Confluent, Inc. (CFLT) Earnings Call Transcript & Summary

How is everybody holding up? First, just a few meetings in and at 3 more days to go. Are you ready for 3 more days. before we get to -- everybody looks tired. No, it's just -- that's just the first launch -- and I think there's a lot of our clients also have flowed from parts of the world. The jet lag has got to be a huge -- not for the local people like me. But Jay, thank you. I think it's your third year consecutively back to back at Goldman Sachs Communacopia and Technology Conference. Thanks for making it once again. We really appreciate your participation.

So I think it used to be easy to start off the conversation of Confluent. Tell us what you do? What is -- now most people understand that. But maybe you could share with us your vision for the company, how has the company changed over time? And where do you see it 5 years from now? Where do you want Confluent to be? What are your goals and aspirations?

Yes. Well, in some sense, the vision hasn't changed that much. Even since the founding of the company, the goal has been to build a platform for real-time data in enterprises. The area of like data warehousing has always been this awesome center of data that's very much like end of the day, ship all the data there. But increasingly, it's about how all the applications and systems work together in real-time to run the business. And that's the role that we want to fulfill. And so in some sense, it's a very boring answer. Like, well, not that much has changed. You go back and read the early blog post about starting a company, and it wasn't that far off from that. But in another sense, it's changed a lot. When we think about kind of the phases for the company, the first phase of the company was around a software product. And as we were going public, that was really the bulk of the business, with then a little bit of the next phase, which was cloud. And we feel like, "Hey, we're kind of entering a third phase here where we're adding what we would call the rest of the data streaming platform." So it's like the rest of what you need to really manage real-time streaming data in the company. And for us, that's all the connectors into different systems, the real-time stream processing capabilities with an offering around open-source technology called Flink and the ability to govern real-time data. And we feel like the role we can play for our customers in enabling applications, enabling workloads is much more significant when you have a solution end to end to how you capture data, how you process it, transform it, work with it in real-time, how it connects up and flows across the organization and how you can govern that in the large. And so we've talked about this chunk of functionality, a few quarters back. We said it's about 10% of revenue now, but kind of outgrowing even the cloud business, which is outgrowing the kind of business overall. So we feel like that's a really strong kind of tailwind to the business as well as an exciting kind of next step in the journey for us.

And that is the 10% of the business today?

Yes, yes, of the cloud revenue.

Okay.

Yes.

And what do you call this? Is there a product that is branded?

Yes, yes. So we think this larger-platform companies want, we would call it a data streaming platform, right? And the -- within the capabilities for that, Kafka is the core stream of data that would get around to the different parts of the organization. But the new things that we're adding is Flink, which is the processing layer. So you can think of it as -- in database terms, people work with SQL, which is kind of the language you would use to [ query ] your data. Flink would bring that to this kind of real-time streaming. So in a database, maybe you'd have a stored set of data, you can write a query like, "Hey, how many customers do I have in California? Who match this criteria?" It would churn through all the data and give you answer, and the answer would be whatever it is, [ 42 ], right? In stream processing, the idea is instead of doing that at a point in time, the end of the day, and having it be out of [ date ] right away, you run this continuously as new customers join or leave or cross thresholds. You have a running calculation that's always correct, right? And you can always look that up. And so when you think about how do you -- not just humans interact with data, which is maybe just at a point in time when you look at the report, but how do applications interact. It's very continual, it's like that. And so this kind of stream processing is really coming to the floor as a key capability in the world of data and a key capability for us around these streams where we're already kind of a leader.

And in substance, the value of any new technology becomes more evident as you build a platform and an application -- set of applications that -- like ERP was the [ kinder ] obligations and CRO was the [ kinder ] application with the cloud. The applications tend to make sense -- it makes sense of the whole infrastructure platform. And all the capabilities with the applications drive a sense of tangibleness to it, right? And that's been one of the aspects of the Confluent. Those applications make it clear as to what is it that you do streaming from integration, connected, et cetera. Where are we in the mainstreaming of applications that are natively built for and [ off ] a platform?

Yes. Yes, that's come a long way. Increasingly, I think, many parts of the data world have to either be built to produce or consume streams of data. And that's a big tailwind for us. When we were getting started, it was very much about bolting this on to a world that really just like wasn't built for real-time. And that was a bit of a hard task for us. Now, this is increasingly something that I think every product is building around. And I would say this is actually a broader theme in the data infrastructure space. I would say we're in a period where -- maybe we're coming out of a period in 2021 or whatever, where there's really kind of 1,000 flowers blooming. And now I think you're seeing more standardization around common interfaces. So for the kind of operational databases, I would say it's really [ Postgres ]. And so everybody has to look like a [ Postgres ] service. There may be many competitors who are trying to do that to cloud providers, other companies, but that's what companies want to have. In the analytics world, technologies like Iceberg that provide an interface to data that's common across different systems have come around. And then for real-time data, the world is very much standardized on the [ cockpit ] protocol, and that's pretty much the thing. And I think that's a positive force for us. When something starts to become a known quantity that many things can build around, then you get many other technologies and partners that integrate into that, that becomes a bit of a force that utilizes...

Utilizes this platform.

Yes, that's exactly right. So I think that's happened in the streaming space. And that's part of just what I think is happening overall in the world of kind of cloud data infrastructure, is this kind of standardization.

I know there's an afternoon after market closed or maybe -- the market opens. you announced the acquisition of WarpStream. I think we're fortunate to hear it from you.

Yes, this is genuine new news.

News streaming.

Yes, that's right. That's right. New events are occurring. Yes, I said this is a company in our space that offered a streaming product with kind of a particular architecture. And we felt like it filled the gap that we had. So we had a product which was self-managed software that you can take and run in your data center, and we had a fully managed cloud product. But one of the things that came around is actually a really good implementation of somebody that's kind of semi managed, right? So you can take it and it runs in your cloud account, the data doesn't leave your cloud account. But it kind of takes streaming out to some of these workloads that would be hard to access with a fully managed cloud product, but where they don't really want to do it all themselves. And so it was kind of a nice way of fully addressing that niche in the market. And we think it's important, at least for us, we want to get all the streams and kind of soak up all the usage of open-source Kafka. And so when we look out at that, there's still a lot of usage of the open-source technology. We want to make sure we have all the kind of packages and configurations that can go and serve those customers. And particularly, we felt this would help us address some of these very large workloads around maybe observability in some of the big tech companies where you felt like, "Hey, we've got great customers, but we're still just scratching the surface of what's there." And so we're excited to add it to the portfolio of offerings. We think it will be a great addition. Doesn't change our plans over the course of the year. It's still an early start-up product, so it will take some effort to kind of fully integrate and harden it, but we're really excited about adding that to our set of offers.

Do you have the cloud which is managed by you or is managed by the customer? This is kind of...

Yes, this is like a cloud product that runs in the customer's account. So if people are foreign to this, the most products that are cloud products are kind of all in the provider's account. That allows you to do things like multi-tenancy and full management. Most of what AWS would build, kind of works that way. But there is an alternative. So like Databricks offers something that's in the customer's account. And that's for customers that want to keep their data very close, but they don't want to do it all themselves. That's a good alternative for them. And so kind of accessing these different models allows us to make sure we can just kind of go soak up all the Kafka usage that's out there.

And where are you going with this? Could this become -- has it been more towards being cloud-like or more on-premise like?

Yes, it's sort of in between the two. And so one of the things when people look at the data space, I do think the assumption has been everything will go to cloud. But then when you look at the actual spend of dollars, what you would see is the kind of data center spend is very flat and cloud is growing. And so in reality, the data center spend didn't really move. And we feel...

We all thought it was going to go down to zero.

Yes. And maybe it will, but like a lot of things in the infrastructure space that moves slowly, at least on the downturn. So what we felt is, yes, there are a set of customers that are set up to manage open-source Kafka. We want to get them into the portfolio in the lowest-friction way possible. Maybe the end state for those is something that's fully managed. But a nice stepping stone is something that's halfway there. And we think that there's a significant opportunity to take that out to customers.

Yes, you're right, the on-prem, it has gravity. I remember people telling me 10 years ago that Microsoft had this product like a server and [ tools ]. And people said "It's going to go to zero. It is still growing," annoyingly. 2%, 3% is a installed base of massive compute. And the more it keeps spotlighting, the more bullish we are because all that stuff is going to [indiscernible]. It keeps getting bigger and bigger.

Yes. That's one of the things we felt like was like, look, there's over 150,000 organizations that are using open-source Kafka, we have on the order of 5,000 customers. Our goal is soak that up, right?

Something that [indiscernible].

Yes, of course, we could be very purist about the architectural style we want to serve, but one of the things we've realized in our space is actually there's strength in covering all the use cases a customer has because getting all the streams and plugging them all together, that's kind of what gives you that central nervous system across the parts of the organization. So we feel that like the self-managed offering strengthens the cloud offering, and we think that this will help plug some of the gaps of what we're covering for customers today. And we're really excited to add it to what we're doing.

So does it also share the same kind of capabilities that your core products have on the Confluent?

Yes. No. I mean it's early, right? Like any start-up product, it doesn't have every bell and whistle. So it will take some time to get there. But nonetheless, represents a nice step in that direction.

Congratulations on the acquisition. I know the question of Generative AI comes in from time to time. What are the lessons learned for Confluent versus a year back or so when it came on the scene, captured [ your ] imagination? What have you learned from your customers' deployment of Generative AI? And how does it inform your product strategy, going forward?

Yes. Yes. I think there's been a set of really exciting use cases in this space. The first part of the business to kind of really pop was actually selling to the AI companies, right? So customers like OpenAI and other companies offering solutions in that space. But along with that, and I think the bigger opportunity is the enterprise use cases. And there, I think there's a sequence of kind of sophistication that starts with kind of these internal chatbots, moves to something that can maybe interface with customers. And the companies would like to get to something that's more like an agent, that's not just taking input and providing text but taking input and providing action. And each company is somewhere on that spectrum. There's some companies that are doing something end-to-end, where there's -- at least today, where it's relatively low risk. But today, a lot of the more conservative organizations, it would be something that's kind of internal focus. Our role in these architectures is about gathering all the data across the organization, getting it into the right form and providing it to be combined with these language models to actually serve customers. And this is actually really important. When you think about the platform for supporting this, inherently, these use cases are about what's happening right now. What -- if it's a support agent, what was that you were trying to do before you called or started chatting with them. If it's something that's helping an internal part of the team, what's the actual state of the business that you're interacting with. So it demands something that's in sync with the rest of the business. And we've started to see these kind of come out into production. We've talked about use cases across companies. Really, from trucking companies to travel companies, a bunch of tech folks have been kind of the fastest movers on it. I think we're still just scratching the surface. So for every application that's come out there in production and referenceable, there's probably a dozen that are kind of in the early stages. I think the way...

I think there are applications that are built on Confluent.

Yes. Yes, that's right. I think the rate at which those kind of come through, that will be something we'll learn over the next year or so.

Any interesting use case or an application comes to mind that was built on Confluent platform using Generative AI as the use case?

Yes. I think one of the examples is probably the easiest to understand that was actually relatively early in the adoption has just been the customer-facing interactions. So a good example -- there's half dozen that we've kind of given publicly. But a good example is a travel company where the bookings are all online. But a lot of the time, the booking isn't quite what you want it, and you end up calling, right? And for whatever reason, the customers can't figure out the interface to change -- make complex changes in their travel arrangements. And so it ends up being that although they're an online booking company, 60% of customers are interacting with them live, which is not the best experience to kind of wait on hold. So what do you need to do to be able to serve them with AI and deflect some of that or reduce the amount that actually have to go through to an agent? Well, you have to have, across a pretty diverse business, the up-to-date view of what happened, right? And for them, they have a number of different properties. And travel is inherently real-time. It's like you reach out when your flight was delayed or your luggage was lost or your hotel was full or whatever it was. And so they need to have an up-to-date view of things or its it's just very frustrated. And so the role for us was really provide that kind of real-time flow of data across parts of the business, get it into the right form and be able to get stored in a way that can be used in what's called the RAG architecture where you are kind of combining stored data with a language model at run time. And I think that's a durable pattern. I think we're going to see that continue to exist. The specifics of how it works may change. Maybe we're not using vector databases in the future, but nonetheless, like we combine it...

I'm curious though.

There's -- if you think about the vector database pattern, what we're doing now is a little bit of a hack where we're kind of injecting data into the prompt. And that's probably not the only way that a language model can interface with stored data, right? So you can imagine that improving over time. But the -- nonetheless, the need to gather together a bunch of data to structure for use in this type of application is very durable. And it's not going to be the case that, that goes away because by having the store data, you can enforce all the kind of access control things that make sure customer A never sees any data from customer B, all the things you have to do in an enterprise context. And so I think that general pattern is going to be very durable and a key part of how these technologies are put to use. And as we transition from kind of chatbots to agents, I think it becomes even a better story. In addition to bringing the kind of input data, you're also then carrying out some action which is going to trigger a synchronous activity in the rest of the business that's the output. And so I think that's a role for us as well.

Got it. I know you brought up agents seeing [indiscernible] what you make of it? Are we -- is that the next step of chatbots and how many [indiscernible] do things on their own on [indiscernible] do you buy into that vision -- how are you going to take advantage of...

Yes, yes. I mean the answer is absolutely. I think the question is what's the timeline right? That's where the biggest probably open question is, I'm close to this space in a couple of different ways. I'm on the Board of Anthropic, a model company. And of course, we see these use cases on the other side. And so...

They are [indiscernible] tomorrow.

Yes. Yes. Yes. It's a phenomenal organization and really exciting work happening there. So Yes, the -- there's no question that informing humans to take action is like good, but you'd rather just complete the task where you can. And now the challenge is it's a lot harder than it sounds. And so I think that probably the gap from the chatbot to the agents is a little harder to do than we think. So like we think about this internally. We have something which we use which has information about our products, which can help internal team members. We open that up externally to customers. And the state that it's currently in is it's available to some customers, but hasn't proven that actually mix either the buying or support experience statistically better. And so they're iterating on it, it will be a partial deployment until it really moves a measurable metric, right? So that's where Confluent is in the adoption. So then we ask ourselves how much better would this thing have to be before we would actually -- instead just answering questions about your deployment allow it to actually operate some of the internal software. We'd love [ that to happen ], right? Like we have a big portion of the engineering team that does this kind of large-scale operations. The answer is it would be a pretty big jump in terms of confidence and quality of decision-making, et cetera, and just understandability for us to unleash it in that way because it's a huge problem if things go down or there's any kind of issue. So getting that human out of the loop. I think it's harder than we think at times. But -- so I think the initial thing of just augmenting is probably got some legs on it. I think in low-risk areas, you would already see this happening where it's -- where machine learning is used today, where it's relevance related, nice to have operations. But I think there is a real requirement on model quality to kind of complete the -- close the loop. But nonetheless, that's definitely the direction and there's a lot of progress on model quality. So it's not like that, it's not moving.

So the loss of jobs greatly exaggerated.

I don't know. I mean, a lot of these is about the pace, right? I think once -- one of the exciting things and scary things about AI is like yes, actually, the aperture of what happens gets pretty wide pretty quickly as you get some years out. And it depends a lot on what you assume. And so it's kind of well beyond my pay grade to predict all of that. For me, I just need to help a lot of course for Confluent.

Exactly. Exactly. But more need for real time information for augmentation leads to demand for RAG and real-time information, Confluent should benefit from being able to beat these LLMs through real time information?

Yes, I think that's right. I mean bottom-line, from a Confluent point of view, there's definitely a set of use cases, which are very data-hungry acquire data broadly across the business emerging, and that's a positive force for us.

The other thing I wanted to get your view as the cloud modality versus the platform. Where are you in your journey to first-rate customers that cloud, of course, you've made an acquisition that kind of last in between the two, but -- where is the company in achieving that sense of comfort with your customers that okay the future is definitely...

Yes. Well, we made a ton of progress. I mean just -- you could see it in the numbers when we went public is -- whatever, 14% or something of revenue was cloud, right? And now it's the majority of the business, and that's obviously a huge progress. So obviously, things have gone well on that front. Now I would say that's still not full unlock to really address some of the most conservative or security-conscious organizations and not just sell to them, but really open up their usage broadly across every use case. There's still more to do, right? And so that's...

What is the unlocking?

It's a really long, boring list of stuff. And so people ask that question in the public sector for the U.S. they just bundle it all together as FedRAMP, and that's one of the criteria. In say, financial services, there's not really an equivalent. It's more like there's a regulatory regime by area, but each organization has their own interpretation of what that means. But nonetheless, you kind of burn down this long, fairly boring list of operational and security-related concerns. And as you do that, you can serve more organizations with less friction. And that's an area that we've made a lot of progress on over the last 2 years. I think there's still more left to do, and that tends to open up more of that opportunity for us with cloud for the most conservative enterprises. And some of those are very big spenders on cloud. So it's important to do it.

Got it. Got it. Excellent. Let's talk about the actually call GTM change, but it's instead of the way you incentivize your Salesforce that has changed.

Yes, it was changed.

It was changed. Then talk to us about what have you learned from the process having implemented it for couple of about 3 quarters now. What are the things that are going to be advantages to Confluent as you as you fully get the benefits of [indiscernible] the way you expect it?

Yes. I'll first just like restate what it was that we changed and then we'll say what's the impact. So one of the things coming into this year, we wanted to change was really orient the go-to-market around consumption. So that the use cases that our customers would take to production that would drive Confluent revenue that would be the incentive for the team. And we wanted to do this for a set of reasons. We felt, first of all, we weren't landing enough new customers. And so we needed to kind of get in there early, even with a smaller land and just kind of plant more seeds, Secondly, as we're getting to scale, we didn't feel like we're driving workloads at the rate that we wanted to really get as many use cases as possible going. And third, as we were bringing these other data streaming platform components, we didn't really have a mechanism to drive it because at the end of the day, what we were selling was like a general commitment to spend with Confluent, not really a particular usage. And so like you have a new product. And despite the general commitment, there's nothing really pushing on taking that out to the world. So for all of those reasons, we wanted to make a switch to consumption. This is something a lot of the peer companies had done over the last few years and had been very positive for them.

It's not like got on it...

Yes. Yes, that's right. Yes, that's right. So we made a change towards that. That's gone pretty well. The -- what we were hoping to see has pretty much played out. So we've seen higher velocity of customer lands. I think Q4 was roughly on the order of maybe 40 net new customers, which we felt was like woefully well, given the number of Kafka users. And most recent quarter was north of [ $300 million ], so a really substantial step up. And we think that can be sustained at a much higher rate. We'd love it to go even higher. And I think that's really important is just, hey, are we getting out to all these new customers, new use cases, are we actually capturing that. Now obviously, these new lands, they really start to contribute over time as they grow. But really, just this year's kind of growth customers or last year's lands and next year's growth customers of this year's land. So we feel very positive about that. Next, we're really tracking and driving the new parts of our platform, the new workloads. Those have been really successful. As with any of these changes, there's a bunch of things you tune along the way in how you're comping different things or what the different incentives are. But overall, it's been, I think, a really positive change and kind of sets us up for what we want to do in the years ahead. So I feel really good about it.

Got it. I'll check anybody with the question just raise your hand. One of the things you'll see in our fireside chat is we will not talk we will not say. I want to segue double-click, we try to use [indiscernible] first.

Okay. Your segueing into...

No segue, no double click and what are the other things that people say very common -- annoyingly common.

The one that gets me is we have to action that which I think means to do it.

Just do it. We to the consumption model, I don't think you can often interpret metrics very differently. Any are -- where do you like the net expansion rate for the company to go to and I think it was a little bit south of what cost you'd expected? What are your aspirations for and where you like it to be?

Yes. Yes. Well, obviously, we'd like it to be [indiscernible].

DBNRR side...

Yes.

So many variants of these things.

Yes, yes, yes. So we have felt a little bit more pressure there. Overall gross retention has remained strong. It's been consistently above 90%. But look, it's been a tighter environment for infrastructure spend with a bit more optimization of the existing usage. And that obviously puts pressure on expansion. And so I think the good news is we feel like there's pretty strong tailwinds with these DSP components, which are kind of getting to the point where they're big enough to start to move things. There -- we feel like we've made good progress on just handing a lot of customers that can kind of drive growth. We feel like we've made a lot of these go-to-market changes. So those are positive forces. But yes, we would like to see that kind of at or above 125%, and it's been a bit below that.

And anything as far as planning for next year is concerned, as you've talked to your salespeople and talk to customers, what is top of mind, what are budgets look like for next year? Is the election, even a thing that your customers have talked about how just that's what I'm planning to ask...

Yes. Yes, it's a good question. So we don't do as much kind of, call it, macroeconomic forecast? Like what will software spend be -- what will the impact of this election and this jurisdiction be all of that absolutely matters. But like our information about that is much weaker than, say, your information about that. So what we do a lot of forecasting of is what are your plans around data streaming. And there, we feel like we have an inside track. The -- it's early to call kind of any numbers or trends for next year. But I would say the good news in this space is look, if you ask people data streaming, what's the role this is going to play and how would you have answered that question a year ago. I think for companies, this is becoming more central, more a part of their kind of big picture architecture they're thinking about in a really serious way. And I think that's obviously a tailwind for us. Even in tighter times where people are also optimizing, et cetera, it's really important that you be part of the next-generation stack that your customers are building towards.

One of the companies we've hosted earlier today, MongoDB, they're talking about streaming capabilities. I know that this came up about a year back or so would say no streaming product, Confluent had to come out and say that this what we do and this is what they do. It's very different. Any changes to the observation? Is there more overlap than perceived or actually less overlap than perceived?

I think there's probably less overlap. So if you think about streaming, it's kind of a paradigm shift in the data world. So every company that deals with data will have some integration with streaming, either you're producing streams or you're consuming streams? Yes. That's net good for Confluent, like a world where nothing works with streaming is not a positive world for us, right? The role we play is kind of at the center. We're like the distribution hub for that streaming data that kind of acts on it -- that's not what Mongo is trying to do. It's not really what any of the other kind of stand-alone destinations are trying to do, nor are they particularly well suited to grow into that role? And that's just the nature of the operational database just fundamentally not the hub that is distributing data in organizations anymore. And so yes, I don't think that there's a lot of competitive level with Mongo, there's -- we actually partner really well with them work on a whole set of use cases and have been really successful with that. So yes, I don't see it as really at all competitive. But you will see more mention of the word streaming, in the data world. And it's actually positive. That means that all of these destination systems, which are stores can now participate in that kind of exchange of real-time data, and that allows us to hook up to them in a much more meaningful way. So I think it's broadly positive. Now there's always some well, we could do this a little bit that's right on the edge and Confluent or in that product. So there's some kind of push and pull there, but it doesn't really mean we're trying to do -- trying to play the same role in organizations architecture or kind of competing for the same thing...

A play of words, mainstreaming or streaming.

There you go.

Final one, if there aren't any questions from our clients here, it looks like there's one can somebody get the mic over to.

We can also try and repeat it.

You can just speak out and I'll work...

Yes...

[indiscernible] so you are talking about optimization starting to attenuate no longer being as much of a headwind. It doesn't sound like you're seeing that yet. I think some of your peers in the consumption space have had sort of similar view that they're not really seeing the headwinds attenuate yet. Can you talk a little bit about why we might be seeing a little bit of a disconnect between hyperscalers and yourselves, given that you normally expect some pull-through?

Yes. I mean it's -- so the question is ultimately like, hey, some of the hyperscalers have said, there's a bit less optimization than there was previously. It's attenuating. You guys have said, hey, there's still optimization. Some of the other peer companies have said there's still optimization. So how can we square all that? I'll try to, although obviously, I can only speak to their experience to some degree. I do think that there's probably a set of easier to optimize and harder to optimize things. Probably the closer to production you are, the harder it is to optimize and make changes. The hyperscalers are kind of a pool of all kinds of things. There's analytics stuff, and there's observability things and then there's like operational databases. And I think you probably feel the effect most quickly on the things that are easy to optimize and then over time, maybe on the harder to optimize things. So I think like one explanation would be, yes, the kind of -- overall, we went into a regime where there was a focus on cloud spend maybe a MongoDB or confluent kind of feels that only with a bit of delay because you're actually making application changes, whereas a data warehouse, maybe you just change data retention tomorrow and kind of drop suddenly or maybe some of the observability things see it that way. That would be one explanation for that kind of difference is you're just kind of seeing the same thing but with a little bit of a time delay on it. Overall, when we look at our customers, if I look at kind of the digital native segment where we talked about that more, it's a mixture of growth and optimization, right, that kind of balance in that in those customer cohorts. It's not like they're doing nothing but optimizing their usage, but it is kind of puts and takes off the overall revenue number. That said, like a similar story to what you would have heard out of these other companies. Yes, there's a few things you can do to kind of optimize your pattern. You can't do it twice, right? So as companies kind of get into the shapes that they want to be in, in terms of their usage, that's a solid foundation and probably stronger TCO to build off of. And so hopefully, that provides a little bit of color.

So on that note, thank you so much for being here once again.

Yes, my pleasure. Thanks, everyone.

Looking forward to have you back in 2025 that's marketing.

Yes. All right. Well.

Thank you [indiscernible] as well.