Good afternoon, I'm Rob Owens. I'm with Piper, Co-Head of Tech Research, and I head our security and infrastructure software practice. Very excited to introduce our next company. We kind of put them in that very cool company bucket in terms of what they do and where the opportunity is. So we have Confluent with us. Stephanie, Rohan, I'll let you two introduce yourselves because you might be somewhat new to this group, and congratulations on your new CFO role, so. Thank you. Appreciate it. Well, hey, everyone, Rohan Sivaram. Been in the role for just over a month right now, so fairly new, but I've been with Confluent for over three years. So, been part of the company since pre-IPO days. Looking forward to the conversation. Hi, all. Stephanie Buscemi. I am CMO at Confluent. I've spent about 25 years in enterprise marketing in tech, and prior to about 2.5 years here at Confluent, I was at Salesforce for about seven years, where I was CMO, and prior to that, about five years at SAP. Let's go right there, Salesforce to Confluent. Right? What, what did you see in this opportunity when you, when you came over? Yeah, a couple things. You know, explosion of data, so we've all seen that. Explosion of applications, so all this data because of all of these applications. When I was at Salesforce, I was out of the tower, the headquarters offices, and I was right down the hall from the executive briefing center, so I would say that I was the A, B, and C team, often getting to talk to customers. What I would always see is that while we were talking about applications, whether that was for sales, service, marketing, whatever that be in an organization, the conversation always came back to the underlying issue of data, the movement of data, the processing of data, the data which feeds the application, the database, the data warehouse. and so I thought all these applications are great, but they're only as good as our ability to get that data to them in real time, and so became a student of that. That's great. How much evangelizing has to go into selling a new customer? I mean, obviously, they, they understand the data problem per se, but this is a bit of a new space, so would love to kind of understand your perspective in terms of sales and marketing and what that motion looks like. Yeah, I think that it's past the evangelical phase. If you look, you know, there's been a lot of open source technologies out there, but if you really look at Kafka, Kafka's become ubiquitous. You know, there's 150,000 organizations out there using Kafka. 75% of the Fortune 500 is using Kafka. This is not a fad. Kafka is it for the stream. And so for us, in terms of sales and marketing, our go-to-market motion, you know, is really being able to go in to those organizations using Kafka. If you think there's 150,000 organizations out there using it, and our customer base is, you know, sub-5,000, there's a lot of surface area for us to cover there. We can go into these organizations and make it pretty frictionless with them through our product-led growth to come in and get started for a simple use case, and then get that success with that use case, using Confluent Cloud, and then go from there. And clearly, in this environment, I think people have been nervous around budgets and where spend happens, right? We've seen this keep the lights on type of spend in a lot of cases, with other areas being cut and cloud rationalization. And it feels like you guys have shifted very well to an ROI proposition. So I'd love for both of you to actually kind of touch on that ROI selling and where the customer is from that perspective. Yeah. Okay, you want to start? I'll kick it off. Stephanie touched on the fact that we have over 150,000 organizations using open-source Kafka, and our penetration is just less than shy of 5,000 customers right now. How we are differentiating ourselves with open-source Kafka matters, and we're essentially doing it on two fronts. The first is truly through technological differences, and the second one, Rob, to your point, is around ROI. And when we think about the ROI that we bring to our customers, it's typically on two fronts. If you're a company using an open-source shop, and you're using open-source Kafka, your spend comes in two categories. One is the infrastructure category, and the second one is truly you have these highly skilled Kafka engineers who you employ. So with Confluent, you get this as a service. On the infrastructure side, we're obviously making sure that we've optimized it, and we are providing you with meaningful ROI. And then on the people side, you just don't need to employ those people. So when you combine it together, that's... I would say, especially in the current economy, that's a huge catalyst. I don't know if you want to add something. Yeah, I mean, it's interesting. You would think in a tight macro environment that people could actually revert to open source, and it's the opposite. We're powering mission- I think there's a great customer example. I don't know how much you're willing to go into it, but when we first went into the pandemic, I think a major customer went to open source and came back rather quickly because it actually hurt your NRR, even though they were back, right? Yeah. The way you count it, so- Yeah. I don't know what you're willing to share on that customer, but maybe you can use that as an example. Well, without getting into customer details, Rob, what I can tell you is, you're right. I think the value proposition for companies using Confluent, Confluent Cloud, has become significantly higher, and we touched on the ROI side and the cost side of it, but what we've done over the last five years with our Confluent Cloud product is we've differentiated ourselves even more, with most recently, we spoke about our data streaming platform at our Investor Day. And what we are doing is we're looking at streaming, but on top of streaming, the other components of the data streaming platform, which is our connector ecosystem, the governance and security we bring to our products, the stream processing side, where we're just scratching the surface right now, and last but not least, our ability to share data between organizations. That put together, is a huge differentiator, and that's not something organizations can actually build by themselves by using open source Kafka. So I think ever since IPO to now, we've had three years, and our Confluent Cloud product has developed significantly, and we continue to drive that differentiation even more. Would love to maybe drill down a step further into what those different components can then mean to the model from a revenue perspective. Right. Because I think you're, as you said, just scratching the surface on some of the newer opportunities. Yeah. You're, you're absolutely right. Today, when you think about some of the growth drivers that we have in our business today, it's mostly we are monetizing the streaming side of it. And the opportunity, as we look ahead over the next 12-24 months, will be the different components of the data streaming platform. And I'll maybe spend 30 seconds on each of them to just give you a little flavor. On the connector ecosystem, that's something that basically brings more data into, into Confluent, and we have 120+ connectors. Although our on-prem product has a high, I'd say, attach rate to these connectors, for Confluent Cloud, we're in early days. So that's vector number 1. Vector number 2 is around governance. We have this free version of our governance product, which is, which has a very high penetration rate. We've launched our paid version about 12 months back, and that's been growing pretty well, and it's one of the fastest growing products for us. And that's obviously vector number three-- two. On the third one is stream processing. We recently acquired this company called Immerok, and which is based on this Flink technology. And we see stream processing as an opportunity for us as we look ahead, and by end of the year, we'll come up with a product which is going to GA, which is going to open up that opportunity for us. So long story short, if you think about broad drivers, each of these components can be large, independent businesses. And the power of this is when they work together, it drives that network effects, and it has that push and pull impact that we want. From our perspective, we see the monetization of this is probably gonna be more towards the end of 2024 and 2025. So the opportunity on these areas is actually in front of us. I think it's underappreciated how many of your customers are running it hybrid, right? How many run platform in clouds? Maybe you can speak to that, and then how are plans changing now? And are we seeing a light at the end of this so depressed software spending tunnel? I'm gonna ask you, because the finance guy won't. He won't answer that one. Like I said to you, I mean, first of all, the land motion is strong. I mean, we see out there... There's, first, increasing volume of data. Second, there is more and more need for real-time data. So as long as those continue to be drivers, it's good, good for us, and it's easy to land and be able to start, even with an initial use case within an account. We're also finding within these large enterprise accounts, our ability to serve one use case and then move to multiple- Mm-hmm ... use cases is easy to do. I mean, as he mentioned, the network effect of our offering, the reusability. One of the beautiful things about, you have the stream, and now we're gonna be bringing the processing into that stream, the reusability of that data to be able to go to any destination, whether you need that for any of your applications. An enterprise that might have over 500 different applications, many of them will want that data. The different databases, the different data lakes, the different data warehouses. So we continue to see, and I would say in the market, you know, we continue to see that the developers are dealing with more data. They want more, they need to do more on real time, and so that LAN motion is strong. On your question on the hybrid customers, we've always said that we need to be wherever our customers' data and applications reside. If that's on-prem, we need to be there with our Confluent Platform product, and if it is in the cloud, we need to be there with Confluent Cloud. And over, say, since the IPO over the last couple of years, we've seen that hybrid set of customers expand quite a bit. And recently, at our Analyst Day, we shared some numbers. 100K+ ARR hybrid customers, we have now over 200. That has increased significantly in the last couple of years, and that's something that we see as an opportunity. And why? Because the customers that have both on-prem and cloud end up having a net retention rate which is higher than the company average. So overall, it's a net positive for us. That's great. It serves us really well, particularly in, like, highly regulated industries, I mean, where they're slower, and they're potentially their move to full being fully cloud, that hybrid is a true differentiation for us. The open source space, Kafka is still growing and growing quite rapidly. Absolutely. We stay very close to the Kafka community. You know, we still are, I think, believe, the largest contributor to the open source- Sure ... Kafka out there. And so it continues to grow. And like I said, it's becoming truly ubiquitous as we speak. So how do you think about conversion and monetization? Is it just as they mature and need more in connectors and governance, and a lot of the value prop that you offer, so there's a natural evolution of that base into the paid? Or how do you think about targeting and actively converting that base? Yeah, I think it's, it's twofold. One, you, you continue to have that land motion, and you continue to do that bottoms up to developers in a very frictionless way, meaning through product-led growth. You're really just trying to, in a very frictionless way, get them into your product, letting them be able to experiment, use for that initial use case. We then, what sort of see happens there, is that you start to see that there is a proliferation of use cases in an enterprise account, and that ultimately then allows us to spend time more with the tech-savvy executives, those CTOs, those CIOs within the organization, who are realizing that they, they want to shore up this data mess that they have in their organization, and this notion that your data is a product. That's when they start to look at it as, "Oh, we wanna standardize on this as a data streaming platform. We don't wanna just use it for one day, two day use cases here. We, we wanna create the reusability of our data, and a data streaming platform allows us to have that reusability." To be able to have the streams be agnostic to any data destination, but be able to serve every data destination and every source. Then in that stream, to be able to govern and process and do all of that. Right. And I think you've said historically that the job of marketing is to be the voice of the customer. Yes. I did a little research. I would love to touch on GenAI- Yes ... and just that feedback that you're getting back from customers in terms of what they wanna see out of Confluent, and where you feel Confluent's best play is. And then I'd love to turn the question to you, just to give you a minute to think about, what technologies are you guys using internally that can drive efficiency from a GenAI perspective? Yeah, so we think GenAI is definitely a secular tailwind for us. We're very excited about it. You know, I'm not gonna hype it, in that I do think it's early innings for everyone out there on GenAI. Where we see our role in it is, you know, if you sort of historically looked, enterprises were focused on building these complex models, and they were doing it for specific use cases, and that was expensive, and time consuming, and hard to maintain those. And so it was hard to get traction and scale around that. Now, these organizations are looking on, "How can we leverage and tap into these large language models and make them useful to our business?" Well, they're not gonna be useful if they can't bring that together with their proprietary, their customer data, their information, their proprietary information. And so our role is to be able to bring that real-time context data there to the point of inference. Think about the prompt. What is the thing? Getting the right data at the right time to answer the right question out there, that's what they're gonna need to be able to do. Mm-hmm. We actually see, you know, it's early, but we actually see customers already doing it. We have an online travel provider who, you know, they can leverage that large language model data, but then they can take real time... They can take not only, like, general transactional customer data, not just like the account and contact-level data, but they can pull in real-time behavior data based on the customer and marry those things together to be able to answer the right question in that prompt, in that chat interface. Yeah. Thinking about GenAI internally, what I can tell you is it's going to be a driver of productivity and efficiency as we look ahead. How much, how fast? That's probably TBD at this point. Internally, we feel that as we are evaluating these GenAI-related systems, there are opportunities in the go-to-market side, in the engineering side, as well as on the G&A side. And we're, I'd say, not just us, if you ask any operator, they'll probably tell you that they're probably in this evaluation phase to what's gonna work, what are the go-to-market, I would say, roadmap for these GenAI products. And hopefully, by end of the year, we can look at one or two areas that we can start standardizing on. So, to summarize, I think it's an opportunity, not only for productivity, but also efficiency. How fast and, you know, how much we get out of it is probably TBD at this point. So you're not gonna let me take my margins up for next year yet? Do we have any questions from the floor at this point? Let's touch on the, Immerok acquisition and just data stream processing, the opportunity longer term product, I think, coming in the Q4. Yeah. So, on the Immerok acquisition, we acquired the company earlier this year, and, this is based on this technology called Flink, which is, one of the larger open source providers out there on the stream processing side of the world. And, we acquired the company, and, we will be coming out with its first commercial version of the stream processing product probably end of this year, which is currently in early access with our customers. Why is this relevant? Because we see stream processing as a huge opportunity because, it provides more context and more, I'd say, value-added views, from the streams business that we are currently in. Overall, from a monetization perspective of this business, we expect that the product's gonna GA end of the year, early next year, and it's gonna, there's gonna be a ramp time, because customers will probably need to build some kind of applications before they start monetizing it. So towards the latter half of 2024 is where we are gonna see start monetizing the stream stream processing product, specifically of the Immerok acquisition. From a marketing perspective, exciting new stuff? ... I'm very excited about it. Two kind of reasons there. One, just if you look at, it is the second-largest open source- Mm-hmm. A growing open source. So Flink is quickly, like Kafka, becoming a de facto standard. I mean, it started later, so it's a little bit—it's not quite there yet with Kafka, but it's healthy and growing really fast. If you look at large enterprises, leading companies out there, the Ubers, the Netflix, the Apple, like, every, like, amazing brand out there that's sort of on a digital forefront, they are using Flink today. So it is becoming the de facto standard for stream processing. So we're very excited about it. You can think about a lot of what we did with Kafka, that playbook we've done with Kafka, we will do again, with Flink. We will build a cloud-native offering, where we will make it a complete offering, meaning that... And it will be able to go everywhere. So we'll be able to go across multi-cloud, have multi-tenancy on that. So from a marketing perspective story, it's very exciting to be able to say, "Hey, it's not just the stream." Much of our business thus far has been the stream, and that's really powered by Kora, our cloud-native engine. Now, not just having the stream, but what are all the things you can do with the stream? Well, now with Flink, being able to have the stream and the stream processing together, to be able to filter, to join, to aggregate, to do analytics, all of that processing, that's the path to truly a data streaming platform. So Rohan, let's move to the model for a second. You expect the operating margin break even by the end of the year. That's a 23-point improvement from where you started the year in the Q1. That's, that's pretty impressive. So a lot of that's come through sales and marketing. Maybe speak to the efficiencies you've found. Help us, you know, help us think about the ensuing year, and you're not starving the business relative to the growth opportunity and achieving profitability so quickly. Then maybe secondarily, talk about some of the gross margin leverage you guys have seen, too, with the cost savings on that front. Yeah. You- 23 points is big in three quarters. You, you stole my line. I had a slightly different one, which is, in the last 18 months, we've seen 30 points of margin expansion. That's a bigger number, well said. That's a bigger number, exactly, yeah. And we've done that while maintaining our top line growth north of 30%. I mean, it obviously goes back to our broad philosophy that we have, which has candidly not changed for us. We've always been focused on driving efficient growth, and you can only do that if you have this ROI mindset and how you think about unit economics. And that's something we have a very high level of discipline within the organization. And when you think about the levers, earlier this year, we decided to pull in our target profitability by 12 months. We went through a restructuring exercise, as you might be aware. And when we were looking at that, of course, opportunities came in the go-to-market side of the world, but we were extremely thoughtful with respect to how we look at reducing our costs or rather rationalizing our costs. And as we were doing that, we made sure that we preserved our go-to-market capacity, because that's gonna be important as we look at our growth ahead. So, overall, I feel pretty good with the progress we've made. And looking ahead, of course, leverage is going to come in a couple of areas. First will be we'll have more ramped selling people as we exit this year than we started the year. That's gonna be a driver of leverage, that's gonna increase productivity. The second is, I've, I've said this before, we're probably in the early innings with respect to our partner ecosystem, which is going to provide us with not only efficiencies, but also growth levers as we think about that. Other areas will be, we'll, we will continue to be very, very disciplined with respect to how we invest and how we think about investing dollars with respect to ROI. Going back to your comment on gross margins, we feel pretty good with the progress we've made in the last 18 odd months. And if you think about our gross margins, we have our Confluent Platform business, which is software-type gross margins, and then we have Confluent Cloud, which has gross margins lower than Platform. Over the last couple of years, our cloud business, from a mix perspective, has continued to become a larger piece of the business. And that, by itself, is headwinds to gross margin, because cloud has lower gross margins. What we've done during this period is, we've improved our cloud gross margins quite significantly, and the levers we've seen are on the infrastructure side, and our engineering team have constantly gotten optimizations on that side. We've also seen, I'd say, improvements with respect to a bigger portion of our customer base is moving to multi-tenant, and that, by itself, for cloud businesses, is a tailwind for gross margins, so. Great. The last one, one from me, excuse me, is just puts and takes around your greater than 130% NRR and sustainability, because we've seen a lot of pressure on NRR from other companies, so maybe you can speak to that. You've talked to new modules coming online, which obviously you can upsell into the installed base. Yeah. We... When I think about our NRR, I think probably there are three, three drivers for, you know, NRR. The first one is just the data volume for your existing use cases. That's a driver. The second one is us being able to sell net new use cases. And the third one, which we touched on quite a bit in our discussion, is around making sure we are selling different pieces of our data streaming platform, the different components. So these are probably the three drivers of our net retention rates. And when I think about it, the best way to think about it is, if you look at our $100K and $1 million-plus customers, we've grown that a pretty good clip. Very, very decent rates. That basically tells you that we're constantly selling into our customer base, and the footprint that we have within our customers is constantly increasing. That's how I would categorize our NRR drivers and how I think about it. Perfect. Stephanie, Rohan, thank you so much. Thank you. Thank you. Thank you.