Alteryx, Inc. (AYX) Presents at Bank of America 2023 Global Technology Conference (Transcript)

Alteryx, Inc. (NYSE:AYX) Bank of America 2023 Global Technology Conference June 7, 2023 1:40 PM ET

Company Participants

Kevin Rubin - Chief Financial Officer

Paula Hansen - President and Chief Revenue Officer

Conference Call Participants

Koji Ikeda - Bank of America

Koji Ikeda

All right, let's get started. Hey, everybody. Thanks for joining us. My name is Koji Ikeda. I am one of the software analysts at BofA on the enterprise software team. We are absolutely thrilled to have Alteryx here today. We have Kevin Rubin, CFO; Paula Hansen, President and Chief Revenue Officer. I forgot [indiscernible].

So, thanks for doing this. And I guess [indiscernible] levels of the conversation here for those in the room may be not familiar with Alteryx and those on the website or on webcast that might not be familiar with you guys, just real quickly what do you guys do, and maybe just a minute or two on your background.

Paula Hansen

Sure. So, our mission at Alteryx is to put the power of data in the hands of knowledge workers, business users across enterprises, and we have for a couple of decades built out a very rich platform to help them turn that data into insights to be able to make better decisions for their business. Our sort of hallmark differentiators with that platform are first, its ease-of-use, you do not need to be technically sophisticated to be able to use our platform. It's a very elegant, drag and drop GUI user interface, low code, no code as well as well code-friendly. So, we really welcome people very quickly on to the platform.

Another differentiator is the broad base and breath of analytic tools that we can apply to your dataset from, of course, starting with the basics of prepping and blending that data for analytics, but then once you are ready for analytics, applying over 300 different building blocks to that data from geospatial capability that might help a marketing person better understand again how to spend their dollars geographically to serve their [team] (ph), to predictive analytic capability that can help various aspects of enterprises forecast their business. And so, that's a second differentiator for us.

The third is just the fact that data is very disparately available across enterprises and various types of data sources, both on-prem and in the cloud, as well as various types of data formats structured, unstructured and so forth. We have the capability through our data connectors to be able to get data wherever it is. So, we are not partial to where data is to be able to bring it in pipeline for analytic analysis. So, we are very excited about the future with generative AI, as well that I am sure we are going to talk about, and the way that just opens the analytic opportunity even broader population within the enterprise. And so, we've made some really exciting announcements about that which I am sure we'll get in to you.

Question-and-Answer Session

Q - Koji Ikeda

Got it. Well, thank you for that. And I am asking everyone couple of standardized questions, kind of one on the macro and one on AI. We will get into that, but on the macro front, could you characterize maybe how the demand environment feels today, June 2023 versus January 2023 versus maybe a year ago? Do you think things feel the same, different, and is the end market talking about [Alteryx] (ph)? I am just curious how the end markets [indiscernible].

Paula Hansen

I think the demand market is very consistent over that horizon. If you look at CIO surveys from last month and CIO surveys from a year ago, data analytics has been in the top three to five investment categories consistently over that time. And in fact, I would say right now as people are looking for more answers in this uncertainty, they are going to see around corners, they want to move a speed analytics can provide them that capability to be agile. Of course, we are seeing the macroeconomic environment. In our sale cycles, we see more scrutiny on the deal. We see maybe a few extra approvals required. It's a dynamic environment, so we have to stay very attuned to that. And I will remind you that in our last Q1, we did hit our guidance, and still dealing with the macro environments. So, what we have done in the go-to-market team to really pivot towards a value-oriented sales motion well before the macro changed, I think is serving us well. That means we are talking at the executive level to be people that have both the budgets as well as the insights into where money needs to spend, and we are able to have conversation with them around not only what the technology does, but what's the value that it unlocks, what's the return on that investment, and that helps us navigate these conversations.

Koji Ikeda

Got it. And just thinking about the last quarter results, I think -- please correct me if I am wrong, but I do remember some [indiscernible] maybe deals pushing out. Have those deals closed? How do you feel about kind of the pipeline going forward?

Paula Hansen

Yes, we talked about last quarter that as we got into the middle of March timeframe and as you all know all too well with Silicon Valley Bank and First Republic Bank, and just all this what's happening in the world of banking, we did see that cause some pausing in customer behavior. I think people were just trying to make sense of it. Trying to get their arms around it, and so, timing wise that was in the final weeks for us, so it did affect some deals for us. There's nothing unusual right now about our pipeline, our pipeline coverage, and the normal maturation of deals within our business.

Koji Ikeda

Okay, okay. So, moving the topic over to AI, you just had your customer conference, great conference I attended. It was a good conference for sure. And a big announcement made. So, can you talk a little bit about I guess mainly for those who are here that are unfamiliar with AiDIN, what is it? What is AiDIN? Why did you guys introduce it? And from a broader picture perspective, what is the AiDIN plus [indiscernible] the Alteryx engine mean from a differentiation standpoint for analytics? And, what it differentiated from the images -- maybe just what I often hear is why I can't I just use an LOM to shove data into it. And, why I can't -- why can't I just replace Alteryx? AiDIN was a big announcement, so anything [indiscernible] super helpful.

Paula Hansen

Okay, it's fair. And there is a lot here, so, I am sure Kevin will want to add. So, we announced AiDIN at our user conference a couple of weeks ago. AiDIN is our generative AI and machine learning engine that's available across the Alteryx cloud platform. And, this is not new to Alteryx, right? We've been in the analytics business for a long time. We have had AI and large language models in our platform for quite some time.

I think we are bringing it more to the forefront now and continuing to accelerate the innovation here because of the significant interest obviously in the marketplace. Some examples of how that's coming to fruition in what we do when you think about analytics, it's a very dynamic process that you have to go through when you are analyzing your data and for less sophisticated people, technically sophisticated people, they want things served up to them in an automated way, in a very easy-to-understand way. So, we have actually had product for a couple of years called Auto Insights, which applies AI to your data analytics. And then, looks for anomalies looks for trends, and sort of serves in natural language insights to the end user about what is happening, what's the data, what's the story within the data, what's being told. We are now extending that with generative AI with something that we launched called Magic Documents which takes those insights and then extends them to an email that can be automated and sent to a distribution list of people who want to get access to those insights or into a PowerPoint if you want to see it displayed in certain type of graphical format.

So, generative AI in our case just sort of extends the visibility of the insights in the analytics that we've been powering now for a couple of decades. Similarly, we run millions of workflows across our customer base and people want to document those workflows. They want to be sure that they can apply governance to those workforces and understand who is getting access to data and who running different reporting, what is it that can be replicated and automated further across the enterprise? And so, we are using generative AI in that capacity as well. We have an open AI connector. And we are just getting started on this. I think the next wave of this we demonstrated at the conference was something called multi-modal analytics which is across the enterprise. Again if you think about this mission for analytics for all, some people will want that user interface to be very GUI-based. They want to work on a whiteboard. They are very visual. There is other people that want to work with SQL. There is still another that wants to work with Python and Jupyter notebooks. And our strong belief is that if you think about business process and you think about analytics, it's going to cross all of these personas. They need to collaborate together on an analytics platform. And having that interface, serves each one of them with the language and the way that they think about analytics, really just increases the number of people that participate in the analytics opportunity. So, we're really excited about where this is going.

Kevin Rubin

Let me just add on a few points. So, if you think about the reason that we have been so valuable and successful in our large organizations is just the share complexity of those environments. Data is fragmented across these large organizations. They are in different formats. They are sitting on-premise. They are sitting in clouds, warehouses. So, they are everywhere. And as a traditional business user, you understand the context of your business as you're sitting in some domain-specific role, but you don't generally understand data structures and data environments. And your ability to go into a Snowflake, and interact with an IDE is impossible. That's not your skill set or your understanding. And so, we have allowed a level of abstraction in the product today to allow those users to go into incredibly complex environments, be able to analyze a very broad set of data, and do so with the understanding and the context of the problem that they're solving.

When you extend to large language models and AI, and how do large companies deploy that within their environments, you haven't done anything to satisfy or change the complexity of the environment and the ecosystem that these business users are dealing with. So, we think that as companies start to deploy these types of technologies, they are going to need a responsible way to manage, deploy, and understand what's happening within their data environments. And that is something that has been core to our platform for a very long time the ability to govern and provide transparency and understand these.

So, you now talk about large language models. We don't believe our large customers are going to expose their sensitive proprietary data to public foundational models. Right, they want to be able to leverage that technology, but do so within their confines of their environment, behind the Firewall, with their data and their data alone. They don't want to be able to have their data used to improve a public model that's going to then help a competitor with their model. So, they want to contain it within their sphere. And we think Paula referenced, we've got millions of workflows in the wild. We have the largest population of analytic processing in the world available to us, and we think we'll be able to take that data, build an Alteryx specific LLM, and then benefit customers by training their own data within their environment.

We'll provide a workbench that allows them to understand what the model is doing, how it's making its decision, understand biases, or any hallucinations that may be in that model. So, they have confidence that the users internally that are using these models and the data sets are doing so responsibly. And so, we previewed in addition to the multimodal, this AI workbench concept at our Inspire Conference as well. And so, those will lead to two separately monetizable SKUs that we expect to have available for customers later this year generally available early next year.

Koji Ikeda

Got it. Thank you. Just a follow-up here thinking about the way AI changes the pain points for your customers out there, does AI and the rapid awareness of AI over the past six months, does it increase the pain points? Does it decrease the pain points, or does it change the pain points of the way your end market is looking at analytics?

Paula Hansen

I think of it as changing the pain points, right. I think that, of course, the benefit of AI is clear in terms of just the way that it can rapidly open up insights for a broad set of people across an enterprise or an organization, which is what we're so passionate about in our business. I think the new pain point frankly, that it introduces is around this concept of governance, right. And I've talked to dozens of companies, probably nearing 100 since Generative AI really hit the world stage in the November, late November time frame. Everyone gets the use cases and the benefits, but the technologists within our customers, the CTOs, the CIOs that we speak with, say I need to make sure that this does not introduce risk into my business.

And how Alteryx can you help us with the governance piece of this, the audibility of it? We're operating in highly regular markets like the markets that you serve in financial services. This cannot be unleashed in a way that introduced risk into the business. And so, we have always been strong in the area of governance. We've invested heavily in that over the last couple of years in particular because we're so focused on the Global 2000. So, roles based access, integration to enterprise vault systems, integration into enterprise authentication systems, versioning of workflows. So, it can be very easily traced and understood every step of the way. What's happening in the data pipeline, AI is going to accelerate that need for governance. And we feel very well equipped to be able to address both that opportunity that I mentioned of the insights and the simplicity that it can drive while at the same time making sure that the governance is in place.

And I think that again, based on the conversations I have with customers, they're excited about the role that Alteryx can play in that because we've been a trusted brand with them with very important data for a really long time.

Koji Ikeda

Yes, and the last question here kind of on AI is the modernization aspect. And Kevin, maybe over to you is you mentioned two SKUs, but where else are you able to monetize? I guess specifically AiDIN, since you already have AI throughout the platform. Is AiDIN going to be included within the platform? Is it going to be in designer? Can we find it in server? Is it going to be able to create new products as we go along or premium SKUs? I mean, how do we think about the modernization?

Kevin Rubin

It's going to cross over that spectrum. So, Paula mentioned two capabilities, one being Magic Documents, and two being the ability to annotate and attach metadata and information to workflows, so customers can better manage and understand what's been deployed and how it's being used. Those we believe are important for those respective products. So, Magic Documents will be part of intelligence, auto insights and just improve the capabilities in that product, specifically designer, server designer cloud this ability to leverage Gen AI to go through annotation, we think that that's important for all of our customers to be able to use. So, it just simply makes those products more valuable, given the price points that they sit at.

The multimodal, the AI workbench, we believe those are incrementally valuable on their own and demand or warrant individual SKUs. And then, as we go forward, there's going to be capabilities that we find on both of those spectrums, additional capabilities that we can roll out to the broad population of users that will just simply enhance those products, as well as other SKUs that we can add over time.

Koji Ikeda

Got it, got it. I'm going to ask you guys one more question and then open it up for Q&A for the audience. If you have a question, just raise your hand. We'll get the microphone over to you. You could ask your question. So, I wanted to ask you there's AI in this question. On the competitive landscape, so with AI, analytics and everything, has the competitive landscape from your lens changed over the past six months? And how would you bucket the competition out there, maybe from how do you think about the enterprise competition? How do you think about citizen data scientist competition? Any sort of help there or color on the competitive front would be really helpful for us.

Paula Hansen

Yes, we have not seen a material change in the competitive landscape over the last several quarters. The reality is that many enterprises are still running very, very manual processes for aggregating the data across the various data sources that they have, applying cleansing capabilities to it, getting it in a mode where they can actually analyze it, and then apply the analytics, and more importantly, the automation of the analytics to be able to scale it across the enterprise.

So, more often than not, we're sort of competing against that landscape of old that companies and organizations are just losing their patience with. It's error prone, it's slow, and it's costly. As we think about the buckets of categories to be truthful, when you think of the citizen data scientist or the lines of business user, there really isn't a competitor that we feel can stack up against the breadth and depth of our portfolio.

There are players that do visualization, of course, and we're not in the visualization space. And that today is still only like 20% of what we feed into at the visualization layer. We see niche players on the very sophisticated technical side of the continuum, right, serving the data scientist community. And that's a small population of users, and it's also a space that we also can participate in, but that's probably a bucket of category of competitors that over the last year, we've seen some people sort of apply their attention to. So, frankly, we feel really good about when where we stack up competitively and still see so much opportunity because of the ways that companies are operating today in these very old sort of legacy ways of analyzing their business.

Koji Ikeda

Got it. Any questions from the audience? Please raise your hand. We got a question here in the front.

Unidentified Analyst

Hi, [indiscernible] here. A couple of questions, I guess, one is on Location Intelligence. I know you guys announced it, put a press release out there, but have you talked about all about kind of putting the numbers around that, I know like ESRI, a lot of your users are using ESRI right now and then using Alteryx to kind of put data in. Is there an opportunity kind of hard dollars if I were thinking about that? And then I guess the second question is on ESG reporting. Obviously, your Inspire event keynote speaker was with HelloFresh. I was talking to an IR team the other day. I won't say the name, obviously, but they were doing everything manually. Huge opportunity there, they have two people and they're trying to do an entire ESG operation of a multibillion dollar company with two people, tons of data. Can you also talk about the opportunity there as well?

Paula Hansen

Yes, so with Location Intelligence, the origins of Alteryx actually were in the geospatial area. So, this is an area that we're incredibly comfortable with. But what Location Intelligence does is just make that much more accessible, right, that again going back to the fact that we want to address less sophisticated, less technical people within the lines of business. And so, with Location Intelligence, it's a guided experience. It's making that understanding of how to apply geospatial capabilities in an analytic pipeline much more in reach for people who may not technically sophisticated. So, we do think it is an interesting opportunity and one that we have a lot of experience with. And I think it's not a standalone business. It's more compelling as a part of the broader analytic platform that we provide.

The ESG piece is fascinating. We see a ton of opportunity there. And this is where our partners are particularly gravitating towards. So, we talk a lot about our partner ecosystem with the Global Systems Integrators, the PwC, KPMG, Ernst & Young, the list goes on. Some of our largest customers as well as some of our largest partners, and they see the opportunity in ESG and they're in the middle of that with their consulting services. And so, we're actually in a couple of places bundling up our platform, their consulting services for off the shelf type of ESG engagement model that we're certain they're going to be in the middle of a lot of those. So, it is a rich opportunity for us, and as you said, you heard from the HelloFresh customer at our conference in terms of the impact that that's had for them.

Koji Ikeda

Any other questions from the audience?

Unidentified Analyst

I got lots.

Paula Hansen

Okay.

Koji Ikeda

Okay. So, I wanted to ask you a question about the analytics cloud, the progression it's had over the past year and a half, specifically around Trifacta, the integration and then the capabilities that the analytics cloud maybe has versus the legacy designer product. Where is it at today? How much more there is to go? Yes, just curious to hear your thoughts on that?

Paula Hansen

So we did an acquisition of a company called Trifacta in February excuse me, of 2022, and their business was all around building the modern data analytics stack for companies that are leveraging cloud data warehouses. So, as companies are moving more and more data into a cloud data warehouse and modernizing that, it comes to reason that they would also look at their data pipelining capability and want to modernize that at the same time. So, when we bought Trifacta that opened up another persona for us within the enterprise, the data engineer who's usually in the middle of those sorts of modern cloud migrations. So, that is a core component of the analytics cloud platform, but it's also the underlying platform as well. So, the Trifacta Multitenant SaaS platform form is what is now the basis of all of analytics cloud going forward?

On top of that, we have a machine learning capability in our cloud platform which again is meant to reduce the barriers to entry for a non-sophisticated user to start building machine learning models and have seen really great interest in that from lines of business users. And we have the Auto Insights capability which I referenced a little bit earlier, which is really around time series data and understanding what's happening with that data from an anomaly and trends perspective and serving up detailed Automated Insights for the non-sophisticated business user to understand what's happening in the business.

Koji Ikeda

One thing I heard at the Inspire Conference from your partners and customers too, was that a lot of designer desktop users have complex workflows created and you mentioned there's millions of them out there. And one thing I heard was that they were super excited about being able to now port those workflows to the cloud. I guess I don't understand that feature all that well. Could you talk about that a little bit more? And what does this mean for maybe as a driver of cloud adoption in the future?

Paula Hansen

Yes. So, I think that if you look at our installed base, which is largely on-prem users, we do see that some of them will want to maybe migrate to the cloud, but a lot of them, frankly want to stay on-prem too, right and so there's this clear sort of hybrid environment that we foresee for certainly the foreseeable future. But as those same users want to collaborate and share their workflows with maybe new users in the enterprise and those that come onto the cloud platform as a new user, this cloud connected desktop, we call it, or execution, cloud execution for desktop means that those on-prem users can continue building their workflows on-prem in the way that they're comfortable on their desktop, but they can publish them to the cloud and share them with cloud users. And so, it just really opens up the opportunity more broadly for collaboration across the enterprise.

Kevin Rubin

And one point of maybe just taking that a little bit step further, customers that have deployed so on-premise customers have deployed server, often by the way that is deployed in cloud services within their environments. So, there's a cloud component, when they deploy workflows that are effectively operational and analytic processes into server, it becomes like dry cement in these organizations. It's incredibly sticky. It's very difficult to port to something else and replicate. And so, what cloud desktop will allow for those users that aren't server customers that same benefit, they can deploy these to the cloud and they can run in their environments without having to manage server deployment. And so, from a durability and a renewability, it provides us with a lot of stickiness.

Koji Ikeda

Got it. I know we're running up a little bit close on time, so last question for you, Kevin, is at the Analyst Day, you put a stake in the ground in your long-term targets. What was giving you the confidence to do that? What are you seeing in the business? Have you seen it in the business the whole time? And now you just wanted to give more visibility, just really curious of why 2028 all of a sudden?

Kevin Rubin

Yes, I think we've described our long-term targets as being a four to six year horizon. It was always calibrated to the growth opportunity in front of us and how we thought about the balance between growth and profitability. Guidance implies we cross a billion dollars in ARR this year. It felt like an appropriate state to be more prescriptive around scale and how that looks. And the general sentiment and temperature in the environment around profitability is clearly different than it was three or four years ago.

I think we've addressed a lot of the points of confidence around the business as we think about achieving the long-term model. But just as a refresher, we have less than 1% penetration into a large market opportunity that we think just continues to expand with generative AI. We have a transformed and growing level of sophistication within our go to market that we think will serve us well over time. We've got roughly 47% of the Global 2000 that have a 131% net expansion rate, which is up three points year-over-year. We have an increasingly growing partner network that we think will help drive significant efficiency and scale as we think about growing the business. And we have a complete suite of products and innovation coming in front of us that we think will help continue to drive growth.

Koji Ikeda

Got it. We're all out of time. Thank you so much for doing this. Really appreciate it.

Paula Hansen

Yes, thank you. Thanks for the opportunity.

Kevin Rubin

Thanks.