Klaviyo, Inc. (KVYO) Earnings Call Transcript & Summary

I still have the old set of question. Do you want a number question?

Good luck. [ One to 13 ], whatever you want to do.

We can kind of do like a little bit of surprises here. Hey -- well, actually, it's not often we have a founder with us, so it's actually really good to kind of start like slightly differently because from a lot of investors, they're still -- I'm still getting like kind of more questions where they're trying to understand the company better. Like when you guys kind of came together and started Klaviyo, like what was the initial thing where you looked at the market and were like, oh, I can solve this, I can do better here?

Yes. So I think there's kind of 2 things came together. So the 2 of us had previously worked at a start-up that was building large-scale business intelligence and testing software for really the Fortune 500, so like the Starbucks, the Walmarts, the folks like that of the world. And the specific problem that we ended up working on, the technical problem, was unifying a lot of their data into this intelligence platform. So -- and then we use that to make -- to help them make decisions. And so as an example, we -- somebody like Starbucks, if they're trying to figure out how much they could raise the price of a cup of coffee, in the Starbucks data systems, the sales system that knew what each individual consumer had bought was totally siloed from the inventory system that knew if that store had enough beans of all the different types. It was totally siloed from the time clock system, so they couldn't figure out if it was understaffed in a given day. And it was definitely totally siloed from the real estate system, which we could then use to figure out, was it raining outside and when was it raining? And so we unified all that data, made it available at very low latency, so we could run these huge-scale calculations. So that was kind of the background that we came to thinking about a start-up with. We -- when we went to found Klaviyo, our approach was we had a belief that if we could find a problem that existed in the world, that we could use software to solve, and that people would hand us a credit card to solve that problem, that we could build a business. And that if there were enough people who had that problem, we could build a very large business. And so as we started exploring what those different problem spaces were, we knew we wanted to bootstrap. We wanted to wait until we really found a working business before we went out and thought about how to capitalize it. So early on, we took what we've learned in the original company to say, okay, let's build this true database customer data source of record that understands as more and more businesses are digital, everything a customer has ever done, when they've been on the website, how long, which pages they looked at, what they bought, what they haven't bought, what made up the basket, any support interactions. Let's just build that pure data source. And at the time, we thought that would apply to -- we -- mostly new software, so the first market we thought about were software companies. But we quickly -- as we built this out and started to find customers, we said, okay, this database is most powerful if we have some way to drive action on top of it. And so we stuck -- we thought e-mail was just kind of getting commoditized. And we said, okay, take this, what's really valuable, this data platform, stick the messaging component on top of it, and then we can use the data platform to both drive intelligence and value and also measure the impact of what we are doing. And so in that process, we got our first 10, 20 customers, who were really all sorts of different businesses, so apps, software companies too. We ended up with a decent chunk of e-commerce businesses. And that's when we really realized, okay, hey, software in the marketing category has really always been about the app, about the messaging. That was, we thought, the easy part. This database part of truly understanding your customers in a personal way and then being able to trigger messaging to them in like a very targeted way that drove results was something we could uniquely do. And that's really -- that was kind of the beginning. And so we had the first 20 customers, half ended up being e-commerce. We leaned into e-commerce really as a way to grow efficiently without needing much capital. And we're effectively off to the races.

I mean -- and the -- congratulations, by the way. Like it's an amazing story. The -- if you think about it in that space, there were kind of quite a few guys. There still are quite a few guys. Like they're not doing that great, in a lot of them. But like -- how do you get that differentiation through because like it's relatively easy to say like, yes, I can send e-mails. And it's like, yes, but it's not like -- it feels like there's like a lot of like educating that you can do this better. Or was it just the demos spoke for themselves and then you're -- off you go?

Yes. I mean, so a couple of things made that a lot easier. So first, we very much focused on this as a -- see if the customer has a problem. And so rather than go in and say, hey, are you sending e-mails, it was very much, look, are you sending this type of e-mail to somebody who's been on your site, and then they don't complete a purchase? Like are you triggering that e-mail? And if they said no, we could say, you are throwing away $100 for every person who does that. That was one, so the fact that we could quantify exactly what the impact was. But 2 is we had a philosophy that even if we weren't going to ultimately have all our growth be product led, if we built the system such that we could get someone up and running in a couple of minutes, we could live on a sales call, set up this e-mail, and they could start to see results over a couple of days. And if we've made that a free trial, they could immediately start to see value. And so the more we can make that sales conversation about revenue and not about, hey, replacing this commoditized system, it was a much easier sale. And we still had the benefit of, look, you've got existing budgets set aside for this. If you take that budget and give it to us instead, it made it easy. So it's a combination of a promise of like, look, you can really see this, plus we could measure the impact, and then it was replacing existing [ apps ].

Yes. Is that where that Klaviyo attributable revenue, I can't pronounce it anymore, coming in?

Yes. Exactly. So it really came -- I mean part of it just came out of like the idea that we were bootstrapping. We couldn't rely on status in any way to sell or anything else. We had to be very good at making the sale. The best way to make an easy sale, when you're like otherwise, we didn't have any credibility as 2 guys basically working out of a basement, was I will show you the dollars you are making. And so from the really earliest days, it was about this data platform and the attributed value was the way of selling that data platform.

Yes, yes. Okay. And then like at some point, then the Shopify relationship came in. Like talk a little bit about that, what it meant, and what it means for you guys now.

Yes. So early on, we realized like a core part of the value is getting the data into that data platform. And so early on, we integrated with as many people as possible, and we had to get very good at building those integrations. So we integrated with Magento at the time, BigCommerce, Shopify, all these different platforms. Shopify at the time, we -- [ upper ] was growing fast in the SMB. We focused on the SMB because the sales cycle was very quick. So as a start-up, we could learn very fast by having short sales cycles. And as we started to have success with Shopify, we had to -- we still had not raised money. We knew we had to find a way to grow that was very capital efficient. And because of that, online ads were an opportunity. We had to rely on other strategies. And so we settled on, okay, one was going to be word-of-mouth. So every customer, we would go above and beyond in service and get them to recommend people. But the second was a belief that platforms could be a tremendously valuable way to grow. So we focused on agencies. So okay, who are the other people who work with our customers and how can we add value to them? And then we focused on platforms. And with Shopify specifically, what we said is, okay, they're never going to do anything to help us, as a tiny start-up, out. What are the things that they're trying to do that we can really help them with? And at the time, they were just launching Shopify Plus. And part of the pitch for Shopify Plus from the Shopify side was, we're going to open up a bunch of APIs and you can use them to grow faster. The problem was most Shopify stores didn't have engineers on the team, so there was no way to use these APIs. So we immediately built functionality into these APIs and offered it via our marketing platform. And then what we did is every time we got a Shopify customer, we say, okay, who is the sales rep who sold you on Shopify? Can you introduce us to them? And we meet with them and explain like, hey, you can better do your job selling Shopify by selling Klaviyo, bringing them up in the sales process. Because when you use the APIs, you'll be able to sell Shopify Plus. And so that was kind of the beginning of this virtuous cycle. And then the deeper we got the word-of-mouth, we expanded in Shopify, the agency model expanded in Shopify. And then over time, we built a closer and closer relationship between the companies, culminating in the investment a couple of years ago.

Yes, that's a pretty amazing story. Yes, well done. The -- and like bringing that now to today, like where are we in terms of that kind of market understanding? And the reason I'm coming with the question -- where I'm coming to this is if you look at the performance of you guys over the last few quarters, you kind of -- it doesn't look like there's a downturn. Like I mean, well, if you look at everyone else, it's like coming down, down, down in terms of growth rate. Is that like a function of people understanding your value proposition better? And like you're just kind of seeing it better? Like IPO gives you more airtime, et cetera? Like how do I think about that kind of difference in growth that we're seeing out there?

Yes. I mean we fundamentally think about it in the same way we did initially, and we think it's the same factors, which is if you have the data platform vertically integrated with the app on top with the messaging, and you can show the value you're generating, it makes you very sticky. And it makes you like a core growth lever that's very important to the businesses you work with. That both helps with new customers because they're -- that's a clear difference as they're coming in the door. It also helps keep your existing customers from spending on -- reducing spend and moving to much more commoditized products. And so we think that's honestly how all software should work is that you've got to be able to prove your value. And the more you have that core data built into the same application, that lets you be -- provide a much better path to value.

Yes. And then like now you kind of -- I mean, you tried it out or you kind of proved the point in the more SMB market. But in a way, like you should -- you're very powerful in terms of what you can -- you should be moving upmarket, which I guess you're doing now. Like can you speak to that? Like I mean, how much -- is there at some point like a scaling question that I need to ask you on the data platform? Or like talk maybe about that motion upmarket.

Yes. I mean even in the first -- in the early years, just by nature of the way some of e-commerce works is we'd have brands who would start with us who were tiny, and then they grow to be multibillion-dollar brands. So very quickly, we had to focus on the scaling problem because our -- the customers we sold to were small and would grow up to be really big customers. Thankfully, that was like -- that was the exact technical problem we spent years solving for other brands, and so that's where we had a ton of expertise. Now I think -- as we think about clearly in the future, what we found is like the core differentiation of like, hey, I need to have all my data and customer understanding in the same system as my marketing, that same problem exists in the mid-market and enterprise as it exists on the -- in the SMB. So I think on the -- in the mid-market, you're more likely to maybe have like a data warehouse. But still, you've got to file a ticket with engineering and get them to hook the systems together. But if you can truly combine those in a single stack, it just is vastly empowering to the marketer to run tests, to see the results, to make everything move faster.

And then, I mean, the one question that comes up a lot is like, as you move higher upmarket, well, one, they're still kind of best of breed, and they -- but they do think about data. So then if you talk up there, it's like, well, there's Snowflake and then I have someone else on top of it, the Snowflake. How do you like -- do you need to be like same as Snowflake? Or like how do you have to think about that kind of as you go kind of higher up in the space?

Yes. I mean, I think what we see is we're typically selling into marketing, and what they typically experience is some sort of Frankenstein stack. So there might be a Snowflake, there might be a CDP that sits on top of it, there might be a marketing application. And the whole thing is expensive and unwieldy, expensive to wire up and to make it do what you want, and it's challenging. So they experience a problem, the current stack is like not getting the job that they want done. On the flip side, like the -- if you think about systems like Snowflake, like there are a lot of -- they're designed to be generic data stores but to empower -- to power all sorts of different workloads, data science to whatever that is. What you actually need -- what the marketer needs is that customer understanding with a very opinionated data model that's built around the person. So people have facts about them, they do things, they don't do things, you need to understand all those things. And if -- you need to structure that data in a specific way to make all that available very quickly in segmentation and to drive outcomes. You're going to trigger an action with that. It's just a different data problem than you need in a classic data warehouse. Every business likely needs both because it is a highly opinionated structure designed for marketing and driving action versus a, hey, I -- maybe I'm going to run an analysis of my inventory and which SKUs and how fast they're moving and which ones the margin is not great on, I need all the data in a -- structured in a very generic way.

Yes, yes, yes. Okay. But like from a -- like it's fascinating because like I used to -- we kind of do a lot around or work around like big data. And we have like Databricks coming in tomorrow as well. It's a -- like if you think from your perspective, like how was your underlying stack? You need to constantly think if am I still on the right thing? Or is there something where I kind of probably might need more scale and my stack need to change? How do you think about that? It's like...

Yes. I mean it was one of the earliest problems we had to solve, and it was one of the problems we had the most experience with because very quickly, just by nature of the business we work with, it was a very large amount of data. And a lot of those customers were growing very fast just because e-commerce was growing very fast. And so from the early days, we had to understand, okay, how does that data scale? What makes this possible? And so we had to think about how we structure and store the data. And then the flip side, the other side of it was we had to really make sure that the messaging component was seamless. And while -- how do we make sure that, whether that was e-mail or SMS or -- in our mind, it was really -- we were channel agnostic. We didn't really care what the messaging channel was, but like we had to make sure the channel got from -- the message got from point A to point B. And so we had to develop a lot of expertise around that as well.

Okay. And then if you think about it now, like leveling it up a little bit, if you think about the growth from here, like how do you think about the different vectors in terms of upmarket, international, more channels, et cetera? Like how do you balance that?

Yes. Yes. I mean we very much think of it as there's a set of short-term things where we've seen significant growth that we just -- we have to capture the opportunity we've seen. So there continues to be a tremendous amount of headroom in the SMB. In international, if we're -- even in the first 2 years, we found that very quickly, we had country -- customers all over the world. But we never until recently invested in: one, providing the product in different languages: two, providing everything around the product, so like all the sales support, success docs, and other languages; and three, providing full product functionality. So the one area of the product that requires localization is really the provisioning of -- providing SMS numbers and local SMS numbers that are compliant with local laws. And so on all 3 fronts, we've seen that there's -- we've built -- we've been building the infrastructure and have launched in the first set of countries. But we'll keep accelerating and speeding up and do -- and launching in more countries more quickly. Capturing that fundamental upmarket, we continue to see a ton of growth, something we have to focus on in the very short term. But between the 3 of those, they're just kind of tremendous short-term opportunities where we're already seeing growth, and it's all about how can we deliver on what we're already seeing.

Yes. I mean you mentioned earlier like the platforms you're working on with Shopify, but then you have like higher up the market, you have like Magento. BigCommerce, well, maybe slightly more on the smaller scale. How do you think about like partnerships with those guys? And is the Shopify -- will the Shopify partnership eventually be like a limiting factor? Or like how do you think about that?

Yes. I mean -- in the early days, we very much said, hey, to make this work, we've got to make the cost of these data sources, these platforms, to be very cheap. And so very quickly, we launched on not just Shopify but Magento and BigCommerce and then ultimately, Commercetools and PrestaShop. So from a just technical side, had to make that very cheap and easy. From a partnership side, it's been very important that, yes, this is -- like it's not -- there's nothing in the product or no reason this would just be tied to Shopify, and so how do we form the similar sorts of partnerships with others. So we have a partnership with PrestaShop, where we're the default platform for PrestaShop in given regions. So I think what we found though is that if we sell a given number of merchants who might be on other platforms, if the -- on average, merchants are moving to Shopify. By the end of the year, we'll see a number of those merchants move from other platforms to Shopify, so the gravity, we tend to follow the gravity of the e-commerce world as well.

Yes. And then shifting gear, on the growth levers. Like so SMS is like, in a way, omnichannel makes a lot of sense. SMS, you're only starting to kind of roll out now. Like what do you see from a customer perspective in terms of -- is it just like, well, I always use SMS, these guys, so I now just use e-mail and SMS there? Or do they realize like it doesn't make more sense? You're coming from a better platform -- data platform anyway. Like so how do you also think about that SMS upside?

Yes. I mean we see it very much varies by brand and it varies by individual consumer within a brand. And so if we just think about any one person, more and more people are willing to experience -- get messaging from a brand across different channels. So it might be that they want their shipping notifications to always be text to let you know that, that product just arrived. It might be that for a new product, you're willing to get SMS for the brands you love the most. But for most brands, you're willing to get many more e-mails with the new product announcements. And so -- and that would be -- it might be one thing for me and a different thing for you. And so what we see is that it's very variable. And then even in the life cycle of brands, if you're a new brand starting on day 1, SMS is probably not economical. It's probably too expensive to invest in too much. So what we see more and more is brands truly are becoming multichannel. They're even moving beyond e-mail and SMS to try out new channels. We're launching WhatsApp in 2025, which we're very excited about. But we expect there to continue to be this like movement towards, hey, I -- as a brand, I just want to give you the right channels for you as a consumer that are most likely to make you happy and ultimately drive purchases. Those might differ across consumers, but I'm not -- I can't be focused on any one channel. I've got to truly play in all the channels.

And how does the channel help you with different markets? So with like me being German, if I think about WhatsApp in Europe, it's like massive. Like is that kind of like -- you almost get a multiplier on both sides then?

Yes. I mean I think the thing with WhatsApp is there's the use case that we've all experienced personally at WhatsApp. At the business level, if you're not kind of like thumbs on phone, sending the WhatsApp, the -- Meta runs WhatsApp much closer to SMS. And so the economics are much closer to SMS. It's a very expensive channel because of -- Meta basically acts like the carrier and takes fees in the way they would from a carrier. And so we've seen that a lot of Europe brands. Some brands have figured it out. Some brands haven't. Even though the WhatsApp is so dominant as a business communication tool, it's still very much in the learning early days, but it's a channel we're excited about.

Yes. Yes, I can imagine. And then think about like other extensions, if you think about like reviews, et cetera. Like is there -- are those -- do you think they can get kind of bigger? Or is there anything out on the horizon that we're kind of missing, like social or whatever, that we should kind of pay more attention to that you kind of [ see ]?

I mean, the things that we think about the most are -- there are always tools like some of the ones we've launched, the CDP, which for us really means like some data tooling and some analytics tooling that sits on top of our database; Reviews, which is kind of a marketing add-on. What we really think about is our core differentiation is this data platform and the strength of the differentiation and the -- what takes us out of the commoditized world of like generic centers is take the data platform, drive outcomes with it, and then measure those outcomes. What are the other software categories that don't have the data that people need? And one that's top of mind is customer support and service, writ large, where almost every service interaction exists separate from the underlying database that understands who this person is, which is why you have to say, oh, I bought this product. I -- it arrived yesterday, and here's the problem with it, like all things that like with much tighter integration, you could provide a much better customer experience. And so as we -- one thing we think about a lot is if we really powered digital relationships, what are the other pieces of those digital relationships that this same data platform can power?

Yes, yes. Is that not where the Data Cloud from Salesforce tries to do it in hindsight?

Yes. I mean I think exactly. We see people trying to do it. And I think it's just the -- we tend to see people come at it. They start with the apps, and this is very true in the marketing space. They start with the apps and they try to build into the database. It turns out the hard problem is database, so you have to really come at it from the database side and then build into the apps. And the other strategy so far hasn't really worked for anybody.

Is that kind of a little bit like what we kind of should think about you guys more and just kind of -- actually, it's kind of the data side moving upmarket? If you solve the data problem, you're actually -- whatever you do on top was not simple? But it's like it's much easier than trying to solve the data problem in hindsight?

Yes. I mean I think that's the -- I don't think we -- that's the insight, I think, we ultimately kind of stumbled our way into, which is the data is not valuable for its own sake. But the data -- like if you have that, the right data engine stored in the right way, that's what can power these outcomes. And like the apps drive the outcomes, but you've got to have the unified stack to actually drive the outcome. So without the data, you won't be able to measure the outcomes to necessarily drive the same level of outcomes. And so it really had to be unified, and so you had to -- we had to solve this data problem first. It just turned out that's what we've spent years working on beforehand.

And then -- sorry, in all the sessions, I had to ask gen AI, so I almost left it too late. Like how do you think about it then for your space?

Yes. So we think about AI in 2 ways and gen AI in a very specific way. So our users generate a ton of content. And early on, we had close partnerships with numerous large AI companies and integrated in our products. And so we've now had years of data of seeing people leveraging LLMs to generate content. And we see that it had great adoption. People are using it to change how they work. The interesting thing we've seen is, if I am the person who goes in and designs an e-mail or designs SMS , if I am going to spend 30 minutes on an SMS in the old world, I still spend the same amount of time today. It's just that my workflow is a ton of iteration, working side-by-side with AI, and then ending up with a much better message once I've used AI to generate a whole bunch of brainstorming. And so we've seen that be really powerful. The way we really think about AI is generative will certainly enhance the way our users work. The other piece though is because we have the unified data platform, how can we best understand what action is best for every given consumer? Is it 3 e-mails, 3 SMS? Which times of day? Under what actions? How do we drive more revenue and use AI to understand exactly what it actually takes for each person and make all that automated basically in a way that it becomes more and more of a revenue engine for our customers?

So is that like a -- do you need an LLM for that? Or like how do you even think about it?

So some of it's LLM. Some of it's much more classic machine learning, but it's really a...

Yes, more of like machine learning. Yes.

Yes, exactly. It's like the combination of all of it, so the LLMs make it much cheaper over time to like create marketing content and test marketing content. But the kind of fundamental underlying -- like I need to derive intelligence from like a large quantity of data, what we would have like years ago called big data -- but like largely, you can use some of the AI techniques. It's going to be very important to the -- is very important to them.

Yes. Yes. I mean -- and then in last couple of minutes, I wanted to shift a little bit on more the outcome in terms of like profitability, et cetera. Like it is -- like you could argue like, look, you started like not taking a lot of money, like kind of out of your garage. And then from that, you basically have like a thinking of, like, yes, I'm not going to spend money for the sake of spending it and just kind of drive -- buy sales guys, et cetera. And that drives the kind of the good profitability level for you, guys? Or is it also the data platform that gives you more efficiency? Like how do you think about that dynamic?

Yes, both. I mean the data platform provides a lot of pricing power. It makes our products stickier, like it provides a lot. But I think just our -- because our core DNA was focused -- tightly focused on efficiency, it meant that we've built a lot of go-to-market motions that let us grow very efficiently. And we largely spent none of the money we raised throughout for a very long time. And even when we did, we spent kind of like small tens of millions on a couple of marketing experiments, but that's it. And so just that fundamental DNA has been very powerful. I think now, as we have a business that can generate lots of cash, it's how do we take that cash and get better and better at understanding which investments and experiments we want to run and where we can lean in and accelerate. But I don't think there's no fundamentally changing our DNA of we're going to approach everything with efficiency and be very disciplined about where we are spending beyond what we're making.

I mean -- and if you think about it, but if you generate kind of this healthy amount of cash, like how do you think about capital structure? And like it's almost talking to Andrew on the spot here now as well. But like in the end -- but how do you think about capital structure, M&A, like et cetera?

Yes. I mean it's not something we've done in the past. It's something that we -- just given the fundamentals of the company can make a ton of sense, given what we see as the size of the opportunity, it's certainly something I think we'll consider over time. And we think about it as like our -- we are a fundamentally product-led, product-driven company. But like where we can move substantially faster, whether that's small tuck-ins, whether that's new market entries, whether that's like much larger opportunities, certainly something that we'll want to consider over time, given the fact that there is -- we're in the position to do it.

I mean do you think, from your perspective, is it because like the -- your core asset is, in a way, is the underlying data platform. So you can't really buy a number there, so it would be more on top or kind of expertise and stuff?

Yes, that's basically right. So I think with everything, as we think about the product road map and where we're going, it's okay, right, there is what we're very good at. So it's less likely to be buying something, a new data platform or anything like that, because we've kind of -- again, fundamentally, that's where our advantage is. But it's what fits around that, whether that's new markets, whether that's new people, whether that's new functionality that could be an app on top of that.

Yes. Perfect. Hey, that's a good closing statement as well. I'll let you go. But perfect, thank you. That was really -- I really enjoyed it. And it was actually really good to go back to the basics a little bit. Hopefully, it was really helpful for the audience as well.

Thanks for having us.

Thank you. Thank you.