Amplitude, Inc. Q4 FY2025 Earnings Call

Good afternoon, everyone, and welcome to Amplitude's Fourth Quarter and Full Year 2025 Earnings 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 first 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. With that, I'll hand the call over to Spenser.

Good afternoon, everyone, and welcome to Amplitude's Fourth Quarter and Full Year 2025 Earnings Call. Today, I'm going to cover 3 things: First, our strong Q4 results and progress in the enterprise. Second, how AI is driving demand for analytics, and our strategy to deliver. Third, a look at our new AI agents in action and a spotlight on customer stories. Q4 represents one of the strongest quarters in Amplitude history. Our fourth quarter revenue was $91.4 million, up 17% year-over-year and exceeding the high end of our revenue guidance. Our annual recurring revenue was $366 million, up 17% year-over-year and up $18 million from last quarter. This was our highest net new ARR quarter since 2021. Non-GAAP operating income was $4.2 million or 4.6% of revenue. Customers with more than $100,000 in ARR grew to 698, an increase of 18% year-over-year. Over 25 AI companies are now included in that $100,000 cohort as well. This quarter was marked by balanced execution. No single deal was over $1 million, yet we had our highest ever number of multiproduct and $100,000 ARR lands. I want to talk more about AI and our strategy. Over the past year, AI coding assistance from Anthropic, OpenAI, Cursor and others have compressed development cycles dramatically. The velocity at which companies are shipping new products has accelerated. When software is this easy to build, it creates a gap between how fast teams can ship features and how fast they can learn if they are working. This shifts the pressure to the right side of the product development loop that you see here, the use and learn side. Understanding how users behave, what works and what doesn't and what actions to take next becomes the bottleneck. The constraint is no longer knowing how to build; it is knowing what to build instead. This is the hardest problem in software today. I say that because builders and their AI assistants need a system of context that combines multiple data streams. They need structured behavioral data. They need the correct retention and funnel logic, and they need the right analytical tools exposed in a way that enables AI to reason effectively. The AI then needs to be able to iterate with that system, test hypotheses, refine queries, identify root causes and recommend actions accurately and repeatedly. This is not something that can be built over a weekend or replicated accurately with an LLM on a data warehouse. However, it is exactly what Amplitude is purpose-built to do. We have worked with thousands of companies over the past 13 years and amassed the world's largest database of user behavior. Our AI can explore patterns, explain changes, and guide teams on what to do next more accurately and reliably than any other system. Over the past 6 months, our Agentic analytics platform has reached a 76% success rate on complex production-grade queries, that is 7x better than a straight text-to-SQL approach. With the new agents we launched yesterday, teams can now move from insight to action in minutes, not weeks, using analytics, cohorts, experiments, and messaging in one continuous Agentic workflow. Through our MCP integrations with Anthropic, Figma, OpenAI, GitHub, Lovable, and Slack, we are bringing behavioral intelligence to teams where they already work. Understanding user behavior now becomes as simple as asking a question in a chat window. This puts Amplitude in a unique position. The frontier labs are pushing the boundaries of AI models, and they recognize the complexity of analytics experimentation and behavioral understanding, so they turn to Amplitude. As I mentioned earlier, more than 25 of the leading AI native companies, including some of the names you see here, are customers with over $100,000 in ARR with Amplitude. In addition, one of the world's largest frontier AI labs is a 7-figure customer as well. They came to us to replace a manual system built from fragmented internal tools and raw warehouse data. Using Amplitude Enterprise Analytics and Session Replay they can now understand activation, engagement, retention, and monetization end to end. With Amplitude MCP, they can offer those insights directly within the AI environments, their teams already use, dramatically improving the ability for them to automate development. And it's not just AI companies; companies of all sizes need a system that gives them trusted data, insights, and action to successfully deploy AI in the real world. So they turn to Amplitude as well. This momentum, combined with one of our strongest quarters across gross bookings and new ARR alongside meaningful improvement in churn. Our go-to-market motion has matured. There is a tighter focus on value-based cases in the enterprise and on expanding with multiproduct deployments. We continue to consolidate the fragmented market. Platform win rates are increasing against point solutions and our newer products are gaining traction. Guides and surveys launched less than a year ago are our fastest-growing product to date. We are also seeing a large increase in AI native usage as agents connect directly to Amplitude. Over the past few months, the total number of queries triggered by AI agents has increased dramatically. In October last year, there were almost none, and today, it is 25%. Agents also drove the vast majority of overall incremental query growth. This tells us that customers are trusting agents with analytics work. It also indicates that our platform offers the accuracy and the context needed in production environments. Taken together, this creates a powerful tailwind for Amplitude as we continue building a durable, scalable company that can unlock the next frontier in software. Over the years, we have intentionally expanded beyond core product analytics and into adjacent workflows. We have continued that work and acquired InfiniGrow, an AI-native marketing analytics start-up that connects spends, behavior, and revenue impact. InfiniGrow brings strong AI native engineering talent to Amplitude. This strengthens our platform as a system of context and expands our ability to bring acquisition, activation, and retention into one continuous feedback loop. Yesterday, we launched our global AI agents, specialized agents, and MCP. This represents the start of a fundamental shift in how teams work with their analytics data. Historically, analytics has required humans to do most of the heavy lifting, writing queries, building dashboards, monitoring changes, interpreting results, and then figuring out what to do next. That process does not scale in the world where teams are shipping faster and faster. AI agents change that model. Instead of asking questions one at a time, teams can now delegate work to agents that continuously analyze behavior, surface insights, and guide action. Our agents understand events, funnels, cohorts, experiments, session replay, and outcomes because they operate inside a context system specifically designed for them. Agents make life easier by doing the work that slows teams down today. That is very different from bolt-on AI tools from SaaS companies that sit outside the data and try to infer meaning after the fact. The best way to see this and understand this is to look at it in action. I want to show you a quick teaser video, and then I'm going to show you a demo of what we've released. Let's go ahead and roll the video. It's a great question all product builders should ask themselves now, what will you build? I want to now walk you through what we've launched in AI analytics yesterday. I'm really excited about the future, and I want to show you Global Agent. Global Agent radically changes how our customers interact with their data. Starting your day with a dashboard is dead. Take a look at this interface—no dashboard, no graphs, no charts, just a chat box and a few simple prompts if customers need help getting started. I can talk to Global Agent like I talked to a colleague. I'm going to go ahead and ask it how's our loyalty program doing? In seconds, it comes back with a summary. Notice, I didn't use any jargon about event totals or taxonomy, just a regular question. It's calling out some pretty concerning numbers. Only 5% of users who view our welcome page actually go on to join the loyalty program. That is low, so I'm going to click in and investigate more. The Global Agent has followed me to a deep dive on this chart. I can keep investigating with another simple question. Break this down by traffic source. Here's the breakdown. Facebook and Instagram are driving loyalty sign-ups at 5.6% and 5.2%, while Google and direct traffic lagged behind. The Global Agent summarizes it perfectly. Social media converts 10% to 15% better. Since social media outperforms Google, I might shift ad spend, but looking overall, all the rates are low. So before reallocating budget, I'm going to go deeper. Is this a channel problem or an audience problem? Let me ask, do new users convert differently than existing customers? Without AI, this kind of analysis takes a lot of time, segmenting users, comparing funnels, pulling insights together, the Global Agent does it in seconds. And here it is, 14% conversion for repeat purchases, 5.4% overall. That's 2.6x higher. That answers my question. It's an audience issue, not a channel issue. I should reallocate my budget towards repeat purchases. Again, simple language, fast answers, and deep learning that anyone can use. Analytics is the perfect use case for agents. So I want to show you specialized agents. Our specialized agents work continuously on specific jobs that would usually take dozens, if not hundreds of hours, monitoring dashboards, analyzing session replays, processing feedback, running conversion experiments, which is legwork now done automatically. We're going to be eating our own dog food on this one. I already have a session replay agent set up to monitor our own session replay tool and have it set in addition to sending a slack when it has a strong finding. This specialized agent has been watching hundreds of replays and sent me some summarized findings. Users with multiple saved filters type search terms, but cannot find filters without scrolling through the full list. Power users cannot preview filter criteria before applying, forcing trial and error selection. These are all things we should improve. We could have had someone watch all those replays. We could have talked to customers for hours on end or we could have let these continue to be issues. Instead, I get these findings served to me on a daily basis with a full report and a detailed breakdown with key findings, suggestions on what to explore next, and even a highlighted set of replays of these issues. Okay. We're going to save the best for last. Finally, I want to show you what I'm most excited about, which is Amplitude MCP. We're releasing a fast-growing library of expert-level workflows that customers can trigger in AI clients like Claude with a simple slash command. I'm going to go ahead and use Amplitude and Claude by typing use slash create dashboard and create a dashboard that tracks our growth conversion performance, hit, I hit enter, and it goes to work. Instead of me manually creating 15 charts, running the segmentations myself, and piecing together an explanation in a document, this skill handles it in one click. With MCP apps, Claude is opening and building Amplitude charts right inside itself. It's done it. So I've now gone to the link it gave me in a perfectly built dashboard with top-level metrics, conversion funnels and segment breakdowns. Amazing. Moving on to customers. We had a great quarter for new and expansion deals with enterprise companies, including one of the largest music streaming apps, the Cheesecake Factory, Asana, PGA of America, CrossFit, Stewart Title Guaranty Company, Crunch Fitness, WHOOP, Once Upon Publishing and NTT DOCOMO. I'm going to highlight 3 examples that demonstrate the power of the platform in different ways. Japanese telecom NTT DOCOMO is using Amplitude across more than 1,000 active users to drive efficiencies at scale. As an early design partner for our AI agents, their data platform team uses agents to streamline analysis across existing dashboards. In 1 project, an agent reduced campaign analysis time by over 90%. Our AI-powered session replay summaries, automatically localized into Japanese, help UX teams identify issues faster and improve the digital journey for millions of customers. We are now working closely with NTT DOCOMO to shape our agents roadmap with feedback on collaboration features and AI-powered insights. Siemens, the $70 billion global technology leader, partnered with Amplitude over 3 years ago to power analytics across its website presence and broader digital ecosystem. By consolidating onto our AI analytics platform from a series of point solutions, Siemens gained a unified real-time view of user behavior. Recently, the team organizing their annual user conference used Amplitude to identify their overreliance on direct email and organic channels. They experimented by reallocating spend into targeted web promos plus paid and organic social. This delivered a 90% year-over-year increase in web traffic and a projected 50% increase in registrations in attendance at their conference. Lastly, we landed one of the largest music streaming apps in the world. We are working with the teams that lead checkout optimization, upgrades, churn prevention, and recovery as they seek to understand the revenue drivers for hundreds of millions of monthly active users. They will use Amplitude analytics combined with session replay to get a holistic view of these monetization drivers. These stories all point to a common theme: from AI start-ups to global enterprises, customers are betting on Amplitude as the AI analytics platform that will help them thrive in this new era. Before I hand it over to Andrew, I want to be clear on how AI is shaping our opportunity. There is a common misconception in public markets that AI makes analytics either irrelevant or easy to replicate. The exact opposite is true. AI has made software easier to create, but creation is no longer the moat. The real advantage is how quickly a team can learn, iterate, improve and automate. Agentic analytics is the key. It unlocks the bottleneck on the right side of the product development loop and enables teams to learn as fast as they ship. AI is a structural tailwind for Amplitude. It is why I believe the opportunity ahead is massive and why I'm excited about what's to come. Now over to Andrew to walk you through the financials.

Thank you, Spenser, and good afternoon, everyone. 2025 was a year of innovation and execution, and we delivered a solid base for our future long-term growth strategy. When we met at our Investor Day last March, we laid out a deliberate road map to capture the enterprise and accelerate multiproduct adoption while leading the industry in innovation. Today's results demonstrate that we haven't just met those goals; we've established a new baseline for durable growth. The enterprise is now our core growth engine. ARR from our enterprise customer cohort is up 20% year-over-year, with higher retention and expansion rates than the rest of our business. This was not by accident or luck; our AI analytics platform has been designed to be enterprise-grade with trust and safety of our customers at the center. Our go-to-market team has worked for the past 3 years to orient our go-to-market motion to focus on the enterprise, increasing customer value through selling our platform and engaging in longer-term contracts. 2025 was the coalescence of this work to focus on our customers' value and creating a durable base for future growth. We sustained growth of current RPO greater than 20% throughout the year. And in Q4, total RPO grew 35% year-over-year. Our average contract duration is now above 22 months. In addition to our success in the enterprise, we have also formulated our product and our go-to-market team to embrace our AI platform strategy. By combining niche point product solutions surrounding analytics into a comprehensive platform, we are able to deliver greater value than stitching together point solutions. We also believe that having a platform is essential for harnessing the capabilities of AI to reduce friction in our customers' workflows. In 2025, we did a great job expanding our multiproduct attach rate for our customers. 74% of our ARR is from customers with more than 1 product, up 15 percentage points from last year. We still have a great opportunity to expand our multiproduct customers as well. Only 51% of our ARR comes from customers with greater than 3 products. Looking at a full platform deployment of 5-plus products, that percentage is 20%, doubling year-over-year. We have a massive opportunity to expand with our customer base. We believe our market opportunity expands dramatically with the inclusion of our new AI products that promise to expand adoption and use cases. The progress in selling our platform is best exemplified through improvement of our retention and expansion motion, with dollar-based net retention now above 105%, after exiting 2024 at 100%. However, our work is not done. At the beginning of this year, we introduced a new pricing and packaging strategy to our sellers. Let's start with what's not changing. We are not changing our core billing metric of events. We believe this is a great representation of the value our customers receive from our platform, and it is also an appropriate monetization strategy as we center AI engagement on our platform. What has changed is we are centralizing the monetization of our other products, such as experimentation, session replay, guides, and surveys to be a percentage uplift on the core platform charge, which is events-based. This reduces the friction of adoption of those products by making it easier to understand for our customers and reduces the need to estimate how many sessions or experiments they want to run in the near term. Longer term, this will also encourage greater consumption of our platform as customers no longer fear using certain parts of the contract or underutilizing others. It's a radical simplification of our pricing that acknowledges our customers' needs for greater cost transparency and certainty on their costs as the volume of data ingested into our platform expands. It also supports our focus on integrating AI into all of our product offerings and expanding customer usage, which can be a tailwind longer term on easier lands and faster platform expansions. In summary, as we've transitioned to an AI analytics company, we have created a more durable base for our business focused on the enterprise. We've driven expansion of our platform through innovation, and we're making it easier for customers to get value quickly and encourage expansion. We've done all this while being disciplined in our spending and driving to non-GAAP profitability with record free cash flow. Looking at the rule of 40, which we measure based on free cash flow yield and ARR growth, we've now improved from a rule of 15 in 2024 to over 24% in 2025. We'll continue to focus on driving top line growth through a disciplined manner in 2026. Now turning to our fourth quarter and full year results. And as a reminder, all financial results that I will 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. Fourth quarter revenue was $91.4 million, up 17% year-over-year versus 9% in fiscal 2024. Fiscal year 2025 revenue was $343.2 million, up 15% year-over-year versus 8% in the fiscal year 2024. Total ARR increased to $366 million exiting the fourth quarter, an increase of 17% year-over-year and $18 million sequentially. 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. Platform sales were also particularly strong. 44% of our customers now have multiple products, with 74% ARR coming from that cohort. The number of customers representing $100,000 or more of ARR in Q4 grew to 698, an increase of 18% year-over-year and up 45 customers since the last quarter, representing the largest sequential increase in this cohort in company history. Additionally, the number of customers representing $1 million or more in ARR grew in Q4 to 56, up 33% year-over-year, demonstrating our ability to land significant accounts and grow them over time. In-period net dollar retention progressed to 105%, led by cross-sell expansions across our customer base. 58% of Q4 gross ARR was driven by expansions across a broad range of customers, with no individual expansion exceeding $1 million. It's still driving meaningful progress in that dollar retention. We will continue to focus on driving net dollar retention higher through our platform strategy. Gross margin was 77% for the fourth quarter, flat to the fourth quarter of 2024 and up 1 point since last quarter. We continue to make progress on optimizing our hosting, driving multiproduct contracts, and monetizing our services engagements. We will continue to look for opportunities to incrementally improve gross margin over time. Sales and marketing expenses were 42% of revenue, a decrease of 1 point from the third quarter. We continue to focus on improving sales efficiencies, driving improvements through our changes in processes, coverage, and expansion of enterprise customers. At the same time, we are investing in future growth while balancing those incremental investments with efficiency gains. In Q1 FY '26, we will have higher sales and marketing expenses as a percentage of revenue, reflecting timing of events and our annual company kickoff. R&D was 18% of revenue, flat to the fourth quarter of 2024. We expect to continue to invest in the talent and capabilities of our team to drive greater innovation in the future. G&A was 12% of revenue, down 4 points for the fourth quarter of 2024. We expect G&A to improve as a percentage of revenue over time. Total operating expenses were $66 million, 72% of revenue, down 3 points sequentially. Operating income was $4.2 million or 4.6% of revenue. Net income per share was $0.04 based on 141.5 million diluted shares compared to net income per share of $0.02 with 135.7 million diluted shares a year ago. Free cash flow in the quarter was $11.2 million, or 12% of revenue compared to $1.5 million or 2% of revenue during the same period last year. In the fourth quarter, we managed our cash collections and made meaningful progress on shifting contracts with annual payments in advance. For the full year, we had a record free cash flow of nearly $24 million or free cash flow margin of 7%. 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. We have already purchased in the open market under our current buyback. And given the strength in our balance sheet and the underlying business, our Board has approved an additional reserve of $100 million to be used for buybacks. 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 roadmap 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 confident we have the right strategy and the right platform to continue to consolidate the fragmented market. We continue to improve our go-to-market motion and are accelerating our pace of innovation. We have the right monetization strategy to encourage the adoption of our AI tools, and we believe those tools will reduce the barrier to adoption of our full platform, leading to greater monetization opportunities. Our strategy remains consistent with our go-to-market being aided by our simplification of our pricing and packaging. We will continue to focus on gaining new enterprise customers and driving cross-platform sales with our existing customer base. We also believe that with the release of our AI capabilities, our monetization of data ingested in our platform, and the cross-sell opportunities of new products gives us the right strategy to align the value of our customers receive with our growth opportunities and grow our business in a profitable way. For the first quarter of 2026, we expect revenue to be between $91.7 million and $93.7 million, representing an annual growth rate of 16% at the midpoint. We expect non-GAAP operating income to be between negative $4.5 million and negative $2.5 million. And we expect non-GAAP net income per share to be between a negative $0.02 and a negative $0.01 assuming basic weighted average shares outstanding of approximately 135.1 million. For the full year of 2026, we expect full year revenue to be between $390 million and $398 million, an annual growth rate of 15% at the midpoint. We expect our full-year non-GAAP operating income to be between $7 million and $13 million. We expect non-GAAP net income per share to be between $0.08 and $0.13, assuming weighted average shares outstanding of approximately 145.9 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.

Thank you, Andrew. Our first question is from Taylor McGinnis from UBS, followed by Billy Fitzsimmons from Piper Sandler.

You announced several exciting agent offerings this week, and at the same time, you've also seen positive engagement with third-party agents integrating into Amplitude's platform. Spenser, you provided a great example of extracting insights using Anthropic Claude. How do you see Amplitude's agents and these third-party agents developing? Could you discuss the differentiation you expect between Amplitude's agents and the current activities of third-party agents?

Yes. So to be clear, they both use the same underlying infrastructure. What will happen with either MCP model context protocol is a way for external products like Claude or OpenAI's Chat GPT or Cursor to connect into Amplitude and request a set of calls. But that is the same infrastructure that both our global agents and our specialized agents use. And so the way to think about it is there's a whole set of tool calls that are available to these agents. You can say, get me a list of events, get me a retention, get me the list of tools you have like retention and funnels, get me the possible properties for this event. And what we'll do is we'll expose that to an orchestrator that we have that basically interprets a query, whether it's in the chat with Global Agent or whether it's external from MCP. And then it'll kind of pull in all the different contexts I talked about and then spit out the answers that you see. So it's the same underlying infrastructure because the nature and type of questions are the same whether you're asking it from Claude or Slack or whether you're in Amplitude's UI.

Q4 was an excellent quarter for new logo ARR. We gained numerous new customers who are realizing value from Amplitude as they start their journey with us. Many of these engagements developed throughout the quarter, leading to a significant portion of ARR being booked later than what we've observed in previous quarters. As we noted, there wasn't a lot of major expansion during this quarter. This situation presents a solid opportunity for future growth with these new clients. Entering a new logo requires strong competition to demonstrate value, which can take additional time. However, we're very happy with the influx of new Amplitude customers, and we believe this positions us well for future expansions.

To start, can you help us understand the improvement in net revenue retention? How much can you attribute to increased upsells and cross-sells compared to improving success in reducing churn within the business?

Sure. So throughout the year, we've seen our customers increasingly adopting more and more of our applications in the platform. When we started off 2025, we specifically were training our sales team how to sell our platform when we're introducing new capabilities. We acquired new capabilities, and we put those into our platform as well. So predominantly throughout 2025, the improvement in net dollar retention was related to our cross-sell capabilities. And as you were alluding to, in the past, we had situations where we were overselling capacity against analytics. And even with some customers increasing data, it wasn't enough really to offset and contribute materially towards net dollar retention. Now that we're past most of those capacity-related issues that we created for ourselves, we're starting to see customers and their data ingested into our platform contribute towards net dollar retention improvements as well. And so now as we think forward, and I've said in the past that we have full intention to continue to set up our customers and expand with our customers, introducing new innovation. We think that both vectors, both data ingested into the platform, meaning upsells as well as cross-sells will contribute to further improvements.

Our next question will come from Billy Fitzsimmons from Piper Sandler, followed by Rob Oliver from RW Baird.

A follow-up there, Andrew, on the pricing and packaging question. So obviously, enterprises really like predictability. You guys have never been a seat-based model. So if you can just help us understand in the context of the new pricing model, clearly, it sounds like it's driving more engagement, a cross-sell opportunity, less of a friction experience. But how does the buyer manage that predictability? And I guess the inverse of that would be, how do you get comfortable on the cost side with AI embedded in?

Yes, that's a great question. We focus a lot on collaborating with our sales team and customers to illustrate how the instrumentation within the platform provides excellent visibility into the data they are ingesting. We assist our sellers in understanding how the incremental data being added to the platform impacts the costs that we will charge customers. We encourage these discussions as part of the sales process, which allows for a more gentle introduction for customers regarding their adoption of Amplitude over time, rather than relying on estimations of their data implementation. We work closely with them to demonstrate how the instrumentation operates. An important aspect that you mentioned is that we have conducted extensive research with customers to ensure that we have the right billing metric aligned with our value proposition. We have been testing this approach for some time. In fact, nearly 20% of the new annual recurring revenue we booked in the quarter came from customers using our newly piloted pricing and packaging. We're aware that customers appreciate this change, viewing it as more transparent and less frictional. Furthermore, we believe this strategy positions us very well, especially as we enhance our platform with AI products, which reduces the barriers to adoption. Customers feel they receive great value for their investment in Amplitude and have less anxiety since they gain better cost predictability and transparency regarding how their usage will evolve over time.

There were two main aspects of the InfiniGrow team that impressed us. Firstly, we are always on the lookout for exceptional talent. When we find a company and an opportunity that align with our vision, we act on it. Specifically, with InfiniGrow, Daniel, the CEO, and the rest of the team have extensive experience in AI analytics and automating workflows over the past few years. They have valuable insights into how the future of this category will develop. We are venturing into new territory with AI analytics, and partnering with someone who has deeply contemplated this is significant for us. We aim to explore ways to collaborate with them. Secondly, InfiniGrow has a strong understanding of marketing analysts compared to product management. As these roles continue to merge over time, and as more customers move away from traditional MarTech tools to innovative platforms like Amplitude, we want to ensure we are prepared to meet their needs and facilitate that transition. They have a deeper understanding of those buyers than nearly any other company we have encountered in the analytics sector.

Our next question will come from Clark Wright from D.A. Davidson, followed by Koji Ikeda from Bank of America.

You noted the cross-selling opportunities continue to be an area of strength, what is the natural pathway you're seeing in terms of product adoption? And what is the role that agents are going to play going forward to help drive additional cross-selling motions?

It's encouraging on both fronts. Analytics remains at our core as an analytics platform, a focus we've maintained consistently. Tracking the core aspects of the user journey enhances the overall value of the platform. This facilitates experimentation because we can effectively target and measure user interactions. It also improves session replay capabilities, allowing us to analyze the actions of users who encountered errors. Additionally, guides and surveys can be tailored for specific users who may show signs of confusion. Analytics is fundamental, and its integration with other features enhances their effectiveness and vice versa. Regarding agents, there is significant potential here, especially with products like AI analytics that we launched recently. These tools can be enhanced by AI. For instance, I shared earlier how our session replay specialized agent works; instead of watching just a few session replays, AI can expedite that process, enabling us to analyze hundreds of them in a fraction of the time, significantly boosting productivity. Experimentation benefits similarly. Many inquire if we have a repository of best practices for web pages and interactions, and our experiment conversion agent can offer suggestions based on our cumulative insights from various clients. This elevates the effectiveness of experiments. The real innovation occurs when these features converge. You can begin with analytics, identify your least satisfied users, and receive suggestions on how to improve their experience. For example, if users are unhappy due to a malfunctioning page, a session replay agent can assist in analyzing that specific page to identify issues, such as a misformatted button. Further, it can propose alterations and even suggest test variants, allowing users who may not have any analytics or experimentation knowledge to execute these projects seamlessly via our Global Agents or specialized agents interface. This represents a substantial opportunity for increased utilization. While we are primarily focused on analytics, I am excited about the other developments. We’ve even seen feedback on social media asking why we limit session analysis to 100 at a time, and we're actively working on expanding that capacity.

I'd say the pricing and packaging is relatively new. So I wouldn't attribute that necessarily increasing win rates. I think that the biggest thing is, one, our sales team has just worked really hard at demonstrating the value of our platform to our clients, and that's really resonating. And the other is you really have to credit our product team for creating just really great products that work well together. A lot of people claim they have a platform, but the reality is it's a bunch of products that's stitched together that doesn't look really well. When you have a platform, you have workflows that are instrumented well and it's easy to interact with the different modules in the product. And that's the way I would characterize our platform today. And every time that customers are adopting more than 1 product, it's because that integration, those workflows seamlessly across our platform are coming through as real value. I mean, I talked to a number of customers myself with the sales team, and they always come back and say, we're just so far ahead of what everybody else is even representing an analytics platform to be.

I want to make sure, YC, I want to make sure I understand what you're saying, you're saying do we see competition from Snowflake and Databricks because they have a lot of data too?

Well, it's not just data. They are thinking about doing the application side as well. I mean if you think about Vibe Coding and then you think of application building, you can make it easier to build. I'm just curious if you see anything blurring between customer talking about just use cases between a customer you can use a data platform like Snowflake to build it, they have Cortex versus what you will see with Amplitude?

One significant observation we've made is that customers are constantly looking for the latest and most advanced capabilities. I recently came across a compelling analogy comparing software to sushi. While it's acceptable for a gas station to offer sushi, renowned chefs like Jiro in Japan will not be facing any business struggles; in fact, their popularity will likely increase. From our perspective, we focus on how to provide the most sophisticated and comprehensive analytics system. For instance, in our benchmark evaluations, we achieved a 76% accuracy rate, whereas competitors like Cortex or Databricks Genie may only reach around 10% or lower. We are working on sharing full metrics regarding this. The text-to-SQL functionality is just a piece of the puzzle; there are two other crucial components. First, we need to consider the context layer, involving the effective integration of various data sources such as analytics data, session replay data, interaction data, and survey data, interpreting them accurately. This allows an LLM agent to query efficiently. For example, an analyst might want to understand their onboarding funnel, identify the primary drop-off point, and discern the differences between users who advanced versus those who did not, which requires multiple precise queries. Setting up the LLM correctly is essential; appropriate tool calls and context are needed. Given that we possess the largest repository of user behavioral data, we have extensively analyzed millions of analytics queries to develop an agent that performs effectively. The importance of context and iterative querying makes the differences in accuracy between options like building your own system or using Genie or Cortex versus Amplitude exceedingly clear. For analysts, a shift from 76% to 10% accuracy represents a significant impact on the effectiveness of agentic analytics.

I think what you're seeing is that the efforts we've been doing on sales and marketing, on our cost to serve, and our G&A and operating more effectively as a company, is not just a one effort, one activity. There are multiple. And certainly, we're introducing agentic capabilities into our own workflows within the company, and that's certainly contributing to it. But there's so many things structurally we've done to the business to create greater durability that that's ending in greater abilities for us to drive efficiencies. I'll give you one example. We've talked a lot about our ability to go drive increasing contract duration to our customers and that our RPO has been growing rapidly. Well, if you don't have to renew your installed base every year or that installed base percentage goes down because you're executing more and more longer-term duration contracts with your customers, then the sales team has more time to dedicate towards selling new and expansion deals rather than working on renewals. And so it's just a great example of a strategy we put in place that's going to accrue benefits for a longer period of time.

In terms of guidance, the full year revenue range seems a bit wider than normal at $8 million. Can you help us understand the reason for this? And what scenarios are contemplated at the low and high ends of the range?

I think when we approach our guidance, we approach it with what we think we can go execute in the period. And I wouldn't read too much into that other than we have a breadth of different opportunities that we're going after both with our product set and with improvements in our targeting enterprise customers. So I wouldn't read too much into it.

And that will conclude our fourth quarter earnings call. Thank you for your time and interest, and we look forward to seeing you on the road this quarter as we attend conferences hosted by Baird, Citizens, KeyBanc, Morgan Stanley and others. Take care.