Datadog, Inc. (DDOG) Earnings Call Transcript & Summary

Okay. Perfect. Good afternoon, everybody. Hope you're enjoying day 2 of the Citi Tech Conference. I'm Joe Lamino. I'm a research associate on the software team and I support for Fatima Boolani in covering Datadog. And so we're very happy to have with us today Datadog's very own CFO, David Obstler.

Thanks. Thanks for having us. Appreciate it.

Thanks for joining us. So David, I think just to start, we'll talk about a brief overview of Datadog. What you're doing for customers today and how you've gone from the core infrastructure monitoring into the observability and security use cases.

Yes. Upfront, just saying in advance that we reported in early August, so all of our comments will be reflective of what we disclosed at that time with no updates. Getting into the history of Datadog. So Datadog has a unified observability platform that offers log -- infrastructure metrics, APM traces and logs all in one platform. We started out as a infrastructure monitoring company. And over the years, expanded the product suite to include all the pillars of observability, adding on the 2 I mentioned, plus synthetics and ROM, which we call the digital experience monitoring suite and more recently adding on such products as network monitoring CICD or predeployment analysis of code as well as more recently security. In terms of the progress, one of the metrics we gave out, and I think it was the conference call before the last one, is that we're about over $2 billion of ARR. And what we said was coming from infrastructure only, about 50% of the revenues now are non-infrastructure. Most of that would be in APM and logs. So from having a single product over the years, we've expanded the platform in order to have this integrated suite, including about half the company in those other capabilities.

And just to stick on that, if you were to try and drill down to a products level or maybe a category level security, APM, logs. Is there anything you can share in terms of the ARR breakdown or revenue breakdown across your product suite?

Yes. So I mentioned that we are a little over $2 billion. And so about half of that is infrastructure, about $1 billion, and half of that would be the other 2 major pillars of APM and logs. The other products are in there. They're smaller products. They've been released more recently. So that's sort of an approximate overview. And I'd like to add that essentially, most of our monitoring is of modern applications. The crew there is containers, Kubernetes, microservices delivered on the cloud customer-facing, where our product is differentially and that is used ubiquitously by lots of participants in the DevOps ecosystem. And most of what they're doing is they're trying to examine the delivery of the applications and analyze and remediate in real time. And that's sort of the Datadog end market.

Fair enough. So just piggybacking off that, when we look at your newer products and your non-infrastructure products, where you really started to see some good traction there. I think your last earnings, you shared 30% of customers adopting one of the newer products from calendar '21. So could you talk about what's really working in that part of the business?

Yes. So most of our clients are trying to get as many signals about their environment in 1 place, 1 single pane of glass. So the the biggest revenue are those pillars. But as we've added on other types of products as signals, network monitoring, security, code analysis, that's what -- and then the RUM products. Those are the products that account -- their newer and they're not those first 3 pillars. And that's what meant buy the 30%. Essentially, many of these are pieces of the observability platform and our clients are using more and more parts of it as we create more value in the platform.

Okay. That's good. So I think the platform approach is something that's well spoken of today, but the fact of the matter is there are other vendors who also claim to have these observability platforms even in the cloud. So could you talk about what differentiates Datadog's platform? And why customers should prefer that rather than some of the other vendors that we hear about?

Yes. So the market is pretty much divided into 3 areas there. Born in the cloud platforms like Datadog, then open source, open source would be you build it yourself. That's a choice that's out there. That's probably the biggest competition. I mean you don't buy a packet software solution, you build it. And then there's a set of companies that were born more on the on-premise environment. So we compete most with those that monitor cloud workloads modern and that, and the open source. Now in terms of open source versus Datadog, it is if you look at the cost to build and maintain the system, it's cheaper to buy a service like Datadog than to do it yourself. If you want that service, if you have a service that needs to be delivered on-premise, that has functionality that's unique to you and nobody else, then you might want to consider building it. In terms of the group of cloud monitoring companies, it's all about the product and the fact that Datadog was built natively with a unified data infrastructure that allows it to bring all the data together, produce reports, correlate it and a user interface and automation and integrations that allow a client to without professional services be able to use it. And the other companies that have been trying to do this have not built a native platform that functions this way. It's either been put together through acquisitions or it isn't able to be accessed by clients in this way, that platform approach from the data, from the components and from the way it gets used is unique to Datadog. And if you did customer surveys, et cetera, you would see that's the reason why they buy Datadog.

Okay. So just sticking on the platform, some of the feedback that we get is that costs can run high, right? Especially with at that usage factor. So with that in mind, we still see customers continuing to adopt the platform. So could you help us understand the dynamic there where there is potential for customers to incur higher costs, but they still prefer to adopt the platform? Or is there a further technological differentiation, for instance?

Yes. I mean we have a usage-based platform that is correlated to how a client sees value. We've put -- we've been a very much of a product-led company. And I would say we're fairly recognized as being best of breed. So our clients generally for this use case are choosing what they think is the best and most useful in both most value-added service. Anytime you're going to be critical to the client, it means essential to their operations. As they grow, you'll grow, and the price will increase, but we are still a very, very low percentage of the clients' cloud expense, low single digits or lower. And essentially, through value-added pricing, have been able to maintain a gross retention in the very high 90s, meaning, as you said, our clients are both staying with us, growing with us. And then we can talk later about our new logos, landing with this as well. So the clients are finding value in the platform and its quality and its usefulness and continuing with and buying more Datadog.

Got it. So Datadog, you're saying on average, is about low single digits percentage of the client overall...

It ranges quite a bit, but we are about 1% -- our revenues, about 1%, the sum of the hyperscalers revenues, whereas we don't know the cloud spend of all of our clients. We have enough data points to know that we vary from from 1% or lower to maybe up to 10%. But generally, our average would be quite in the low end of that relative. So most of the spending and most of the essentially costs and the opportunity are really around the cloud and then we monitor that.

Okay. All right. That's interesting to hear. So I guess the financial benefit for Datadog, there's also something over there in terms of customers consolidating on your platform, yeah, so can you talk about some of the uplifts mechanics over here?

Yes, definitely. So we started out with only infrastructure. And then over 4, 5, 6 years, we developed the logs, the synthetics and the APM product and got it to the part where -- the place where it's rated best of breed aswell. And so it was always recognized that having everything in a single platform allows you to do what you need to do, which is seeing all the signals, see what's happening and remediate fast. So that was always there. And as we sort of improved all the components, clients endorse that by consolidating on Datadog. And so that's what's been happening for a while. And then as we've launched more products, they've consolidated other point solutions. This, I would say, has been going on. And then in the cost environment we're in, it is also can be quite economical and cost efficient, for a client to have 1 vendor and 1 platform, which means that you can see everything and maybe pay less.

Okay. Got it. And do you see perhaps some sort of uplift when they're adopting maybe moving from the security or observability APM? Could you talk a bit about that?

Yes. So essentially, a couple of different metrics. One is, as I mentioned, half of our revenues are non-infrastructure. So the upsell of all the products, weighted average is 2:1. They're equal. So we only had infrastructure and now we've doubled with the other products. So that's one thing. Two, is with our net retention we are about 1/3 new products that you didn't own and 2/3 or so increased volume of what you already own. So that shows the impact of the cross-sell. In terms of security, it's too early to tell. We have good traction. We have 5,000 customers. We have 26,000 approximal customers. We're selling to certain use cases. I think we mentioned on our call the number of customers that are spending $1 million and $100,000. So we have some scale customers. But we're just scratching the surface. We haven't even finished building out the suite. We think that the market could be doubling in TAM, but we need more experience in the market to see if like the other products, this can be a doubling like the other products were.

Okay. And we'll talk on security later on. But maybe just to shift to the competitive environment. If with about 19 products, I think you have today, there's certainly a multitude of vendors that you would compete with, and then not to mention, of course, the expansion into security. So could you talk about some of the competitive dynamics and especially when we get into the security space.

Yes. So in observability, as I mentioned, you have us and there are a couple of other companies that were native to the cloud, New Relic, APM. You have open source their names like Elastic Grafana. And then you have companies that were born on-premise like Splunk and Dynatrace and AppDynamics. And some of them have expanded into cloud. That's the competitive landscape in market research and market share that's been put out by Gartner and others, we're the largest. Of course, we weren't 5 years ago. We were not even, but we've become the largest, which is evidence of the gaining of the market share. We've always been the largest company and the market share winner in cloud native, cloud deliver types of applications. So that's where we've excelled versus on-premise. And that continues to be the case. And probably over the last few years, I mentioned with the product has even become more of a Datadog market leadership. In terms of security to sort of set some groundwork, there are a number of companies out there, some very large companies like Palo Alto and CrowdStrike and others that have most of the revenues in endpoint, network, e-mail, we're not in that business at all. So they're in what they do in their main part or for that matter, Splunk in security logs, we're not in that business. So we are -- is in cloud security, which has 3 major components. It has cloud, cloud security or in the infrastructure side of it as application and it has Cloud SIEM. And most of, this is a pretty new area. It's not very penetrated. You do have Palo Alto and some of the others in there. I would say that where we're selling, which is not the centralized CISO case right now, but as an add-on into DevOps, a lot of it's greenfield. There are some Palo Alto, Prisma, some with, et cetera. So a lot of that are new use cases. And what we said is that we're in build mode, our strategy right now is to attach it to the use cases in DevOps. And as we continue to build, we may attack a bit more of the CISO market, which we're not in today, but that's down the road.

Okay. That's fair enough. So like you said, there's a lot of greenfield in this.

There is.

So there's certainly other pure-play cybersecurity and even large vendors playing here.

There are.

So when you go to customers to pitch Datadog security, what are the key points of differentiation that make the products better than...

So right now, we're only doing attached to our existing customers, which means that they have our infrastructure, APM and logs. So the pitch is; one, we already have your eyeballs; two, we already have all the data in a single pane of glass; three, we already have your metrics traces and logs, you don't have to reinstrument, so our pitch would be as part of the platform. Also, most of the security is not architected for realtime type of use. So that's what we're doing. That's the use case we're working on now that we may evolve to a broader use case in security but that's the differentiation we have today.

Got it. And so when you think about your security strategy today, is going after those net new customers, going beyond this pitch to your existing customers. Is that part of your strategy?

No, to existing customers who are already using Datadog for DevOps, who are beginning or using or having what's called the DevSecOps and have all our data and our instrumentation. So it's a upsell extension to our existing customers. That's what it is today. That may not be what it is when we finish building it out, but that's what we're doing today.

Got it. Okay. Fair enough. And I think some people wouldn't be happy if I didn't ask you about AI. But I promise not to spend too much time on it.

Yes. Sure. No problem.

I think it's pretty clear that there's a lot of vendors who are coming out and saying that AI is a positive exposure of them, right? Other vendors that you compete with. But I guess the question is, what is the level of differentiation, right? Why is Datadog and Datadog's ambition with Generative AI? How is that different from some of your competitors?

So we are essentially agnostic to the monitoring of applications however they're created. Our advantage is that we, as applications get created faster and are more data-intensive, we have it. So it's very likely. It's very early because most of the enterprise clients are just thinking about, they don't actually have the LLMs in their enterprise models yet. But if history repeats itself, any time there's an acceleration of application creation, new ways that applications are created and more data, that's Datadog because Datadog gets their rewards based on the CPUs, the GPUs or the hosts, the tests and the data that flows in. It's -- we are very likely -- if it repeats, again, it's too early. We don't know how it's going to shake out in this end, but it's very likely since we are a follower, we're basically monitoring what our clients do that -- if our clients create a lot of new applications that are LLM enhanced, we will be a net winner. So that's sort of the product line. Then there's the our platform. So we are attempting and we made a number of analysis at Dash, this is about us as a software company, not what our clients are doing and what we're monitoring. We're essentially injecting, we have been for a while, but we're increasing the pace. Large language models and pilots in our platform. We introduced a copilot, we introduced an LLM suite. These are all in beta. But what does that mean? What does that mean? That means, essentially, that enables our clients to access our platform for whatever they're doing, and then automate more functions in our platform using pilots or automated tasks, which means they can do their job faster and that would be a Datadog platform side. And then the last piece of this is Datadog as a monitoring tool for tools vendors, who are in this industry. Now why is that interesting? We said 2% of our revenues now and growing. Why is that? If everything works out, these tools vendors are going to need to provide a service that is mission critical and won't go down to their clients and that has to be remediated in real time, and that's a perfect customer for Data -- and a data of intensive one. That's a perfect customer for Datadog. So the third is do we have an effect from a sector that grows very rapidly, and we're providing products to. Those are the 3 ways that Datadog can monetize in AI.

Got it. And that's the 2% of ARR.

That's the 2%. Yes. Yes.

From the next-gen AI customers. Okay.

So that's the most definitive thing we said on the call because why? That's servicing clients who are already doing it. The other parts of it are, one, we launched a bunch of products, but they're in beta. And people say, "how is it going?" Well, there's enthusiasm, but clients aren't really using them yet, the beta. And then the last part is probably the biggest part, which is the knock-on effect from what we all believe, I think, is going to happen here which is that if the dream is realized that in the injection of all of this, this is going to improve the speed and the innovation of application launch, and that is where Datadog wins.

Okay. Perfect. And then I think just the last one on AI. When you think about the investment that's needed to capture this opportunity, right? The hype started recently, but we also understand Datadog has been pursuing AI for a while. Are there incremental investment dollars that you need to put into the model in order to compete and to capitalize on this opportunity?

Yes. Like I said, we are, we have been -- so we've been, our R&D is our largest cost element, it's -- if you look at it versus the other or product line company, it's in the 30s. And this is a component of it. And yes, we're basically prioritizing between all the different demands. And this demand is getting a higher percentage than it did a year ago within the envelope of our R&D spend.

Okay. So today, AI investment is higher priority would you say or higher production...

There's -- our priorities include our platform. About half of our -- and part of this is -- our platform, security, being built. AI. So there's a number of different priorities. But within that envelope of sustained innovation and R&D launch is AI. We haven't and we're not going to basically go over a headcount of what's doing what, but it's a component in that.

Fair enough. So I think that's a good segue into the financial market, which I wanted to talk about for a little bit. You've had this strong sales efficiency, and I think that's helped by the higher R&D. Can you just talk about some of the drivers even as the top line sees these macro pressures that are enabling you to maintain respectable levels of profitability?

Yes, yes, yes. So a couple of different things. One is that we invested substantially in the previous 2 years, but not so much. We didn't bend. We didn't have to do risk. But we had a good amount of investment, and we're now harvesting that like salespeople. We hired a lot of salespeople. Retention is getting better. They weren't ramped. So another ramp. So essentially, we've been able to, in this environment, make productive the investments we made in the previous years. We've also prioritized R&D, as I mentioned. So that's been the area where we've protected the most. And because in sales and marketing, we did do this capacity expansion, we are essentially a little bit more this year in productivity enhancements in improving that. So that's why we've been able to rebalance this a bit, yet we've always said big long-term opportunity, we're going to continue to grow the investment a lot but in a prioritized way. Another thing is we've never invested -- I've been there 5 years since -- I've been there -- never invested above the top line growth. So when you essentially look at conservative top line growth, and then you develop your investment envelope that provides you a lot of flexibility, but protects margins as well.

Okay. And maybe a specific question on profitability. So gross margins, I think in the past couple of quarters, at least last quarter, you talked about cloud cost efficiencies. I was just hoping to dig deeper and talk about where these efficiencies are coming from? Are you renegotiating contracts? Are you optimizing your own cloud usage? Just talk about that.

Definitely. So first of all, we are rearchitecting the platform on an ongoing basis to make it more efficient. We announced our log store, Husky. That's an example of that. So we are in our own instances and platform investing in efficiency. Two, as we get larger, there's economies of scale and negotiating with the cloud vendors. Three, I think we've gotten better in capacity planning. We've always been pretty good, but I think we -- in terms of how much you want to be on demand or not. We think we've gotten good at it through analytics and things. So all of that has allowed us. But we know that there might be times when an investment in the platform will have a very strong ROI, but you have to leave the existing platform up while you're innovating. And that's why we've been careful to caution everybody that there could be fluctuations. Having not done that a year or 2 ago, we wouldn't have this margin and this way of sort of getting scale to our customers and Flex Logs and others.

Okay. Got it. so I think another key theme around Datadog is these cost optimization, right? So if you could have to spend a few seconds a few minutes here talking about the trend of cost optimization that you've seen? And then also recently, we, of course, heard about some normalization in usage growth. Why not talk about that as well?

For a long time, we had a set of cohorts that operated -- I think we gave metric above 130% growth, and that was, I would say -- and we've had a pretty weird set of events in COVID and then. And so then we had a caffeinated level of spend. We had essentially an escalation of spend. This could be -- this is a number of reasons. I mean low interest rates, the prioritization of growth versus profitability, very strong demand environment. So essentially, what happened started -- when the Fed started raising rates and everyone gets started getting more cautious is that we started just like inflation unwind, we started to have a little -- some unwind here. And that's what's been weighing on the net retention, meaning we're not having customers land differently. We're not having them -- those ones that land, ramp them differently. We're not losing customers. It's the rate of expansion of the customers. And so what we said was we had a group of customers that were identified by being cloud native, having grown very, very fast, potentially overspending and maybe that was because of the prioritization on growth versus profitability. And we said starting in Q2 last year and in affected industries, maybe their industry and demand. And that place we got hit hardest we said this now for over a year. And we've been through a year cycle with them. And I think what we said on this call was that, that group of most affected customers seems to have stabilized. We don't know there's not going to be other waves. And some of the reasons we can see that they've stabilized, that a number of them have signed commitments. Sometimes for 3 years at this level. Now they haven't begun to grow substantially yet, but the pain from them unwinding, so that is sort of the most pain. So that is a sign that we may be getting later stage in this optimization. Now we still have, in our broader customer base, a cost-focused world of one that is constrained relative to what it had been in the highest growth, and we have different degrees of freedom. So since this is not something that we control, this is what our customers, we try to be always cautious in both our planning and in our guidance. And that's why we said we may be getting to a trough, but we can't tell you when the rebounds going to happen. And we expect that we're going to have a cost-conscious environment in the rest of this year. In terms of what's happened in the quarters, I think this -- we've gotten this question a lot, there's a lot of variability in a frictionless model, cloud model. And what we saw last quarter, we've been very transparent. We saw a stronger start to the quarter. And then we saw in the middle of the period, the most intense optimization. And then we had a little better towards the end of the quarter in July. But we wanted to say that given we don't control this, we essentially have not said -- wow, we're not claimed victory or anything. We're saying this is going to continue. This is going to be volatile. So let's take the weighted average and then discount that for guidance. That's a question that's been asked quite a bit.

Got it. And maybe just to try and dig into something here. If these customers that were most impacted, as you said, with the optimizations a year ago, if 1 year later, we're seeing them sort of normalize, right? Maybe what are the reasons why we couldn't take that and say, okay, if there's other customers that are less impacted, then maybe they take a year to come back and normalize, why not?

We can say that. We basically, we've been using cohorts. We have other cohorts where it hasn't been extreme. It wasn't as tough for them. They maybe didn't do it right away, they might have been more hopeful, but there are ways of this, and we don't think we're through it. Also, customers, and this is in Datadog throughout its history. We have a rolling set of optimizations always going on. And so it's harder to predict. So we think it's a mix, but there's some light at the end of the tunnel, but we don't feel comfortable predicting that our customer base broadly has finished doing their work.

That's fair. And then just one more on this issue. I think if we look over the past couple of years, right? There was the onset of COVID, which had a little bit of a negative impact on the business. And then post-COVID, there was this COVID-driven demand strength, right? From people going cloud, then we get hit with the macro. So there's been a lot of volatility when you think how spent the model and the usage growth rates, how are you thinking about a sustainable medium- to long-term usage growth rate? Or how do you think about usage growth for Datadog over the next couple of years?

Yes. I mean we essentially look at longer term that only 20-some percent of the workloads are in the cloud that many of our clients, particularly as you get into enterprise are very immature in their cloud journey, that we're landing great new customers and consolidating, building out more products, and we have a weighted average time series that before all this happened was more normalized. We don't know for sure, but there's data points that when we said this above 130, that wasn't in net retention. That wasn't like just random. That was because the data was sort of centered around that, and that was maybe a conservative version of it over a longer period of time. But Yes, things got volatile, and it isn't going to be a straight line, but we use that type of evidence. We also have 26,000 customers and companies like AWS have 1 million. And even if you want to discount that down, we have very low penetration in the end customer base.

We're coming up on 5 minutes here. I'm just going to pause, see if there's any questions from the audience for now. Otherwise, I'll keep going. Okay. Perfect. So I also wanted to talk about the contract structure dynamic definite that you have, right? So you have this situation where customers commit to a certain amount. And then if they go past that, they'll pay you overage, right? So just curious how you saw that change during the macro?

So it didn't change in that -- customer is still committed. They still went to overage and because of the burst nature, because of the, their service is not the same every day, they will always do that. What did change is that, they -- and we do. So basically, we recognize revenues largely on usage, but underneath of it is a take-or-pay commitment. So if they don't use it, we have, we recognize it. But what was happening with growth being higher, they were getting into another commitment situation faster. So they might get into something that was beyond the overage. They might -- a lot of customers operate with, I don't know, 5%, 15%, 20% overage because they want to do that operationally, but they got beyond that faster, and they did it in their contract. And what's happened is taken longer to get to that period. And that's what's been going on in the contracting. When we look at the RPO and the billings, which is not exactly correlated but it was relatively strong, it is a sign probably that they are now getting to the point where they can predict their business more and can contract in a confident way, as I mentioned, with the most affected.

Yes. Okay. So -- and speaking of customer behavior and customer expansions. Now historically, expansion has been a very strong driver for your business. I think a couple of quarters ago, it was about 80% of the year-over-year increase in revenue. I think more recently, it's about 60%, right? So when you look at that dynamic, how much of it is as a result of pressure on expansion, of course, because of the macro versus how much of it is actually internal execution improving and your team just getting better at landing these new logos?

The new logo -- so essentially, what's changing is the net retention. So the new logo, as we said, has been fairly consistent. So if new logo stays roughly the same and your net retention goes down, your new logos will be a higher percentage. So most -- that tells you that most of what's going on has been the economic environment. Some other things that tell you that is gross retention in the very high 90s, upper 90s. Continued growth, weighted average, meaning the customer base is continuing to grow. The customer base not leaving us. The flow, the balance of things flowing towards us, meaning consolidation. So what that tells you is that we believe, and I think is that most of what's happened has been either the deflation of escalated spend or an overall cost environment largely caused by macro.

So therefore, when we think about the medium term and the long term is the idea that as we exit the tough macro environment, the Datadog will return to these higher weighted towards expansion, this type of growth? Or do you see this new level as a comfortable?

No, I think that, that would be -- what would be our -- a greatest prediction. We have said when we were at 80%, 70%, 80% which implies a net retention quite a bit higher than that -- sorry, then today, we said you shouldn't expect this. This is not normal. We basically said that. So I think that's right. That's what is likely of course. Of course, we are working on in many ways to control we control, which is grow our sales to grow our partnerships, grow our alliance, everything to try to get more and more lands. In fact, I think we've done very well holding that because the economic environment certainly, in fact, affects weighted average landing, it's just we've gotten bigger and better on the other side of that, and it's balancing out the stability in lands.

Got it. Then with 2 minutes left, I just wanted to toss in a question on the cloud cost management. It's something we've heard a lot of conversations about. Could you talk about the advantage of adopting the cloud cost management use case with the Datadog, especially because hyperscalers tend to have their own native capabilities for this use case, so why would customers [indiscernible]...

Yes. So it's like a lot of things for Datadog. So we are -- we put all the clouds, all the cost on that. We have all of your usage. So we already own your usage data. And then we also are putting in all of the cloud. So you can see a unified cloud. And the groups of people that are responsible are not in the same department but work with the DevOps. That would be -- those would be our advantages, of it. But you're right, I mean, we haven't come out and said, oh, expect $1 billion of revenues. We think this is a good product. It's healthy. It helps our clients get healthy. But we don't know yet whether it's going to be one of the main pillars or smaller.

Okay. Perfect. And I think the question, a perfect question to cap the discussion is just, when you look at the near term, right? Near to mid-term product portfolio, you look at the market. What are some of the areas that are low-hanging fruit that can really help you drive [indiscernible].

That's a very good question. So I think some of the low-hanging fruit are: one, being responsible to our clients so that we help them optimize so that we have them being Datadog advocates on the back end, which we're doing; two, consolidation. That's already happening, but I think we can even do more of that because of how we're set up in our product and our platform. Three would be we've seen areas that we haven't really penetrated very well some of the emerging markets, et cetera. So put our money there and -- or government is an example, and basically lean into more things. And lastly would be to work on productivity and enablement of our sales team and continue to build out the product.

Okay. Great.

Thanks.

Well, David, I think it's a perfect place to cap the discussion. Everyone, please have a good rest of the conference.

Yes. Okay. Thanks a lot. Thank you, everybody, for listening. Thank you.