Chief Advocacy Officer, Libby Duane Adams. With that, it is my pleasure to welcome to the stage our Chief Executive Officer, Mark Anderson. Sure. Perfect. Thanks. Yeah, that safe harbor statement, I insisted we go from two slides down to one slide, you're welcome. Okay? Well, welcome everybody. Thank you so much for joining us. As you know, I'm Mark Anderson, the CEO here at Alteryx, it's just my pleasure to welcome you here to our 2023 Analyst, Investor Day. You know, listen, the technology world is changing pretty dramatically, right? Like, 10 years ago, I remember Marc Andreessen famously said, "Software is eating the world." You know what? He was right. You know, software has really permeated every aspect of our lives, you know, in terms of how we learn, how we play, and how we work. It's been incredible. Today, though, I think we see some very similar prophetic statements about artificial intelligence. The public has really only seen a sample of large language models and generative AI today. We're only just starting to scratch the surface of what this powerful technology can do. At Alteryx, we established our foundation in automation and transformative data analytics. We've long been incorporating AI and machine learning capabilities throughout our platform. Today, I get to steal Suresh's thunder and make, I think a pretty bold announcement here. We're announcing AiDIN. Sorry, a new brand of generative AI technologies powering the Alteryx Analytics Cloud platform. With AiDIN, generative AI meets trusted analytics. You can go from insights to impact even faster. Suresh is gonna talk a lot more about this in just a few minutes, gosh, I mean, I've been doing this for a long time and disruption, transformation, and technology evolution. Sometimes what feels like a hype cycle can truly be the brink of something very big. It can foretell change to come, it can create opportunities of a global scale. In all cases, I've found that the formula for success is remarkably similar. One. you need a compelling technology that solves critical challenges in new and effective ways. Two. you need people to appreciate the undeniable value of this technology. Three. you need a vibrant, large addressable market landscape. Four. you need an effective team to carry forth the mission and execute the opportunity. I think we have all four. We've got an incredible opportunity ahead of us. Artificial intelligence, and more specifically, generative AI, introduces new and compelling ways with which we can expand our platform capabilities right away. We've identified a clear path to unlock new use cases and empower new personas with artificial intelligence, and we look to continue driving forward this emerging wave of innovation. With the introduction of AiDIN, we're excited to launch enterprise-ready, AI-powered capabilities for our customers today. Not in 2024 or 2025, but today. Alteryx has been at the forefront of evolving data analytics technology for many years. Over the past decade, we've seen large organizations across the globe increasingly embrace data analytics. The volume of data for sure has grown exponentially, and savvy business leaders I talk to every day have been leveraging analytics to drive real, tangible value. At the same time, it became quickly apparent that while the demand and urgency for data analytics was going up, the ability to effectively scale analytics throughout an organization was a big-time challenge. All of these investments were being made in the ecosystem. We talked about cloud data warehouses, data management, and data visualization. All of this and only a handful of employees could actually engage. I'm talking like the data scientists, statisticians, and the data engineers within IT. The reality is that centralized data teams are overwhelmed with the most challenging operational problems, and they're far too removed from the actual use cases and the context to drive timely and actionable insights. Let's face it, data is dirty, and it's everywhere, and the business analysts don't have the ability to independently analyze and automate. This current dynamic simply does not work. This is where Alteryx fits in. The Alteryx analytics platform is the data orchestration layer for the enterprise. Alteryx provides an easy-to-use analytics platform that enables the business user to synthesize across a fragmented ecosystem of data types and data sources. Our technology partnerships further abstract the complexity for business users, connecting them to modernized data destinations like Databricks and Snowflake. When I became CEO a little less than three years ago, our differentiated offering and unique market position was crystal clear to me. We empower the business user, the accountant, the supply chain person, the woman that works in HR operations, the merchandising analyst. Our platform enables these functional experts that have the context to run their own analytics, to automate their own processes, to drive their own insights without friction or dependencies. Just before I came here, I had a meeting with a CIO from a really large advertising agency and one of our sales reps. This CIO's function, one of his primary mandates in 2023 and beyond is to really empower and democratize access to these kinds of tools. He was very strict about the guardrails that he wanted to be able to impose. It was like he was stealing from my keynote tomorrow. I mean, our platform actually enables these functional experts, right? We do this with the necessary guardrails that provide the kind of governance and security that that CIO is gonna support and bless. Especially when you go to the cloud. You have to have really strong governance and security, or you do not pass go, you do not collect $200. This is how you scale analytics across an organization. This is how you bring efficiencies to the data analytics journey. This is what we call analytics for all. I believe that Alteryx is uniquely positioned to drive this democratization thematic at an unprecedented scale within our customers. We've executed so many transformational initiatives that have put us in this favorable market position in just the past few years. We're sustaining a rapid pace of innovation. You're gonna hear a lot more about that from Suresh. We're embedding AI throughout the platform. We've accelerated our cloud platform innovation roadmap, and we're enabling enterprise-grade data analytics governance. We focus our go-to-market motion on the largest global organizations, where we think the vast majority of the total addressable market exists and the greatest opportunity for net expansion growth. We've upleveled our sales team under Paula Hansen to drive greater executive engagement with our customers. We're meeting with CIOs now instead of training our salespeople to duck and jive around them, right? We've embraced a partner ecosystem to further scale our sales and customer success reach. We've expanded our customer success capabilities to help our customers create value with our platform. As we look ahead, there are so many exciting dynamics underway as larger organizations have to embrace data analytics. At the forefront of this evolving movement is artificial intelligence. We've long embraced, as I said, AI and ML throughout the platform, providing capabilities to run things like predictive analytics, build and calibrate machine learning models, and create AI-driven root cause analysis visualization tools. As for generative AI, we recognize the immense potential and are excited to now introduce our initial generative AI capabilities to our customers this week. While Suresh will provide much more detail here, I wanna make two key points on the recent hype regarding generative AI. First, generative AI concepts we've seen introduced recently, they do not fundamentally change why customers are going to Alteryx. Walk around here in the next few days. Talk to some of my customers. I was talking to Steve earlier today. He was one of our smart investors that decided to go to the general lunch and sit down with a couple of tables of our customers and got some good G2. You're gonna find zealots in these people that walk around here. We've got double the people at this Inspire over our last Inspire in Denver. We really think this is a huge tailwind for us. A narrative has emerged that a large portion of our workloads are essentially easier forms of Excel work. This couldn't be farther from the truth. The reality is that approximately 70% of Alteryx usage in customer environments engages with four or more data sources beyond Excel. Customers are leveraging this data within the Alteryx platform to solve mission-critical, you know, and very complex challenges with analytics and automation across an increasingly complex data stack. Second, under the branding of AiDIN, we have some really exciting ways that we're leveraging generative AI to enhance the Alteryx platform today and in the future. Another thing we'll dig into today is cloud, which is a super important initiative here at Alteryx. Our customers are in the early stages of incorporating cloud throughout their data stacks. Cloud for us opens up a dramatic new total addressable market for us. Customers that want to provision Alteryx innovation that don't have a Windows device or don't want a thick client on their device. It also delivers a whole new level of complexity for customers to manage and increasing the need, we think to, you know, to use Alteryx as the data orchestration layer for their environments. Today, we'll explore how Alteryx helps companies optimize these hybrid cloud stacks, plus ways that we're bringing analytics itself into a cloud environment. Our goal today is to explore these underlying themes within the data analytic ecosystem. We wanna help you break through the noise and understand what is truly impactful to our customers. We wanna discuss the opportunities that we see emerging and the initiatives that we have underway to enable us to best serve our customers and to further distinguish ourselves as the platform of choice for data analytics for all. We have a massive opportunity in front of us. Companies are leaning into data analytics now more than ever. Every customer I talk to continues to ask for help in their journey. They're doing this to embrace not only top-line efficiencies to drive greater sales, but also to optimize profitability. Business users are leveraging Alteryx workflows to automate, analyze, and enabling them to work more efficiently and more effectively. Analytics helps users see around corners and optimize time spent, which is critical, especially in this dynamic macro environment that we find ourselves in today. It never ceases to amaze me how much untapped potential data analytics can and is unlocking within large global organizations. I stopped getting surprised hearing from CFOs that they're using, you know, manual software to close the books. It happens every day. A recent IDC study found that 9 out of 10 respondents said that less than half of their knowledge workers are active users of any analytic software beyond spreadsheets. 63% of organizations are not using the full breadth of data types available. Why is that? It's because the business users have not had access to proper analytical tools. We've got to be able to cater to their needs and their skill sets. Companies are either dependent on smaller centralized data analytics teams or forced to have business users do with analytics, you know, in a manual productivity suite that just doesn't scale. With all of this in mind, it's not a big surprise that less than 18% of organizations indicate that their data access policies were moderately effective or better. Meanwhile, 73% of the organizations expect analytics spend to outpace that of other software investments. Who better than Alteryx to meet this rising call for greater democratization of data analytics? In terms of addressable market size, there's the tangible $81 billion spent annually on largely disparate, fragmented silo tools. Per IDC, this spend alone is expected to grow north of $150 billion by 2026. This market needs a disruptive end-to-end provider with capabilities to empower a larger user base with analytics and insights. Who better than Alteryx to provide a comprehensive set of analytics and a comprehensive analytics platform for a broad range of user personas and use cases? We strongly believe that our solutions stand alone in this increasingly complex data analytics ecosystem. Modern analytical processes are comprised of several key stages, including data discovery, data transformation, reporting, visualization, and predictive forecasting. The market landscape is certainly fragmented, but the reality is that many of these vendors focus on very narrow, very specific pieces of the puzzle and target specific types of users. This has led to all kinds of confusion and certainly vendor fatigue on the part of customers who have to integrate these various standalone tools into a functioning analytics workflow. Customers require a layer of abstraction to democratize this data and create opportunities for actionable insights. Who better than Alteryx to provide this easy-to-use platform and enable analytics for all? This commitment to analytics for all truly differentiates us. Yes, we have a broad growing range of capabilities from spatial analysis, predictive analytics, automation and more. It's the fact that we can provide these capabilities in a highly unique platform for everyone. You don't need to be a data scientist to use Alteryx or a coder or an IT analyst or a data engineer. A general knowledge worker can fully embrace what Alteryx has to offer and unlock data across the stack with a breadth of advanced analytical tools. For our users, this is absolutely career-changing. For those joining us in person, this will quickly become apparent as you engage with our community over the next few days. The enthusiasm is undeniable. For our customers, this enables an unprecedented ability to scale data analytics across the company. With that, let's take a quick look at the platform. On the left, you've got our flagship solutions Designer and Server. Designer enables users to create data automation and analytic workflows through an easy-to-use graphical user interface. With more than 300 drag-and-drop automation building blocks and hundreds of connectors to both on-premise and cloud-based data sources, it allows users of all technical abilities to conduct advanced analytics in a modern hybrid data stack. Server scales these processes by allowing users to publish their workflows to the entire enterprise and then automate, federate, and schedule those workflows. On the right, we have our Alteryx Analytics Cloud platform, which was introduced in 2022. Alteryx Designer Cloud is a more agile, cloud-native version of our flagship offering. This expands our platform to new personas and enables them to access analytics anywhere on any device by anyone. Alteryx Machine Learning enables business users to create and calibrate machine learning models through an easy-to-use guided experience. Alteryx Auto Insights enables non-technical users to simply enter time series data and use AI-driven discovery to perform root cause analysis of business trends. A recent addition to our platform, Location Intelligence, which introduces cloud-native geospatial analysis capabilities to our customers. The breadth of our solutions and the platform-first approach provides our customers with a highly differentiated, scalable analytics solution for their data analytics journey. I felt this when I joined the company, and I feel it now more than ever. I am so excited about the opportunity ahead of us. I believe we've got the right team, we've got the right platform and technology, the right go-to-market motion to carry us forward on our key initiatives of democratizing data analytics throughout our customers' organization. What's next? What are we at Alteryx doing to address this huge opportunity? First, AiDIN's a major step forward in this to bringing generative AI to our customers. Our underlying goal here is to abstract complexity associated with data analytics for the masses, and we've been working on some really compelling enhancements, leveraging generative AI to expand our reach to new personas. Of course, cloud. In 2022, we announced the Alteryx Analytics Cloud platform and integrated the Trifacta platform into the user interfaces of our customers' favorite apps. Now with general access to Designer Cloud in early 2023, it's time to get these offerings in the hands of customers. Our success with flagship Designer and Server offerings has earned us the permission to explore new use cases, to empower new personas with our customers and prospects, cloud will be a key enabler of this motion. We're driving ahead with cloud, introducing new capabilities, new offerings, and better integrations across our on-premise and cloud offerings. Second, our enterprise go-to-market motion. Last year, we upleveled the sales team to stage appropriate executive-facing reps and refocused our efforts on the largest organizations in the world. Partners are certainly a big part of this, expanding both our sales and customer success capacities. We've seen great success with meaningfully increased Global 2000 penetration, higher Global 2000 net expansion rate of 131% last quarter, and our highest ARR growth consistently coming from cohort of largest customers. Folks, we're just getting started. We mentioned that exiting 2022, roughly one-third of our Global 2000 customers are at less than $50,000 of ARR, and many are still in their first two years of an Alteryx license. With this breadth of offerings, we're focused on engaging with these customers to identify new ways to unlock value and deepen our penetration. Third, we're committed to growing in a disciplined, profitable manner. We made some important investments in our business early last year, and now as we move beyond that investment phase, we're laser-focused on not just unlocking efficiencies of scale in the model, but demonstrating rigor and thoughtfulness in terms of our spending. Earlier this month, we nearly doubled our 2023 non-GAAP operating profit outlook to $80 million-$90 million. To speak more on these key initiatives, we've got a great lineup for you today. First, you'll hear from Suresh Vittal, our Chief Product Officer, giving you more key substance on our exciting new offerings. Super excited about what he's bringing to cloud and generative AI. You're gonna hear from Paula Hansen, our Chief Revenue Officer and President, joining us to discuss go-to-market strategy. She joined us two years ago, as the impact that she's had on our business is just incredible. I'm so proud of Paula. Paula's done a great job of enhancing our sales motion to the level that we need to break through that billion-dollar ARR milestone. She'll provide some additional color on how that sales motion has evolved, the results that we're seeing, and what's ahead. To wrap up, we have Kevin Rubin, our amazing CFO, who's gonna provide additional insights as to how we're thinking about growth and profitability with some thoughts on the long-term financial model. For those of you here in person, I strongly encourage you to engage with our community, talk to our users, listen to their stories about Alteryx has transformed their output and their lives. Listen to our customers as to how Alteryx is deeply incorporated into their data stack and accelerating the scale of their democratization of analytics. Engage with our partners to see how Alteryx is resonating with their customers and how expanding our how expanding our cloud platform opens up new incremental opportunities for them and for us. Of course, check out the solutions yourself on the floor. Nothing is quite as impactful as interacting with the platform firsthand and seeing the magic work. With that, I'm gonna hand it over to our Chief Product Officer, Suresh Vittal. Thanks, Mark. Hello, everyone. It's great to be here with you today. I'm Suresh Vittal, Chief Product Officer, and I have the privilege of leading our product and engineering teams here at Alteryx. While Mark just spoke about our business goals, I'm gonna dive deeper into the product strategy and our roadmap. I'll talk to you about our product portfolio, our cloud platform, and especially our generative AI capabilities manifested in AiDIN. As you'll see in a few moments, we're expanding access to more systems, more data sources within every product vertical. We're also accelerating our cloud platform and how the cloud platform's experience intermingles with our flagship product experience. We believe a single experience layer, regardless of where the user is using our innovation, is key. It drives deeper engagement, it drives higher utilization, and delivers more retention for us. Speaking of our customers, let me just highlight for you a few simple case studies on what our customers are doing with Alteryx products today. Right? Let's start with Mastercard. Mastercard is an innovator in responsible data innovation. They're working with Alteryx for multiple years now, and their goal was to open up the access to all of the data that Mastercard has to thousands of non-technical users within the enterprise. They built a platform that allows for integration into a variety of source systems, brings data into the hands of the technical users in a responsible fashion. It allows for the business users and IT teams to collaborate by building synthetic data on top of the Alteryx platform. Mastercard uses groundbreaking AI to create statistically equivalent, privacy-compliant synthetic data. In the hands of the business users, this allows them to create new products responsibly. Teams can derive insights from all of this data. They can understand how their consumers work and the kinds of data products they need. Using Alteryx, Mastercard generates the synthetic data solution. It helps their users understand and create value out of these products. Second example here, Genomics England. For those of you who don't know Genomics England, they're a whole sequencing and genomics analytics company that focuses on the British National Health Service. We learned that their team was struggling with large amounts of data and that different roles were not getting access to all of the data they needed to drive their sequencing efforts. They were wrangling upwards of 60 petabytes of data and sequencing 3,000 new patients every single month to enable scientific research that goes towards curing disease and finding new treatments. They've leveraged Designer Cloud in their mission to sequence over 100,000 genomes. The genomics team was able to broaden their analytics capability throughout the organization, aimed at that 60 petabytes of data, and it's cut delivery times for their research by 50%. Now they have squads of citizen data workers, and this is biologists and real scientists, not just data scientists, working on this data. Alteryx Analytics Cloud is part of their everyday work. Let's look at the example of T-Mobile. At T-Mobile, the accounting team was pressurized to complete critical reports by the end of the month. They were grappling with data prep, aggregation, summarization, formatting, and this was becoming a hard challenge for them to deliver solutions in time. With Alteryx Designer, they could automate these analog month-end manual processes that were error-prone. With Alteryx Server, they were able to allow their teams to share these workflows across the organization so every other team could get better. These accountants and analysts have turned into data workers, and they're partnered with their IT teams to ensure they're in compliance with the expectations of the enterprise. These three customers are examples of the power of Alteryx that we bring into the hands of these users. These are just examples. If you look across all of our customers, they're doing three things almost universally. One, they're handling large, complex, disparate datasets from enterprise systems, Salesforce, Adobe, Workday, Snowflake, and many more. They're building mission-critical applications. They're serving multiple personas: the analyst, the data scientist, the data engineer, the IT teams. Based on my recent conversations with our customers, I want to focus my time with you on three main topics. I'll talk to you about our plans for generative AI. I'll share with you where Alteryx fits into the complex data ecosystem. Finally, I'll spend some time on our product roadmap, share with you what we've delivered so far and what's coming here over the next six to nine months. As Mark said, AI is leading the world every single day. We see generative AI and large language models lowering the barrier to entry to ask questions of the data. It's helping break down silos. Equally importantly, it's engaging our imagination in how we understand data. Nothing could be more in line with our mission of analytics for all. The good news is this domain's not new to us. We've been utilizing these technologies since 2019 with the introduction of language models through our Intelligence Suite product. Language models drive our capabilities for text mining, for natural language processing, for PDF data extraction in Intelligence Suite. Since then, our teams have been iterating, identifying new ways in which we can incorporate more of this technology into our portfolio while others are still lacing up for the race. Here are some key opportunities that I see as a result of this investment into generative AI. The first incremental market opportunity is that new personas will emerge as they wake up to the power of generative AI. As we blend generative AI into our analytics stack, we're observing something that I call multimodal analytics. Analysts, data scientists, data engineers can all collaborate in real time to develop analytical insights. Powered by AiDIN, each persona can leverage their tool of choice, from prompt interfaces to no-code and code-friendly workflows. All of this is live translated by generative AI into a single analytical application. By attacking the friction that already exists between these roles, we foresee this capability dramatically accelerating how analytics is delivered inside the enterprise and how collaboration happens. We believe this democratizes analytics in very powerful ways. The second area that we see tools with generative AI. We see unparalleled appetite for generative AI capabilities inside the Alteryx products. The demand for generative AI capabilities is there to reduce the tasks, not the roles. We've been investing in creating features and capabilities. We'll allow customers to apply the time saved on the transformative insights. We build the time to value that our customers realize. This I believe has a positive impact on customer expansion and customer retention for us. AI applications and build contextualized AI applications on unique customer datasets. If you break this down, Alteryx itself. Think about this as the biggest repository of analytics activity, right? Millions of analytics building blocks that deliver breakthrough insights for our customers. Starting with an industry-standard foundational model and then fine-tuning it with the library of best practices, access patterns, partner customization, and then finally allowing our customers to bring their own data. This creates a personalized proprietary AI for each customers. This personalized proprietary AI is rich with context from the Alteryx ecosystem. It's also rich with the specifics of the customer's business, enabling, I believe, analytical outcomes that no other vendor can replicate. I believe the system can train and deploy these models inside the customer's infrastructure, so we can bring generative AI to the customer's data. By doing so, we become the trusted partner for every CIO to safely harness their most precious assets in the company, their data. Mark talked about AiDIN. Our efforts to take advantage of this massive opportunity that generative AI represents is really part of our broader AI strategy. Today we're announcing a major step forward in the strategy with the availability of AiDIN, AI-driven insights that enable decisions. AiDIN is the engine that infuses the power of generative AI inside the Alteryx Analytics Cloud platform. AiDIN encompasses so much more for us. It's the new features, the new experiences and enhancements that we're adding to our existing products. You can check out our latest generative AI and capabilities powered by AiDIN as you walk the show floor today and tomorrow. You'll see demonstrations of these capabilities coming to life in the hands of our customers. Regardless of whether a brand is new to Alteryx or you're an ACE is how we describe our experts that use our product, we see benefits for our customers. Imagine a world where you're generating text-based content, predictions, recommendations, and scenarios that can inform critical business decisions. Imagine if you're locating new patterns in your data that were previously undiscoverable. We can help them automate repetitive tasks, reducing manual effort that they put into analytics. Equally importantly, we can ensure that the data and analytics processes are transparent, they're compliant, and they're auditable within the regulatory environment that company operates in. While it's early stages, we think AiDIN is gonna transform the monotonous aspects of our work and allow more room for human ingenuity to thrive. Let's imagine data scientists can use AiDIN trained on workflows, pre-existing workflows to create new workflows in a matter of minutes. This process would have typically taken them a week. Take the case of IT professionals getting freed up to see how the data is being used inside the enterprise, how it moves, who has access to the data, and the provenance of the data and the insights that they're creating. Think about business users moving away from a static panel of measurements and actually working with a dynamic interface that is tuned to their business context and provides them real-time insights. Similar to having a one-on-one conversation with an expert. We're advancing our capabilities at rapid pace. The recent excitement about generative AI has many vendors starting to talk about their plans for the future. Meanwhile, at Alteryx, we're delivering these capabilities now. Here are some examples of what's currently available in product. First, let me talk to you about Workflow Summary. This is an AI-generated documentation of the workflows that exist in Designer. This was a real prevalent feature that was asked by our customers in initial testing, where documentation for subroutines, for workflows, for entire libraries of Alteryx creations can be documented in a matter of seconds. We're creating an entire layer of metadata and governance that didn't exist before. Magic Documents is an AiDIN-created summary report. It's available in email, PowerPoint, and in Word based on the audience's context, their needs for faster insights and synthesis. You'll see demonstrations of that tomorrow as well. OpenAI Connector, which will allow our customers to use GPT in Alteryx Designer workflows to transform how data is communicated and shared. I'd like to show you one of our most impactful AiDIN-driven offerings. I mentioned earlier, with AiDIN, we are pioneering multimodal analytics, where analysts, developers, data engineers can collaborate in a single pane of glass in their preferred way. This is unique because every persona will use the analytical tool of their choice, of their choosing. It can be a conversational prompt or a no-code workflow or Python or SQL. Let me show you what I mean by sharing with you an application the team's actively working on. We have Asa Whillock here who's gonna walk you through the demo here. Hi, everyone. My name is Asa Whillock. I'm the Vice President and General Manager of Machine Learning here at Alteryx. I have the distinct privilege of showing you AiDIN, particularly our multimodal analytics capabilities. A new application powered by AiDIN that's going to demonstrate some amazing capabilities powered by generative AI. Imagine for a minute, you are working at an organization that is growing like gangbusters. You're coming out of the most recent recession. You have a brand-new budget. Your role is to hire over 1,000 people. Specifically, you need to answer a question today about what are the right cities to start your recruiting campaign in? Normally, if you were to go about this particular question, you might find your analytical experts, your data experts. You'd bring them all together to put this together. We might be talking days, more likely weeks, to answer this sort of question. Pretty typical. What I'm going to demonstrate for you now is the ability weaved in between Alteryx and generative AI to answer that question in seconds. Moreover beyond that, to enroll the power of your organization to make sure that answer is not only fast but correct. Let's dive into it. I'm Luisa in this case. I've been charged with this particular question. The first, most direct way to go about this is I literally just ask the prompt, "Hey, where should I hire next year for best fit and affordable talent?" Simple enough. Let's see where it takes us. You start to see AiDIN here, where it lays out a whiteboard. I have my conversation up and to the right from Luisa who asked her question, and AiDIN's already on it, right? It's proposing to put together data from Workday, Indeed and Outlook, knowing that HR data is corresponding with what we have in Workday. Offers made and accepted are going through Indeed. Outlook has context for what we're trying to accomplish here, and we wanna weave all these together. Let's do it. AiDIN creates a predictive model, and just like that, it weaves together Workday, Indeed, and Outlook, and there we have it, right? Instantly, we're looking at something that says, "Hey, our top three hiring markets, New York, Chicago, Los Angeles." I can dig in, and I have my visualization that gives me a heat map that visualizes the nature of this in that exact regions. I can go further. Up in the left, Denver. These are the areas where we want to start. Super. I can even dive into this initial results grid, look a little bit further in terms of what this data looks like. Awesome, right? Ask a question, get an answer. This is the generative moment we've been experiencing. Are we done? Is this it? In 30 seconds, we answered the question we came to? Well, not really, right? We know that answers don't just have to be fast. They have to be correct. Alteryx knows that analytics is a team sport. We need to engage the most valuable source of information, our own teams and business experts who understand the shape of the data that powers our enterprise. We need them to look at this to make sure that these are the correct answers, that it passes the sniff test, and AiDIN understands that. If you click for it, Louisa says, "Let me send this to Annie, the analyst, the key member of my team who understands the shape of this data to understand, is this really true?" AiDIN can take care of exactly that, transporting this over to Annie. Let's go take a look at her screen, see what her experience looks like. AiDIN brings this over, welcomes Annie to this, shares the same whiteboard experience that we've been putting together. Great. Indeed, she can dig into the same data that we've been looking at previously. Aha, there on the lower right. All these hires that generated this particular analysis from 2020. That was three years ago, pre-pandemic. Very different hiring market. We need updated information to produce a useful outcome in this, and we know exactly what to bring to bear. Annie calls for adding Vertice data. For those of you who are unaware, Vertice is a portfolio company with Alteryx. We've invested in them. They have an outstanding suite of products, one of which is the capability and data to map out extended offers and accepted offers across the United States that are up to the minute. Super valuable information we've used in our real use case to accelerate our own hiring and others. In this case, we need this to produce the correct result. Awesome. We just ask for it. It's right. Oh, nope, it's not in the right spot. It's actually after the predictive model. That's not where we want it. Simply enough, we can just pick it up, drag and drop it to the right place. Right? Seems elementary, but the nature of this is that every panel within AiDIN is editable. Whether I want to do that in conversational prompts or modify it directly on the whiteboard, that's accessible to me. Easy enough to do. Let's double down on the point by bringing this over to SQL, right? As an analyst, the ability to click over to this beyond the whiteboard and see, well, what's the code exactly doing? Show me the SQL capabilities, is something that adds a lot of confidence. I now know exactly what this workflow is doing, and I'm pretty confident about it. Previously, this would have been inaccessible to me. Easy translation between different experiences in an analytical context, workflow to SQL, just wasn't something that was available. The power of generative AI gives us this ability to translate between these different kinds of analytical systems seamlessly and empowers Amy, in this case, to be able to do what she needs to. We're feeling pretty good about this now, but there's a model involved. Let's get this over to Sam, the scientist. Real data science to actually map out how our model looks. AiDIN takes care of that. Let's go over there. Now I'm Sam, right? Sam, in this case, has something interesting that pops up at the top. It says metadata has been generated for this project. PII and other salary information has been restricted away. Pretty innocuous when you get down to it, but imagine what that's like today. If we're engaging different members of the team who have different access to data, this can stall out your project for days, if not weeks. They need to be added to access control lists. We need to switch data pipelines. AiDIN takes care of all of that. You let the system do the work. Its awareness of the systems that are involved, the different matching data pipelines, Sam's own role within the organization, allowed you to transition seamlessly, where Louisa and Annie didn't need to know about this, and Sam gets the appropriate scaled-down information access to him where he can still operate. I click over to canvas mode and you see, if you're very familiar with this one, an existing Alteryx workflow version. This is key because it underpins the same capabilities that our customers know and love today within this AiDIN interface. It's a great way to visualize what you're processing. Up into the left, you can see these PII controlled data sources we were talking about. In the lower left, you have the Vertice component. On the upper right, you've got this blue logo for Alteryx Machine Learning, which is powering our prediction. Well, great, this works. You know, for Sam, he actually wants a slightly different experience for this. He's gonna go over here, clear away this dialogue, and he's gonna click to generate Python. Right? Again, we're greeting this particular user with the system and the analytical tool of choice. For a data scientist, access to Python, where they can actually examine the code about how this model is built, is critical. It's a language that gives him great confidence beyond the whiteboard itself, beyond the canvas, that he understands how this is working, which is great. We can do better. Sam says, "Hey, I want to see this as a Jupyter Notebook. This multidisciplinary tool will be perfect to viewing this." For AiDIN, it's a snap. Press a button, expand this out. We have a Jupyter Notebook that allows us to process and look at this 'cause Sam has an intuition. With all that hiring and these high-cost geos, we expect that we might have some bias involved in terms of predicting that we're gonna have to pay more for these different environments. We employ Alteryx Machine Learning, which has built-in libraries triggered by AiDIN to measure it. Indeed, that's true. We do have a little bit of a bias basis. In fact, it's proposed a code snippet to normalize that and get us back to a correct predictive result. What's more, you notice at the top that insight has changed. It's changed live. It's now saying Raleigh, Indianapolis, and Pittsburgh. Interesting. Let's go a click deeper in that. Yeah, exactly right. Look, the heat map's changed too. This is a really interesting outcome. We have to get this back to Louisa. For Sam, he puts a note on this, says, "Look, the flow looks great. I changed some of the model bases to eliminate bias. Check it out. Take a look." Now we're back to Louisa again. Louisa sees the outcome. She's welcomed back. "Hey, Annie and Sam have taken a look. They've removed bias from this. They've adjusted the data. You can now see the adjusted outcome." Louisa's excited to dig in. Hey, there it is. Raleigh, Indianapolis, Pittsburgh, and further, Houston, Seattle, Denver, and Las Vegas are on the top of our list that we need to go through. Wow, this is much, much better. The reason we're so much more confident is not only did the answer take less time, but we've had the benefit of our expertise leveraged for this to make sure that it's correct. One more thing. Louisa loves it so much she says, "Hey, can I get this every Monday? Please operationalize this report." Normally, very difficult thing to do. You have to transition out of the sandbox into production pipelines. For AiDIN, again, a snap. That same metadata management that understands this is a sandbox pipeline, that's a production one. To be able to put this in through integration with plans and automatically have it scheduled for changes, this is a very easy thing to let the system do. Normally, really difficult. Here with AiDIN, totally a snap. Okay, we've seen, looking back on this, the ability to ask a question, get an answer in seconds. That real key generative conversational moment. We've also seen how you can enroll your entire team in making sure the answers that come from those generative moments are correct, a critical thing for enterprise. Third, we've seen the capability for every one of those individuals to be able to leverage their tool of choice to accelerate their ability to develop analytical insights. Finally, to really put it on rails with the governance and operationalization every enterprise needs. This is multimodal analytics. This is our first capability released and powered by AiDIN. Super excited to share this and more coming with you. Thank you very much, guys. Suresh? Thanks, Asa. That was awesome. When you look at the data ecosystem in a modern enterprise. Was there a question? Is it GA now? No, it's in early design with our customers. It'll be GA towards the end of the year. When you look at the data ecosystem in a modern enterprise, you'll see tremendous complexity and a sprawling network of data sources and storage repositories there. Enterprise data comes in all shapes and sizes, from cloud applications like Salesforce, Workday, file storage environments, S3, SharePoint, legacy databases, Teradata, Oracle, SQL Server, the modern cloud data equivalents, Snowflake, Databricks, BigQuery, mainframe servers even. When I talk with our customers, they're looking to modernize their legacy data environments into the modern cloud equivalents. The reality is that this transition from legacy to modern doesn't happen overnight. There's so much more to consider that goes into cloud modernization. The people, the process, the technology change that needs to happen. These cloud modernization projects typically take years, and they'll take many more. Many of our customers today are in the midst of this transition. Some are just getting started, some are halfway through it, and a few are even at the starting line. Most of our customers need to go through this cloud modernization, which means they'll operate in hybrid environments for a while. For so many of our customers, this actually means cloud is making things harder, not simpler. Increases the complexity. I've generally found that this kind of transition will come in phases for our customers. They start on the left side with their systems of record. Over the past decade, you've seen that they've modernized from homegrown legacy ERP systems to modern enterprise SaaS applications. As the source systems get modernized, they begin to modernize the database layer, giving rise to our partners like Snowflake and Databricks. You see something similar also happening on the data side of the house, on the right side of the house. There's been a move to cloud analytics tools and related technologies like app builders and reverse ETL to get data back into the cloud systems. Similar to the data side, on the analytics side, there's also a big sprawl of tools and technologies. This is kind of a paradox we're all living today. The data and analytics ecosystem gets messier and more complex as it modernizes. This transition to the cloud will solve problems of scale and solve problems of access. It introduces new ones of cost and of making sense of all of this information. Data sources and systems across the enterprise fragment just as readily as they consolidate. In a report by Enterprise Strategy Group, they found that 55% of those surveyed indicated that cloud has made data integration harder, not simpler. The end goal remains the same for all of our customers. No matter what stage in the cloud transition they are, they want to leverage this data to create business insights that can be actioned upon. There's this messy middle of this ecosystem, where many critical steps are taken to prepare the data for analytical consumption. Year-over-year, every time you see an analyst report show up, users continue to express that 80% of the work with analytics is making sense of all of this data and preparing it for all of the analytics that they wish to do. Today, so many users go through error-prone manual processes. They have to jump between tools. They have to check that the data matches, that the insights are accurate, the numbers don't look right. Modernizing this messy middle is critical because it fixes the analytics process. It creates value for both the left-hand side with the data source systems, as well as the right-hand side, the analytical applications. Fosters greater collaboration. We think this modern enterprise data analytics layer is too big for any functional team to own. You see it every day. There's need for collaboration. Most of our customers understand this, and they're rising to the challenge by enabling the non-technical users, the business users, to get access to this data in responsible ways. The IT and central teams are able to curate and certify these data sources and make them available to the analysts. The IT teams need to understand who has access to the data, where, what use case are they using it for? Where is the insights getting delivered to? They have to do that while taking the complexity away from the less technical users. In every customer discussion we've seen an ask on how do you bring analytics capabilities to the cloud with Alteryx? The biggest reason why customers continue to use Alteryx as their analytics automation platform is that it enables the collaboration that I was just talking about across the organization. It makes light work of the most time-consuming tasks in the messy middle. Gone are the long hours around manual processes and checking and rechecking and cross-checking data. Gone is the analyst scripting VBA that they don't know exactly what's going on. Not only are they using it for these time-consuming prep and blend, but they're using it for the consumption layer as well. Machine learning, AI-augmented analytics, analytical app building. It's really a single platform that goes from the data to automated insights and recommendations by bringing all of the different users together. Data sources become more productive for everybody, for the IT teams on the left and for the analytics teams on the right. Analytics tools become much more easier to use because they have the freshest, most up-to-date data. IT teams can tie their projects back to business outcomes. For an outsider's perspective on Alteryx's role in today's data and analytics ecosystem, I'd like to invite to the stage one of the key representatives of one of our partners and one of our large ecosystem partners. Krishnan Parasuraman from Snowflake will discuss how Alteryx and Snowflake work together to help our customers with their biggest data and analytics challenges. Please help me welcome Krishnan to the stage. Thanks, Suresh. I'm super excited to be here at Inspire and share some of the things that we are seeing in the industry. I'm part of Snowflake's go-to-market leadership team, and I work very closely with some of our largest customers. I wanted to talk to you a little bit about what we are seeing in the market. One of the things that I see is we have a lot of joint customers who are using Alteryx, and they use Alteryx for analytics, and they use Snowflake on the back end for scaling and for getting access to the Data Cloud. Just to summarize, what is the opportunity for an Alteryx user when they combine the power of Alteryx with Snowflake, right? First and foremost, Snowflake is a scale-out data platform. What that means is for traditional Alteryx users who have operated in a highly constrained environment, right? Where they had to sample data from traditional databases, or they had to work with IT to provision their back-end data systems. Now they have a mechanism by which they can access pretty much the entire organization's data at scale. They don't necessarily have to worry about things timing out or having to call IT and provision more infrastructure. All of that works magically, right? And that's the power of Snowflake. We are a scale-out platform. We help organizations manage data at scale. We can do both scale up, scale out, and scale across. What that means is as your data complexity increases and as your queries become more and more complex, and they deal with larger datasets, we automatically scale up, and we provide enough compute resources for those tasks to complete. We also scale out, which means as an Alteryx user goes through the different stages of data automation, be it ingest, be it prep, be it data science, be it predict, automatically, we are keeping up with that and scaling on the back end. We also scale across, which means as you publish the output of an Alteryx workload and you now have hundreds of thousands of users within an organization concurrently accessing that dataset, we are able to support that. That's the power of the cloud, that's the power of the Data Cloud, and that's one of the most important capabilities we bring to the market. In addition to that, we also help Alteryx users tap into the vast network of Snowflake's Data Cloud. Here's what that means. If you look at that picture, that's a real picture. Every dot there represents a Snowflake customer, and every line represents two customers of ours sharing data with each other, right? In a very secure, privacy-compliant manner. We have a vast network of our customers collaborating with each other. When you start a Snowflake journey, our customers typically start on the periphery, where they are not necessarily sharing data with each other, so you have these dots. As they mature, as they start evolving in their Snowflake journey, they start collaborating with others, and you start seeing these clusters emerge. Think of these clusters as a value chain. Think of that as a large supermarket company like a Kroger or an Albertsons that is sharing data with their CPG companies, right? What they are doing is they are sort of exchanging information on inventory, and they're figuring out when is the right time to do pull-based replenishment. It could be a manufacturing company, which is actually sharing and collaborating data with their entire supply chain. You're thinking about, you know, what is available to promise, what does supply chain risk look like, and so on and so forth. For Alteryx users, not only do they have access now to their first-party data set through Snowflake, but through our Data Cloud, they can collaborate with their entire ecosystem. That's the power of the Data Cloud that we bring to the market. Our partnership is very unique. We have a deep engineering-level partnership. Our engineering teams actually work on early access of our products to figure out what's the best way for Alteryx and Snowflake to interoperate. We also have a lot of customers who work with us jointly, right? For Alteryx customers, what it means is they now have access to large volumes of data. For Snowflake customers, the benefit really is now you have a highly orchestrated mechanism by which you can manage your entire analytics pipeline, right? We are able to expand our offering to the analyst and the business users within an organization. You don't necessarily have to be extremely technical or extremely data-savvy to use Snowflake, right? This model gives our customers, our joint customers, a very unique opportunity to do something that was previously impossible, to democratize their access to data. The stack in terms of how we work together is actually very interesting. Through the entire life cycle of an Alteryx user, going through their experience, be it starting with data ingest, combining data, doing prep, data science, and so on and so forth, they are constantly on the back end interacting with Snowflake, right? All the different integration points are optimized for Snowflake. We do something called as Pushdown Optimization, which means the SQL queries are actually pushed down into Snowflake, and they execute there. A typical process which could have taken hours or minutes would now, in this new model, take seconds to execute. That efficiency and that performance and that cost savings are pushed back to the customer. The other interesting thing that Alteryx does is a lot of Snowflake capabilities are abstracted and provided as building blocks for Alteryx users. Alteryx customers can run Snowflake SQL. They can push down Python code programming into Snowflake. They even have the ability to run our CLI directly inside of Alteryx. You can bring different types of datasets, be it structured, unstructured, semi-structured data, and so on and so forth. As we start investing and expanding our AI capabilities, we'll be making some announcements on generative AI, and so on and so forth. Those capabilities also become available to Alteryx users in the near future. The most compelling thing is really the access to this massive repository of third-party data that's available in Snowflake. We have over 390 data providers that publish over 1,800 data products on Snowflake Marketplace. What this means is Alteryx users can now have access to weather source data, over 30-plus data sources that represents ESG information, supply chain risk information. When you're building your data science models, you have additional signals that you can incorporate in those models, and that improves your prediction accuracy, right? That's another benefit that Alteryx users would see. This has been an incredible partnership for us. We really see that the combined value that we bring to Alteryx users is greatly beneficial to them, and this is something that we are successfully seeing in the field in terms of our customer traction, in terms of what we are seeing in terms of how fast they're adopting Snowflake and how we are helping Alteryx users. Thank you very much. Thank you, Krishna. That's such a powerful message about how Snowflake and Alteryx are working together. Next month at Snowflake Summit, you'll hear more about some game-changing innovations that accelerate the power of Alteryx on the Snowflake platform. We talked about generative AI and our product plans and strategy around generative AI. We talked about the data ecosystem and the complexity and the cloud journeys that our customers are going on, the amazingly productive Snowflake and Alteryx partnership. I'll now segue to talk to you all about the roadmap. Before I start getting into the details of the roadmap, I'd actually like to share with you kind of what did we release in the last six to nine months. What were some of the key capabilities that came out? First and foremost was our release of the Alteryx Analytics Cloud platform in February. The Analytics Cloud platform, think about this as the culmination of the integration, the acquisition, and integration of Trifacta. This enhanced platform, which includes Designer Cloud, Alteryx Machine Learning, and Auto Insights, offers an approachable, easy-to-use drag-and-drop interface that's accessible to all the employees at different skill levels without actually compromising on the governance and security expectations of the enterprise. You'll see us offer cloud licensing models and further this mission with time series analytics that we're delivering and forecasting by the end of this year. You'll see a variety of AiDIN-powered capabilities as well that are showing up in our products. Now that you've seen kind of what we've delivered so far, and you've seen some of the AiDIN-powered capabilities, I'd like to walk you through the rest, the roadmap for the rest of the year and some of the innovations we're bringing to market. Across the Alteryx Analytics Cloud platform, we're focused on increasing our TAM by expanding the platform's reach to Azure and Google Cloud Platform. You'll see us make the data planes available in regions for our multinational and international customers. Snowflake has been our launch partner for the Alteryx Analytics Cloud platform and is already well-served. You heard Krishnan talk about the push down capabilities. It's really well served with those capabilities. We're intending similar support for Databricks, including the recent Unity Catalog launch. Integration of the Unity Catalog is next on the list for us. Push down into Spark and SQL as well. You'll see us launching two new cloud experiences for our customers. Location Intelligence, which democratizes spatial analytics on the cloud with full push down into Snowflake, I must add. Alteryx Exchange, a powerful platform for our partners to create, list, and transact their solutions on top of the Alteryx platform. We know our customers choose to transact through different cloud marketplaces. They use their AWS credits to buy software. Through private offers, you'll see Alteryx be available on the AWS Marketplace. Azure and Google product marketplaces are next, and they're coming later this year. That's the innovation that's coming on the cloud side of the house. Let me shift gears and talk to you about some of the innovation we're delivering on Designer and Server. Our big priority, as you heard Mark and me speak about, is enterprise readiness and governance. We want our customers to be able to treat Alteryx workflows as part of their core SDLC process, just as they would treat any code. Integration into source code repositories like Git. An Alteryx workflow is treated just like code written by a software developer. Data Connection Manager is our technology that helps protect and manage credentials across the platform and do it securely, so you're only updating in one place. We're making that even more powerful and easier to adopt with integrations into cloud wall storage environments. We delivered HashiCorp. You'll see 23.1 is delivering AWS Secrets Manager, and so much more will be coming here in the next couple of quarters. Our team's also giving customers most wanted requests from our community side of the house. Our product feature that was most in demand from our community was something called that allows our users to schedule which part of the workflow runs at what time on different sources of compute. That's called Control Containers, and that's being delivered. We're also delivering Dark Mode. Last but not the least, later this summer, you'll see us deliver Cloud Execution for Desktop. Cloud Execution for Desktop is our first major initiative to support Designer and Server customers who may need more flexible hybrid options for their enterprise. Cloud Execution for Desktop has been designed to take existing workflows in Designer and Server. Think about this. Hundreds of thousands, even millions of workflows that exist, unique IP that exists in the hands of our customers. They want the scalability and flexibility that cloud offers. We'll allow them to do that with the flip of a switch. No migration needed. Publish these workflows into our cloud platform, run them in the data plane of choice. This execution is just part of how we're continuing to bring this hybrid experience for our customers on their adopted cloud. We plan to add more capabilities to this experience. The ability to push datasets from desktop into the cloud. The ability to allow for complex workflow orchestration with cloud environments through our plans capability. We want our customers to succeed on their cloud journey, on their modernization journey, no matter which stage of the journey they're on. No one will be left behind, and no one will have to start from scratch in rebuilding their workflows. I know I covered a lot of ground today, from generative AI to the data and analytics ecosystem, to our product roadmap and what we've delivered over the last 12 months and what's coming. I want to leave you with three things. First, with Alteryx AiDIN, generative AI meets trusted analytics. You can go from insights to impact even faster and create disruptive applications by allowing multiple personas to collaborate. We believe there's real opportunity to contextualize large language models for a customer's unique dataset. The journey of generative AI in the enterprise is only just getting started, and we are right at the forefront of that. Second, please remember that Alteryx is the enterprise choice. Our customers choose Alteryx because it enables them to handle disparate, large datasets. Think about genomics handling 60 petabytes of data. It allows them to create mission-critical workflows. It helps them serve multiple personas. We're giving them the cloud capability, so they have the flexibility of running workflows on-premise or in the cloud. Third, we're simplifying a really complex data ecosystem. Data fragmentation persists today, and we help our customers make sense of that messy middle and that ever-growing complexity and bring clarity into their data ecosystem, into their data ecosystem strategy. Thank you for your patience. Thank you for listening to me. A big thanks to Krishnan again from Snowflake for sharing how Alteryx and Snowflake work together. With this, let me hand off to Paula, and welcome Paula to the stage. She'll talk to us about our go-to-market strategy. Welcome, Paula. All right. Good afternoon, everyone. It's wonderful to see you here this afternoon, and a huge thank you to Suresh and his team for sharing that incredibly inspiring amount of innovation. I'm personally thrilled to be able to introduce all of this to our customers and our partners here over the next couple of days, and as Mark said, encourage you to spend time with them to see how all of this great innovation is landing with them. Our go-to-market team has been on a transformational journey for the last two years, and that transformation has all been about the mission of driving durable ARR growth, high levels of renewability, and consistent expansion across the customer base. When we do this for Alteryx and drive this incredible performance, the good news is that through that, we're also driving great outcomes for our customers as well. Let's just take a look at some of the changes that we made in 2022 and how that's driven impact for our business. A year ago, I at Investor Day, I outlined a three-pillar go-to-market strategy that we had. First, enterprise focus. Second, winning with partners. Third, customer success and services. Now I wanna walk you through how we've reforged that strategy for 2023 and beyond and added a 4th underlying pillar as well. Over the past few years, we've realigned our sales motion to focus on the largest enterprises out in the market. You might ask, "Well, why would you do that?" Well, when I joined two years ago and started talking to customers, three things became incredibly apparent to me. First is that customers were yearning to become insight-driven organizations. They had plethoras of data. They knew that they could unlock potential from that data, but they were seeking a platform and a capability to continue advancing their ability to make better, more informed, confident decisions. Secondly, organizations of all sizes were very early in that journey, in some cases even nascent. You can look at research from the International Institute for Analytics that talks about a 5-stage analytic maturity model, and most enterprises are sitting at a 2.2. There's lots of runway ahead for these organizations to mature. Then thirdly, we at Alteryx have been uniquely positioned for 2 decades now to help large enterprises embrace analytics at scale for a broad base of personas across the organization, from business analysts to data engineers to data scientists to knowledge workers and the executive team. Those are the reasons that it was obvious to me that we should point our go-to-market engine at the enterprise space. We also know that our platform is incredibly impactful for the largest of enterprises because they're the ones with the largest volumes of data and the most questions that they have to address on a daily basis to be able to remain competitive and drive their business forward. The more complexity in environment, the more value Alteryx drives for those organizations. In the enterprise, our wins are sticky. When you deploy a Designer workflow and integrate it into a large enterprise routine, it drives incredible value for the organization and great gross retention for Alteryx. It's fairly difficult and disruptive to remove Alteryx from an environment, and in fact, it rarely ever happens. Additionally, enterprises of course have immense amounts of opportunity for us for cross-sell and up-sell opportunity as our platform is continuing to grow and expand with the rich set of capabilities that Suresh just walked you through. It means that we continue to earn the right to go back to these organizations and have greater investments with them and drive higher value for them and bigger customer lifetime value for Alteryx. This has shown up in our results. As we reflect back on 2022, we've shared many of these numbers with you on earnings calls. Our net expansion rate increased 2% year-over-year to 121% now. In the Global 2000, up 3 points year-on-year to 131%. We also enjoy the highest retention rates with our largest customers. Right now, our average ARR per account is $100,000 of ACV, which is up more than 20% for 3 consecutive quarters now. We've now reached 650 customers that are spending over $250,000 with us per year, and that's up 30% year-on-year. As exciting as these numbers are, there's still so much opportunity ahead for us within this customer base, and these organizations are still early in their journeys. To operationalize this enterprise focus on the most important, largest organizations in the market, we had to uplevel our sales organization. The first focus area for me was bringing on well-tenured, stage-appropriate sales reps that are used to selling in the complex enterprise software space, and they had an impact on the business right away. We now have 47% of the Global 2000, which is up two points year-over-year and up 8 points from just two years ago. As we've said on our earnings call, our largest customer cohort is also our fastest-growing. We now have, as we exited 2022, over $140 million-plus ACV customers. We have a repeatable sales motion now that we can take out to the rest of the 900 of the Global 2000 customers to bring them on the journey over $1 million of ACV. Second, with that more well-tenured sales organization, we've built the resources, the programs, and the overall go-to-market strategy for them to engage at the executive level, to have a conversation about driving democratization at scale with an enterprise platform. We're now engaging with all C-levels, CFOs, CIOs, CHROs, COOs, heads of supply chain, and the list goes on. When I joined, I inherited a sales team that was selling licenses a dozen at a time. Now our sales organization is selling hundreds of licenses to many customers. With the strategy that we have and the initiatives in place, I can envision a time in the near future where one year ago, here at Inspire, we held our first ever executive summit, because historically, Inspire was mostly focused at the user base. We had a wildly successful executive summit last year. This year, I'm pleased to tell you we have more than twice the number of executives that are joining us to have the conversation about the continued journey in their analytic maturity and the role that Alteryx can play as their trusted advisor. By engaging at the executive level, we not only position Alteryx as an enterprise platform, but we get visibility into the strategic imperatives that those executives have, and then we can meaningfully represent the value that Alteryx drives in those strategic imperatives. We're helping CFOs close their books on shorter cycles. We're helping heads of supply chain make sure they get the right product to the right location at the right time. We're helping CHROs predict what risk there is to employee retention. We're helping sales leaders like myself to look across the entire continuum of the customer journey and identify various points where they can have opportunity for the highest level of revenue growth. Our initial land into a customer for a brand-new customer might be a single line of business. It's often the office of finance because it's such a data-rich environment. Because we now have a deliberate, intentional sales motion that we back up with the right resources and partnerships, we have an ability to scale out from one line of business to multiple lines of business in a predictable way, and that's where the enterprise platform conversation and sale is enabled. We saw this with Copa Airlines, which is one of the largest carriers in Latin America and a member of the International SkyTeam fleet. We've been engaged there with their CIO, Julio Toro, who now has full visibility across the enterprise to the impact that Alteryx drives across over 40-plus business-critical processes. They leverage Alteryx to help them manage their dynamic pricing. It's a fully automated business process. They leverage Alteryx to help their passengers pick their seats through the mobile app for their flights, and so much more. Another example is at Symphony Care Network, which is a Midwest-based transitional care network. We're engaged here with the CIO as well, Nathan Tyler. Because of the work that we do with him and have done for a number of years with Designer and Server, he knew the power of Alteryx, but wanted to start exploring how he could be more relevant to his sales organization. He invested in Auto Insights so that his sales team could get insight into which physicians had the highest referral rate. They could be more targeted in their sales and marketing campaigns to drive pipeline and top-line revenue growth. Another pillar of the strategy that we've released is with enterprise license agreements or ELAs, which we've talked quite a bit about on earnings calls. This is a sales vehicle which provides great flexibility to our customers with predictable pricing, more access to the broader portfolio, integrated services to help them with the post-sale, as well as burst capacity. Burst capacity gives a customer up to 50% incremental licenses above what they're contracted for the first year of the contract. This is meant to facilitate and accelerate the expansion opportunity. They also get access to servers, so they can drive broader levels of automation and services from our customer success team to help them on their journey. Now, to be clear, ELAs are not a new concept. You've all heard of them before, and many of us had ELAs in our former lives. The unique thing here at Alteryx when I joined was it really felt like there was pent-up demand in our customer base, that they were spending a significant amount of time on license management. Because our land and expand sales motions over the years created so many contracts for our customers, we were missing the opportunity to really take an enterprise view. We thought if we gave people access through burst capacity, it would give them the opportunity to move faster on their analytics maturity. The hypothesis was proven out. Frankly, I'm exceeded my expectations of what the response rate would be. We shared on our last earnings call that we did a number of ELAs throughout 2022, a large set of them in Q4. Just 90 days later, within Q1 of 2023, 25% of those customers were already leveraging the burst capacity. Reducing that friction, providing that flexibility and the supporting resources to help customers accelerate their expansion is working. A great example here is actually a customer that our Snowflake colleague mentioned a little bit earlier, CUNA Mutual, who you may all know as a financial services organization with $41 billion of assets under management. They've been an Alteryx customer for a number of years. Like many customers, they started with a handful of licenses and continued to invest and grow with us to deploy server. We had immediate impact on some of their most important business processes. We helped them redesign their sales forecasting process. We helped them redesign their sales commissions process. We continued to investigate use cases in the Office of Finance in HR. We partnered with Snowflake to bring incremental value to those investments, and it was clear to CUNA Mutual that the enterprise license agreement was the next step for them to make in the Alteryx relationship. Our second pillar of our go-to-market strategy is winning with partners. Partners are absolutely vital to our success and, frankly, our customer success. They add accretive value to our value proposition. They help us reach new markets, new customers. They bring relationships to us in both the customer lines of business as well as in IT, and they build incremental value on top of our platform with their IP and their services. This provides scale to our sales motion. This helps us drive higher levels of expansion and retention. We'll take a little bit of look over what we've done in the last couple of years as it relates to our partner relationships. When I joined in 2021, partners were mostly being utilized for transaction fulfillment and user training. We started really spending time to figure out how we could continue to expand their service offerings, redesign our programs, recruit more partners into the ecosystem, and broaden out the reach into the marketplace. In 2022, we recruited a number of new GSIs and solution providers. We redesigned the program so that the incentives that our partners have are directly aligned with our core financial goals, and the results were very, very positive for us. In 2022, we saw partners influence more than 50% of our new ACV each one of the quarters. As we look ahead, we're now getting even more interest in the ecosystem that we've built for our customers. We've recently brought on some newer, large, broad-based solution providers that are well-known in the enterprise software space. Over the last few months, we've signed partnerships with Presidio, with Converge, with SHI Stratascale, with Protiviti, with NTT DATA, and many more. These are trusted solution providers in the enterprise IT space with hundreds and thousands of sellers that are going to take our go-to-market motions and help scale more broadly across the marketplace. While we're just a little over a year now in this fully integrated go-to-market strategy with partners, I'm very confident that these partnerships are going to lend well to the durability of our revenue growth as well as our expansion with our customers. With that, I would love to have you hear directly from a partner, I'm going to invite onto stage Greg Sarafin from EY. He's a global managing director for alliances. Welcome to the stage, Greg. All right. He's got the yellow EY shoes on, if everyone noticed that. Welcome to Inspire. Thank you so much for being here, and thank you for being a great customer and partner. We're actually gonna start first with the customer side of things because the partnership's relatively new, but EY has been a valued customer of Alteryx for a number of years. Why don't we start with the value that you've seen from Alteryx in terms of helping your employees, your consultants, and the relationships you have with your clients? Sure. First I'll say that EY probably is a great example of your transformation on your go-to-market. We started very organically at EY, like many of your customers. We did not require our consultants when they're serving their clients to pick any one tool over any other tool. As you know, in the provision of services for a client, you can't do anything of value without getting at their data. Yeah and doing analytics on the data. Sometimes you'll do an engagement, and the client will have the data in a form that you can use, and that's great. More often than not, they don't, right? You've got to work with the client to get the data, pull it all together from different places, aggregate it, join it, filter it, et cetera, et cetera, before you can then get the insights that you need to deliver the value to the client. As I said, we certainly supported Alteryx from an IT perspective as a package that people could use. The really interesting thing about Alteryx is that it basically won the democracy of usage at our firm, right? Our people could use any of a number of products to do this type of work. Over the years, just word of mouth and success, and frankly, time to value, right? If we charge a client $500,000 to do an engagement, we spend $499,000 to get to the data. Yeah that doesn't leave much left to do the insight piece, right? Alteryx really won because it was the most efficient tool at helping us get the data insights for our clients and serve them better. A couple of years ago, of course, as you know, we've made the transition now from sort of using it departmentally and organically to saying, "Hey, this is sort of the standard tool, standard EY for doing this type of work and service delivery." You know, as you can imagine, our clients are asking us to come in and help them navigate change and disruption. That's a lot of what we do. One of the big areas that we use Alteryx is in tax. If you look at regulatory change, right? The regulatory rate of change for finance is like this, right? Everybody sort of a new IFRS or FASB, right? If you look at tax right now, regulatory change is like this. Yeah. Right? You've got digital tax regimes, you've got, now you have eco taxes, ESG taxes, CBAM, et cetera. When you look at all that change, clients have got to react to that almost every quarter. Yeah. Right? They don't have there's not some magic database or some magic query they have in Snowflake, although a lot of them do have Snowflake now to sort of make it easier to get at the data. Ultimately, you still have to join it in new ways, manipulate it in new ways, and to get the answer for the client. Tax is just one of many examples of where we really couldn't provision services without the tool Alteryx. That's the basic premise of why we use Alteryx as an enterprise client now. Yeah. Fantastic. Obviously you're a trusted brand when it comes to tax transformation. Yeah. There's an incredible amount of joint opportunity for us, which kind of leads to the next question. Right Which is, you know, evolving from Alteryx customer to now an Alteryx partner. Talk to us a little bit about what made you decide? Yeah T hat was a right way to take our relationship. First, a little context for the audience here. EY, we're a $50 billion firm. We do a lot of partnering. Six years ago, when I came in the role, we did almost none. We were doing about $1 billion a year in partnering. We're now at $8 billion, right? We've grown $7 billion in six years, and we've done it by doing it a little differently than you may be used to. We don't partner with everybody. For example, my competitors will partner with SAP and Oracle and Workday, and they'll basically sell against each other in the marketplace. At our firm, we pick one partner for ERP, one partner for hyperscale, one partner and so forth, right? For us to now choose Alteryx as a partner tells you something. Yeah. Right? Because of the value. We think we're one of the biggest users of the value of Alteryx that we can imagine, right? It's so prevalent across our enterprise and our service delivery now, we have a very good handle on how to harness the value and power of the platform. It was that combined with the fact that our customers need to do the same things that let us say, "All right. Well, we're using it. Our customers, when they see us use it, say, 'What is that? How do we get access to that?'" We've had a lot of just sort of, I'll call them accidental sort of, engagements with clients to help them adopt Alteryx. Now we said, "Look, this is it. Alteryx is our standard. We are going to both use it as a standard, and we're gonna go to market with it as a standard to help our clients. The thing that we really value about it is the ability to gain new insights, right? Mm-hmm. You gave some examples of where, you know, using it to, you know, create what I'll call instrumentation. Mm-hmm. Analytics packages, dashboards, process intelligence, and those sorts of things. Those are all great as well. For us, what we really try to help clients do is empower your people. This goes back to. Yeah R ight? A firm like EY, we spend probably $2 billion a year on IT, right? I can sit here, and I can give you return on capital calculation on that $2 billion that we deploy on IT. Much of it is in data and analytics and so forth, our data fabric and so forth. When I look at the return on capital, what we're getting with Alteryx, simply because we democratized it and all of our people are using it. Mm-hmm that multiplying effect just completely flips the return on capital. Yeah. Right. That's the power of a platform like Alteryx, is that you get the full breadth and depth of human capital using it, and your return on capital is through the roof, as is your value creation. For us, we're bringing this story to our clients, we're helping enable our clients on their insight journey. How do you become an insight-driven organization? How do you unlock the power of human capital with this technology to innovate continuously, to find new insights for better ways to operate your company, to serve your customers, to serve your employees, to serve society in general? Yeah. These are the things that we value about the partnership. Fantastic. Great. As you're having those conversations with your clients, there's so much happening in the industry right now, a lot of buzz around AI. Yeah launched AiDIN this week, which we're super excited about. What are you hearing from clients of what are the, you know, top two or three things that they're really looking for when they're thinking about the data space and the analytics space? You know, every client says the same thing. We have all this value that we know is sitting there- Mm-hmm Like oil in the ground. Yeah. Our IT organization is punching these little tiny holes, right? To suck that oil out at a rate that is not fast enough for us to fuel our innovation, to fuel our growth. Mm-hmm. Right? What they need is they need to get access to that value, and they're not gonna get it through just standard capital allocation strategies. They're gonna have to unlock the power of everybody accessing data and driving new insight with it. That's not, b y the way, I love, you know, I'm over at Dell Technologies World right now. And of course, at every conference, including SAP Sapphire last week with SAP, everybody's talking about what? They're talking about AI, generative AI. Yeah. That's great. Generative AI is really powerful, and there's already a long list of use cases that we've already put in production and clients are putting in productions to take advantage of that particular type of AI. Mm-hmm. It's only one type of AI, by the way. AI still pales in comparison to the power of people. Mm. The power of human capital. AI is just another tool in the arsenal that you're gonna provide. Everybody's gonna have generative AI in their platform. I actually don't view generative AI necessarily as a differentiator. To me, what's a differentiator in your platform is the ability of people to get access, to pull that oil out of the ground faster. Mm-hmm Right? Than they can with just a few pipes that the IT organization drills for them. That, to me, is what we're hearing from customers. We need to get the unlock from our data. We need to be insights-driven. We need to be able to react in real time to things as they're unfolding, and the only way to do that is to democratize that data and make it available to everybody. Yeah. Great. Fantastic. Well, we very much appreciate EY as both a valued customer as well as a partner, as we both go out into the world and try and help customers become insight-driven. Thank you so much for being with us here today, Greg. My pleasure. Thank you. Thank you. Thank you, everybody. Okay. The third pillar, that I wanna talk about within our go-to-market strategy was customer success and services. We talked about this major investment that we'd made over the last couple of years to focus on the post-sales engagement model, which is absolutely key. If customers are going to make investments in analytics, you need to make sure that value is delivered. Customers right now in our conversations wanna spend more time with us on the how part of the analytics journey than the what part. They're absolutely convinced, as Greg said, that they can unlock more value from their data. How do they do it? How do they scale across the enterprise? How do they do it with governance? How do they find the next set of use cases and value to unlock with their organizations? That's why we built customer success and services, to help with user onboarding, with driving feature adoption, with helping customers build a center of excellence, so they really can scale across the enterprise. We're enabling customers to do this with governance and with security because as IT departments become more involved in managing this platform, they wanna understand that we're doing it responsibly. A great example here is Disney, who presented with us very recently in Florida at the Gartner's Data and Analytics Summit about how Alteryx plays a pivotal role in their global rollout of Disney+, as well as their acquisition of Hulu. They leverage Alteryx to help them overcome global tax regulations and to reduce outstanding electronic invoicing by 300%. They specifically pointed to customer success as this trusted advisor team that helped them on their journey to be able to deploy the analytics in support of these strategic initiatives. When you deliver a powerful, innovative platform, and you match that up with the types of resources from a post-sales perspective that help people quickly unlock value, it means they renew at high rates. It means they expand at predictable rates. It means you earn the right to come back and ask them to continue to invest and grow with you. The proof is really in the pudding when you look at the numbers in terms of the influence that our customer success team has on the customers that they work with. Gross retention rates track 5 points higher. Net expansion rates track 10 points higher. Now in 2023, as we've shared with you, we have our largest renewal base in the company's history, and customer success is pointed very squarely at some of these very large renewals to help us take advantage of these great opportunities from a retention and an expansion perspective. We've taken the lessons learned from the customer success teams of people and put that into a digital platform as well that now helps us to scale customer success across the entire customer base. I talked about enterprise, I talked about winning with partners, I talked about customer success and services. The fourth pillar that we're adding, and we have added in 2023, is an underlying pillar which is helping customers scale with Alteryx Analytics Cloud. We've incorporated cloud into every aspect of our go-to-market. It's in every one of our conversations, and the response from our customers has been overwhelmingly positive because they're looking for someone to advise them on their journey as they modernize their data stack. We launched our first-ever cloud analytics survey at the beginning of this year just to ask for that feedback from our customers and understand what role cloud analytics can play in their most important strategic imperatives. 89% of respondents felt that cloud analytics can contribute meaningfully to their profitability, which I don't have to tell those of you in the room here how important profitability is for every organization right now. 81% of those organizations felt that analytics would have a positive impact on their ability to manage through uncertain times, economic uncertainty right now with inflation, wanting to see around corners, the amount of scenario planning that organizations are doing. Analytics plays a critical role in organizations' ability to be agile and gain insights. For Alteryx, it means that we unlock new use cases and new personas and are able to go advance the conversation with customers on their maturity of their analytics journey. Kingfisher is a great example here. Kingfisher is a European home improvement company that's been a longstanding customer of Alteryx with both Designer and Server. Recently invested with us with Alteryx Analytics Cloud, not only to leverage Designer Cloud, as they're modernizing their data stack, but also for Auto Insights. Their executive team wanted to have more insight into what was happening specifically in HR and in operations with a dynamic AI-driven interface. This is a classic example of the opportunity that the go-to-market team has to go to existing happy customers with on-prem investments and expand the personas, expand the opportunity with Analytics Cloud. The strategy continues mostly from 2022 with the three pillars I talked about and scaling with the cloud platform as the underlying fourth pillar. We've had some really positive early momentum with cloud. In Q1, we saw our cloud t he number of customers that invested in cloud was up 30% year-on-year, and the business is growing at a really positive rate. The momentum is strong, the pipeline is strong, and we're confident that this is going to be accretive to the overall opportunity that we have. One question that I often get from investors is, do you lead with cloud or do you lead with on-prem as a sales team? If I'm being honest, that's actually the wrong question. We lead with value. Our entire focus in the go-to-market team is to understand the business problems that our customers are trying to solve and to apply our expertise in helping them understand how to put the analytics platform to work for them in solving those problems and becoming insight-driven. Value means we have to articulate the ROI. It means we have to show up with a business case. We have to show up with proof points. If you go to G2, which is a customer review site, you'll see that customers on that site say that the average ROI with Alteryx is six months, which is half the time of the other 250 analytics providers that are reviewed on that site. We're incredibly focused on making sure that we deliver value realization and that we can articulate the value of the investments that our customers make with us. When we apply this value sales motion to the opportunity that we have, it means that we can accomplish our mission of durable ARR growth, high levels of renewability, and consistent expansion. Number One, there is strong demand in the market for what we do. Mark talked about it at the beginning, an $80 billion market opportunity for us. Even in today's macroeconomic environment, you survey CIOs, analytics and AI are in the top three investment areas. Number Two, cloud and services are not only growing pieces of our business, but they are a validation that our customers see Alteryx as a long-term trusted advisor that they want to partner with as they continue to advance their analytic maturity. Number Three, with the launch of AiDIN, we're incredibly excited to now recement with our customers our innovation that we have in terms of helping them bring the power of AI and ML across the enterprise. Number Four, we have the largest renewal base in the history of the company. We have high levels of renewability against that and a defined sales motion for expansion. Lastly, our enterprise license agreements have become very well-received in the marketplace, helping our customers to accelerate their journeys with us and drive higher levels of expansion. With that, I will end with where I started. The analytics for all mission in Alteryx in the market is absolutely resonating with our customer base, and that's what is enabling the mission that we have on the go-to-market team to deliver consistently on the revenue growth, the renewability, and the expansion opportunity. Really excited that you chose to spend time with us today. Hope you have the opportunity to talk more with our customers and partners that are here with us. With that, I'm now going to hand the floor over to our Chief Financial Officer, Kevin Rubin. All right. Thank you, Paula, and thank you everybody for joining us today. This is what you all came to see, I'm sure. Okay, we have a lot of many exciting initiatives underway here at Alteryx. We have an $80 billion plus market opportunity. We are enhancing our go-to-market motion to meet this demand efficiently and effectively and to drive customer success for our customers. We're also rapidly innovating and expanding our platform of analytic solutions and cloud with AI. Underpinning this momentum is a robust financial model in which we have demonstrated durable growth and expanding non-GAAP profitability. Today, I'd like to provide additional color on our growth and profitability drivers and on how we expect those to enable us to progress towards our long-term financial model. Let's begin at the top with annualized recurring revenue or ARR, which we believe is the purest metric in terms of scoping the growth trajectory of this business. We are a subscription business with gross retention rates around 90%. ARR provides a clear measure of our active book of business, a book of recurring business. We'll take a closer look at the composition of ARR, how that has changed over time, and the drivers that we anticipate ahead. I'll also discuss how this bridges to revenue on the P&L. We'll help understand the nuances of revenue recognition and how that can influence short-term linearity of the P&L. While we strongly believe ARR is the best measure of our normalized growth rate and is a key measure we manage the business to, we know that our revenue mechanics are important as you build out your models. Next, we'll explore profitability. As we move beyond the recent investment phase, profitability is a key focus for the team in 2023 and beyond. We will discuss the incremental drivers that we expect to contribute to delivering our 2023 non-GAAP operating income guidance of $80 million-$90 million. We'll provide the incremental drivers to build on this momentum going forward. Finally, will take us to the long-term model. We'll provide an update on the long-term model, plus a framework for how we strive in terms of achieving this model. With that said, let's jump into it. Exiting Q1 of 2023, we generated $857 million in ARR, up 25% year-over-year. As a reminder, this comps against Q1 of 2022, in which we acquired Trifacta and reflects a five-year CAGR of 35%. Our annual guidance for the year, $1.015 billion-$1.025 billion, which we provided as of April 27th, implies continued durability of this growth at 22%-23%. What's driving the success we've seen and what are the incremental drivers ahead? Let's begin with the strategic focus on large enterprises that Paula discussed. Winning with large enterprise customers is so important to our model, and it really comes down to 2 key factors. One, larger customers have significantly higher upsell opportunities across multiple lines of business, larger employee bases, and a wider breadth of potential use cases. Two, larger customers have a higher gross retention rate. We have found that once Alteryx is incorporated into operational processes and embraced as part of a strategic vision in these large customers, the platform is like dry cement. In summary, we believe large enterprise customers provide a significantly higher lifetime value. Let me provide additional insight into our growing traction with this cohort. Over the past two years, we've nearly tripled the number of $1 million plus ARR customers to over 140. In addition, over the past five years, we've seen the average customer size in this cohort significantly increase. We're benefiting from growth in just. We're benefiting from growth in the number of large customers, as well as bigger wins with those customers. Let's take this one step further and expand the cohort to customers generating 250,000 or more in ARR. This chart illustrates the growth that we have seen in the number of 250K+ customers over the past 5 quarters. This growth has been remarkably durable even as we scale and facing law of large numbers. We've now reached approximately 650 250K+ customers in Q1 of 2023. This is clear evidence that our go-to-market efforts are bearing fruit, and our platform is resonating with large global organizations. Going forward, we plan to provide the 250K plus customer cohort on a quarterly basis, as we believe this better reflects how we're actually managing the business. This will replace the total customer count we've previously provided, as it's largely influenced by very small accounts with very low ARR. The true magnitude of the go-growth opportunity is very much in front of us. In any given quarter, the vast majority of new ARR is with existing customers. We achieve this expansion through proper enablement and implementation, close engagement with our partners and our customer success teams, and with the flexibility of the ELAs that we discussed earlier. The value proposition quickly becomes undeniable. The customers expand. This chart shows how our dollar-based net expansion trends over the past 5 quarters for both total business and Global 2000 customers. As I described, this is driven by expanding implementations, reflecting deeper penetration in existing lines of businesses and expanding to new personas and new use cases, as well as robust pricing. With approximately one-third of the Global 2000 customers still at less than $50,000 in ARR, we have ample runway ahead to continue to drive these net expansion rates going forward. In addition, with cloud now fully available, we see cross-sell as an incremental layer of growth within the base. More on that shortly. We're having problems here. Thank you. Back one, please. All right. Let's touch a little bit on our annual cohort of customers. The first thing that is abundantly clear, customers that stay with Alteryx are with us for a very, very long time. Once we demonstrate value, it is essentially cost prohibitive and disruptive to rip us out. Nearly three-quarters of our ARR today comes from customers that have been with us for over five years. Second, we have a long runway of opportunity ahead based on our foothold in 47% of the Global 2000. To wrap up on ARR, we are seeing positive trends in terms of both total ARR growth and the quality of the ARR. Over 80% of ARR today comes from 100,000+ customers, and over the past three years, that's increased 10 points. As we win with bigger customers and more deeply penetrate those customers with both our flagship and our cloud solutions, we expect this mix to continue to shift towards the higher LTV cohort. Okay. With the context of ARR in mind, let's move on to revenue. I want to reiterate that we do still believe that ARR is the best metric to measure true subscription revenue growth for any subscription model. That said, it's important to understand how this drives revenue and our P&L in terms of linearity of revenue recognition. Let's begin with a brief overview of how this works, and then I'll walk you through an example. Because our flagship solutions of Designer and Server are deployed on-premise, meaning they're on a desktop, they're on a laptop, they're in a server, we are required to recognize a portion of the deal value upfront. Since 2022, this has been about 50% of the deal value, and the remainder is recognized over the contract value as PCS. To be clear, there's no impact on ARR and no impact on revenue or profitability over the life of the contract, but it does impact P&L linearity in 2 ways. 1. We see an outsized portion of the win in terms of revenue and profitability in the quarter that the license is deployed. 2. Changes in contract duration, while having no impact on ARR, does influence P&L linearity. Let's take a look at an example case study of a hypothetical deal worth $100 per year under a 1-year contract versus a 3-year contract. ARR is the easiest to see. $100 of annual value translates into $100 of ARR. Duration has no impact at all. For about half of the contract value with the remainder recognized over the subscription, meaning $150 would go into subscription license revenue in the quarter the subscription terms begins, and the $150 additional would be recognized ratably over the remainder of that contract. What are the key takeaways here? First, we believe ARR is the best measure of normalized growth of the business, and it is not impacted by contract duration. Second, contract duration has no impact on the total recognized over that period, but in other words, in both scenarios, $300 is recognized over three years, whether it's a one year or three year, but the timing of the recognition is different. Third, quarterly linearity is, of revenue, is influenced by contract duration. In the example, the difference between a one-year and a three-year contract resulted in 3 x more revenue in the quarter the license starts and approximately 2 x more revenue in the first year. When you scale that up to ACV bookings of around $700 million, this impact is amplified. For example, the difference between an average contract value of one point four years versus one point five years is as much as $35 million in upfront revenue on an annualized basis. One last comment on revenue recognition as it relates to the cloud. As we layer in incremental growth from our cloud offerings, this revenue will be recognized ratably, better aligning overall revenue and ARR linearity. Before I move on to profitability, one last slide to show the net. the effect of net ARR, net new ARR, and revenue by quarter over the last several years. The intent here is to show the linearity of the year. There are a few dynamics that are influencing this slide. First, we've historically seen a greater share of net new ARR and revenue in the second half of the year. As we focus on enterprise, we've seen this further shift to H2 linearity. Second, given skewing of multi-year contracts to Q4, the revenue linearity is even more pronounced in net new ARR. You can see the 2023 guide given as of April 27, 2023 implies very similar linearity between H1 and H2 in terms of net new ARR, revenue, and profitability. We have a significantly larger renewal base in Q4. You heard Paula reference that earlier. We have confidence in the guide given our proven track record of retention and expansion that we've demonstrated now for years. This is evident by consistently delivering gross retention rates around 90%, improving net expansion rates at 121% and even higher in the Global 2000 at 131%. Top of this, we have an enhanced enterprise sales team, a deeply engaged customer success team, a strong book of ELAs providing incremental upsell opportunities, and an expanded basket of offerings with the cloud. That was a lot of mechanics on the model. Let's go back to the fundamentals, profitability. Make no mistake, we have a significant opportunity ahead of us, and we are positioning the company to capitalize on both product innovation and go-to-market scale. At the same time, we are instilling a culture of discipline in our spending decisions, and we are committed to delivering on our recently increased non-GAAP operating profitability outlook. Let's begin with the historical trends and what we expect to be in 2023. As this chart shows, we have historically delivered strong non-GAAP operating profit across multiple years. Now over the last couple years, we executed on a strategic plan, enhancing our go-to-market motion to drive success with large enterprises and meaningfully accelerate our cloud innovation path. This was intentional, proactive decision that we made to put the company on a path to surpass a billion-dollar-plus in ARR. As we move beyond this investment phase, we believe the business is well aligned to carry forward the strong growth momentum, and we are committed to achieving this scale with disciplined spending and expanding profitability. Underlying this view is our 2023 outlook for $80 million-$90 million in non-GAAP operating profitability that we gave on April 27. What gives us confidence that we can achieve this? On one hand, we do have a proven track record of being able to deliver healthy non-GAAP operating profitability. At the same time, I can appreciate that the guidance calls for significant improvements in 2023 and would place 2023 as the strongest non-GAAP operating profit year we've posted in the history of the company. Let's spend a little bit of time on the drivers. Q4 of last year, we significantly reduced our real estate footprint. Given the distributed nature of our workforce, we were able to reduce our footprint by approximately 40%. This alone unlocked $15 million in annualized cost savings. This last quarter, we announced that we reduced our workforce by about 11%. While we certainly do not take this decision lightly, we are taking a disciplined approach to evaluating all of our spend. Having grown our headcount in 2022 by nearly 50%, we believe this will drive improved productivity without impacting our 2023 growth. We expect the workforce reduction to provide approximately $40 million in annual savings this year. Third, sales productivity. As Mark and Paula mentioned, we meaningfully upleveled our sales team in 2022. We brought well-tenured enterprise-facing reps, we upgraded our partner program, and we built out an enterprise-grade customer success motion. We've since closely tracked our net new ARR per sales rep as a way to gauge pacing of the ramp and overall sales force productivity. We did see productivity uplift in 2022. Sales ramps take time, particularly for enterprise, and especially in the current macro environment. As we look to 2023, we continue to optimize pipeline management, sales enablement, and we accept contributions in this front to pick up throughout the year. That said, our productivity assumptions incorporated in the guidance prudently reflects the dynamic macro environment. Finally, we expect operating expense efficiencies as we move beyond the investment phase of recent years and scale the business. The bottom line is here we are instilling a culture of discipline throughout the organization. I've laid out a bridge on how we expect to get to our 2023 guide, but it certainly doesn't stop there. Gaining traction with large enterprises inherently drives greater productivity, meaning higher ARR per account and per rep for the years ahead. Partners provide scale, unlocking higher quotas per rep. Cloud offers incremental upsell opportunities within existing accounts, also contributing to higher ARR per account and per rep going forward. Simply moving beyond this investment phase will enable us to normalize our OpEx growth and unlock efficiencies of scale. All of what we've talked about today culminates in the long-term financial model, which we've aligned to fiscal year 2028. We expect gross margins of 80%-85%. This reflects the impact of the Alteryx Analytics Cloud platform as we continue to expand with new users and new personas. While this is a relatively small portion of ARR today, having just made Designer Cloud generally available, we are assuming a greater mix of cloud in the years ahead. This model also has non-GAAP operating profitability at 25%-30%. The most meaningful opportunities to improve our operating model are in sales and marketing and G&A line items, which were both the primary focus of the recent reduction in force. As I highlighted earlier, we expect to make meaningful progress towards this long-term model in 2023. The midpoint of our outlook implies 9% non-GAAP operating margins. As for how we plan to achieve margins from 2023 levels to the long-term model, we aim to deliver 3-4 points of margin expansion each year. Where we land in this framework each year will depend primarily on calibration of ARR growth and pacing of incremental cloud contributions. This model finally assumes an ARR growth rate of at or greater than 20%. While we don't specifically guide to stock-based compensation, we expect it to progressively come down as a percentage of total revenue to the low to mid-teens% over time. Additionally, 2022 had an uptick tied to performance-based grants early in the year, which will roll out of our stock-based compensation over the next few years. In terms of share count, we are managing to about a 3% dilution per year. Finally, we have free cash flow at 20%-25%. Pacing of achievement should generally track our non-GAAP operating profit. This means the model soon should be generating high volumes of cash, strengthening our capital structure, and providing greater flexibility with our outstanding debt due in 2026 and 2028. With that, I'll wrap up with a quick summary of the day. We began the day discussing the data analytics opportunity. We have a significant opportunity ahead as the largest companies in the world are increasingly embracing data-driven cultures. We believe Alteryx is uniquely positioned to be able to enable these companies to scale analytics throughout their workforces with an easy-to-use, powerful platform to solve complex business challenges across an increasingly complex and fragmented data stack. We took a closer look at how that data stack is evolving and the opportunities this unlocks. We are seeing greater interest in data analytics in the cloud, an increasing interest in how we can enhance our platform with generative AI. Of course, we had some exciting generative AI announcements under the AiDIN brand that is available to customers. We discussed the strategic initiatives we are executing on to put our sales team in a position to help drive our customer success. Finally, we recommitted to our long-term operating model with a clear framework on how we expect to achieve our goals. With that, I'd like to invite Mark, Paula, and Suresh to join me on stage for some Q&A. If you would like to ask a question, please raise your hand and wait for the microphone so that we can make sure the webcast audience can hear. Thank you. Yep. Okay. Hi. Over here. First of all, thanks for the presentation and for the detail. It's very helpful and inspiring. Inspire. I guess I have a long question for you, Mark. Clearly the focus on enterprise is understood. That's where the big dollar ticket is. That's where the big opportunity is. I'm trying to get my hands around the model. When I look at your largest customers, your $1 million customers, even at the current pricing, that still implies only 200, 300 seats per customer, meaning your user penetration into any account is extremely small, even in your largest customers. On one hand you'd say this is a great opportunity to expand. On the other hand, at $4,000 per head, it's hard to expand. I'm, you know, maybe disclosing my age here, but going back to Tableau's days. When Tableau went to market, it was a $1,500 per year kinda license per person. It was very expensive. They hit a wall, and then they changed their pricing completely from creator to viewer. My question is: Is there a way to evolve your pricing model to ones that are more involved with the platform or perhaps with generative AI right now? People could do a lot of things without coding that maybe you lower the pricing of a license and create some differentiation where you truly can get into 5,000, 10,000 seats in a $100,000 type of customers. It feels like that even at $1 million, which seems like a lot, you're not really necessarily strategic to a company 'cause you're only 200 seats. You're a nobody in the context of how many people is in the organization actually using you. Yeah. I'm gonna show my age too. I think that's Itai. I can't see that well. Yes. because you're far away. Sorry about that. Great to see you, Itai. Thanks so much for coming. I think your question is a good question, but it has a fundamental kind of fundamentally incorrect assumption around pricing. Our pricing is very elastic. It's very dependent upon number of licenses. Yes, our list price for Designer is $5,000 if you buy one license. Our largest customer, PwC, publicly stated they have 150,000 Alteryx licenses that I wish they paid $5,000 a license. They don't. The large banks, you know, many of whom you'll meet if you walk around in the next few days, you know, many of them have tens of thousands of licenses. I do get your point more broadly speaking. I think, you know, I'd like to think we inherited a company that was good at selling dozens of licenses, and I think over the last few years, we've built out the capabilities, and involves largely people, process, and of course, technology, to be able to sell hundreds of licenses to lots of customers. I think our future state is selling thousands and thousands of licenses to many customers. The best, I'd say proxy that you can look at is the size of our the Alteryx community. It's well over 500,000 users today. Of course, not every user registers for the community, but it's doubled in the last, you know, two and a half, three years. My expectation is, you know, just given the, the incremental price elasticity of software and part of the rationale, Itai, for us going to the cloud, which will allow us to get our unit cost economics down, you know, to much more attractive rates with larger deployments, I expect that we'll get, you know, into the millions. Okay, great. Then as a follow-up, machine learning, the demos were great, Suresh, and very impressive. I can see how intuitively it works well with Designer users. I guess my question is: What% of your base right now is already engaged with machine learning capabilities or just still only Designer and Server type of customers. Then on the complement for that, Kevin, you're talking about the tail that is kind of rolling over small customers that, as you've seen in the decline of customers this last quarter. How much more of a tail is there, do you think, value at risk that can churn off because of your focus on the larger ones rather than on small customers? Thanks. Maybe I'll go first. We have machine learning tools inside Alteryx Designer as well, right? I talked about the fact that we'd introduced language models with Alteryx Intelligence Suite. I'd say about 25%-30% of our customers start to use those Alteryx Machine Learning tools and AI tools inside of Alteryx Designer. As you know, the Alteryx Machine Learning product and the Auto Insights product as part of the Alteryx Analytics Cloud platform, we just launched them earlier this year. We're seeing Paula Hansen mentioned this, we're seeing real good traction towards those capabilities. We actually gave you the case study of Kingfisher, who kind of uses Auto Insights, and there's many such customers who are using these capabilities, we expect that also to continue to grow. On Designer and Server, as we look at kind of the maturity of the customers, I see growth there too in using the machine learning tools, the R tools and the AI tools in there. Yeah. The question with respect to the long tail. For the last two years, we have had a go-to-market motion that has been focused on the largest customers in the world. We don't deploy resources against small deployed customers that don't have an opportunity to expand. We mentioned on the call that the churned accounts were less than $15,000 in ARR. They're not really contributing to the model. I did provide a couple of metrics this afternoon. 80% of our ARR is with customers over 100,000. To the question about whether or not it's a headwind, they're really small customers. We sold them a few handful of licenses over the years. They've renewed them. Maybe they've stopped. We don't put any resources against them. They're not a meaningful contributor to ARR. From our perspective, I think it's just noise in the system. Thanks, Itai. Thank you. Hey, Guys. You guys hear Mike's? Hey, Guys. You have Mike Cikos from Needham. Sorry to blast everyone. Thank you for doing this. I wanted to build off of Itai's question regarding the AI and the AiDIN brand. Just to clean up my understanding here, one of the things that we've been hearing from a lot of organizations is the idea of responsible AI, right? I think you guys alluded to it several times in your comments, but can you further hash out that responsible AI? Like, is it the governance? What is it you guys are doing there that differentiates you and gives enterprise customers that level of confidence to take the next step? As you know, Alteryx is used for really complex analytics automation, and there's a role for us to just be responsible with that analytics automation as well. Forget about the AI portion of it. Even with the analytics automation, where we're making sure all the time that they're using the right kinds of datasets. We're tracking those workflows. We're giving them insights into how those workflows are built and used. It's customer-managed telemetry and things like that. AI adds another layer of complexity to the responsibility question that you're asking, right? Is there bias in the models? Are the results flat out wrong? You saw in the demo that we showcased, there were subtle hints about kind of the governance and responsibility. We're also having the AI learn from the user's actions and from the user's data to start to call out where they may be out of bounds in how they use the data. you'll start to see us. Let's be clear, this is very early innings for generative AI inside an enterprise. There's gonna be surely a layer of governance and auditability that CIOs are gonna demand and even add on their own on top of this, right? From a product experience standpoint, we wanna make sure data lineage, understanding the metadata, enriching that metadata, flagging at the right times based on the persona and the rights that people have access to on the data. We're able to control which data gets used for what kind of use case, and we'll continue to do that. Got it. Just for the follow-up to continue that conversation, I guess if I'm thinking about where we are in the development, is the early access program open? Like, are our customers in beta on this? When is the GA? How do you think about it playing into, I guess, that longer term model, the 28 target that we have, is this something that is viewed as like a vector, an upward vector to your growth, or is it more just continuing to build value for those customers and ensuring that retention? Like where does this play into that long-term model as well? I'll do the GA question that you had. I mean, we approach this as any software company does, right? We have a small set of design partners we're actively building with. We'll put in early access towards the end of this year, and early access becomes general availability over time, right? We have customers actually pounding away at our generative AI capabilities. There are some capabilities that are already all access. Tomorrow you'll hear us talk about Workflow Summary, Magic Documents that are already all access, and customers have the ability to start using them inside the products. The multimodal analytics, we are in early stages with the customers where we're designing actively with them. We've got a handful of them using about 15 to 20 customers using it with us and giving us feedback, which I think is really important as you introduce this kind of technology into the enterprise, right? Again, enterprises tend to move slowly. They're super careful. They don't have the same appetite and enthusiasm for it as many of you might have about this technology, because they've got lots and lots of questions they need to answer. They're very willing to partner with us and go deep into the design process with us, and we're doing that today. I expect, as I answered, somebody asked the question, I expect to put this in early access by the end of this year, and then as we're gonna scale it up, it'll become generally available. That's the answer on how we're rolling this out. Yeah. I guess with respect to the long-term model, I guess what I would say is this is just another, you know, key piece of capability and functionality that's inherent in the basket of offerings that we'll have for our customers. I don't want to give you the sense that it's a different vector per se, but we do think it'll be an important piece as to why this product continues to expand within our customer base. Great. Thanks. Thank you. Thank you. Sanjit Singh from Morgan Stanley. I had a question for Suresh. When I think about generative AI in the context of data analytics, I kinda think about SQL being abstracted away and maybe sort of the, like, the depth of the dashboard, because we can get direct responses to direct questions. In a world where literally every data analytics provider is gonna be integrating, you know, generative AI technology into their product portfolio, where do you see the source of value in terms of what customers will pay for? Is it in the metadata management? Is it more governance? Is it more policy? Where do you think in a world where everyone's sort of integrating this technology, where do you think the source of value to customers in terms of what they will pay vendors for? I, First, as I said, it's super early days of where value realization will happen. We believe we've got a point of view, clearly, right? We believe the opportunity for each persona to interact with the analytics in their preferred medium is gonna be super important. Even while I agree with you that it'll abstract away the process of creating the SQL, the analysts will still want to cross-check, will still want to append, will still want to change, will still want to test. The power, and you've all covered the analytics space for a long time. Collaboration's really hard in analytics, right? Being able to create those insights, iterate on those insights, share with the organizations that are asking, there's always a backlog on this stuff. We believe there's gonna be real acceleration if you're giving the different personas, whether it's a data scientist looking at a Jupyter Notebook, whether it's an analyst looking at SQL code, whether it's another analyst just writing a generative prompt, we think there's real value in kind of fostering the collaboration. You kind of need the guardrails that CIOs expect, then no CIO is willing to allow this loose in their users' hands unfettered, right? I was speaking with a large insurer who's here today at the conference, and I asked him, I was like, "What, what are you doing with generative AI inside your organization?" He said, "Look, unsupervised learning comes with its own challenges. We're not sure we're ready for it. We're not sure we're ready to put this in the hands of the customers. We need to partner with people like you to help us introduce this in a responsible way into the organization. Then I see the next layer of building generative AI applications. I talked about adding your customer, adding their own unique datasets on top of some of the fine-tuning that we're already providing, right? I think that process of bringing their own data to models that we've already fine-tuned, creating new insights, and then making those insights available is gonna be a big exercise as well. I think there's multiple stages of evolution that this is going to go through. I really appreciate that vision. It sounds quite exciting. Kevin, I guess my question for you, I really appreciated the bridge from ARR to revenue. If we sort of look at 2023 guidance and look at some of those components between the ARR guide of 22%-23% and the revenue guide of 15% versus 60% last year, how much is duration an impact? How much is the inorganic contribution from Trifacta an impact? Then sort of mix, you know, typically 50% of TCV, is that changing this year? Any way you can sort of map 2023 guidance to that framework that you introduced to us. Yeah. Thanks for the question. Keep in mind, like, let's go back a little bit. 2021, we had an intentional decision from a pricing perspective to focus on ACV, less about TCV, we saw revenue growth in 2021 come down significantly while we still maintained ARR growth. In 2022, we saw the normalization, if you will, of contract duration, revenue growth had the benefit of a really low revenue number in 2021. 2022 had an outsized effect, whereas you saw continued stable growth in terms of ARR in 2022. 2023, my comments on the Q1 call was we saw slightly lower contract duration in Q1. We expect that to slowly return to more historical levels as the year goes on. There is an element of contract duration that is playing into the 2023 revenue guide, but you're not seeing that play out in ARR. I'm sorry, you also asked about Trifacta. Excuse me. Trifacta contributed $22 million last year in ARR, lesser in revenue just by the nature of rev rec. There is no change this year in the upfront percentage, so we're still recognizing for on-premise deployed technology about 50%. We got some up front. Great. Thank you. This is Alexander Sklar with Raymond James. First, Suresh, I wanna ask about if you could elaborate more on the Cloud Execution for Desktop, just kind of intentions behind that. Is it intended to be kind of an accelerator for customers to migrate to the cloud? Is this just a way to get them to kind of adopt new workloads in the cloud? Elaborate more on kind of the intentions of that solution. Thank you for asking a question that's not about generative AI. We've always maintained that we want customers to use our innovation wherever they want to use it, right? You use it on the desktop, you wanna use it on the cloud, you use it on the cloud, and increasingly use it in both places, right? It's very clear customers have hundreds of thousands, maybe millions of workflows and real IP that they've built into their business process where they wanna get the benefit of the cloud. They wanna get to use compute they've already paid for. They wanna be able to scale up and scale down as they need to. We partner with companies like Snowflake, where we're doing aggressive push down, so they want to leverage some of the investments they made there. We don't want to put them in a place where they have to rebuild these workflows for the cloud, right? We design deliberately this technology that allows them to publish the workflow, which is an asset in the system that just gets published, and it's immediately available in the cloud. Then you decide which data plane you want to run that workflow in, right? We think this unlocks a lot of latent capacity for the customers into existing cloud investments that they've already made. That was really the intent there. It was not to say, "Hey, why don't we migrate you off of this thing?" We just wanted to say, "We want our innovation to be used wherever you prefer to use it." There was a gating factor in that which was the desktop workflows, we need to make them compatible with the cloud and run them in the cloud data planes of their choice. The teams delivered an amazing piece of technology that allows the workflow to work anywhere it's published. Thanks for that. Just a quick follow-up on, sticking with cloud for a second. Mark or Paula, I appreciate that fourth new cloud pillar that you laid out, Paula. Can you just talk about are you incentivizing the sales force to sell cloud at all differently than in the past? It sounds like you're selling value, so maybe not. Just curious if there's any specific quota related to cloud or any different compensation structure. Thanks. Yeah. We're very much focused on having the field understand what the customer is trying to do rather than incent them to, you know, put a certain type of technology or deployment model in front of the customer. There's nothing specific there. We're really trying to go at the pace that our customers wanna go. We find that there's customers at all different places on this cloud journey. We might talk one day to a customer that's cloud only, cloud first, that's great because we have so much to talk to them about. We might have, you know, some of our existing customers that are looking cloud as purely where they're going to add new users. It really varies widely from customer to customer, and we wanna go at the pace that they wanna go and sort of, follow their lead. Yeah. Alex, you know, just a quick add on there. Reps are primarily paid on annual contract value. That's the vast majority of their commission. That truly aligns very nicely to ARR. As Paula said, we're starting with business outcomes and use cases that we've templatized so that, you know, we can approach a customer and have a discussion really about their priorities on spend and the way we can help them, you know, address those priorities and then work backwards into whatever the technology is best for that, whether it's a cloud-delivered product or a, you know, a desktop-driven product. It really doesn't factor into it until the third or fourth meeting. Anytime you put a new product in a salesperson's bag, they're gonna focus on trying to sell it. You don't have to incent them. They love- Yeah. They love new tech, so they're quite excited by all the innovation that we're releasing. Okay. I think we are actually out of time, so we're gonna wrap up on that one. Thank you very much. For those on the webcast, thank you for joining us. For those in person, we're gonna take a brief break, and then we'll continue with the interactive customer panel. Thank you all. Thank you, guys.