Hi, I'm Clarke Jeffries. I'm on the software team here at Piper Sandler. Really pleased to have Criss Harms, CFO of Amplitude. Thanks for joining us in Nashville. Thank you. Yeah, absolutely. Maybe you could start off with an intro, you know, about Amplitude. Who is the solution for? What friction point are your customers really trying to solve? Got it. Well, in buzzword terms, we're a digital analytics platform, providing a self-serve to our users within our customers that allow them to understand a customer journey and find insights from those so they can take action. I find it best to use kind of three quick examples to make that a little bit more tangible. We think of Atlassian, really large customer of ours. They have both a digital product, which is a software product, and they have a digital customer experience, which is every interface that you have with your target user's customers, both in the product as well as the website, both as well as the digital and others, right? That's a technology-leading company with both a PLG digital product as well as customer experience. We like to use Chick-fil-A. Chick-fil-A sells chicken. They're gonna continue to sell a physical product, but they're gonna engage with their customers in a digital customer experience. The easiest way to conceptualize that is their app for ordering chicken. And every element of that experience, both from first accessing the app to kind of utilizing the app, is part of a digital customer experience. And I use Ford Motor Company as the last, right? They sell cars, but they increasingly sell digital cars, right? Digital is part of that vehicle, and that product is embedded into a physical product. Both the kind of the facet of the product is kind of in this new concept, as well as the experience of how they-how you interact with that digital product. That's what we talk about when we think about the role that we play. The value that we bring to that customer set is the same that's existed since the first websites went up, right? You wanna be able to understand your customer journey, and it has historically been served through kind of combination of products and data scientists and machine learning people. It was a combination of software and people, and we're into the kind of next generation, where we're enabling the self-serve part, where the product manager or the marketing individual or the data leader, they're self-serving on that insight without having to leverage data scientists. They're able to speed their time to insight and their ability, their speed to take action to kind of shape that experience. The use cases are pretty simple, just to round out the picture at a value. If I'm our customer, they're seeking new customers, right? They whether those are coming to the website or the mobile app, or I like to use a digital media company, where they have two different streaming products, and they're trying to engage with that customer to both acquire them the first time, to cross-sell them and sell additional products. They are trying to then make that a really incredible experience, and therefore, retention and making it very sticky. And then the monetization, which for me is always a good way to conceptualize both the digital media companies that are trying to upsell into other streaming products, or I use the Chick-fil-A, right? Once you're an identified user, right, now they understand your preference through our experience. How did you navigate the app? What were the things that led you to increase your cart value? Because we know, we're telling them through the solution set, "You like strawberry milkshakes," and so reminding you to go order the strawberry milkshake. That's all being enabled because we've given them that insight, and they've shaped their digital product, and they've shaped their digital customer experience to take advantage of it. That's the value we do. That's kind of the legacy of the solution set evolution, and what really separates us is being able to do it in a self-serve way without having to leverage data scientists, machine learning on the side, and therefore, shrinking that cycle down considerably. Yeah. Maybe we can drill down and talk about the economic unit of the platform. You know, how does a customer go from $10K of spend to $50K- $100K or even seven-figure? Yeah. You know, what are those, you know, economic units that scale through to be a large account? Yeah, a multifaceted answer, but let's also recognize we have customers that are starting to approach 8 digits with us. Whoa! So just to start framing just how big this can be when I think of the Fortune 500 or the Global 2000, and we're still so early. So that journey can be... Look, I'm gonna pick a large enterprise. I am a product manager, and I've purchased it, and now I'm discussing it with my other product managers in other business lines and product units. So now we're selling into different buyer units within that large enterprise. You know, we've got certain customers where, you know, 20+ different PL, product lines, business units. So we'll grow that way, right? We'll grow on a kind of a horizontal basis. We'll grow kind of vertically. I talk about that customer journey. Every, every moment in there, whether I'm clicking through a link or I'm swiping right, right, I'm, I'm doing some engagement with the digital experience, that's an event. And, our customers are interacting with their customers and their prospects. That is driving more volume of events, and they're procuring from us kind of capacity structures of what those events. So as their app or their solution or their, is reaching more of their end customers and prospects, that drives volumes up. And then we, we, we offer what we think is kind of the complete platform across kind of four major components, and our ability to sell those additional components is the other vertical. So our core is what you would expect, the analytics. That's 90% of our top line is really tied to that core analytics, getting insights to drive action. But within that, we also sell what we call Experiment, and Experiment is very much what we call the A/B testing, right? You find a spot on that journey, and you wanna figure out how to maximize it. So you do two different versions of it or three different versions, and you find out which one resonates most with your customers' experiences, what drove the most volume, and whether that's design layout or the copy you're using or the images you're using, or a combination of there. You can run different tests, A/B testing. Do they work better, or B work testing? That's the next big leg of it. We have what's the kind of the plumbing, the CDP portion, right? Really the customer data profiling of aggregating all of that customer data insight into one spot, to give a 360-degree view from an aggregated view. That's kind of the third leg. And the fourth is coming, which we think will kinda complete the picture of the platform, which is in the kind of the experience place, segment, which is Session Replay. While we haven't GA'd the product yet, people are familiar with what that offering is from other products that are in the market. And it allows them to watch how the individual navigated through the screen. You can watch how the cursor moved, you can watch how the app was engaged with, and the time spent on certain pages and others. Those are the four of what we consider our platform that are currently today sold in those individual units. Yeah. And certainly I love the story about this, you know, purpose-built product analytic solution. You allude to the fact that as all the Fortune 500 become digital companies, they all have a digital product to manage. We see that the leading edge with somebody like Atlassian, but it's gonna trickle down all the way to- Yeah ... any kind of consumer-led company, and they need to have a purpose-built back-end to front-end to understand that, that digital product. I wanted to talk about the landscape because I think this might, you know, also talk about what are the options that companies currently try to use or successfully use to really understand their digital products today? What are the landscape of vendors that you kinda compare yourself to or maybe even replace? Yeah. So, not as straightforward of a question as you had hoped—'cause it's reflective of where we are on the maturation. So let me profile it this way, right? Really across three different kind of market segments, and this isn't just Amplitude saying this, right? This is Gartner saying the same, that these three are converging, and you could argue there's actually four and five, but I'm gonna focus on the top three that are converging. That very much being the product analytics space. And let me list the three: the product analytics space, the marketing analytics space, and the experience analytics space. Those are all starting to converge. If I double-click into each, product analytics space is really where Amplitude, other kinda next-generation self-serve players like, Mixpanel and Heap and Pendo play, but candidly, relatively small market today. If I take the revenues of all of us together, right, we're the only public company of those, right? Everyone knows our revenue's just south of $300 million. If you added everybody's up, it's a sub-$1 billion market. But you have then the marketing analytics, where Adobe and Google Analytics play, very much geared towards the office of the CMO, a much larger portion of the wallet share. And then you have the experience space. Well, it's the third. I won't spend as much time there. Those are all converging, right? To the second part of your question, like, who's serving it and with how? There's a really important part here, and this gets back to those operative words of self-serve. So much of what's being served today is either through a combination of homegrown solutions with data scientists or Adobe, various products within their portfolio and data scientists, and Google Analytics and data scientists. It is not an apples to apples comparison of comparing their software offerings and our software offerings. There's a much different TCO, compelling message that we bring to the table in the self-serve. But that's how it's being served today. It's very people-intensive, and it's very, like, not time-friendly. Mm. When you ask a good question, you run through the cycle, you get back the answer, but that might be a day or a week later, and then you're like: "Great, now I can ask the next question." Whereas in a self-serve world, self-serve world, specifically Amplitude, we can get you that insight. By we, I mean, the product can get you that insight really quick. Yeah. Yeah. Oh, yeah, it seems that, you know, even the customer that may approach eight figures, you know, it's because developing this on a bespoke basis would be complicated. It would be creating a data structure. It would be inventing a database for this task, and- The fundamentals of just Data Governance is just really complex and hard, and we'll get to it later, but it's one of the areas we're leveraging some of the generative AI and other pieces to make better and easier, but... I jump ahead. Well, let's talk about it. Let's talk about AI. Let's go to product strategy. You know, you have some product announcements that you talked about in the last quarter. You know, maybe just at a high level, could you describe the AI solutions that you're talking about today? And, you know, what are the most important use cases for, maybe some of the more advanced analytics, opportunities in the future? Yeah. So, kinda, try to answer it in two parts. Today, we're weaving it into existing workflows. We're making those easier and simpler, and we called out two of them in the last earnings call, which is when you're first starting to use the product, rather than identifying the chart or table or insight that you're wanting, just ask us. Ask us through the product what you're seeking, and we'll deliver it to you. Using AI to just let you type in what you're seeking, and let us give it to you, making it simpler and easier. It's a big part of our corporate strategy around Win Simple. And the other is around Data Governance. That's, you know, the, the trust you can put in the data allows you, the user, the customer, to start leveraging it sooner. And our ability to say, "Look, these are the... These are the things you should focus on in this priority stack to improve your data governance," another area where we can use AI, but very much within existing workflows, making them iterative, easier to use. Spenser, our one of the three founders, who's our CEO, has really started to talk about, in the future, right, where AI is creating new workflows. That creates an opportunity for us then to put it together in a productized way, right? Today, we're very focused on just making the existing things work better using AI, but clearly opportunities for us to create new things to do, but that's definitely part of a longer roadmap. Yeah, absolutely. You know, obviously, I mean, we talked about it this morning with a keynote about AI. How do you think about monetization? Because, I mean, when you take your platform, and you embed AI, this is making it better. But, you know, how do you think about some of these features that can be monetized and be kind of pillars in the platform in the future? Do they fall into that? They fall into what I just tried to characterize. When we start to create kind of something we consider more of a tipping point of something substantive- Mm-hmm. Then we can talk about it as an incremental. What I wanna emphasize is while we do sell it, the platform, in that productized framework, it is very much built around... We think those four tenets reflect where we're going, and customers will be buying more of that structure as we move forward in a platform. Anybody who does any homework on Amplitude and does any technology backchanneling is just gonna hear, look, this is, this is the technical solution today and for the future. It's extremely advanced, it's extremely robust, but it has some weaknesses, and the weaknesses is getting started is a little bit cumbersome, right? Our ability to get data ingested from your various spots and start to leverage is cumbersome. Our ability to use that AI to make things simple, like, those are the areas that we need to improve on. Those are the areas that we wanna leverage AI, in terms of today, just kind of making the overall solution set a little bit easier for that first-time customer, that first-time user. I'll close with... Look, before I joined Amplitude, tried to do my diligence, reached out to a lot of my tech leader friends and had them reach out to their Slack communities and Twitter communities to get feedback, and just got overwhelmingly positive on technology product architecture. One of the wonderful things since I've been here six months is nothing but validation on that. Mm-hmm. It is really robust. But where it's- it needs the improvement is Win Simple, and that's one of the things we really prioritized around for the engineering team. So kind of weaving- Yeah ... all those, all those pieces together to your question. Faster, faster time to value. I mean, always iterating- Yeah ... on that is beneficial to the customers. One thing that you mentioned in terms of data ingestion, that maybe we could turn the lens towards a partnership with- Snowflake. -Enterprise, yeah- Yeah ... enterprise data warehouses. You know, can you tell me a little bit about that partnership? I mean, when you think about the, the data that you interact with today versus the data that's in that, you know, cloud data warehouse, I mean, what is that incremental opportunity for you? How excited are you about? Yeah. How material could it be to start, you know? Yeah. Look, I'll start with the benefits to Amplitude and sort of, kind of, the benefits to that customer set- Mm-hmm ... and the persona specific, and it's, it's really compelling. I will qualify, we're still shaping what that product looks like. Mm. We're still very early with Snowflake about what go-to markets would look like. We have done the product announcement, but we're, you know, we haven't set a GA date. Yeah. There's still plenty to do on that journey. Let's talk about the benefit to Amplitude, right? It absolutely will make things easier. The onboarding of just putting kind of the application layer and the insights layer onto an existing warehouse, the time to value gets decreased- Mm ... dramatically 'cause we don't have to deal with the, and the customer doesn't have to deal with, the movement and the ingestion of data from their different spots into our vertically integrated. That's very much beneficial, both Amplitude and the customer. It is, it opens us up into a new marketplace that we're not hitting as directly as we could, in terms of the Snowflake customer base that's out there today. It's a good differentiation for Snowflake... and really does speak to one of the key personas that's in these enterprises, both digital-native and traditional companies, which is the data leader, right? That data leader is looking at it from two lenses. One is, I've already spent all this money to get it aggregated into a data lake. I know it's in-- I have it in other places, but it's predominantly there, and I really, the data leader, really hate having to incur the cost with you, Amplitude, to move it into... and allow you to ingest it into your fully integrated solution, right? I should get some cost savings for all that I've done there. Very valid point. Second point is, hey, look, sometimes this data shifts, and the data drift comes into play, but I know it's not drifting if it's sitting in my warehouse. Yeah. Those are some of the positives, both for us, for speaking to that persona, but it has some limitations that play to the other personas that are in those enterprises. It has some limitations for the product managers and some of the marketing users of it, the time to process, the compute costs that's associated with it, definitely goes up, right? Mm-hmm. There's value in how we've the Data Governance side of how we've organized and our ability to do speed. At a minimum, it's gonna be a great kind of stepping stone for that customer set, where they'll get quick time to value, can get some kind of basic utility, some early use cases, but as that becomes more robust, we really see it as a lily pad of them moving- Mm ... to the more fully integrated solution. One of the key themes in that, I'll just close with that, is, look, we're trying to meet customers where they are with where their data resides. This is absolutely aligned to that sentiment. Yeah, absolutely. Let's maybe talk about the customer base overall and maybe, you know, we've had all this discussion about some of the larger enterprises, the Fortune 500. You'd be forgiven to think that this is an enterprise-only company. And so maybe just talk about how big is enterprise relative to SMB in the business? You know, what metrics might be a good map or guide on that. Yeah. Maybe we'll keep you there, and then we'll dive into maybe some more. Okay. Yeah, look, we do have roughly about a 20% concentration into the SMB, right? 80% are established companies. That is the base of our ARR. Within that 20% of what we call SMB, we have $1 million+ accounts and $100,000+ accounts. So SMB is much more synonymous with just VC-backed technology startups than the ones that are very engaged with their customer sets, even though they have a small employee base, are very large customers for us. But that's kind of our, our mix today, kind of 80/20- Yeah ... between the two. You didn't ask, but this is important to me, and I think it's important to the investor base, is there's another lens by which to slice this, right? We talk about digitally native companies, companies that come out of the technology space. We gave Atlassian as a great example. Mm-hmm. We're still very highly concentrated in the digital native space. We are still very early in our penetration into traditional companies. I used the Chick-fil-A example, I used the Ford example, but that's where the lion's share of the broader market is going to be, and we're still, as our overall market, still early in that. What's the last piece that's really important about that distinction is, look, that's where Adobe and Google Analytics plays, with the marketing and analytics solution. The reason that's really important that those markets are converging is, the ultimate winner of this space is gonna be the one who can bring the technology a solution that is there for tomorrow, right? Mm. Which is gonna be a much more kind of robust assessment of insight to actions, built around a digital product and converged marketing. But those dollars are owned by the CMO predominantly, right? We talked about the size of that, and it has been Adobe and Google Analytics that have kind of own that wallet share. A really fundamental part to why I think Amplitude is really well positioned is it has a leadership team that recognizes that. Those are the key buyers within that customer set. Our ability to speak to them effectively, to convey our value and differentiation, those are all things that we're putting in place as part of recognizing that it's not a peanut butter. We don't have a single group of customers, that the split across enterprise, commercial, and SMB isn't adequate. You need to also be looking at it as digital native versus traditional company, and how we align our go-to-market and how we drive our SaaS Magic Number up, is all a function of aligning our go-to-market sales around that distinction. So, I'm increasingly talking about we have six customer segments, not three, right? Not just enterprise, commercial, and SMB. Yeah. It's also split between digital native and traditional companies, and we're building kind of the quantification of where our own install base is around that, how we map up our marketing message of how we communicate from expand motions, how we enable the field organization and the marketing with insights into the customer accounts they're going after and the profiles that fit there, so that we're speaking in terms that resonate with that customer set and that persona within- Mm ... those customer sets. I think, you know, lastly, if... There have been a lot of dynamics in terms of, in those digital natives. Obviously, a lot of them were, maybe participating in the economy at disproportionate levels during the pandemic, had big accelerations in their business, and, now spend is under a different level of scrutiny. And, maybe not to give a six-part-level health checkup on all these different things, but, you know, where are you seeing the most green shoots? I think you've kind of characterized it as flying blind versus trying to rationalize spend, as there's a very different pattern of behavior between the digital natives and the people that are just- Yeah ... starting their digital journey. That, unfortunately, is a more complex question than I'm gonna be able to answer in 20 seconds. But, I'm not gonna look, enterprises, right-sizing. We have contracts from 2021, 2022 up for renewal that we're resetting based on their volumes with their end customers. But we also have 20% that we're exposed to the SMB. They're going through a really tough time. Mm. A lot of those are just struggling to survive. Those are the ones that are flying blind. 80% of our business, going through its optimization to the levels that are commensurate with their end customers. 20%, going through a rougher time. Mm. Clearly, not all 20% of that is exposed. Like, we did 2 large expansions that we called out in Q2. Yeah. Yeah, no, it's good stuff. Yeah. Yeah. All right. Well, that's all we have time for. Criss, I really appreciate you making the time. Appreciation. Bye bye.