Looping Back on Confluent (CFLT)

Following disappointing results from the hyperscalers, Confluent was one of the first independent software infrastructure providers to report Q4 earnings on January 30th. Further complicating the picture, they preceded that report with an announcement of an 8% staff reduction on January 26th. That filing included top-level Q4 results. This flurry of news overshadowed two key points from the reports that subsequently became clear.

First, Confluent largely maintained their revenue growth target for full year 2023 that had been previously set in Q3. Initially, the market seemed to be anticipating the standard q/q raise. However, subsequent earnings reports from other software infrastructure providers made it clear that just maintaining guidance represented a positive signal, as several companies made fairly significant downward revisions to their full year revenue target from either what analysts had modeled or the company’s own guidance from the prior quarter. Among peers, Confluent was one of few companies that kept the target about the same.

Second, the driver of the layoff was a desire to pull in profitability targets by a year. Because force reductions can be organizationally disruptive and sometimes a signal of worsening demand, the layoff was initially interpreted negatively. The full Q4 report provided sufficient evidence that demand was softening, but not falling off a cliff, as new customer activity was strong.

The upside to the staff reduction is a significant decrease in expenses. Confluent’s revised operating margin target for end of year 2023 is now Non-GAAP break-even. If achieved, Confluent will have improved their operating margin by 2000 bps (20%) for two years in a row. They have telegraphed that FCF margins will follow a similar path.

These two factors, along with other positive signals in the report and earnings call, make Confluent’s results look more favorable relative to peers. When compared to subsequent reports from most other software providers, Confluent is forecasting less revenue growth deceleration and a marked improvement in operating margin.

This momentum may well provide investors with a favorable set-up going into 2024. Looking to next year, we could have a situation in which Confluent is growing at 30%+ revenue with positive operating margin. The stock appears fairly valued now, implying that further price appreciation could increase proportionally to revenue growth.

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Furthermore, as we transition into 2024, Confluent will be ramping up a new product offering, the result of their Immerok acquisition in January. This would provide a new revenue stream, which management has indicated could eventually be as large as that for core Confluent. Addressing the other half of data infrastructure that makes streaming useful, stream processing would serve another set of use cases, closely aligned with application developers.

While the remainder of 2023 is needed to integrate Immerok’s Flink-based offering into the core Confluent platform, the new product would start to generate revenue in 2024. Flink is a popular stream processing solution, with some of the largest data-generating companies as users, including Uber, Apple, Netflix and Goldman Sachs.

This new revenue contribution from stream processing would layer on top of what appears to be a fairly reliable assumption for durable growth, as more enterprises migrate their existing Kafka installations to Confluent’s managed cloud offering. Kafka already enjoys over 100k installations and has penetrated more than 75% of the Fortune 500, providing a large upgrade pool. The Confluent leadership team expects Confluent Cloud to make up 50% of quarterly revenue by end of 2023.

As the largest provider of solutions for “data in motion”, I think Confluent is well positioned to benefit from a renewed focus by enterprises on mining their internal data for insights that can drive more sales, lower costs and improve customer service. With an influx of generative AI models, enterprises are realizing they can harness their own data to create new products and services.

I think this renewed focus on enterprise intelligence will bring increased investment in modernizing data infrastructure. Some of this started during Covid, as lockdowns forced many business processes online. Post Covid, investment has slowed down, as enterprise IT budgets come under new pressure from a challenging macro environment. As the macro backdrop stabilizes, enterprises should ramp up their investment in creating comprehensive, timely and governed data sets for AI models. Competition will soon make these efforts a priority again.

For my own portfolio, I am leaning into a few companies that support this theme of modernizing data infrastructure and expanding the data ecosystem. These include SNOW and MDB for storage, NET for cheap transit, distribution and security and DDOG for operational data (keeping in mind that observability isn’t restricted to just infrastructure monitoring). To capture the need for enterprises to feed their AI models with near real-time data, I recently opened a position in CFLT. As the leading provider of data streaming solutions, Confluent is positioned to capture their share of this growth.

This post reviews Confluent’s market, their product fit and how an increase in demand for data services will benefit them. After setting that foundation, I will loop back on their Q4 report. Combined these inputs should allow investors to determine if they want invest in CFLT. We will get another update on May 3rd with their upcoming Q1 earnings report.

Confluent Product Positioning and AI

I had previously published a deep-dive on Confluent, exploring their product offering and financial performance from Q3. I will review some of that content in this post, discuss recent announcements and try to project potential benefits for Confluent from the rapid emergence of AI as a new driver of investment in modernizing data infrastructure.

Confluent sells a cloud-native, feature complete and fully managed service that provides development teams with all the tools needed to support real-time data streaming operations. The platform has Apache Kafka at the core, supplemented by Confluent’s additional capabilities to make Kafka easier to scale, manage, secure and extend. Some of these value-add features include visualization of data flows, data governance, role-based access control, audit logs, self-service capabilities, user interfaces and visual code generation tools.

All of these features are designed to simplify the effort of streaming data from multiple sources to multiple destinations. As more producers and consumers are introduced into an organization’s data infrastructure, flows cannot be managed through traditional one-to-one, batched data pipelines. A many-to-many system based on the publish-subscribe model becomes necessary.

For organizations that rely on a continuous stream of data to function, Confluent provides the underlying data infrastructure. Use cases extend beyond obvious applications like in financial markets and AdTech. As enterprises realize the benefits of optimizing their business operations and performance through data mining and machine learning, then broader and faster distribution of data in near real-time yields incremental benefits. Confluent is leveraged by leaders in multiple industries to address an expanding set of use cases like fraud analysis, transportation/logistics, healthcare and telecommunications.

At a high level, the investment thesis for Confluent’s growth revolves around the reality that Kafka usage is already widespread among enterprises. During their Investor Session at the annual Current conference in October 2022, leadership shared that more than 100k organizations worldwide currently use Apache Kafka. This includes over 75% of the Fortune 500.

Of the Fortune 500, most of the largest companies in each industry utilize Kafka for data streaming. Kafka was developed at LinkedIn and open sourced in 2011. It graduated from the Apache incubator program in October 2012 and has been broadly available since then, allowing it to grow into the most popular data streaming platform. As a data platform, Kafka is sticky. Once an enterprise builds data infrastructure on top of it, the cost of switching to another solution is high. This explains the broad adoption. Additionally, its timing was favorable, as the decade of 2010 was when most enterprises updated their data infrastructure to enable this type of data distribution for new Internet and mobile-delivered applications.

Since Kafka usage is prolific, Confluent’s challenge and opportunity is to convert this installed base to the commercial version of Kafka that they provide. To justify the upgrade, the Confluent team has built additional, proprietary functionality around the core Kafka distribution. These address tricky problems in scalability, security, management and ease of integration with data producers and consumers.

These capabilities are only available to customers who subscribe to Confluent’s distribution. If the customer is self-hosted, they download and install the enhanced Confluent package onto their servers. If using Confluent Cloud, management is seamless as the Confluent team handles all operations for the customer. Of all open source software infrastructure options, I can tell you that Kafka is one of the most difficult to manage, requiring dedicated engineers well-versed in the nuances of running large data infrastructure at scale.

Besides Confluent, the hyperscalers offer hosted Kafka services. AWS has Amazon Managed Streaming for Apache Kafka (MSK), which is a fully managed, highly available Kafka implementation. AWS offers a separate proprietary product called Kinesis, that is a data streaming processing engine. This is not based on Kafka, but leverages many of the same concepts around the publish-subscribe pattern. Azure and GCP offer data streaming solutions, but also have strong partnerships with Confluent for managed Kafka.

Amongst the hyperscalers, Confluent Cloud distinguishes itself by offering more add-on services and broader availability than any of the cloud providers’ solutions. The hyperscaler products are usually just cloud-hosted versions of Kafka with a few extensions and integrations with their internal services. For customers with a multi-cloud deployment, being able to access the same Kafka installation across different cloud providers and on-premise systems represents a big advantage. This capability would be limited in usage for any of the individual hyperscaler offerings.

To attract these Kafka users to the Confluent offering, the product team emphasizes three categories of advantage:

• Cloud Native. Kafka has been redesigned to run in the cloud. The Confluent Cloud solution comes with tools for DevOps, serverless configuration, unlimited storage capacity and high availability.
• Complete. Confluent has added features in a number of areas that provide supplemental capabilities not available in open source Kafka. These are designed to appeal to developers, allowing them to reliably and securely build next generation applications more quickly. These include over 120 connectors to other popular systems, stream governance, enterprise security, monitoring, ksqlDB and stream processing.
• Everywhere. Confluent Cloud is available on AWS, Azure and GCP. Confluent Platform is also available for customers that self-host their infrastructure. Cluster linking provides a bridge to connect instances across different cloud providers or to physical data centers.

For Confluent Cloud, the advantage to customers is that all aspects of managing the Kafka instance and adjacent services are handled by Confluent. With self-managed installations or Kafka cloud hosting from other providers, the IT organization is left to manage most or some of the functions in the Kafka installation.

With Confluent Cloud, the customer outsources all aspects of managing the Kafka instance to the Confluent team. These include partitioning, scaling, software patches and monitoring. Provisioning, configuring and running a Kafka cluster at high data volumes can be very complex. Having the engineering team that wrote the software managing one’s infrastructure is a big help. Resources that were dedicated to running the Kafka installation can be redeployed onto projects that create real competitive advantage for enterprises.

Due to this inherent advantage, the hyperscalers have been increasingly partnering with Confluent. AWS signed an expanded agreement in January 2022. Azure soon followed in April 2022. GCP and Confluent have a long-standing relationship. As with other independent software companies, the hyperscalers are discovering the advantages of partnering with these providers versus trying to compete with them.

Additionally, as on-premise customers plan their cloud migration, independent software providers like Confluent can bring that customer to the hyperscaler. If the hyperscaler has an antagonistic relationship with a preferred software provider, that hyperscaler would be less likely to win the overall hosting contract. We have to remember that the hyperscalers are much more concerned with winning business over another hyperscaler than giving up an opportunity to sell a single add-on developer service.

Market Opportunity

When I first encountered Confluent, I was admittedly skeptical about the size of the market opportunity. I understand the benefits for real-time data, having managed a large-scale Kafka installation at an AdTech company in the past. However, outside of online advertising, financial markets and a few other categories, I struggled to see the broader value proposition for spending incremental IT budget to accomplish what appears to be faster batch processing.

My reasoning has changed as the value of data for enterprises is increasing. While data has always been an important input for top-level business metrics, like executive dashboards, it is more commonly being harnessed by enterprises to identify growth opportunities, lower costs and deliver better customer service. New capabilities in machine learning and AI are rapidly increasing the ability to make use of this data in an automated fashion and even begin shifting decision-making from humans to machines.

As the realized value of data increases, enterprises will be incrementally more willing to invest in data infrastructure. Training and running AI models can be computationally expensive. Consolidating, pre-processing and cleansing data ensures the inputs are focused on what is most likely to provide insight. Additionally, AI-driven systems can’t just “go find all the data” (at least not yet). Like any analytics, they require a data source and some context. If the data source will be an API, then investment will still be required in the software infrastructure required to serve, refresh and secure that API.

Additionally, data governance remains a requirement. If data is shared with an AI model, the system still needs to track who has access to what and the level of obfuscation required. Modern systems of data infrastructure have data governance and secure sharing as a core design tenet. For example, if patient health records are fed into an AI model to create a more/better diagnosis service, then the system still has to ensure identifiable patient data is protected.

Industry analyst IDC recently published their “Future of Intelligence” report for 2023. They created an index of companies measuring the state of their enterprise intelligence programs. Enterprise intelligence goes far beyond basic business intelligence (BI). Enterprise Intelligence represents the totality of relevant information available to an organization, combined with the organization’s ability to extract real value from that data.

To achieve enterprise intelligence, organizations need to collect and combine data from all internal and external sources available to create a complete 360 degree view of the organization, its customers, products, supply chain and market. It must be fluid and flexible, and have the ability to quickly fuse data, perform advanced analysis, support multi-role decision-making and identify actionable intelligence to uncover hidden opportunities, reduce costs and deliver better service to customers.

They found that those companies with the most mature and effective enterprise intelligence programs also exhibited the highest revenue growth and speed of execution.

IDC’s enterprise intelligence benchmarking research shows that maturity in enterprise intelligence makes a material difference to business outcomes. Top-quartile enterprise intelligence performers are 2.7 times more likely to have experienced strong revenue growth over 2020–2022 and 3.6 times more likely to have accelerated their time to market for new products, services, experiences, and other initiatives.

Improving enterprise intelligence performance will often require concerted investment and action at multiple levels: from data platforms (to enable more openness, flexibility, scale, and connectivity) and pipelines and processes (to enable more effective, consistent processing of data to make it “insight ready”) to tools (to build and deliver analytics and insights), decision-making and action-taking processes, and culture.

Organizations that invest in enterprise intelligence will find that they are more digitally resilient, agile, innovative, and dynamic than their peers.

IDC Future of Intelligence Report for 2023, December 2022

Building a robust enterprise intelligence program requires investment in data platforms to store the data, pipelines to move it, processes to make it “insight ready” and tools to analyze it. Decision-making and direct actions are often drawn from the analytics and insights generated by the enterprise intelligence program. When applied to business decisions continuously, the result is better enterprise performance relative to peers.

The recent flurry of announcements and improvements in AI capabilities will only make this need for enterprise intelligence more acute. In order to be “AI ready”, enterprise data has to be passed through the same systems and processes as for prior baseline analytics. Only now, the value of the data is even greater as machine learning and AI promise to unlock deeper insights and make data more actionable.

If the value of comprehensive, consolidated, cleansed, semi-structured data is higher as an input to new AI processing and mainstream enterprise intelligence, then the effort to make it real-time is justified. As the current macro pressure on IT budgets normalizes, I believe we will see a doubling-down by large businesses of investment in their enterprise intelligence programs. Underneath an effective enterprise intelligence program lies robust data platforms, pipelines and tools, including a focus on timeliness.

A simple prediction of enterprise investment to improve their data intelligence capabilities recently came from Alibaba. Daniel Zhang, chairman and CEO of Alibaba Group and Alibaba Cloud Intelligence, said, “We are at a technological watershed moment driven by generative AI and cloud computing, and businesses across all sectors have started to embrace intelligence transformation to stay ahead of the game.“

Besides the AI system and raw compute providers themselves, beneficiaries of new AI-focused investment should include the same enablers of modern data infrastructure that drove the ongoing effort towards enterprise intelligence programs. As a baseline, enterprises will want their data in a modern data store (ideally cloud-based like Snowflake or Databricks) to serve as a curated and governed data source. Migrations off of legacy data warehousing solutions and isolated data silos should experience a new sense of urgency.

With an ever-growing number of data sources and business systems, enterprises will also need an open, flexible, scalable and governed system for collecting all of this data and getting it to the right destination. The more up to date, the better. This provides a new catalyst for data streaming platforms and stream processing. Built on the most popular platform for data streaming, Confluent is extremely well-positioned to capture this demand.

Granted, a lot of Confluent’s growth opportunity is tied to the migration from self-managed Kafka to the Confluent product offering (Confluent Cloud). With the increased reliance on this distributed data infrastructure, I think most enterprises will choose to “upgrade” to the Confluent offering. Self-managed Kafka can be expensive, risky and complex to maintain. Only the largest enterprises can afford to dedicate a team to run open source Kafka. Additionally, as time passes, Confluent is adding ever more capabilities to their platform that exceed the feature set available in open source Kafka.

As part of their report, IDC closed with ten predictions representing trends they expect to see across enterprise intelligence. One of those relates directly to Confluent. By 2025, IDC expects that 90% of the Global 1000 will leverage real-time intelligence to improve business outcomes by using event-streaming technologies.

Prediction 6: By 2025, real-time intelligence will be leveraged by 90% of G1000 to improve outcomes such as customer experience by using event-streaming technologies.

IDC Future of Intelligence Report for 2023, December 2022

On the Q4 earnings call, Confluent’s CEO mentioned a separate IDC study in which 80% of companies that are already using data streaming have plans to invest in new streaming capabilities over the next 12 to 18 months. These trends will provide large tailwinds for Confluent, as they are the recognized leader in data streaming platform providers.

Immerok Acquisition

On January 6th, Confluent announced its intent to acquire Immerok. Immerok was founded in 2022 and consists of the leading contributors to Apache Flink. Immerok’s primary product is a cloud-native version of Apache Flink. As a fully managed, serverless offering, Immerok Cloud provides customers with an easy way to run Apache Flink without setting up their own infrastructure. This is a similar benefit to other cloud-native services based on an open source product (including Confluent Cloud for Kafka).

Apache Flink is a a large-scale data processing engine and stream framework. It was designed to focus on real-time data and stateful processing, making it an ideal solution for handling large amounts of data. Flink usually runs self-contained streams in a cluster model that can be deployed using resource managers or standalone. Flink can consume streams and then forward data to other streams and databases. Most commonly, Flink is used in combination with Apache Kafka as the storage layer. Flink is managed independently, allowing teams to get the best out of both tools.

With Immerok, Confluent adds significant stream processing expertise to the organization. Confluent plans to accelerate the launch of a fully managed Flink offering. This would be compatible with its fully managed Kafka service, Confluent Cloud. The benefit to customers is that they could choose from several stream processing tools natively on Confluent Cloud, including Flink, Kafka Streams and ksqlDB, based on their use case.

On the Q4 earnings call, the Confluent management team provided some updates on their intention and progress with the Immerok acquisition. They are currently working to incorporate Flink into the Confluent Cloud offering in a truly native way (not just hosting Flink on the cloud). This will include a full integration, available in the same cloud locations as Confluent Cloud. They are targeting the first version to be launched in Confluent Cloud later in 2023.

In terms of our product plans, we plan to launch the first version of our Flink offering in Confluent Cloud later this year. We want to follow the same key principles we brought to our Kafka offering, building a service that is truly cloud-native, is a complete and fully integrated offering, and is available everywhere across all the major clouds. We think this combination of an open popular interface, offered with a deeply differentiated cloud-native core, is the key to success for cloud data systems. We think that, over time, this offering can be a substantial driver of growth in our business, comparable in size to Kafka itself.

Confluent Q4 2022 Earnings Call, January 2023

Confluent’s CEO made the assertion that the future Flink-based stream processing product could drive as much business as Confluent’s current data streaming platform. While it will take some time to ramp up, this expands Confluent’s potential share of their estimated $60B TAM. The CEO feels that the Immerok product extension could drive this amount of revenue for a few reasons:

• Confluent will be able to better monetize application development that occurs around data streams, in addition to powering the core streaming operation (through Kafka). This should expand spend with existing large customers
• By making streaming easier for customers, Confluent’s solutions are suitable for a broader set of customer workloads.
• The processing of streams can generate more streams that would feed back into demand for core Kafka, data connectors and data governance services.

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Q4 2022 Earnings Results

When I first published my first Confluent deep dive, Confluent had not published their Q4 report. Interestingly, the results turned out to be largely inline with expectations, including for forward growth. This consistency turned out to be a positive signal, as subsequent software infrastructure providers set lower revenue growth targets for the next year than analysts had expected. In cases where management had provided a preliminary view of 2023 as part of their Q3 results, Confluent was fairly unique in actually maintaining their target.

In Confluent’s case, their Q4 report had little impact on the stock. This was partially because they pre-announced a workforce reduction through an 8-K filing on January 26th. Additionally, with their Q4 report preceding other software infrastructure quarterly reports by a week or more, the market had few comparisons to realize their relative outperformance. As of this writing, CFLT stock trades at about the same level as at the end of January and has shown relative strength over the last couple of weeks. This may point to institutional accumulation or a realization from investors that the Q4 story was actually pretty good.

To better understand how the Q4 report is being interpreted by the market, let’s dig into revenue, profitability and customer activity.

Revenue

Revenue in Q4 was $168.7M, up 40.7% annually and 11.2% sequentially. This beat the company’s guidance from Q3 for a range of $161M-$163M, which would have represented 35.1% annual growth at the midpoint and 6.8% sequentially. This means that Confluent beat their prior guidance by almost 5% on a sequential basis. The sequential revenue growth is impressive, as many software peers saw lower sequential growth, masked by still reasonable annual growth rates.

For Q1, management set preliminary revenue guidance for a range of $166M to $168M, representing annual growth of 32.4%. Analysts had been looking for $169.6M. The Q1 guidance is slightly below the revenue just delivered in Q4. While that might sound unfavorable on the surface, Confluent took a similar approach when setting guidance for Q1 2022. They finished Q4 2021 with $119.9M in revenue and then set the next quarter’s sales target for a range of $117M – $119M. In that case, the actual revenue was $126M, representing almost 7% of outperformance. Confluent’s revenue is usually seasonally high in Q4.

For the full year of 2023, leadership set the revenue target for $760M-$765M, which represents 30.1% growth over the FY2022 actual revenue of $585.9M. Coming out of the Q3 report in early November, management had provided a preliminary range for 2023 revenue of $760M – $770M. In the actual Q4 report, they lowered the top end of the range by $5M. Compared to the magnitude of reductions made by software peers in subsequent Q4 reports, Confluent’s adjustment was minor.

This compares to the following results from peers:

• Datadog: Analysts expected 32.6%. Actual was 23.8% above 2022.
• Snowflake: Management had estimated 47% growth for 2023 (FY2024) as part of the Q3 report. In Q4, they lowered 2023 guidance to 39.5% annual growth.
• MongoDB: Analysts had expected 25.6%. Actual was 16.4% above 2022.

Confluent Cloud is experiencing strong growth. In Q4, Confluent Cloud added $11.5M in sequential revenue for a roughly 20% q/q growth rate. On an annual basis, Confluent Cloud revenue increased by 102%. While this annual growth rate has been decelerating quickly from over 200% in 2021, it appears to be settling into a sustained growth rate much higher than overall revenue, at least based on annualizing the sequential growth rates from the last couple of quarters.

For Q1, as part of forward guidance, management expects Confluent Cloud to add $5M in revenue. That would decrease the sequential rate to 7.3%, but a drop in the net additional revenue from Q4 to Q1 is normal and happened a year ago. Management expects the sequential gross amount of Confluent Cloud revenue to ramp up through 2023, similar to the pattern in 2022, with a more pronounced ramp in the second half of 2023.

At the end of Q4, Confluent Cloud contributed 41% of total revenue, up from 28% of revenue just a year ago. This increase is significant, as the higher growth rate for Confluent Cloud will prop up overall revenue growth as its contribution increases. Management expects this ramp to continue with the contribution from Confluent Cloud reaching 50% in Q4 2023.

One advantage that Confluent currently enjoys for higher revenue growth is their size. Even the 2023 guide is well under $1B in revenue. This smaller size may support above average growth for another 1-2 years. RPO provides another data point for future growth. Remaining performance obligations grew 48% year over year to $740.7M. This represented an 11.6% sequential growth over $663.5M in RPO as of the end of Q3.

Of this, 62% or $459M is expected to be recognized as revenue over the next 12 months. This would provide 60% of the current estimate for FY2023 revenue. While nearly 12% of sequential RPO growth from Q3 to Q4 appears strong, Confluent management was hoping for more. For comparison, in the transition from Q3 to Q4 2021, Confluent increased RPO from $385M to $500.6M for a 30.8% sequential increase.

RPO growth was affected by deal slippage in the fourth quarter. The CFO explained that they experienced “less urgency by customers to sign deals in the last couple of weeks than we typically would see in a calendar Q4.” This was primarily in the enterprise segment, as some customers opted to delay their purchases to FY2023.

Confluent didn’t experience any material changes in discounting, contract duration or win rates relative to the previous quarter. Additionally, the CFO reported that a number of the Q4 deals that were pushed did close in Q1, indicating that demand is still intact for Confluent’s solution.

Profitability

While any layoff in mass will have impact on company morale and potential knock-on effects for future hiring, the objective financial impact of Confluent’s 8% workforce reduction is a full year improvement in the path to profitability. Leadership now expects to exit Q4 of 2023 with breakeven Non-GAAP operating margin. This would be a significant improvement from the -21.5% operating margin delivered in Q4. Additionally, management clarified that quota carrying sales capacity and key areas of R&D were being preserved.

If we go back to Q4 2021, Confluent had delivered -41.4% Non-GAAP operating margin. This was a step backwards from Q4 2020, in which operating margin was -31.7%. Confluent actually became less profitable in 2021, primarily due to hiring in anticipation of a large growth opportunity. This shift in focus back to profitability is refreshing and would represent roughly 2000 bps or 20% improvement each year in 2022 and 2023.

For Q4, Confluent delivered a substantial improvement in Non-GAAP gross margin, increasing from 68.2% in Q4 2021 to 73.0% this past quarter. This improvement allowed gross profit to grow much faster than revenue at 50.5%. Like other software infrastructure providers, Confluent can drive higher gross margin for their subscription offering. The Cloud service has a lower gross margin, as the hosting fees paid to the hyperscalers falls under cost of goods sold. Confluent’s great mix of revenue from the Cloud product will lower gross margin.

At the same time, ongoing efforts to optimize the operations of the Cloud service can lower costs and improve gross margin. As use of the Cloud service grows, Confluent can negotiate better volume discounts with the hyperscalers. These factors all combined to deliver the nearly 500 bps in gross margin improvement year/year.

Improvements in gross margin combined with efficiencies across department spend to drive a 20% increase in operating margin. For Q4, Confluent reported a Non-GAAP operating loss of ($36.3M) for an operating margin of -21.5%. While still negative, this improved significantly from a loss of ($49.7M) or -41.4% operating margin a year ago.

Besides the increase in gross margins, Confluent was able to reduce the relative percentage of revenue for all three major personnel departments. On a Non-GAAP basis, total operating expense as a percentage of revenue decreased by 1500 bps from Q4 2021 to Q4 2022. This reduction was enabled by a 400 bps decrease in R&D expense, 790 bps in S&M and 330 bps in G&A.

These improvements drove a Non-GAAP net loss per share of ($0.09) for Q4. This improved by $0.10 from a year ago. Additionally, it beat analyst expectations for ($0.16).

Looking forward, Confluent leadership expects further improvement in operating margins through 2023. This is being driven by the headcount reduction executed in January, as well as further optimization improvements in spend. For Q1, they set a Non-GAAP operating margin target of -27%. This would represent a step back from the -21.5% delivered in Q4, primarily a result of some one-time expenses to be incurred in Q1. On an EPS basis, this translates to a range of ($0.15) to ($0.13). Analysts were looking for ($0.16), so Confluent outperformed here.

For the full year, leadership set a target for -15% to -14% operating margin and a Non-GAAP per share loss of ($0.28) – ($0.22). This also beat analysts expectations for ($0.51), by roughly half. Further, management expects to exit 2023 with Q4 operating margin at break-even. This would represent a nice achievement, as operating margin was -40% two years prior.

Free cash flow improvements have lagged operating margin, but management expects a similar trend to apply. For Q4, Confluent had negative free cash flow of ($30.9M). This was down from ($26.7M) in Q4 2021, although slightly better on FCF margin due to the growth in revenue. FCF margin for Q4 was -18.3%, as compared to -22.3% in Q4 2021.

Long term targets provide a favorable opportunity for Confluent. Management has set growth and profitability goals for the mid-term (let’s assume 1-2 years) and long-term. In the mid-term, they can keep Non-GAAP gross margin near 70% and push operating margin up to 5%. FCF margin has a little more upside at 10%. Over a longer period, management feels they can reach a peak of 72-75% Non-GAAP gross margin, with operating margin above 20% and FCF margin exceeding 25%.

Included in the mid-term target is an annual revenue growth rate of 30% or more. For FY2023, they have already set the preliminary target for a hair over 30%. They will likely raise this a bit as the year progresses, assuming the macro picture doesn’t deteriorate significantly. Looking to FY2024, analysts currently have $979M of revenue modeled for 28.4% growth over their FY2023 estimate. Going out to 2025, they have revenue growth of 26.5% modeled for a total of $1.238B.

If Confluent can keep revenue growth above 30% for the next 3 years and reaches a FCF margin of 10%, valuation starts to look reasonable. With an EV of about $6.0B, the EV/S ratio drops to about 6 from its current value of 10.1. Any further outperformance on revenue growth from 30% over the next two years would increase this spread. Growth of 35% would drive about a 2x increase in valuation by end of 2024. While this represents a slowdown from the 41% revenue growth just delivered, Confluent would inflect to profitability by this point and be approaching 10% FCF margin, supporting the higher EV/S ratio.

Customer Activity

In spite of more scrutiny around deal closing and a slowdown in large customer expansion, Confluent is still adding new customers at a healthy clip. This is similar to other software infrastructure peers (like DDOG, MDB, SNOW, NET), which continue to onboard new customers at rates close to those prior to the recent spending slowdown. This provides investors with some assurance that the digital transformation and cloud migration pipeline is still intact, even with the revenue growth compression currently driven by less aggressive expansion by existing customers.

In Confluent’s case, they added 290 total customers in Q3, up 6.8% sequentially and 31% y/y. Earlier in 2022, Confluent adjusted the sign-up process for Confluent Cloud to remove the requirement to enter a credit card. This had the effect of injecting a one-time delay in time to count a customer as paying versus free. Previously, once a customer entered a credit card, they would incur a small charge and be counted as a paying customer. Now, those customers can remain a “free” user until they are ready to turn on Confluent Cloud.

This transition to support “try before you buy” is ultimately better for the customer. In the near term, the paying customer count froze between Q1 and Q2 2022 and then marginally increased in Q3. In Q4, customer growth appears to have worked through the one time reset, with new customer additions increasing to 290 in the quarter, versus 120 in Q3. The sequential growth rate of 6.8% annualizes to 30% annually. Combined with a strong net expansion rate, this should support elevated revenue growth.

Large customer activity remained strong as well. While Confluent leadership pointed out some deals that pushed from Q4 into Q1 as an impact on RPO growth, the rate of customers moving into higher levels of spend appears largely intact. Customers spending more than $100k in ARR increased by 70 in Q4 to 991, representing 7.6% sequential growth and 35% annually. This is the largest total increase in this segment of large customers over the prior 4 quarters.

Additionally, these large customers contributed more than 85% of revenue in the quarter, providing some insulation against potential fall-out from the failure of SVB and the knock-on effects on start-up spend. At their Investor Session in October 2022, management shared that 172 members of the Fortune 500 are customers.

Confluent delivered a similar step up for customers with $1M+ of ARR. They added 20 of these sized customers in Q4 to reach 133 total. This represents more than twice the number added over the prior 3 quarters for nearly 18% sequential and 51% annual growth. Leadership reported that growth of even larger $5M+ customers was higher, doubling in FY2022.

Confluent tracks its dollar-based net retention rate (NRR) like other software infrastructure companies. In Q3, they reported that this rate was above 130% for the sixth consecutive quarter. In Q4, NRR dropped to “just below” 130%. Management added that NRR for cloud only and hybrid lines of business were both “comfortably above” 130%. They also shared that hybrid NRR continued to be the highest, which makes sense, as those customers are likely transitioning to the cloud version and expanding in both directions for a period. Gross retention rate remained above 90%, reflecting the decrease in NRR wasn’t a result of a significant increase in churn.

Confluent leadership even mentioned that they now have “a growing number of $10M+ ARR customers.” This speaks to the larger opportunity for Confluent. With just under $600M in revenue last year, expanding more large customers into the $5M and $10M range would have an outsized impact on total revenue. Confluent doesn’t require a whole lot of $5M and $10M customers to get revenue over $1B.

Confluent has shown evidence of a very elastic spending threshold. At their Current conference in October, leadership presented a number of examples of companies that have significantly increased their Confluent spend over time. These examples include multiples in spend of 8x to 31x over a relatively short period of time (5-6 years for the largest increases).

If we look at one example with the Fortune 50 Bank, Confluent leadership laid out the rapid expansion progression over 4 years. They even think the customer has a line of sight to $20M in ARR.

To have a single customer approaching $20M in annual spend is significant. That one customer would represent over 3% of total revenue for 2022. With 133 customers now above $1M+ spend and a doubling of $5M+ customers in FY2022, we can see why Confluent’s focus on growing their largest customers makes sense.

Investment Plan

In my prior review of Confluent, I had listed a number of tailwinds for the company and a few potential risks. Risks included concerns about the size of the market, a slowdown in customer additions and lack of expansion into adjacent product categories. Over the last few months, I think those risks have dissipated. Confluent’s total customer additions returned to the prior trendline in Q4. The Immerok acquisition moves Confluent into a new product category with management projecting that the stream processing business (Flink) could match the core event-streaming platform (Kafka) in revenue in the future.

The concern about size of market still exists, but I think is more balanced by the renewed interest by enterprises in harnessing their data to improve business outcomes and drive efficiencies. The insights generated from platforms of intelligence are dependent on a large set of comprehensive, clean and up-to-date data. As the value of these insights increases and are further bolstered by new AI models, then further investments to modernize data platforms and make data transit near real-time will be justified.

I think these forces tip the scales in favor of Confluent. As the most popular provider of an event-streaming platform and soon stream processing solution, Confluent is well positioned to capture the incremental demand from enterprises seeking to modernize their data infrastructure. I think these tailwinds will benefit other providers in the data infrastructure landscape, like Snowflake and MongoDB, but Confluent appears unique in dominance of their product category.

So far in 2023, CFLT stock hasn’t moved much. It started the year just over $22 a share and has been bouncing around the $22-23 range recently. If they achieve 35% revenue growth for this year and next, they will exit 2024 with about $1.07B in revenue. This growth rate only requires about a 5-7% beat over current analyst targets for growth in both years. With the opportunities that I discussed, this should be achievable.

In parallel, the company is rapidly progressing towards Non-GAAP operating margin and cash flow break-even. Confluent should exit 2023 with a 20% improvement in these measures from Q4 2022. By Q4 2024, I think the trajectory of improvement will continue and could reach a 10% FCF margin, which is half the margin increase projected for 2023.

At a current EV of $6B, the 2024 exit valuation would be an EV/S ratio just under 6. Maintaining CFLT’s current EV/S ratio of 10.2 would provide appreciation of about 70% over the next two years. Given what I see as an improving risk/reward trade-off for CFLT stock, I have initiated an allocation of about 2% in my portfolio at a basis around $22. We will receive Confluent’s Q1 report on May 3rd and can check back on progress then. If the report includes significant outperformance or the stock dips before then, I will likely increase my allocation further.

NOTE: This article does not represent investment advice and is solely the author’s opinion for managing his own investment portfolio. Readers are expected to perform their own due diligence before making investment decisions. Please see the Disclaimer for more detail.