Amplitude, Inc. (AMPL) September 2026 Conference Transcript

Thanks for being here.

My name is Billy Fitzsimmons. For those of you I haven't had the chance to meet, I cover application software here at Piper Sandler. Started at the start of 2026. I covered the hyperscalers, apps, vertical, and we're really excited to have Andrew Casey with us, CFO of Amplitude. Thanks for being here.

Appreciate it.

I think a good place to start is people in the room might be familiar with Amplitude, but a lot's changed over the last year. Can you both take us through quickly what Amplitude does, the problem itself for customers, and some of the recent changes?

So I've been with Amplitude for two years, and maybe I'll start with the two things that were going on right when I arrived, which is this effort to, one, sell to more enterprise clients, and two was a postulate that our founders have about all the applications that are kind of surrounded product analytics were some of it a bug. They really thought that they should all be integrated in one platform. So it gets back maybe to the first question you asked. What does Amplitude really do? You've got to think of Amplitude as this observation observability layer with every piece of software that gets created. And our foundations were around product analytics where we were – our motto was we help people build better products. And so you're getting feedback on how people are interacting with a mobile application, a website, a kiosk, Anything that had software and a way of digitally engaging with clients is what businesses were deploying, and they were using Amplitude to get that feedback. And a lot of it came in the form of, how do I make this website better? How do I make the feature get demanded more? How do I make sure that a marketing promotion is being seen and being acted upon? How do I make sure that cart abandonment isn't a big issue? Those are types of issues that every one of our clients who are using classic product analysts was trying to determine. Now, they may also be deploying applications like Session Replay to get qualitative feedback or using an experimentation platform to understand if they're making changes to the product, what the impact would be. They might have used the CDP or an activation software, then take cohorts of customer information and feedback and move those into a Klaviyo or a Braze. They might have used a Pendo or a WalkMe for a guide and a survey to kind of point people in the right directions on how to interact with their product. And all of those, as I said, were created around product analytics. They're taking their feed from the insights that the product analytics engine was providing. And so we decided that we should drive a strategy around building those products and adding them into a core platform in which customers could easily migrate or move between those different application environments, not have to learn a new UI, not have data taxonomy issues, and just drive greater and greater workflow optimizations. And so I'd say over the last two years, we've kind of executed on those visions of, one, selling more to enterprise clients, which are increasingly trying to engage digitally with their clients, and consolidating all these other applications into a single platform.

I think one of the interesting things in the last earnings call that we talked about was, walk us through this. So over the last two years, it sounds like monetization has maybe lagged underlying data ingestion growth. And then on the last call, we talked about how those trends are starting to potentially reverse. Can you walk us through the dynamics around that?

So when I arrived at Amplitude in Q2 24, there was a backdrop of decelerating growth. There was a lot of discussions around how many contracts actually have entitlements, which are much higher than their actual utilization rates. And what Amplitude had been seeing is contraction and logo churn. especially with what I would consider overselling capacity and some really poor sales practices. In fact, one of the stories I tell is one of the largest contracts we had, I think I told you this before, but just for the audience, one of the largest contracts we had, I realized that it was a 12-month contract, and upon renewal, the customer was entitled to a 33% reduction in the rate of data that they were ingesting in the platform at the same volume. And so I look at that contract and say, well, you need to take into consideration the likelihood that the customer would renew and incorporate that into your ERR. Well, that didn't happen. And so we were looking at a customer who was renewing. They were using the product, but we were going to get over a million dollars in contraction just because they had this renewal opportunity. So that's an example of a really poor selling practice that you just wouldn't do. And today, if that were going to happen, I would tell the rep, well, if that's the way the customer wants to do it, to make sure they're not going to have an increase in data ingestion fees in the first year where they're wrapping up, that may be okay, but I'm going to pay you as if that second year does get renewed. So those are types of the behavioral changes, the, I'd say, maturation that we drove throughout the go-to-market team. Yes, we replaced a bunch of them. Yes, we made them enterprise sellers, gave them a selling process and criteria and pipeline analytics and, you know, just really moved amplitude more and more into what I would consider a very mature enterprise selling process, along with executing against this consolidation play we saw out there. I think all of that culminated into the Q2 earnings announcement. We said, look, remember when we were talking about so many quarters about the churn that was happening, what the reasons were behind it. We kept using, you know, hey, this is inhibiting our growth. And if you would have looked back at that time in Q2 24 and said, what was the real percentage utilization, customers actually using their data versus what the entitlement was, and I would tell you that was around 60%. So it just portended that there's going to be more additional churn happening. If you take that same view this last quarter, I'd say that the data utilization versus entitlement was at 85%. And that's a very healthy place to be because it incorporates not only customers who had just signed and they're ramping up, but also customers who are now getting to the point where they've either been upsized or expanded into that entitlement. And so now we're looking at a lot more renewals where customers are coming in and they're having to increase the amount of data ingestion that they're contracting for because they've outstripped that entitlement.

I have a lot of questions on everyone's favorite topic, AI. Can you contextualize for us Amplitude's role in kind of this AI era? There are kind of several products you have to address AI. But let's start with Wave, agent analytics, and your custom agents, and kind of the opportunity that gives you going forward.

Can I do it in reverse order? Because I think it's more of the evolution of the product itself. I would tell you that for a long time, Amplitude looked at AI as not very interesting. And then there was a period under which increasingly it became clear that AI was going to be a key enabler for us, and that more and more of software is going to be created using agentic capabilities. And the exponential increase in software was going to create a much broader surface for us to deploy Amplitude for instrumentation observability. And when I explain Amplitude to friends and people about what we do, I say you've got to imagine that software slowly but surely had many, many, many, many layers over a period of time. And Amplitude has the opportunity to be that key observability instrumentation there for every piece of software that gets created. And because there's more software being created, that expands the relative TAM for us. Now, we started implementing AI with a global agent and with these custom-built agents that you're talking about. And we also enabled our MPCP server for a couple of reasons. First, we wanted to make Amplitude easier to use. for a long time, and this predates you, but a long time people would say, Amplitude's hard to use, and it's hard to implement. And we'd say, well, we agree, and we're working on it. And we did a number of things to make it easier and easier, like a single line of code, we consolidated the SDKs, but it was still an interface that was hard to use, unless you were a data scientist or an analyst who was very accustomed to working with within an application environment that brought all these disparate data sets together. And so by implementing a global agent, Now, increasingly, our customers are interacting directly with that prompt and just saying in a natural language way, I'd like to see what the actual conversion rates are with a new marketing program that's rolled out. Can you draw a couple of charts? And then once that's done, interface again in that way to augment, or can you give me feedback on the cohort of customers that would be most likely to demand this product based upon historical frameworks? And so that interaction is one that was really focused on how do we get more and more of not just the real data scientists and people who are very interested in the amplitude instrumentation, but rather the broader marketing analysts, business analysts, pricing analysts that could use the data and do it in a much easier way. So broadening the adoption, broadening the use cases was why we started integrating the MCP server. that brings in more data sources and the global agent. And we figured if we charged directly for that, that would create a pricing barrier where adoption would be mitigated. And so we didn't want to do that. It was so important for us in this consolidation strategy to make it broader, more use cases. Now, as we implemented these modules, experimentation, guides and surveys, activation, you started including specialized agents that actually worked within those applications. And session replay is probably the one that had the most immediate impact because session replay gives you qualitative visual feedback on how anybody is interacting with an application or website. And engineers would actually have to get used to, have to go look at all these different recordings. It could be thousands of them. Variably what they're doing is they're sampling. Well, agents are really good at accumulating lots of data and summarizing it for you. And so that's exactly what the session replay agent was doing, is it's giving engineers the ability to say, I want to look at 10,000 session replays. It's still for me the most important aspects, and what does that tell you from? What is the insight you get from it? And so customers were, again, looking at these specialized agents and says, wait a minute, this is enabling me to do my work more efficiently. And by the way, having the agents deployed in a consolidated platform, not individual products, but in a consolidated way, And how do you start to stitch together workflows? So let's say now, from a product analytics perspective, you get some insight on your website. Hey, there's a lot of people who are interacting on this banner that you say is a promotion, but it's actually not driving conversions. Like, okay, that's the insight. Run the experiment. What's going on? Let me see the qualitative feedback. And the agents are actually prompting these interactions. And if you don't have paywalls and they're all integrated well, suddenly the customer starts getting experience, which is much more holistic about how you actually drive that engagement to ultimate conversion. And so both those instances, the ease of use with the global agent and the specialized agents provided this framework where the power of all these things working together in a platform was really exemplified, even if the customer wasn't entitled because we dropped some of the paywalls and they could start testing experimentation or testing the activation product. And invariably, they'd run out of entitlement, and they'd either have to contract with us or stop using, but that then provided us a greater and greater pipeline for customers who might be interested in expanding with us, even if it wasn't in the moment. Okay, so that's kind of the backdrop on how we started making really good progress using AI to drive our consolidation strategy. And then I'd say when you talk about products that are for fee, like AI Feedback, Agent Analytics, and Wave, which I'll explain each one, AI feedback, you've got to think about it. It's a way of getting customer sentiment and feedback beyond surveys. Think what Qualtrics does today. They do a bunch of surveys. They get community information. What's the response rate? It's pretty low, usually, with surveys. And it's not really product data. It's sentiment data. So you want to marry product data, sentiment data that you're getting potentially in surveys. That's guides and surveys. But then how about just this holistically around Reddit feedback, LinkedIn feedback, what they're saying about the product, your customer support, all that's connected through AI feedback. And so you get a more holistic view of what the customer's actually perceiving your product to be good or bad at. And that influences what you would build next. Agent analytics is, we kind of stumbled upon as we were building our own agents and realizing that there really was no application environment to measure how well your agents are doing what you intended them to do. And think about the implications here. Every enterprise is building or deploying some type of agent to enhance customer service, improve workflows, to drive increasing monetization efforts. All of these things then are surfaces in which you may want to understand exactly what your agent's doing. In some cases, make sure they're actually doing exactly what you want to do as opposed to compromising your environments or providing personal information externally. In fact, there's increasingly more and more healthcare companies that are looking exactly to us to help them not only with the modernization of their web interfaces with their clients, like Sutter Health, but they're also starting to look at how the agents and the chat and the customer service environments can be better instrumented and managed to provide the outcomes that you're actually looking for. So if you say, I want an appointment set up, great, agent may jump in. If that agent doesn't actually get you to the point of booking the appointment, then it failed. And if you don't understand the reasons why it would fail or be successful, because you're not actually understanding everything that's happening outside the agent, then the investment's wasted. So agent analytics is this opportunity for us to really help instrument and manage workflows that are being increasingly directed by agents. And sometimes it's not agent to human, sometimes it's agent to agent. And so we just went GA with agent analytics. we're charging based on the number of sessions. So think of that in terms of you engage with a customer service agent. That would be a session. And so the real critical aspect of getting this right as we evolve and add more customers is what's the right yield? And what are you seeing in the session volume versus the yield that the customer is getting, whatever their outcome is? And I don't think that we're absolutely locked in on the price point and the strategy at the moment. I think that we're increasingly going to find that there are variations in sessions by use cases and by verticals, and then that may cause us to have a different framework as we start deploying the agent analytics in different ways. Wave is a really revolutionary product, and it comes from Spencer and Curtis, our founders, talking about that software really should be able to improve itself. One of our taglines is we help people build better products. Well, the next step from that is why can't the product build better for themselves? Why can't it constantly update and understand what's going on in security patches or integration issues or personalization? So AI has really enabled that vision to come to fruition in a product. And you've got to think about Wave as a product manager, a data scientist, and a developer all rolled up into one. And the customers that are beta testing us today, they're using Wave in lots of different use cases. but one is using us in loan origination, and WAVE is already surfacing ways in which the web artifacts that customers engage with should look at not just the rates of the loans, but what the payment is for the customers should be paying. I know that's intuitive, but sometimes you kind of surface these major little things that cause loan conversion to be increased because the customers now associate with, oh, this is the payment I'm going to make associated with this, and not only that it surface what the issue was, it built the code, deployed the code, tested the code, updated it consistently. And so as there's more feedback from interactions, Wave is actually driving continuously improving products with those interactions for that use case. And so where would this go? I mean, I think every business that wants to digitally engaged with a client is using it in an agentic way is going to have a workflow they want to observe, and they want to drive autonomous engagement around, and they want to drive improvements. One of the interesting things we're seeing now from all these router optimization companies like Open Router and Fireworks AI and others, they're looking for ways in which they can get the best, lowest cost model to meet the performance that companies are expecting. But what if you could actually do better? What if you could actually take token spend to outcome based upon the use case, optimizing for the router in between for the cost? Suddenly you've got a really, really strong feedback loop associated with the ROI. You're making investments in AI. So that's kind of some of the initial use cases of Wave. I would say just in product development is another major one. You have Wave deployed in your code base and constantly making upgrades and testing based on changes that your development team is contributing to your repositories, to security fixes that are coming on, to bug fixes. And so there's really a broadening TAM associated with where Wave can be deployed. I think our initial focus will be in e-commerce and retail because everybody's got a digital interaction where they're trying to figure out how they optimize conversions and reduce cart abandonment. But Wave and Agent Analytics don't need core amplitude. They can actually be deployed in data environments of our competitors. They can be deployed in different data sets that we haven't been exposed to in the past. So it's a very interesting opportunity and growth.

Having new things. And I want to make sure I ask you about Statsig as well. So this was technology Amplitude got from OpenAI. I think when it was announced, frankly, my read talking to investors, there was a lot of confusion kind of around it at the time. I think people understand it a little better now. Can you just size the opportunity for us, what the technology is, what Statsig does relative to core Amplitude, and then come to the cross-sell opportunity here.

So first, I think you have to understand a couple of things are going on in the market, too. First, every major enterprise is figuring out how they're going to manage your data strategy, and a lot of them are consolidating with data warehouses like a Snowflake or a Databricks. So that's happening. The other thing that's happening, we talked about earlier, is all these new AI development tools, they've done nothing but create exponential increases in how fast that software can be developed. Now, what hasn't been really addressed is how good that software is. Is it actually doing the things you intended it to do? So there's a bottleneck in what we call the QA and test and learn process. You deployed something, what was the impact? Well, that's increasingly becoming a tool called experimentation in the product development process. And StatSig, on its own, had done a very good job about appealing to software developers and AI developers to go test and experiment their new code as they're putting in production. A customer of ours, Granola, they do a great job of using core amplitude, but they're constantly running experiments on their code and deploying it based upon what they're learning from the process. So they develop, they experiment, and it's happening just in a flywheel consistently. And so the software development process for them, the whole product development lifecycle, is increasingly turning very, very rapidly, And that's allowing them to drive new innovations and fix bug problems. And so Stats did a really good job of appealing to that use case, data warehouse native, and development teams, engineering teams. Now, Amplitude had an experimentation product. It was mainly focused in cloud-based environments and for other use cases. And so as we've seen more and more, this full product development lifecycle starts to open up opportunities for us. we saw a key opportunity, one, to get the best technology in the marketplace associated with data warehouse experimentation, and two, appeal to an increasing group of developers, both internal and external, that are going to want to use and have a tool set for running the experiments at scale. And so since we've brought StatSig on, I would tell you that I've had numerous conversations with both existing StatSig customers and with potential, and they're just reinforcing the thought process there. This is going to be an enormous opportunity for Anthem to take that technology forward. Land with new clients, land in G2Ks, sometimes not associated even with the core use case that they built, but rather appealing to, if companies want to become AI native, one way they can do that is by changing their development cycle and incorporating experimentation at scale to do it.

I want to tie all these things to financials. We've talked about a lot of new products, accelerating product velocity. You have a lot more to sell. How do we just think about, like, the growth rate of the business going forward for Amplitude? And then I also want to get your perspective on margins and margin structure. Do you provide leverage over time?

So let me start there, because ever since I've become the CFO, I've been using this phrase called growth with leverage. And if you look back at any of our transcripts, I talk about it. And that we're going to go drive growth. We're going to make investments around it. But that does not mean it's at all costs. We have to be profitable. And, you know, that was the first year people kind of were very cynical about it, whether we could do it or not. In 2025, we broke even. And, you know, the first couple quarters in my guidance for 26 would suggest we're continuing to do that. And so that's just been my mantra, and it will continue to be. Like, we're going to continue to make investments, but we're going to grow with leverage. Spencer has talked a lot ever since our, frankly, our investor day in March 25th. He believed that 20% was like the minimum for this business. I think we're very early. It's still developing. There's lots of opportunities, but we really should be growing much faster than we were. And when I joined, it was like growing 6%. So we've gone from 6% to 22%, some of that, you know, majority of that organic, some of it inorganic with the static acquisition. But the reality is I'm not really satisfied at where we are. I think we've got a tremendous amount of opportunity. We're still pretty much a direct sales model with relatively few reps. Those reps aren't as productive as they could be, and we really don't have a major distribution channel. And I think some of these new products could afford us broadening of distribution channels. So I think that there's opportunities for us to continue to accelerate growth, and certainly there's areas for optimizations in our cost structure as we go forward and getting greater and greater leverage. Now, I'll talk about gross margins because you asked about it, but when I set the budgets for the company, We set a budget where we expect to have revenue growth on it. And the fact that our RPO has been growing for six consistent quarters over 30% gives me greater and greater visibility of what revenue is going to be. And then we set the target for expenses at a lower percentage of revenue than was the prior year. And that means that they're going to have less to make investments, and they have to figure out and prioritize. And that's everything from R&D to our sales and marketing and G&A. Sales and marketing, I first joined, I think it was like 47% of revenue, way high, very inefficient. It's down to 39, but it really should be more like 32 at our scale. So we still have some path to go there. On R&D, I will try to keep my best around 18% to 20%. We were actually underinvested when I first joined. It was 16% of revenue. You might see that get higher in any one given quarter if there's acquisitions to do or investments to make, but I think that's a good range for us. G&A was 17% when I joined. I mean, it's down to 12, and I think there's still room for us to drive greater and greater efficiencies. So we're going to get leverage on the OPEX side. I know we're almost at that. Gross margins were impacted this last quarter for a couple things. First, we took on Statsig. Statsig's hosting environment, we had to slam into place in order to ensure that customers did not have service disruption. It really wasn't optimized. We didn't have a long-term structured contract with Google. We weren't even sure we were going to keep Google. So there's a lot of work to do there. but I have a lot of confidence we're going to go work in static environments to be more consistent with amplitude. The other thing is, which are not necessarily bad things, but we had costs associated with inference and data ingestion. They're outpacing our ability to actually drive conversions. And the inference costs were somewhat our own strategy, broadened adoption, broadened usage, but those are starting to show up in new contracts and customers expanding. And on the data ingestion side, we've had a pretty good history of once customers are above their entitlements and moving that towards a monetization. So I would look at that as like a precursor to increasing ARR in revenue. And we're 99% of our business is ARR. It's description-related. And so, you know, when you see those types of things happening, it's really about us seeing success associated with adoption.

Perfect. We'll wrap there. Thanks for joining us. We appreciate it.