Amplitude, Inc. (AMPL) Earnings Call Transcript & Summary

Good morning, and welcome to Amplitude's Second Quarter 2026 Earnings Conference Call. I'm John Streppa, Head of Investor Relations. And joining me today are Spenser Skates, CEO and Co-Founder of Amplitude; and Andrew Casey, Chief Financial Officer. During today's call, management will make forward-looking statements, including statements regarding our financial outlook for the third quarter and full-year 2026, the expected performance of our products, our expected quarterly and long-term growth, investments and our overall future prospects. These forward-looking statements are based on current information, assumptions and expectations and are subject to risks and uncertainties, some of which are beyond our control that could cause actual results to differ materially from those described in these statements. Further information on the risks that could cause actual results to differ is included in our filings with the Securities and Exchange Commission. You are cautioned not to place undue reliance on these forward-looking statements, and we assume no obligation to update these statements after today's call, except as required by law. Certain financial measures used on today's call are expressed on a non-GAAP basis. We use these non-GAAP financial measures internally to facilitate analysis of our financial and business trends and for internal planning and forecasting purposes. These non-GAAP financial measures have limitations and should not be used in isolation from or as a substitute for financial information prepared in accordance with GAAP. Additional information regarding these non-GAAP financial measures and a reconciliation between these GAAP and non-GAAP financial measures are included in our earnings press release and the supplemental financial information, which can be found on our Investor Relations website at investors.amplitude.com. And with that, I'll hand the call over to Spenser.

Thanks, John, and good afternoon, everyone. Welcome to Amplitude's Second Quarter 2026 Earnings Call. Today, I'll cover 3 things. First, our Q2 results. Second, how we transformed Amplitude into an AI company and why every company I talk to now wants to learn how they can do the same. Third, a look at our product and a spotlight on our customers. Let me start with the numbers. Q2 revenue was $101 million, up 21% year-over-year. Total annual recurring revenue was $410 million, up 22% year-over-year and up $36 million from last quarter. That was made up of 2 parts; inorganic ARR from Statsig of $17 million and organic ARR growth of $19 million. Andrew will walk through the details. Non-GAAP operating loss was $1.5 million. Customers with more than $100,000 in ARR grew to 824, an increase of 30% year-over-year. Both AI natives and large enterprises are driving this growth. Let me step back and tell you about our transformation and then how we're helping customers along their AI journeys. We help companies build better products. Every company wants to transform to deliver software products in an AI-native way. We've made that transformation at Amplitude over the last 2 years, and now our customers are looking to learn from us. Becoming an AI company starts with the organization. Two years ago, we first transformed our engineering team by bringing in AI engineers who built with it for years. Then we moved into adjacent functions like product management, design and the more technical parts of go-to-market. We also brought in AI expertise through acquisition. Founders and other members of the team from these companies have taken leadership roles across Amplitude. I have focused on bringing in leaders who are former founders and who have a technical background. Gab, our Chief Product Officer, started multiple companies, including Loom Systems, which sold to ServiceNow in 2020. In addition, Nate, our Chief Commercial Officer, has a degree in math and physics and started his career as an engineer programming in C++ and Java and building databases. Most recently, we added Angela Ferrante as SVP of Marketing. Angela founded Laudable, which went through Y Combinator Summer 2021, sold it in 2025 and is a technical marketing leader who builds apps with AI in her spare time. In addition to all of this, we're continually reeducating everyone at Amplitude through initiatives like AI Week, unlimited token spend and a living token leaderboard. This has all resulted in 3x the number of pull requests in 6 months. We've reduced our pull request cycle from 5 hours to 44 minutes. Bug reports are down 55%. 5% of our pull requests are submitted from designers and product managers with no engineering involvement. We've leveraged AI to shorten our closing process by a day. We built customer health dashboards that enable our sellers and leaders to track customer usage, bringing our own Amplitude data alongside Salesforce data and data from other sources. When I talk with our customers, they are all focused on how they can transform their business to be AI-native, like we have done at Amplitude. The AI landscape is changing rapidly, and they want to learn how to adapt. Our customers are on a spectrum of AI adoption. Our job is to meet them where they are and then educate them on how to take the next step. We work with leading AI companies to learn what the bleeding edge in product development looks like. We use that knowledge to educate the rest of the market, including the largest enterprises deploying at scale. More than 40 AI-native companies now pay us over $100,000 a year. Those customers include Harvey, Midjourney, Character AI and one of the leading foundational AI model companies. On the enterprise side, enterprises are now more than 68% of our ARR. This quarter included agreements with Paramount, Jaguar Land Rover and Domino's Pizza. We've improved our pricing and packaging. We reduced down to a single meter to make it simpler for enterprises to add additional products. We increased the amount of data on our free plan, so we're the best for those just getting started. Amplitude has the best pricing, whether you're a start-up or a large enterprises. One of the biggest changes with building an AI-native company we're seeing at Amplitude and with our peers in private markets is in the cost structure. A lot of inference spend is required in order to deliver AI-native products, which increases the amount spent on cost of goods sold. On the other hand, you do not need to add as much operating expense to continue to grow a business at scale. We are embracing this change in cost structure as part of our transition to an AI-native company. For now, we expect gross margins to stay in the low-70s. We will offset that with a commensurate reduction in operating expenses. That allows us to continue to show the same leverage in operating income as we have planned. I am continuing to drive Amplitude to a 20%-plus operating margin business over the long term. We offer 3 products to meet customers wherever they are on their AI journey. Amplitude gives you the deepest understanding of how people use your product. Our agents increasingly do that discovery for you. Statsig gives you feature flagging and experimentation built on the world's most advanced SaaS engine with an engineering-first view. It's also integrated natively with data warehouses. Wave is the future of product development, self-improving products where we automatically recommend what to build next based on signals from users. While we're early here, I'm actually excited to show you a demo today. Together, these 3 products close the product development loop, understand what's happening, measure what ships and ship what matters. That loop is how AI-native business is built. Let me go deeper on Amplitude. Global chat is becoming the primary way our customers interact with their product data. You ask it a question in plain language and it does the analysis, no dashboard building required. It's become the de facto way many companies do product analytics. Global agent finds the root cause behind 75% of customer questions and hands you the answer. There are 1.3 million global agent interactions every week and root cause discovery rates are improving by 1 percentage point every month. As of today, over 40% of all insights come from AI agents as opposed to humans, and we expect this to continue to grow. Today, for our demo, I want to show you custom agents, Statsig and Wave. Let's start with custom agents. Custom agents are teammates that automate recurring workflows on your product data and push that work to other tools and systems. This is our chat interface. An increasing number of users are interacting with Amplitude mostly through chat and agents. I'll ask a question. Which group of users are most likely to purchase next week? Chat can now write its own code to perform this analysis. This unlocks the ability to run deeper analysis and create powerful new graphs and artifacts, including diagrams like you see here, out-of-time decile lift, an ROC curve, segment propensity. You can dig in by seeing the actual code used and step-by-step analysis. This type of deep analysis has never been available before in analytics tooling. We are no longer bound by the constraints of a UI. We can also create automatic and recurring agents that run in the background. I give it these instructions. I want this analysis run every Monday morning, cross reference with marketing activity and Confluence, DM me the results in Slack. Amplitude then creates the agent that you see here. This is the entire prompt, including connectors to Atlassian and Slack. It will run regularly every Monday and push the results to me. We are building the best analytics agent across all data sources. Statsig is the leading product for experimentation and feature management. Statsig runs experiments natively on your cloud data warehouse, whether that is Snowflake, BigQuery, Databricks or Redshift. Let me show you what this looks like. Here is the results page for one of hundreds of experiments that an e-commerce customer is running. This experiment is testing a larger product image versus the default size. There's a lot of statistical machinery behind a good experiment, but the UI makes it simple for an engineer to run. Up top, they can monitor exposure, which is saying if the experiment is healthy or not. We expect to see a 50-50 split, so we're doing good. And as you can see over here, we're getting a healthy check. We move to the scorecard that has the results. This has a confidence interval of 95%. Statsig uses advanced techniques like CUPED and sequential testing that allows engineers to speed up time to decision. We have those turned on. In monitoring, we see specific events we're tracking for this experiment. We're seeing positive results. The checkout event is up by 27.4%, plus or minus 2.3%. Cart conversion is up. Total purchase dollars is up, while cards per session is down. For the rollout of this feature, we have a progressive rollout, starting with employees moving to early access users, then early release and a scheduled rollout for everyone else. Statsig has a variety of advanced experimentation capabilities for rollout like feature gating, dynamic configs and automatic rollbacks. Together, these are the mechanisms that a team uses to ship a change gradually, tune it while live and pull back automatically it goes wrong. Last, I want to show you Wave, the future of product development. Wave allows for self-improving products that automatically recommend what to build next based on signals from your users. Wave is magical. Wave looks across all the different data sources you have; analytics, experimentation, session replay, Guides and Surveys, feedback and many others. It then synthesizes that data into a set of product recommendations, plans those recommendations and then helps you create those changes in your product. I'm going to walk you through a real example Wave suggested and built for Amplitude's documentation site. On our documentation site, Wave found a spike in failed searches through looking at Session Replay and analytics data. The core problem was that search on our docs page fired on every key stroke, typing a single letter to start a search returned an empty no result state before the person finished typing their search leading to a bad experience for users. Wave explains the reach of this issue. Every user who uses search, it has an expected impact of decreasing total search failures by 80%. Then Wave has automatically created a visual example of the problem below, so it's easy to understand. It also has a full explanation of the evidence. For the plan, Wave sketches a wire frame of the recommended update, setting a 3-character minimum and a 200-millisecond debounce to trigger the search. Wave can also drive execution. It automatically created the poll request and cursor wrote the code. Mark, our Technical Writer, was able to merge this poll request and ship this. No engineers, no designers and no product manager. Finally, Wave measures the results of the change. There is a massive decrease in total search failures. Simply amazing, simply amazing. Now, let's talk about some of our customers. We had a great quarter with both new lands and expansions. We added or expanded our relationship with customers, including Paramount Global, Jaguar Land Rover, Teladoc Health, Chime, Disney ad platforms, F5 Networks, Coursera, Grammarly, Kraken and Crunch Fitness, among others. I want to tell you 3 stories about how these customers are leveraging our platform. First is Coca-Cola FEMSA, which sells to hundreds of thousands of small shops across Latin America. Every shop is different, but for years, they have to run the same broad campaign to everyone because there is no way to tailor a message to that many retailers by hand. AI changed that. They began sending each retailer its own recommendation every week written by AI. Their own teams were actually skeptical. A different message for every shop every week felt risky and no one knew if it was going to work. They used Amplitude to find out. Their AI campaigns actually had an 11% click-through rate, 4x higher than their previous approach. Our cohort analysis also showed that this lift lasted. Once a retailer engaged, its revenue stayed higher in the weeks that followed. That evidence turned skeptics at FEMSA into believers, and they scaled from a 2,500 store pilot to 690,000 retailers. The second is Replit. Replit is an AI app builder that allows non-technical builders to turn an idea into an app using AI. Replit has a large global user base of passionate builders that provide feedback. Replit is using Amplitude AI Feedback to understand how customers are engaging with their agents. They've connected AI Feedback to Zendesk, App Store Reviews, Twitter and Reddit and surfaced and prioritize what problems should be solved to increase their retention and engagement. It changed weeks of manual work on their end into a simple click with Amplitude. This is the next generation of product development at work. Third is The Economist. The Economist is a print magazine that's in the midst of a transition to digital delivery and subscription. Their research arm built an AI assistant called Lens that answers questions for analysts and strategists using The Economist's content. Their normal analytics could show what users did, but not whether the AI's answers were any good. The team was reading sessions by hand, but they couldn't keep up. Amplitude Agent Analytics now scores every answer Lens gives automatically. They went from reading a handful of sample sessions to being able to see across all of them. Today, Lens holds a 96.9% task success rate and weekly failures are down 84%. That is the loop working, build with AI, measure whether it is good and fix what is not. To wrap up, the companies on the bleeding edge are choosing Amplitude. We've transformed Amplitude to be AI-native, and we're building the future on what can be done in analytics. Self-improving products are closer than ever with Wave. Our pace of innovation continues to accelerate, and we're building in a way that can scale with leverage. I'm extraordinarily excited about what's ahead. With that, I'll hand it over to Andrew to walk you through the financials.

Thank you, Spenser. This was a strong quarter and a clear step forward in our execution, bringing our vision of how products will increasingly be developed and improved. We crossed $100 million in quarterly revenue. ARR reached $410 million, growing over 22% with the addition of the ARR assumed from the Statsig business and free cash flow was a record quarterly high of $23.7 million. We also returned $69 million in capital during the quarter as part of our share repurchase program. We accomplished these milestones while integrating the Statsig technology and customers, managing through our own AI-native evolution and implementing our new pricing and packaging strategy. AI is changing how customers use Amplitude. The more our customers build with AI, the more they need to measure. Customers that adopt our AI into their workflows run nearly 10x the number of analyses compared to those that are running things manually. This increases the value that customers receive from the data ingested into our platform and makes it more likely that they'll both ingest larger amounts of data and expand into additional products, which is the basis of our growth. Our new pricing and packaging is working. It supports our market consolidation strategy by providing customers with a lower overall cost if they consolidate applications onto our platform. It provides customers greater cost predictability and simplifies the quoting process for our sellers. In the second quarter, 70% of the ARR we closed was on the new model, up from 25% in the first quarter. Now, 28% of our total ARR is on the new pricing and packaging. This is leading to average ARR increasing, higher multi-product attach and longer contract duration, which all contribute to greater durability of our revenue. Our margins reflect the choice. These are investments we are making to drive future growth with increasing profitability. Our gross margin was down over 1 point versus Q1 due to the integration of Statsig. We are working to optimize the new hosting environment and cloud structure, but it will take some time to improve from the low-50s gross margin closer to our expectation of 70-plus for the Statsig business. We're also experiencing higher customer adoption of AI capabilities and greater data ingestion into our platform, which combined has increased our costs and reduced our gross margins by an additional 2 points versus Q1. We have long maintained that we will grow with leverage. This investment in the cost of revenue places greater emphasis on the management of our operating expenses to a lower level in order to achieve the leverage. In Q2, we've managed down our sales and marketing to below 40% of revenue and G&A to the low teens, which is contributing to an increase in operating margins. We will continue to manage both areas lower as a percentage of revenue over time, and we will continue to invest in R&D to drive innovation. We are instrumenting our business to accelerate growth, capture market share and show leverage. One key metric we monitor is the usage of data compared to the entitlement for our customers as this is a primary monetization metric. Today, that metric is at an all-time high. This is the output from better pricing packaging and more usage driven by our AI features. We have increased the durability of our business through our RPO growth and reinvented our internal processes to capture scalability that AI offers. We are running the AI opportunity and taking share as we go. Turning to our second quarter results. As a reminder, all financial results that I'll be discussing, with the exception of revenue, are non-GAAP. Our GAAP financial results, along with a reconciliation between GAAP and non-GAAP results can be found in our earnings press release and supplemental financials on the Investor Relations page of our website. Second quarter revenue was $100.9 million, up 21% year-over-year and 8% quarter-over-quarter. Total ARR increased to $410 million, exiting the second quarter, an increase of 22% year-over-year and $36 million sequentially. This includes $17 million of incremental ARR from the Statsig business compared to the $16 million we expected to add when we shared our first quarter earnings. Total remaining performance obligations grew 35% year-over-year to $483 million. Current RPO was up 30% year-over-year and long-term RPO was up 47% year-over-year. Here are more details on the key elements of the quarter. We had a strong quarter for both new and expansion deals in the enterprise and platform sales were again particularly strong. 48% of our customers now have multiple products, with 80% of our ARR coming from that cohort. We have over 26% of our ARR from customers with 5 or more products, up 2x since the second quarter last year. In period, net dollar retention was 105% on a pro forma basis, led by cross-sell expansions across our customer base. This pro forma basis includes Statsig and Amplitude customers. Gross margin was 71% for the second quarter, down approximately 4 points from the second quarter of last year and down 4 points sequentially. This was driven by continued growth in inference costs as customer adoption of our AI tools accelerated, along with the integration of the Statsig business in its hosting environment. Sales and marketing expenses were 39% of revenue, down from 44% in the second quarter of last year. G&A was 13% of revenue, down 1 point from the second quarter of last year. R&D was 21% of revenue, up approximately 3 points from the second quarter last year, reflecting investment to scale the Statsig opportunity and support for those customers. Total operating expenses were $73 million, or 72% of revenue. Operating loss was $1.5 million or 1.4% of revenue. Net loss per share was negative $0.01 based on 129.4 million basic shares compared to $0.01 a year ago. Free cash flow in the quarter was $23.7 million, or 24% of revenue compared to $18.2 million, or 22% of revenue during the same period last year. We ended the quarter with approximately $162 million in cash and investments. We have conviction in the long-term value of our platform and have used and will use our cash to minimize the impacts of dilution. Our balance sheet position remains strong and allows us the opportunity to be more aggressive in our M&A strategy to accelerate our R&D road map when appropriate. Now turning to our outlook. As a reminder, the philosophy of how we set guidance is through the lens of execution. We are pleased with our overall progress on consolidating point solutions to our core platform and the adoption of our different AI technologies. We've instrumented our business and selling process to make it easier to use more of our platform. We believe that we are well positioned to continue to accelerate our growth in a profitable way. For the third quarter of 2026, we expect revenue to be between $105.6 million and $108 million, representing an annual growth rate of 21% at the midpoint. We expect non-GAAP operating income to be between $2.5 million and $4.5 million. And we expect non-GAAP net income per share to be between $0.02 and $0.03, assuming a weighted average shares outstanding of approximately 133 million as measured on a fully diluted basis. For the full-year 2026, we are raising our expectation for full-year revenue based on the performances in the second quarter to be between $407.2 million and $411.2 million, an annual growth rate of 19% at the midpoint. We are also raising our expectation for the full year non-GAAP operating income due to performances in the second quarter and actions taken in the first half to be between $6.3 million and $9.3 million. We expect non-GAAP net income per share to be between $0.06 and $0.08, assuming weighted average shares outstanding of approximately 137.1 million as measured on a fully diluted basis. In closing, we are accelerating our pace of innovation, and we're growing the value that we can deliver to our customers. We have confidence in our ability to scale a durable and growing business while also bringing agentic analytics to the world. With that, we'll open up for Q&A. Over to you, John.

Thank you, Andrew. Going to Q&A. [Operator Instructions] Our first question today will come from the line of Mark Cash from Raymond James, followed by Jackson Ader at KeyBanc.

Yes. If I could start with Spenser, I really wanted to ask around Wave. I appreciate it's still limited beta, but I think you've been using internally for several months now. I guess, do you see Wave, if it could cause maybe a shift -- a company shifting away from using bespoke agents for specific use cases towards a broader AI-native product development platform from what you're seeing? And if so, how could that change your buyer, maybe the budgets you see in the addressable market over time?

When you say bespoke, like, say, more on that, like...

Yes. Instead of using particular agents to do a specific task underlying because you have like a swarm of agents doing things underneath for Wave. So...

I see what you're saying. I see. Okay. So yes, let me separate out a few different things. What we have on the Amplitude side, and I showed with custom agents is you have these agents that can look across the data and find insights for you and get to the root cause of questions and do that on a regular basis and kind of send it out. What Wave is doing, in particular, to your point, is it's kind of -- it's looking at all your data all the time and then saying, hey, here are points of friction. Here's something that's not working out should be. Here's a feature that I think you should emphasize more. Here's something that I think is a best practice that you're not doing. And so it's operating at a kind of higher level. In terms of the persona, I think what we're seeing is a convergence between engineers, product managers and designers into this AI builder persona. It's not really like you have engineers who are thinking about what to build and you have product managers who are also just chipping code. And so the best -- if you look at where the AI-native teams that everyone is aspiring to be, these roles are melding. So it's still the same problem we're solving, which is how do we help you build a better product, but we're just automating more of it because we're saying, hey, we're going to look at all the data all the time and then suggest recommendations. That -- like I've been -- we've been talking about self-improving products here at Amplitude for about 9 years. And so I'm actually been blown away by what is possible with the technology today where it's just -- it's the perfect problem for AI in a lot of ways. The data sets are massive and complex. So, you can't get any human to look at them. And then the synthesis of, okay, here's what I think could be better and best practices is actually like extraordinarily impressive. And so what that means is that just by the fact that someone is using your software, like it's getting better because it's just translating recommendations. You no longer need someone to go into an Amplitude or to any data system and say, oh, here's what my interpretation of these results. So, I do think -- in terms of budget and persona, I do think, again, that means instead of having these distinct roles, you have engineering, product management and design merge. You're still doing digital product development, and that still rolls up to some leader, the same executive before. But yes, the way you do it looks different. Did I hit it on what you are looking for?

Yes. Absolutely. And if I could follow up with Andrew real quick. If my math is correct, the guidance for the year was raised by more than 2x the beat for revenue and operating income. So, I was wondering if you could just go to the key drivers of lifting growth expectations Why you saw some pressure on pro forma expansion sequentially there in the quarter? And then what you're considered regarding margin leverage and levers while you're facing COGS pressure and ramping token spend internally?

Yes. Sure. So, a couple of things. One, that when we look at our ability to actually generate revenue in the out quarters, one, we start with the strong balances we're booking that are showing up in our RPO. When you've got commitments from customers for a longer-term duration, you start to have better and better predictability about your future revenue. So, that's the first thing. It's one of the reasons why we emphasize that metric so much. The second thing is we look at how much our customers are actually responding to some of the initiatives we're putting out, and that comes in the form of our new product capabilities, our new pricing and packaging, areas where our sales team is running new promotions and activities, all those are bolstering our ability to see a stronger and stronger pipeline, and that pipeline progresses faster through its stages, which gives us greater and greater confidence that we'll add more and more in net new ARR. Now from a revenue perspective, as you know, the predominance of our business is all coming from our subscription revenue. So, those key factors on understanding what's the baseline, what can you see in your pipeline, what you expect to convert is what I refer to is our ability to go execute against the plans that are in front of us. And sales teams have been doing a really good job of driving consolidation in the market, and that alone with our products is driving great conversions. So, that's the first thing. On some of the margin areas, I would tell you, look, -- we just -- in the case of the Google environment that we got or the Statsig, we're going to be focused on driving optimizations in that environment over a period of time. It's definitely lower. We said in the low-50s from a gross margin perspective. That comes from us taking on a whole new environment. Most of Amplitude, all of it, in fact, is on AWS. So, we took on a whole new cloud and hosting environment, and you have to go through the paces of really optimizing how you run those environments for customers. Our first objective was integrating, making sure there were no disruption in service. Now, we're moving quickly into how we can optimize those environments. So, that's one big lever on the gross margin side. And we're constantly looking at how we can make investments to go drive greater efficiencies across all of our operating expense areas.

Great. Our next question will come from the line of Jackson Ader from KeyBanc, followed by Scott Berg.

I was curious on -- I guess, Andrew, kind of sticking with you and talking about rather than on the cost side, just on the operating expense side. We've seen really nice acceleration in organic ARR from the business. But if I take kind of a longer-term view, even on a non-GAAP basis, we're still around breakeven, right? And so I'm curious, as you're thinking about like driving more leverage and more incremental margin that you've talked about before on the income statement, what kind of impact should we expect that to have on the organic growth number, if at all?

Well, I'd tell you that, one, we still expect from an organic perspective, we've got a great set of products. Spenser just walked through a number of them that are brand new to the market. We think they have an enormous total addressable market that we can go after. So, revenue growth will be the predominance where we'll see increasing operating income. As far as leverage as a percentage of what that would be a percentage of operating income, I do expect over time that we'll be able to drive better and better gross -- cost to start revenue and increase gross margins over time. It just takes time to go do those things, especially when you're seeing such a demand inflection from customers and increasing data lines. As I mentioned, we're at an all-time high for the amount of data ingested in the platform versus entitlements. When I first joined, that was in the low-60s. We're well into the 80s now as far as percentage of what customers have ingested versus what their entitlements are, and that portends increasing expansions on upsell, which is usually where we've had a lot of problems in the past of overselling and having to right-size contracts. For the first time, we're past those things and we're starting to see really good upsell, not just cross-sell driving growth. So, revenue growth is the predominance of the first aspect of driving improving profitability. As far as the leverage goes, I think gross margins will improve over time. It's just going to take a while. And we still have a long way to go on sales and marketing is reducing that as a percentage of revenue. I think G&A has room. And I do think that over time, we'll see greater and greater efficiencies with the R&D organization as they adopt more and more capabilities to build products at a faster rate.

Okay. And then just a quick follow-up. Can you remind us, should there be any -- now that we're on a different kind of pricing and packaging model, a little bit more variable, I guess, if you will. But should there be any difference in terms of the seasonality of your revenue ramp or recognition as we move forward with the new packaging?

So on revenue, I'd say, you get a fairly predictable pattern under which revenue is recognized because as I said, most of our revenue in the future periods is designated by our RPO, the committed contracts. But ARR will follow a very typical seasonal pattern. My expectation are bit more on in the enterprise selling basis. Q1 will always be our weakest as far as net new ARR adds as we're adding new territories, adding new reps, implementing new strategic initiatives. This year, in particular, we're educating the sales teams on not only the new pricing and packaging, but a lot of the new products we have. So every year, you're going to have that. And so it will be a slow start and then pick up. This year, too, just to remind everybody, we also had some big changes in our sales and marketing leadership, which is predominance of what you see now flowing through and the cost benefit from a lower sales and marketing as a percentage of revenue, and that's from efficiencies we're driving.

Our next question will come from the line of Scott Berg from Needham, followed by Billy Fitzsimmons.

Spenser, Andrew, nice quarter. I wanted to follow up on sales enablement that Andrew was chatting about there. We did a couple of different customer checks in the quarter. And the one thing that we came back is, I don't think your existing customers are quite aware of all of the different modules and innovation that you've rolled out this year.

Yes. Totally.

I see Spenser smiling. I know that's a function of timing, obviously. And one customer didn't even know that you had acquired Statsig. So, I guess where are you kind of in that journey? When is the sales force properly ramped in that? I mean, the quarter's sales results were good as is, but obviously, better awareness there can be even more helpful.

Yes. To your point, I think a lot of people still bucket us in the analytics company, and it drives me absolutely crazy. Honestly, just sharing, hey, we have Statsig now, and this is bleeding edge feature experimentation. And you can use it too, and this is the same infrastructure. OpenAI runs internally, like awesome. A lot of customers don't even know that. And then same with Wave, I think they're just starting to understand Wave and then same with our other products. I think if you remember from the prepared remarks, like we do see ramping. So, we're moving customers from 1 to 2 to 3 to 4 to 5 to more products, but it's much slower and that drives me crazy. I think there is no substitute for the work of like, hey, we built something amazing. We have to educate the hundreds of people we have in our field. And then they have to educate the thousands of customers in market. Like that's just work and that's just the whole thing. Something I'm spending a lot of time with Nate, our Chief Commercial Officer as well as the rest of the executive team on in terms of how do we get that and do that more efficiently. We just had a kickoff a few weeks ago where we showed off a lot of what you saw today with Statsig and Wave and custom agents. But that's not even to say the -- all the other products we have like Session Replay and Guides and Surveys and AI Feedback that can displace point solutions. So anyway, that is -- I think last year, we said it was the year of the platform. I think we still have a ways to go on educating people on it. I will say that the good news on it is the main thing customers are looking for is, hey, prove to me you guys are at the bleeding edge of where this field is going. And so my view is that analytics and the whole data -- behavioral data ecosystem is going to go through the same shift that coding has in the last 2 years, like that is still going to happen. And so they want to -- we see it in like a lot of the stuff we've been demoing and our customers see it, too. And so they want to know, hey, am I working with the company that's bleeding edge on this? And so even if they're not necessarily ready to adopt a Wave or even a Statsig, I know that, okay, you at least help me take the first step to using some of the basics on these capabilities, and then I can add more even if it's maybe too overwhelming for me right at the start or I'm not ready as a company. So anyway, that's all to say we still have a bunch of work to do to make sure our field is equipped. There's definitely areas that do it extremely well, but then there's areas we need to do a better job on this. So, I appreciate you calling that out.

And then from a -- my follow-up question is on the integration traction with Statsig. You all had a pretty aggressive goal, obviously, to move that asset into your organizations. Kind of where are you with it? Because the other customers that we spoke with were super excited about that. A couple of them were Statsig customers, et cetera. So, just kind of understand, have you hit all your goals around that? And are you kind of at that point where now you can just deliver on product and sales versus just having to integrate the organization?

Yes, yes. So as you imagine, Statsig as been around for 5 years, and there's a lot of work with getting it from a whole group of people who have never seen the code base or sold it or whatever else. I think we've kind of gotten through -- there's always stuff, but we've gotten through all of the urgent fires in running and delivering Statsig. So, that's great. Customers are very excited about how it's landing. We want to make sure to give -- the fact that it's our main focus as opposed to at OpenAI, it was a little more of a side thing for them. It's all been received positively. So, that's good. Now we're starting to think about, okay, what's coming next for Statsig? So if you look at like statsig.com/updates, we're shipping stuff. We've been shipping stuff for the last few months. We're continuing to build on the road map. We're continuing to integrate with Amplitude much more tightly so that if you're on both, which a lot of our customers are, you get the benefits of being able to use data from one and the other. And I think a lot of -- the other thing we're seeing with Statsig is that there's a lot of demand from AI-natives in particular. So, one of the reasons we're really excited to join forces with Statsig is that they -- like a lot of the way the future product development is being run, like people are choosing Statsig for that. So it's engineering-first teams that tend to be much more technical. They're building out whole software development harnesses. They want to manage how stuff is deployed in that harness and Statsig is set up really, really well to scale. As I mentioned, OpenAI runs a version of that infrastructure internally for themselves. And so they've tested that in tons of different ways over there, and we're doing the same thing except with everyone outside of OpenAI. And so there's a lot for us to do in terms of how do you set Statsig up to be a core part of the software development harness for all these bleeding-edge AI customers, and it's where kind of everyone wants to go over time. So, that's what we're focused on.

Our next question will come from Billy Fitzsimmons from Piper Sandler, followed by Clark Wright from D.A. Davidson.

Good to see the results and guidance. I think one of the exciting things about Statsig is potentially the cross-sell opportunity. I know there are some things to do first. But last time I looked or last I checked, I think there were 80 of the 400 Statsig customers are on Amplitude already. So, there's a lot who aren't. Can you just help contextualize for us how we should think about the potential cross-sell opportunity of Amplitude into Statsig or potentially vice versa and how we should think about that flowing through the model long term?

I think probably the much bigger opportunity is to take Statsig to Amplitude customers. I think Statsig customers, as I mentioned earlier, tend to be much more bleeding edge from an AI innovation standpoint. And so that's where everyone is trying to get their organizations to over the long term. It is a very -- it's like a more -- Amplitude is historically focused on product management and then Statsig is much more tailored towards engineers like it has tons of customization out of the box. It has like all the statistical testing. Now, like I said, those 2 personas are merging, but it's early days on that. So, I think the opportunity is as more of our traditional Amplitude customers look and try to build like AI-natives, introduce AI to their software development process, try to build out a harness, eventually try to get to self-improving products, all of those are opportunities for us to bring Statsig. Now, we definitely do see places where Statsig customers are also very interested in Amplitude, but there's a lot more both from a number and ARR basis that are Amplitude.

Perfect. And then if I could ask a second one. Can you just contextualize maybe how either your hiring needs have kind of changed year-to-date or where you're seeing the best ROI from AI-driven efficiencies internally within Amplitude?