Confluent, Inc.

Hi, everyone. Welcome to the Confluent Q1 2022 earnings conference call. I'm Shane Z from Investor Relations, and I'm joined by Jay Krebs, co-founder and CEO, and Stephan Tomlinson, CFO. During today's call, management will make forelooking statements regarding our business, operations, financial performance, value creation potential, and future prospects, including statements regarding our financial outlook for the fiscal second quarter of 2022 and fiscal year 2022, the potential growth runway for Confluent Cloud, and future year outlook for non-GAAP operating margins. These following statements are subject to risks and uncertainties, which should cause actual results to differ materially from those anticipated by these statements. Further information on risk factors that could cause actual results to differ is included in our most recent Form 10-K filed with the SEC. We assume no obligation to update these statements after today's call, except as required by law. As a reminder, 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 financial and business trends and for internal planning and forecasting purposes. These non-GAAP financial measures have limitations and should not be considered in isolation from or as a substitute for financial information prepared in accordance with GAAP. A reconciliation between these GAAP and non-GAAP financial measures is included in our earnings price release and supplemental financials, which can be found on our investor relations website at investors.confluent.io. With that, I'll hand the call over to Jay.

Thanks, Shane. Welcome everyone to our first quarter earnings call. We had a successful start to the year, exceeding our guidance on all metrics. Total revenue grew 64% year over year to 126 million, a notable milestone as we surpassed the half billion dollar revenue run rate mark. Confluent Cloud grew 180% year over year, representing 31% of total revenue. Confluent Cloud is not only the fastest growing part of our business, it also serves the vast majority of our customers. And in the first quarter, we closed the largest deal with Confluent Cloud in our company's history. This eight-figure multi-year expansion deal with a massively high-scale tech company is significant on a number of fronts, which I'll touch on further in a few minutes. Today, I'd like to use the time to dive a little deeper into the use cases driving Confluent's success and the rise of data in motion. The underlying trend behind this shift is that we are experiencing a phase change in how companies use software. Software is moving from siloed applications on the edge of the business to fully connected applications that drive core parts of the customer interaction and the production of goods and services. This transition is imperative for companies to compete in the modern economy. At the heart of all this software is data, and we're seeing an equally large shift in the underlying data architecture to enable this software-driven business. Historically, data infrastructure has been built around data storage, and there's a rich set of file systems, databases, and data stores that allow applications to store and retrieve bits of data. However, increasingly, this storage-centric paradigm is not sufficient. As companies drive more of their core business with software, that software has to have an up-to-date view of the business and be able to react and respond intelligently as the business carries out its core activities. This has driven a shift from a storage-centric world of data at rest and has enabled the rise of data in motion. Broadly speaking, data in motion is about connecting disparate systems by extracting data as continuous streams and allowing these streams to flow to the rest of the applications and infrastructure that need that data, as well as allowing companies to react and respond to that stream of data in real time. Data in motion is now adopted incredibly broadly, with a presence in hundreds of thousands of companies in virtually every industry. Giving a complete overview of all use cases is an impossible undertaking. However, in today's call, I'd like to give an overview of some of the patterns we see illustrated with some examples. To engage with customers in the most meaningful and personalized way to foster brand loyalty, companies need to have a data-rich 360-degree view of all aspects of their customers across all interactions. Confluent not only captures changes to the data as it happens, but stitches together data from disconnected databases, files, and custom applications to deliver a real-time view of all customer interactions, enable real-time engagement across all channels, and reshape experiences. Once this continuous view of the customer is present, stream processing enables the real-time reaction to customer events to drive personalization so that the right suggestion, experience, or recommendation can be delivered to each customer at the right time. And that's what Deutsche Bahn does for their passengers. They have a single source of truth for all the vital travel and train information and make it available on the mobile app, website, even station displays and public announcement systems. Real-time data is what improves the customer experience and gets happy passengers where they want to be on time. Another common pattern our customers embrace Confluent for is data mobilization across hybrid and multi-cloud architectures. Most enterprises on a cloud journey are finding it harder than expected to realize its benefits as the transition to the cloud is often an incremental multi-year effort that's arduous and expensive. So whether it's modernizing their legacy on-premise data warehouse to fully manage cloud-native systems like Snowflake, BigQuery, Redshift, Synapse, and Databricks, or modernizing their monolithic applications to the cloud, one microservice at a time, many of our customers use Confluent as the unifying persistent bridge, enabling data to flow freely between the old legacy stack and new cloud applications wherever it resides, on-premise and in more than one cloud. This pattern of running Confluent to span environments is increasingly common and Confluent is becoming a critical data fabric for enabling integration across multi-cloud and hybrid cloud environments. That was certainly the case for Security Scorecard. They're the global leader in cybersecurity ratings and the first in their industry to offer digital forensics and incident response services. Operating in 64 countries, they continuously rate more than 12 million companies. Their business requires a hybrid cloud architecture, so they turned to Confluent, and now data streaming helps them communicate instant ratings and constantly scans for threats to improve their customers' security posture. We're proud to work with them on their mission to make the world a safer place. In financial services, data in motion has become a mainstay of modern architectures, from small fintechs to the largest banks in the world. In capital markets, for instance, we're driving better business outcomes by enabling enterprises to gain a firm-wide view of their trades and risk. Retail bankers use Confluent for secure real-time payments so their customers can transact with their banks with speed, confidence, and trust. Fraud has become a growing problem in payments and is harder to detect than before, and many customers in the payments industry are using us for real-time fraud detection. Adoption of data in motion in financial services is incredibly broad. The top 10 largest banks in the US are all Confluent customers, and we're seeing ongoing demand across the world. For instance, at Bank Rakyat Indonesia, BRI, the largest bank in Indonesia, and the largest microfinance institution in the world, Confluent powered their digital transformation with an event-driven architecture for real-time credit scoring, fraud detection, and merchant assessment services. Now they're able to detect ATM skimming in real-time, block and disable the cards of their customers, and proactively protect their customers from fraudulent transactions. They've also been able to reduce loan disbursement times from two weeks to two minutes with automated digital verification by processing massive amounts of information in real time, all flowing through Confloat. In retail, Confluent serves as a massive advantage for retailers looking to meet the demands of heightened expectations from customers for real-time omnichannel personalized experiences. Many retail organizations use Confluent to fundamentally transform inventory and supply chain and for hyper-personalization to enable real-time customer interactions that build brand loyalty. In the US, nine of the 10 largest retailers are Confluent customers. Confluent helps Sainsbury's reimagine their supply chain and inventory management by enabling a continuous view of product inventory. By using Confluent, they can drive lower inventory levels while avoiding selling out and enable agile response to changes in supply and demand. Confluent also acts as interconnectivity between different cloud providers like AWS and Azure, adding a secure fault-tolerant real-time data pipeline that spans these environments. This means they're protected if any cloud service experiences downtime, which greatly improves the strength and flexibility of their supply chain. One very compelling area of opportunity Confluent is growing into is the tech sector. Tech companies were the earliest adopters of open source Apache Kafka, but were not typical customers for earlier open source companies. The emergence of cloud as a delivery model has changed that. Our cloud services reach scale, cost efficiency, elasticity, and reliability that no internal self-managed data system can hope to achieve. As we've done this, we've seen some of the earliest Kafka adopters start to shift to our service, including companies like Square that have been mentioned on a previous earnings call. This customer segment is significant because it has built around data in motion in a very foundational way and has amassed huge scale. As I mentioned at the beginning of this call, this quarter we added a very significant new customer in this segment that runs one of the largest Kafka installations in the world. New Relic is the leading observability platform that helps engineers plan, build, deploy, and run great software. New Relic is fundamentally a service for providing powerful insights into streams of observability data. Indeed, Kafka is the backbone of all observability and log data ingested into their platform. New Relic is the kind of company that's always thinking about their customers and always innovating. Our expertise having re-architected Kafka for the cloud and the proven scalability and reliability of our fully managed multi-cloud platform are foundational to this new partnership. Customers can benefit from the joint product innovations we have planned with New Relic, allowing for more insights from their Confluent Cloud telemetry data. We're very excited for the future and how we'll innovate and create even more value going forward. The success with all these customers is driven by the deep differentiation of our product. Confluent builds this differentiation around three key pillars, being cloud native, being a complete offering and being everywhere. We've discussed these pillars quite a bit on past calls, but we continuously add to them to deepen our competitive mode and better demonstrate value to internal IT teams who might otherwise be building around open source Kafka. First, being cloud native, we announced various new capabilities that make our platform more scalable, more elastic, and more reliable. These include greater scalability and unlimited data retention in Azure. Now across all three clouds, customers can scale up to enormous data throughput and store data in Confluent Cloud forever without any limits on size or retention. Finally, we improved our observability and security capabilities, allowing richer insight into the use of Confluent and more detailed audit tracking of actions taken with our product. These capabilities come together with our operational practices to allow us to deliver industry-leading reliability around data streaming. In recognition of this, we've strengthened our contractual SLA to 99.99% as Confluent Cloud offers unmatched reliability for real-time streaming. Our second pillar of being a complete offering continues to be an area of ongoing innovation for us. We expanded role-based access control to help enable secure usage across large companies and teams while still ensuring tight control on data access. We added six new fully managed connectors to Confluent Cloud and our Oracle Change Data Capture premium connector went GA. This allows us to set in motion the vast amount of data at rest locked up in Oracle databases, a mainstay technology in our enterprise customer base. Our third pillar of product differentiation, being able to run everywhere, manifests in our ability to run across all our customers' environments, whether on-premise, hybrid, or multi-cloud. In this area, we simplified the user experience for cluster linking, our proprietary technology that allows transparent linking of clusters across cloud providers and on-premise clusters. We also had a major release of Confluent Platform, which supports on-premise and private cloud environments. This new release brings hundreds of new features and improvements that were first launched in our cloud to our customers' on-premise environments, enabling richer hybrid cloud deployments. A key part of our strategy to be everywhere is our relationship and integration with the three major cloud service providers. These relationships are critical for us, but also mutually beneficial for the cloud providers because Confluent enables data flow from on-premise environments into the cloud, unlocking use cases that would otherwise be tied to legacy environments by data gravity. In our last earnings call, we announced a significant deepening of the partnership with AWS. This quarter, we followed that up with the announcement of a new multi-year strategic partnership with Microsoft, which extends our existing relationship with joint technical, marketing, and sales investments across our organizations. This deeper partnership helps us tighten the integration with Azure as well as better serve joint customers. We also had two notable announcements around our partnership with Google Cloud. The first was the founding of the Data Cloud Alliance, a new initiative that aims to make data more portable and accessible across disparate business systems, platforms, and environments, with a goal of ensuring that access to data is never a barrier to digital transformation. Additionally, we have been recognized in the Google Cloud Ready BigQuery Validation Program, which ensures the best possible integration with BigQuery using our fully managed connector. As part of this program, Confluent will collaborate closely with Google Partner Engineering and BigQuery teams to develop joint roadmaps and continue improving our products. By driving deeper alignment, we are making it easier for our customers to connect and migrate hybrid and multi-cloud data to BigQuery to power real-time analytics. To wrap up on the point of product differentiation, the value of Confluent versus open source Kafka can be summarized from the findings of Forrester's total economic impact study, which they recently conducted for Confluent. The study specifically quantified the cost savings and benefits that businesses can achieve when they offload the burden of self-managed Kafka to Confluent. Overall, Forrester identified TCO savings of more than 2.5 million for businesses that used Confluent, which translates to an ROI of 257% with a payback period of less than six months. The two key areas of savings included development and operations cost savings of over 1.4 million, plus scalability and infrastructure cost savings of over 1.1 million. And perhaps most importantly, Confluent enables organizations to free their teams to focus on strategic efforts that drive competitive differentiation versus managing the underlying data infrastructure. We think this is a key point of understanding that's emerging. Confluent is not just better and faster than self-managed open source, but can also be cheaper as well because of the high expense of cloud infrastructure and developers. We have vast economies of scale by offering these services to thousands of customers. We think these cost savings are a critical aspect of our value proposition and have made us successful in both companies focused on innovation, as well as those focused on cost savings and efficiency. A great example of this ROI is Swiggy, India's leading on-demand food ordering and delivery platform that serves millions of customers every day. To connect those customers with hundreds of thousands of restaurant partners and all of their drivers requires real-time data. Originally, they managed their own Kafka clusters, but the time and energy spent on that deviated from delivering key business goals. Swiggy needed a fully managed solution to refocus their engineers' time, reduce costs, and handle significant spikes in demand. Speed of delivery is a competitive advantage for Swiggy and Confluent is at the heart of their data-in-motion architecture. Another key competitive advantage for Confluent is our customer growth go-to-market model. We discussed this in detail on our last call, but I wanted to provide a brief recap on our strategy and some updates that we've made since last quarter. Our go-to-market effort is product-led, consumption-oriented, and purpose-built for data in motion aimed at driving customer lands and growing usage of our product from early experiments to large-scale central nervous systems. We continue to innovate at each stage in this journey. In the first quarter, we made it even easier for developers to get started with Confluent by allowing signups with existing Google and GitHub account credentials, as well as removing our credit card paywall, allowing developers to test drive our product without the hassle of adding payment information. We also expanded our developer learning center, developer.confluent.io. We launched expanded training materials for Kafka and Confluent, including material written and presented by one of my co-founders and Kafka's original co-creators, Jun Rao. In addition, we've started a library of code samples and step-by-step tutorials to help customers apply Confluent in common use cases, such as the ones described in the call today. This effort helps us to train the next generation of Kafka users on Confluent Cloud. It helps us more quickly progress our customers to additional use cases and applications. The success of this strategy was reflected in a significant increase in signups and another record quarter of customer additions, growing our total customer count 62% year over year to approximately 4,120. This has continued to be a driver of the strong growth in our customer base with 100K or more in ARR, which grew 41% year over year to 791 customers. Finally, the spread of use cases, the powerful network effect, and the customer's desire to build out their central nervous system with Confluent is reflected in the growth of our largest customer base. Growth of customers with 1 million or more in ARR accelerated to 62% year over year, ending the quarter with 97 customers. We are still in the very early innings of this opportunity and look forward to what's ahead. We also recently hosted our first in-person Kafka Summit since 2019. More than 1,200 members of the community gathered in London, and many more tuned in for the live stream. And I've got to tell you, it was great to be back together again. The prevalence of Kafka is also evident in the huge growth of the community. We've seen hundreds of thousands of organizations adopt Kafka, tens of thousands attended meetups throughout the pandemic, and a rich community working to document, improve, and contribute to Kafka. We also announced our new data streaming industry event, Current 2022, the next generation of Kafka Summit, where we will bring the preeminent thought leaders and experts in data streaming together in Austin this October. We invite you to join Current 2022 to learn more about data streaming and our leadership in this space. It's hard to measure open source adoption, but I'd like to share one illustrative statistic today that shows the strength of the growing movement around Kafka and data in motion. Many stats such as downloads aren't available for Apache projects, and stats like GitHub stars aren't very representative of actual production usage. One data source we do look at is the active unique IPs using the Kafka Java library, which is available from the company Sonotype that distributes those libraries. This is a comparatively rigorous measure. Because of the deduplication by IP address, full companies may appear only once. Duplicate and automatic downloads are suppressed, and users who do not remain active fall out of the measure. On a trailing 12-month basis, Kafka downloads grew over 50% year over year. People often ask about the competitive landscape, and the reality is that we don't feel Kafka has a close competitor in terms of scope of usage, breadth of ecosystem, or developer mindshare. As an illustration of this, it's worth considering the adoption rate of one of the most commonly mentioned competitive systems, Apache Pulsar. Apache Kafka sustained a significantly higher growth rate on a percentage basis than Pulsar, despite the fact that Kafka's growth rate is off a user base that is over 10x larger. This sustained superior growth at scale is what has made Kafka the de facto standard for data in motion and is a tribute to the strength of the Kafka community, the massive ecosystem of integrations, the network effect inherent in data streaming, as well as the simplicity and superior performance that Kafka offers. Before turning to Stefan, I want to highlight a key leadership hire in the first quarter. Gunjan Agarwal, our new Chief People Officer. Gunjan joined us from RingCentral and is a 20-year industry veteran whose team's efforts have been widely recognized with an A-plus culture rating and a host of awards for diversity, happiness, and leadership. I look forward to working with Gunjan as we continue to scale our team and culture. We intend to continue to attract top industry talent in every function and create an organization that gets even better as it gets bigger. With that, I will turn the call over to Stefan to walk through the financials.

Thanks, Jay. Q1 was another excellent quarter of operational execution and delivering on our commitments, with results exceeding the high end of our guidance on all metrics. Key highlights include strength in our cloud business, accelerating growth of remaining performance obligations, robust revenue growth, and strong unit economics. Q1 RPO accelerated to 96% growth year over year, reaching 551.1 million, of which we anticipate approximately 60% to be recognized as revenue in the next 12 months, representing 66% growth year over year. Confluent Cloud is powering the growth of RPO. We closed the largest deal in the company's history with Confluent Cloud. This eight-figure, three-year deal had an immaterial contribution to current RPO, and the vast majority of the contract value is reflected in our non-current RPO. Additionally, we recognized zero revenue from this deal in Q1. The deal marks another key milestone for our company and enhances the durability and visibility of cloud revenue growth in the out years. Total revenue in the first quarter grew 64% year over year to 126.1 million. Subscription revenue grew 68% year over year to 113.9 million and accounted for 90% of total revenue. Within subscription, Confluent platform revenue was 75 million, up 39% year over year, and accounted for 59% of total revenue. Confluent cloud revenue exceeded our expectations, up $5.1 million sequentially, and up 180% year over year to 38.9 million, accounting for 31% of total revenue, a 13 point increase from a year ago. We're also encouraged by the underlying momentum we continue to see in our cloud business. For the second quarter in a row, Cloud accounted for more than 50% of new ACV bookings, marking a mixed shift in the business we had anticipated, but that is happening faster than we had expected. Cloud also represents the vast majority of our total customer count and has a world-class net retention rate of greater than 150%. Our outperformance in cloud reflects not only the substantial progress we've made in all the verticals we've been serving, but it also demonstrates our ability to expand into the highly strategic digital native segment. This momentum is being powered by our customers' strong desire to have our fully managed multi-cloud offering, which has superior product capabilities and lower total cost of ownership compared to open source. Confluent Cloud abstracts away the complexities of managing and scaling open source Kafka on their own. and relieves the burden of hiring Kafka engineers in the current labor environment. We see a long runway for Confluent Cloud to grow substantially over the long term. Turning to the geographic mix of revenue, revenue from the US grew 60% year over year to 79 million. Revenue from outside the US grew 70% year over year to 47.1 million. Because we price in USD globally, we don't expect foreign exchange to be a direct hit one to our top line. The net retention rate in the quarter remained above 130% for the fourth consecutive quarter, as we continue to see very strong gross retention and expansion across both of our product offerings. As mentioned previously, NRR for cloud was greater than 150%, and NRR for hybrid customers running both platform and cloud continue to be the highest. Moving on to gross margins and profitability, I'd like to note that I'll be referring to non-GAAP results unless stated otherwise. Total gross margin was 69.7% and subscription gross margin was 75.5%. We're pleased with our ability to maintain healthy gross margins, even as cloud accounted for a larger share of revenue. We've driven substantial improvements in cloud gross margin, and we remain focused on increasing our cloud gross margin over time. In the near term, we anticipate total gross margin to fluctuate near our midterm target of approximately 70%. Turning to profitability and cash, operating margin was negative 41%, which represents a beat relative to our guidance. This was partly driven by increased sales productivity, lower than expected spend from in-person events, travel and real estate, and other operating efficiencies that were continuing to drive throughout the business. Free cash flow margin was negative 46.3%, which was better than our plan. As mentioned on our Q4 earnings call, we expected free cash flow margin to be the lowest in Q1, driven by the timing of cash flows related to our new corporate bonus program and employee stock purchase plan. Net loss per share was negative 19 cents, using 272.9 million basic and diluted weighted average shares outstanding. We ended the first quarter with $1.99 billion in cash, cash equivalents, and marketable securities. Our exceptionally strong balance sheet gives us the flexibility to fund our growth plans as we steadily move towards profitability. Looking ahead, I want to continue to share our plan for managing growth and profitability, which is guided by our framework. The level of investment is informed by our track record of delivering on our commitments, our significant market opportunity, and by assessing unit economics, such as our consistent and strong NRR, increasing sales productivity, and improving cloud gross margin. We remain committed to delivering high revenue growth and annual improvement in margins in 2022 and plan on accelerating the rate of margin improvement in 2023. Based on our current top line growth projections and investment priorities, we plan to exit Q4 24 with a positive non-GAAP operating margin. Turning now to guidance, we're raising numbers for the quarter and the year. For the second quarter of 2022, we expect revenue to be in the range of $130 to $132 million, representing growth of 47% to 49% year-over-year, non-GAAP operating margin to be approximately negative 41%, and non-GAAP net loss per share in the range of negative 21 to negative 19 cents, using approximately 279 million weighted average shares outstanding. For the full year 2022, we now expect revenue to be in the range of 554 to 560 million, representing growth of 43 to 44% year over year, non-GAAP operating margin to be approximately negative 38%, and non-GAAP net loss per share in the range of negative 79 to negative 73 cents, using approximately 282 million weighted average shares outstanding. I'd also like to provide some modeling points. We expect sequential revenue dollar growth for cloud will be the lowest in Q1 and will increase each quarter for the remainder of the year. Our margin guidance for the second quarter and full year takes into consideration an increase in expenses related to travel, in-person events, real estate, and facilities throughout 2022. And as a reminder, free cash flow margin is expected to be the lowest in Q1, followed by Q3, and we expect free cash flow margin to trend roughly in line with non-GAAP operating margin. In closing, our strong first quarter results underpinned our momentum and leadership position in data streaming. Since becoming a public company, we have proven our ability to execute and deliver on our commitments consistently. Capitalizing on the secular trend of digital transformation and cloud migration, we're well positioned to drive continued high growth and deliver annual margin improvements. With that, Jay and I will take your questions.