Good morning, everyone. Corey Carpenter, Internet Analyst at J.P. Morgan. Pleased to have, David, CFO of Yelp, with us. Thanks for coming.
Thanks, Corey, for having us at the conference. We'll be making some forward-looking statements during the conversation today that are subject to risks and uncertainties. Please refer to our SEC filings for more information on the risk factors that may affect our results.
All right, and if anyone has questions, you can submit them via online, and it'll come up to this iPad, or we'll pass the microphone later in the session. So David, just to kick off, Yelp's changed a lot over the years. Just for those newer to the story, could you kick things off with how Yelp has evolved and just the state of the business today?
Sure. So, for those of you who haven't checked in on Yelp in a few years, we've really transformed the company over the past six years, and it's changed in three important ways. The first, Yelp very much grew up as a sales-driven company with growing local sales headcount leading to growth and revenue. A number of years ago, we shifted to a product-led strategy, and we'll talk a lot more about that. The second, of course, is that Yelp is very well known for restaurants, and in the first quarter, nearly 65% of our revenue came from services, so that's the second transformation.
And then the third theme that's more recent, and again, we'll, we'll get into more detail around this, but we're really focused not only on continuing to generate a significant amount of organic traffic, but we're very focused on off-Yelp traffic and tapping into that, and we've done quite a few things to enable that. So overall, in terms of the state of the business, you know, we've really invested in creating a great experience, really updating the experience, and have a significant amount of momentum.
From a reporting perspective, you split the business between services and then RR&O or restaurant, retail and other. Could you just walk through at a high level the trends impacting each of those segments? And then we'll go deeper into each.
Yeah. So, on the restaurant, retail, and other side, we talked about this on the Q1 call, we actually talked about on the Q4 call, we started to see this in late December, actually. It definitely carried through the first quarter on the restaurant, retail, and other side, which is inflation continues to be a real pressure both for consumers and for businesses. On the consumer side, things have continued to get more expensive, and I think consumers have become more resistant to the price increases that, particularly in restaurant and retail, those companies were able to pass through. On the company side, input costs have continued to rise. You've all seen, I'm sure, the increase in minimum wage, particularly in California, but it's not unique to California, so, labor costs have gone up significantly, input costs continue to rise, and margins have been compressed.
So when you combine weakening consumer sentiment with pressure on margins, that's definitely impacted advertising and restaurant, retail, and other from where we sit. On the services side, in contrast, through the first quarter, momentum continued. It does seem like the significant increase in home values has really enabled homeowners to continue to spend on projects. They may not be doing the biggest projects, but they are continuing to do projects, and in the first quarter, we continued to see real strength in our home services categories and across services broadly. So it's quite different between those two sides of the business right now.
So let's start with services. I think you mentioned about 65% of your revenue. What are the main subcategories within this segment? You know, what's resonating well right now that's driving the double-digit growth you've been seeing for a couple of years now?
Yeah. So services itself encompasses quite a broad set of subcategories. The one that we are really focused on is home services, but it's not only home services, it's local services, it's auto, it's professional, it's actually pets, like grooming, things like that. So it's a broad set of those categories, and, you know, particularly in home services, we've really been focused on creating the best experience for consumers meeting pros in order to do the projects they need to get done.
So, last quarter, and you mentioned this earlier, you announced plans to expand paid search to acquire services projects. What did you see in testing that gave you the confidence to ramp this up, and, and kind of why did you get to that $40 million or over $40 million number?
Yeah
... in terms of level of SEM spend this year?
So just to step back, for those of you who aren't familiar with the product, we have a, a feature called Request a Quote. Request a Quote is meant to enable a service pro to respond to an inquiry from a consumer, and we take the consumer through traditionally what was a question tree to really refine what it was exactly that they were looking for. Now, we've introduced our own AI chatbot, which we call Yelp Assistant, which we can talk more about as well, which we're really excited about. But that's gathering that information, and then we take it, we go, we use a matching algorithm, and we match a set of pros with that consumer.
So, one thing that we said that we'd start doing at the beginning of last year was build out the capability to acquire traffic off Yelp through paid search. And the way that we are doing that is, we are advertising on Google, and then we are bringing people onto Yelp, into the Request a Quote flow, to create projects. Now, projects in the first quarter were up approximately 20%. That's overall, compared to the fourth quarter of last year, when they were up 5%. Now, that's a combination of both the improvements that we're making to get people to use the Request a Quote product, but also bringing people into that funnel. We increased spend in the first quarter to $7 million from $2 million in the fourth quarter of last year.
And overall, for the year, we said that we'd spend $40 million or more on acquiring projects through paid search. So, $40 million less 7, $33 million, about $11 million a quarter, roughly through the rest of the year. And the reason that we've taken the level of spend up is severalfold. The first, the metrics that we look for, first and foremost, of course, is: Are we actually growing the total project volume? That's the first indicator. Now, Yelp is a marketplace. We are matching supply and demand, so the supply is consumers coming to the site. Demand is advertising dollars. We run an algorithm to find the market clearing price. And so, when we bring more projects in, what we'd expect to see is increasing clicks in the categories where we're buying and declining CPCs, and we'll get to how that interacts with revenue in a moment.
Those are the first metrics that we look for: increased projects, increased clicks, and declining CPCs in the categories where we're buying projects.
Could you just talk a bit about what categories you're initially focused on for paid search and a bit, maybe a bit more on how you're measuring ROI?
Yeah. So, we're looking at home services categories in particular, and it's across a number of the fairly obvious ones that you'd expect in those categories. But we're testing, we're looking at the different categories, and we're also doing it by geography. One of the things that's very important, obviously, with paid search to get to ROI is to be able to purchase projects in the categories that you care about at a price that is gonna yield a return in the geographies where you want them. And so 2023 was very much around building that scalable platform. I'd say we're still relatively early in the total capacity or capability of that platform, but obviously advanced enough for us to spend $40 million or more. But it's that targeting by category and geography that ends up mattering a great deal.
In time, what we do want to do is be able to also focus on particular audiences. Homeowners, obviously, would be attractive for us, and we want those folks to come back. So you need the economics for the economics to work, you need to bring someone in, get the matching, have the advertising, have advertisers spend more with you, and get consumers to come back. So, that's still very early in terms of being able to determine what the ROI is, and we are very much experimentation driven. I believe we're very disciplined financially, and so as we see those results come in, we'll adjust the spend depending on the performance. But obviously, we want to generate margin out of this effort.
So, you kind of alluded to this earlier, but on, on earnings, you talked about this dynamic of a lag between advertisers seeing the lower or seeing the clicks and the lower CPC and then improving their ad spend. Could you just expand on that dynamic and then also how it impacted your outlook this year?
Yeah. So, we're definitely, as I said, are finding the market clearing price, so supply and demand. What does that mean in effect? It means that we are not trying to control clicks or CPCs. It does mean that if we deliver more value through lower CPCs or more competitive CPCs compared to other sites, we believe that will generate more demand. It's a very simple premise, which is that there is elasticity in advertiser demand for advertising on publishers like Yelp, and that takes time to show up because those publishers actually need to see that improving return on their investment in advertising on Yelp. And so, well, in the past, when we have done this, we've seen clearly that we've been able to generate more ad budget.
That's not always true, but in general, that's what we have seen, and so that's what we're banking on in terms of the current effort. But of course, we have to see it all play out.
Let's talk more about Yelp Assistant, which you mentioned. Excuse me. Conversational AI feature, it helps match consumers to service pros. Could you just expand on how this works and the initial customer response you're seeing?
So obviously, we've all been very focused on the advent, introduction, widespread use of LLMs. We're very, very excited about this technology. Just for background, Yelp has been using AI models for more than a decade, so this isn't something that's new to us. I just underscore the very important element of being able to leverage these models as a publisher is first-party data, the technical capability to train those models, and then, of course, to deploy them in a way that generates revenue. In terms of Yelp Assistant, we have built our own chatbot. That chatbot, you can find if you download the app or use the app. In the Project tab, it'll be in the lower right corner there, so you can see it for yourself.
It dynamically will ask follow-up questions when you start to talk about a services project. Now, there is a supervisory model resting on top of that, like other sites, to prevent the person from trying to make queries that are really not relevant to Yelp or, or get the chatbot to say things that maybe it shouldn't. But once that has been cleared, personally, I found that it is very intuitive, and the questions that it asks aren't always obvious when you go in to starting the Request a Quote process and make you think about what it is that you need to accomplish. We gather a lot of information out of that, and it improves our matching significantly.
So, from our perspective, the more information that we can gain with the fewest number of questions, the better off we are from being able to match and create a great consumer experience. So, we've rolled that out in the Project tab to 100% of iOS app users, and we're looking at how to introduce that across the rest of the site. So, one thing that we've, you know, is also a theme is, well, when are you gonna roll out a chatbot for every category? And one of the things that we've learned recently that's really, I think, very interesting, and was a bit of an aha for me, was not every query wants a chatbot in order to efficiently find the results. And let me give you my current favorite example.
I happen to love chocolate chip cookies, so I looked for best chocolate chip cookies on Yelp. And what comes up? Best chocolate chip cookies where I live, and it came up instantly. Now, if you do that same search on Google, it varies 'cause they are often changing the interaction. It also produces a list of bakeries with great chocolate chip cookies. Do you need an LLM for a chocolate chip cookie query? Not really. So I think what we're gonna see, and even on Google, you'll see, even though they're rolling out these summaries, you'll see that for some queries, they're not going to summarize because there is nothing to summarize. That's not the most efficient way to get the consumer to the business that they're looking for. So, that has been very interesting.
I think it's gonna evolve a lot, and I think consumer preference and for how to interact is also gonna evolve in time as we all figure out how to deploy these models.
My next question was how you plan to roll it out to other categories. I think you answered it.
Yeah.
So, I wanna talk about you announced a deal with Perplexity. You announced an AI API.
Yeah.
Could you just talk about your approach to data licensing?
Yeah
... and how you think about this as a monetization opportunity versus something you want to keep in-house to power things like Assistant?
So we have been monetizing data for some time. We have two products, Yelp Fusion, that's where we're providing content to other sites. I'll come back to Perplexity and the AI API. And then we have something called Yelp Knowledge, which is much more for marketing to businesses, so it's a B2B insights product. We do sell both of those. That revenue shows up in other revenue, and we have quite a few different customers existing for the Yelp Fusion product. Again, that's the content piece. So we're in Apple Maps, we're on Alexa, and most recently, we licensed that to Perplexity for their queries. For those of you who aren't familiar, Perplexity is a LLM-based search site that aims to compete with Google to produce results. So, we're definitely participating there.
Now, the AI API is so we have the API, which is, I'm gonna return results from Yelp. The AI API is where we get a free-form text input, process that with a large language model, and then return results. And that is an advancement because obviously, the site does not need to worry about filtering that query for us. We're doing all the work. And we're working. You may have seen that Expedia's working on their own chatbot, I think it's called Romie. That's in early stages of testing, but that's a place where we are powering content for them. So Perplexity is one, Romie's another. Now, we obviously want to continue to license our data, but I think there's really three layers to value in this entire ecosystem.
The first is training data, which I think is the lowest order value. One thing that has definitely emerged as a theme for folks who are building out their own LLMs is high-quality, human text, and one of the beauties of Yelp is its user-generated content. We're constantly generating more of this text, so it's a great source of value to us, but the lowest order of value is for us to sell that as training data. The next step up, of course, is be able to do something with that, and that's where you're using a large language model as an input with that underlying set of data to improve an experience. So we have something called review snippets. That's where we're pulling out little pieces of text from the reviews to really summarize what's going on with a particular business.
So I'd say that's the second order. What we're talking about with Expedia is another example, where that AI API, you're processing the query and returning a result. The highest order value, though, is something like Yelp Assistant, where you actually have this interaction where both the consumer gains value, you gain value, and the value you gain, you're able to pass on to the service pro, and that's where we're really focused. We're really focused on this second level and this third level of value, as opposed to this first level of just selling training data, especially since we think that we have a very unique position with the breadth of data that we have, the quality of data we have, and our ability to generate insights from that data.
So, moving to restaurants, retail, and other, which is now the minority of your revenue, you mentioned this earlier, just some of the headwinds facing the category. I mean, we've heard from a number of QSRs about kind of pressure on the lower-end consumer. The question for you is: How broad-based is what you're seeing today? Like, is this just happening at larger restaurant chains, or are there other categories impacted as well? And maybe if you could just give us some examples.
Apologies, Corey. Let me just go back one, one last point to make on services, and then I'll come back to restaurant, retail, and other. Just as another example of the way that we are really focused on acquiring off-Yelp traffic, so I talked about paid search, I talked about what Expedia is doing, I talked about Perplexity.... You may have seen it. We're working with Mozilla and Firefox, so we're returning search results, as a person types in a query that can link them to Yelp. And then, just in the past few days, Apple has rolled out a feature in Apple Maps, which is called Quote, and that's for services queries, so not a restaurant or hotel, but they're actually looking, say, for a plumber or electrician or smog check.
When you press Quote, that takes you into our Request a Quote flow. So just this theme of broadly accessing consumer traffic off of Yelp to connect them with great local businesses through these other platforms is a big theme for us. Now, just turning to Restaurants, Retail, and Other, we have seen this as very broad-based. It is not unique to the restaurants category, where consumers appear to feel pressured and businesses also seem to be squeezed on margins. So, that's definitely at the enterprise level, the mid-market level, and in SMB.
Okay. Also, I wanted to ask, on the earnings call, you mentioned perhaps seeing in that channel some spend to platforms such as Uber Eats, DoorDash. Could you expand a bit on what you're seeing there in terms of allocation of budgets? And then, you know, how big of a concern is this to you?
Yeah. So it's not precise, but what we certainly know is that we are competing for ad dollars with other platforms, and clearly, in particular, DoorDash, I think, has been quite successful rolling out their ad product. And so that's just another player in the market who is gonna be considered by restaurants. One of the things that's advantageous for DoorDash in that setting, of course, is that when you advertise, you immediately see the ROI. You bring in that order. So that's. There's a very close linkage between those two, but it's definitely at the margin for us. So we just wanted to point it out because there is a dynamic around this. The exact magnitude of it, we think, is small, but it's certainly out there because it's another player for restaurants to advertise with.
So, if you had your crystal ball, could you just talk about RR&O trends? You know, how optimistic are you on them improving this year? Is this something that you think takes time, as kind of cost and prices better come into balance?
I think I'm not sure this is the rate forecasting session. Maybe that's another session during the conference. I think it's really, really hard to know what's gonna go on with the U.S. economy. I think we've all been surprised by the overall strength, and then also, I think there has been this theme of rolling mini recessions within the economy, and you've seen this, like, home builders at one point, and so I think it's very much there's a lot of pressure on the restaurant and retail side right now. How that exactly plays out, when the Fed cuts rates, where consumer sentiment moves, I think is very hard for us to predict from where we sit. And so what are we focused on?
We're focused on continuing to execute against the themes that I started out with: product-led strategy, services opportunity, and continue to tap into off-Yelp traffic. That's what's in our control, and fundamentally, as a publisher monetizing through advertising, we have to deliver ever more value to advertisers to be competitive and successful.
So, let's talk about your go-to-market channels for a bit. One of the early first question ways you evolved was from sales to product, and that's certainly showing up in your go-to-market. You have local sales force, multi-location, self-serve. Multi-location slowed a bit this quarter. Self-serve customer adds continue to be at record, record highs. Could you just talk about the trends impacting these channels?
Yeah. So, I'll start with self-service. That's been successful for us. This was part of the transformation that started six years ago, which was the most expensive way to acquire a customer, of course, is to have someone speak to them. It's much better if they self-serve through a flow. We've made that flow better and better over time, and it's very consistently grown around 20% or better for quite a few years, and so we're very happy with that. The beauty of it, of course, on the B2B marketing side, is that the more efficient you are or the higher conversion you have in that type of flow, the more that you can spend on advertising.
So, we are going after. The new thing for us is going after consumer traffic through paid search, but we've been doing search engine marketing for some time on the pro side, and you drop them into the funnel, and we start there, and then you get a qualified lead if they don't complete the sign-up, and then you follow up with a rep. So that's a much more efficient model for us, and we've been very happy with the performance there. On the multi-loc side, it did slow. Now, our multi-loc advertising customers are predominantly in the restaurant, retail, and other categories, so you're gonna see those converge in terms of financial performance, especially as I shared, it's very broad-based. It's not limited to SMB, what we're seeing in terms of that softening.
It's definitely been in enterprise and mid-market, so those are gonna go hand in hand.
Yelp Audiences, so, your off-platform ad product, could you talk about the demand you're seeing in for this, or maybe give an overview of what it is?
Yeah
... for those not familiar, and then just, is this something that you expect to be impacted by privacy changes around Google and-
Mm
... and what they're doing with cookies?
Great question. So, Yelp Audiences are retargeting product for large advertisers. That's where we're building an audience for them, for them to target consumers off of Yelp in categories that may or may not be Yelp categories. So it could be for your large national restaurant players, where they're retargeting someone who's come in and looked for, say, espresso or a café near me. It could be in financial services. We like, like this because it expands the set of potential advertisers. It could be auto OEMs and so forth. Someone came in looking, say, for a dealership in a particular type of car. This product, though, is very much centered around your restaurant, retail, and other categories, and so definitely is gonna be impacted or affected by whatever's going on broadly in restaurant, retail, and others.
So, that's another area where it goes hand in hand. That being said, we've been really excited about this product because, once again, on this theme of being able to tap into off-Yelp traffic, what we are doing is taking Yelp traffic and tapping into those same consumers off of Yelp. So it's a different aspect of being able to reach consumers away from Yelp in order to generate revenue. So, another way to think about it is that we're increasing the overall monetization of the traffic we have because we have additional information that we can use to help advertisers to better target consumers.
So I have some questions on financials. If anyone in the audience has a question, raise your hand. Happy to get to that as well. 2024 revenue guide, you're expecting some re-acceleration in the second half of the year. Could you talk about the drivers of that and just what you're assuming on the macro side?
Yeah. So, in terms of the guide for the year, we've guided $1.42 billion-$1.44 billion on the year. We definitely have had to reflect the softness that we were seeing in restaurant, retail, and other, especially given the performance in the first quarter, in the guide that we gave for the second quarter and for the full year. Fundamentally, though, we remain optimistic about our ability to continue to generate value for advertisers and to continue to execute on this product-led roadmap. We clearly have made a lot of progress and continue to make significant progress in home services. That still grew about 15% in the first quarter, so we're pleased with that. Obviously, we're adding traffic there, so that's a component.
But we did not reflect any expected revenue from rest, from home services projects that we might acquire in our outlook for the year. So there's an interplay there. Again, it takes time for that revenue to show up, but fundamentally, we aren't expecting significantly different economic conditions in the second half of the year. It's continued execution against the roadmap that we've set for ourselves that's fundamental to the expected revenue performance for 2024.
Okay. And then on... You mentioned you're not assuming any revenue coming in from those, that paid marketing, but, you know, could you talk about how that's impacting the EBITDA guide-
Yeah
... for the year, given that revenue lag?
Yeah, and just to clarify, I'm giving 2024 guidance. I'm not making any comment about revenue in 2025. But if we're spending $7 million in the first quarter, and again, just for this conversation, $11 million in each of the three quarters, and you're spending $22 million in the second half, and if it takes time to show up, where you'd expect it to show up would actually be in 2025, not in 2024. So that lag is a big part of the reason why the revenue guide for the full year doesn't assume revenue from this, and we're still learning and becoming more efficient overall in doing that. In terms of EBITDA for the year, we did narrow our guide. We had $315 million-$335 million.
We narrowed that to $315 million-$325 million to reflect this increased level of investment. I would remind folks that we've taken a big step to reducing stock-based comp. That meant that we had to increase cash comp. So we're obviously, if you're, you have less stock-based comp, you're adding back less. This is adjusted EBITDA, so there's some pressure there. But fundamentally, we think we've gotten a lot more efficient because let's say we weren't gonna spend the expected $40 million in paid search in 2024. At the midpoint of that range, you got $320 million + $40 million, that's $360 million. Against the midpoint of our range on revenue is a 25% adjusted EBITDA margin, which is what we achieved last year.
So we think that we've continued to drive efficiency, and by the way, that's with us moving from stock-based comp to cash. So we think overall, the business has become much more efficient. We only grew total expenses by 1% in the first quarter of 2024 compared to the first quarter of 2023. So yes, absolutely, paid search is impacting the guide for the year, but I just point out to folks, underlying that, we've gotten, as a business, I think, much, much more efficient over the past four or five years, and that enables us to deploy spend against an opportunity like this in paid search.
Any questions from the audience?
Just to follow on that point, the stock comp, as I recall, you're issuing 65% less, fewer shares this year.
Right.
So from 6 million shares to 2, 2 and change. And then, so if you make some assumption about how much cash comp is up to compensate for that, and then you add the $40 million SEM spend, by my math, your EBITDA is growing, apples to apples, really growing based on your guide, about 11 or 12% year-over-year. Does that sound about right?
So I'm not sure on the exact math of 11% or 12%, but there's definitely an adder. So we did $320 + $40 + something more, for sure, for stock-based comp. And for folks who aren't familiar, we've committed to reducing stock-based comp as a percentage of revenue to less than 8% by the end of 2025. To make that happen, we have reduced our expected grants in 2024 by 65% to employees compared to 2023, from about 6.6 million to about 2.2 million. As the grants of the past run off, and we have been a very consistent buyer of our own stock, and we committed to return capital in excess of a target cash balance through share repurchases, you should see the reduction in shares start to widen out.
Now, the point I think that's being made is, if you look underlying that, what is the significance of this increased efficiency is we obviously have this option to invest in the business or return, allow that revenue to flow through to EBITDA. So, we are in a good place because we've gotten more efficient, we are able to generate free cash flow. We're. I believe we'll be able to reduce share count while we continue to drive that underlying EBITDA margin. So, we feel like we've taken all the steps to be really efficient, stewards of capital for the business. We remain committed to driving shareholder value, and we look at that across creating value through the core business, of course, by growing revenue and becoming more efficient, but also by reducing share count.
We've definitely heard from you the focus on stock-based comp, and I think we've taken a huge step towards that objective of getting it below 8% by the end of next year.
I would just add one thing, that it's the stock comp number that's running through the P&L is meaningless because it reflects the last three years.
Yeah.
What's meaningful is the 2.2 million shares-
Yes
-you're going to issue versus 6 and change.
Yeah.
As you pointed out, your buyback is actually going to now start... It's going to shrink the cap because you're gonna be buying back more shares than you're issuing.
Subject to market and economic conditions.
I think we have one more.
Thank you. So, Google announced quite big changes to Google Search last week. Can you talk a little bit about your reliance there and any numbers that you can share, and just any first thoughts about those changes that they announced?
Yeah. So, more than a decade ago, Jeremy Stoppelman, who's our CEO and co-founder, very much focused on diminishing our dependence on Google, and we were very early on building out the app and making a great experience and really shifting consumers to the app. So, that has continued to be our strategy. We continue to evolve it, improve it. We've redone our home feed, we've made it more visual, we've invested heavily there. Of course, we don't mind SEO traffic, and we certainly are taking the appropriate steps in order to generate SEO traffic. But you know, Google is making decisions that are in Google's best interest, and so, you have to remain focused on the fundamentals: delivering value to consumers and delivering value to service pros.
There are algorithm changes that benefit companies, there are algorithmic changes that hurt companies, there are changes in the experience, and Google has been pushing organic search results down the page for a very, very long time. This is not new. I would just go back to an earlier point, which is that summarizing results and/or using large language models won't always be the best experience for a consumer, and that's not unique to Google. They obviously will look at their performance and see how that helps or hurts their own ad revenue business based on the query that they're seeing. I think there's a lot that's gonna play out here in terms of consumer experience, expectations, what's the best way to give information to consumers, what's the best way to match them with advertisers?
But fundamentally, we have faced this challenge from Google of pushing organic search results down the page for a very long time, and we're gonna stay focused on our unique, what we think is a unique market position, being driven by a great roadmap and being product-first.
Great.
I just underscore, we're incredibly well-positioned, I believe, on AI. We have the first-party data, we have user-generated content. We are very sophisticated in our ability to use these types of models. We've been using them for some time, and we're able to present those results in a way that we think add value. So, just in, as a theme, and then a final point that I'll just make on AI that we didn't get to, is we're also looking at ways to use large language models operationally across our business. I think there's tremendous opportunity there, whether it's increasing dev productivity. Even in my own functional areas in the finance organization, we're finding ways to apply it that I think are really interesting. I'll just give you a quick example that's just emerged. It's still very early.
I don't think this is unique at all to Yelp, but, for capitalized development, the accounting team needs to interpret all the projects that the engineering team is doing, and engineers speak in engineering, and accountants speak in accounting. Being able to actually translate between those two and apply rules, from accounting to what gets capitalized is a super interesting area. And in fact, you can even ingest more information about the projects to make the best possible determination for Cap Dev. So, I think this is gonna emerge as a big theme for most companies, which is: How do we use these tools to get more efficient as a business?
Great. We'll stop there. Thank you, all.