All right. Fantastic. We will go ahead and kick it off with Klaviyo.
Hey, Abe. Thank you for joining us. We're really excited to have you.
Yeah.
I know that you've always had a really specific vision of what you think Klaviyo can look like longer term. It feels like this year we've really started to unlock the next levels of that vision beyond all the success you've already had in the marketing side. Paint us a picture a little bit. How do you think Klaviyo is evolving, or in what ways do you think Klaviyo is evolving, and how does that fit into where you think the company can be 2, 5, 10 years from now?
Yeah, yeah. When we started, the genesis of Klaviyo was I had, I'm a big runner, and I built this website to help people find, you know, 5Ks and marathons and stuff like that. I worked with all these little race organizers. I remember doing this thing manually where I'd go to each one and be like, hey, you should listen to my website. You should listen to my website. I was like, boy, this is really annoying. I wish I could clone myself and just, like, be everywhere to all of these folks at once. That is, like, the driving ethos of Klaviyo. I really do think we ought to be able to scale people, like, you know, either individual humans, or teams, companies, and just make them infinitely accessible at scale all the time.
We talk about empowering, you know, the world's businesses, the world's creators to own their destiny, and the way to do that is to help them be themselves at internet scale. Be authentically you one-on-one. In the early days of Klaviyo, our approach for that was we said, okay, if we're going to mimic the way that humans, you know, act and operate, we'd better build, like, a really good brain for that. That became, like, the database that we built. It's, you know, stores, indexes, data, you know, in real time, makes it available in real time, and, you know, stores, indexes, all these interesting ways, the ways that, like, we actually think. Then we layered on top of that, you know, a bunch of, like, tools that actually, you know, allow you to communicate. That was, like, marketing and messaging.
The key thing that always kinda irritated me about that was you still needed somebody to log in and sort of set up all the rules and, like, modify them all the time. Somebody had to, like, build a marketing campaign or build a marketing automation and then come in and check on it a week later and make some edits and review the data. I was like, that's not really scale. I mean, it's a one version of scaling ourselves. You can deliver this experience to, you know, millions of people concurrently. It's kind of the lesser version. Wouldn't it be awesome if it could kind of, you know, just self-optimize? I think, you know, and obviously, messaging is just one facet of, like, the customer experience. I mean, that's great. It's very proactive, but, like, we're sort of missing the other pieces of it.
You know, the last year we've said we want to attack, we want to improve on two dimensions. The first is I think this era of, like, SaaS software, you know, really where you think of it as, like, it's tools, you have to log in, you have to set it up, configure it, log, you know, be the person that's using it. I think it's just over. You know, it'll take a little bit of time for people to adapt, but we've already talked to a lot of our customers who are like, yeah, I'm bandwidth constrained. Look, if you can do a good job, like, yeah, I'll check it before it goes out, but, like, I can delegate that work to you.
That's why what we're doing on marketing is I think the future of that is really an autonomy layer on top that's like, yeah, we'll develop a marketing strategy for you. We'll figure out all the campaigns and automations you should be running. We'll draft them all out. We'll make them awesome. We'll set them up, and all you have to do is go in and, you know, tick the box, tell us that you approve. By the way, when you get tired of clicking all those boxes because we're already always doing a great job, we'll just turn on autopilot mode and off you go. That is going to happen, I think, to the marketing side of the customer experience. We are solving for that there. I think this autonomy layer, it's coming for all different facets of the way that, say, a business interacts with its consumers.
Messaging is one. It's the proactive side. Hey, everybody's not always, they haven't always opened up your app. They're not always, you know, on your website. You're not always top of mind. How do you know when to reach out to folks so they don't have to come pull and look for you? You know, the flip side is, like, when somebody has a question, and it's not always, like, I think it's not just customer service. It's not just break-fix. When somebody needs some advice and they want to have a real-time conversation, like a chat, and this could be over text, could be over, you know, could be audio. Actually, in the future, I think it'll be video as well. Any of these modalities, like, what do you offer them?
That's the Klaviyo Customer Agent product that we launched to our private beta program in June, and we'll soon be releasing more broadly. Finally, we've done some work on what does it mean to personalize the experience? Maybe when you're not chatting with somebody, but you're, like, in somebody's store, so to speak. Right? You're on their website, and that's our Customer Hub product. It basically would say, like, yeah, tell us, remind us who you are. Like, we probably know who you are, but just authenticate so we can show you all your important information. When you're on a, you know, when you're on somebody's website, like, yeah, we're going to rewrite the whole thing for you so that it's tailored to you. This could be a website. It could be a mobile app in the future, any other digital, you know, digital service.
I think that's kind of it. I think this is how we take, you know, the ethos of what a business or a brand is and make it ever present. I think that should be, you know, it should be self-configuring. It should be, you know, learning on the fly and improving. We have metrics, you know, whether it's engagement or revenue or LTV that we can optimize for, and obviously, somebody can set. This should be possible. I think this should exist certainly for every consumer business, where, you know, internally, we have a saying. It's like, look, if you're Boeing and you're selling airplanes, then yeah, you probably shouldn't use Klaviyo. If you're an airline and you have millions of passengers, then yes, you should definitely be using Klaviyo. For consumer businesses, this is obvious.
We need to bring it to all of those customers, you know, large and small around the world. By the way, one of the things that's been really interesting to me is enterprises love this idea because one of the things we've learned that was maybe a little surprising, but maybe much like not so much. Another couple Klaviyo scale might see some of these patterns even internally. We have to kind of fight against them. They love this idea of AI playing more of a role in the generation and delivery because they care so much about compliance and approval chains.
When I talked to a major, you know, global retailer like a sports apparel brand, they were like, yeah, this would be awesome if you guys could generate this stuff because we could plug in our brand guidelines and guardrails and make sure that everything that our hundreds of marketing teams are doing is consistent. Yes, how fast can you deliver this? This is a major unlock for us. It will speed us up. Right? We'll make sure we're maintaining compliance. I think all of that's going to exist. I think this era of software just purely as software as a service, just as you gotta log in, you gotta configure it, you gotta do it all yourself, I think that era is over.
We're definitely building to be the CRM first for consumer businesses, and then we'll work on everything else that is really AI-driven both on the generation and then the end customer experience delivery.
Let's do it on this idea of the era of SaaS. I think since 2022, arguably, we've started to see consolidation in the front office stack where there was a lot of overbuying in 2021. My question for you is, why do you think now is the time where you see an incremental shift in the landscape? From a competition standpoint, this is a question that we've been debating all conference and for the last several months now. There's incremental competition from the AI-native competition. There's potentially incremental competition from the front end models. A lot in there. Why now? What about the extra competition?
Yeah. You gotta kinda parse apart. If you just go purely to this, like, hey, we're going to use the software to deliver these experiences, there's many services, there's many different formats, different channels, etcetera. Look, this consolidation story is rooted in a pretty basic thing, which is everybody wants the experience to be driven off the same brain, so to speak, the same data set. It's really frustrating when these different pieces of software either, one, don't connect back to each other—disparate data systems—or they're just lacking a brain. They all have to connect back to something, and then the latency gets really bad.
I think this idea that all of the different various marketing channels are going to converge, and then they'll—by the way, because these basic things of, like, if I send somebody a message, I send them a text message or an email, and then they open up their phone or they open up the website and it's inconsistent, it's just a terrible experience. It shouldn't be that way. Why is that? It's usually because there's not one back end. We obviously solved that. I think it's why you see a lot of consolidation there, plus the normal things of, like, people don't wanna learn five different systems. They want one. They want it to be well designed, all this kind of stuff. The AI-native stuff is then very interesting. I do think there's—I spend most of my time, I'm most excited about the companies that are AI-natives that are new.
The same way that when the web started out, it was very fun watching the companies that were trying to figure out the web, but it was hard and it was messy. I remember going to a lot of the developer conferences back in the early 2000s where there was only, like, 30 people in a room, messing with the latest and greatest technology of how to build an interactive website. You're like, wow, this is great. It's not really there yet, but you can tell it's going to be. This has to be entirely the same feel. It's fun hanging out with the AI-native companies because they're thinking and dreaming that way. The challenge for a lot of the incumbents is there's some of them are like, yeah, we'll see if it happens, right? You know, I don't know. Give it another 5 years. We'll see if it goes.
That's boring. I'd be like going and talking to people that are building, you know, software for CDs, right? What's interesting, though, is I've actually been, you know, kind of frankly unimpressed, like, in the marketing space. The customer service space is a lot more interesting. I think it's a lot more interesting.
Yeah, for sure.
The marketing, I've been unimpressed by, like, people going for this, building better tools to do content creation. Look, we've got a project internally as part of defining your marketing strategy. I think every modern brand should also be, in part, should be a media company. Like, you know, we're working with this outdoor retailer, and, you know, they sell all sorts of camping gear, hiking gear, climbing gear, all kind of stuff. We're like, why aren't you also the go-to destination to go find, like, great climbs or great camping sites? Why aren't you both? They're like, oh, we never really thought about that. We just don't have time to do it. I'm like, but you can. All that information is there now.
You can provide more value to your customers than you were doing before. They're like, that's a great idea. We just didn't know how to execute. I'm like, now you can do that. That's the kind of stuff that I was hoping you'd see more of, but I just frankly haven't seen that much of it. We're going to get deep in there. Hopefully, there will be more companies that are kind of focused on that in the next couple of years. The customer agent, the service side, I think, is more interesting. That's where actually we're just very focused on this idea that I think you're thinking too narrow if it's purely about customer service. I think every business is going to provide some AI that is going to be the concierge, the representative to that business, and it's going to solve all sorts of problems.
We have these fascinating stories when we turn our agent on for our customers. We have this whole classifier that will look at the kinds of conversations people are having. It's crazy. There are basic things of, hey, I need to return this product, or I have a question about sizing or whatever. When you see people, I've seen conversations with some high-end, some fashion brands. I've seen people plan entire, like, we're having a wedding, and I'm wondering what the bridesmaids should wear, but also, I have questions about what my mother-in-law likes. It's like, holy smokes. There's this whole conversation going on, and they're just querying away at this product catalog to try to understand what's there. These are the kinds of conversations that I think are going to become the norm. You already see this happening with the larger LLM.
I think it's fascinating who is going to do that well. Obviously, we're benchmarking like crazy, our models, our end agent. How good is it at retrieving the right information and actually answering questions? What we found is, at least in a retail and e-commerce context, because of the access to data we have, both about the business, product inventory, catalog, as well as that consumer, their data set, we can just provide better answers. I think the AI natives are the most interesting companies. We spend a lot of time there. Oftentimes people ask, like, hey, do you see this collapsing of the user interface where does everything devolve to, like, you know, ChatGPT or Gemini?
To give you a little bit of an analogy, I always felt like even in retail and commerce, it felt a little simplistic to me that the entire world was going to collapse down to Walmart and Amazon. It just felt like, yeah, it's possible, but you're not really going to have, there's no room for different experiences. In theory, it's possible, but in practice, it just doesn't seem likely. I do think for a lot of productivity-based things, I think we probably find ourselves, I certainly do. I just love the LLMs for a wide variety of personal productivity tasks, and things that are more adjacent to that, it makes sense those would get pulled in. I think as you get more domain specific and you're getting deeper into it, I have a hard time. Maybe it's possible.
Maybe we'll have to see how it rolls out that, if you're thinking about, say we give you a marketing strategy and here's a campaign we think should run, and then we want you to review it. It's hard for me to believe that entire interface and then the editor and all this kind of stuff will fit inside of that box. I think we're still a little bit like the explosion of web apps. I think we're going to find there's some patterns that will be discovered of good ways to interact with LLM. I think we're going to find there's also some specific to each application, or at least some of these high utility, high value, domain specific applications, where the UX needs to be a little different. As we want to be really great at that too.
Yeah. Fantastic. All right. I wanna talk a little bit about CRM.
Mhmm.
Klaviyo's expansion from marketing to sales is significant. Historically, we've seen a little bit of a nuance here between B2C and B2B. Talk to us a little bit about where you feel you're strong today from a sales feature and functionality standpoint and where you need to go to be best in class.
Yeah. We built Klaviyo to focus on B2C use cases first, frankly, in large part because there would be less friction. Right? I mean, it was one thing if I had to convince a, you know, salesperson, it's like, hey, we actually believe the entire customer experience should be delivered through software. Kind of a tough pitch. Right? Given the, you know, the job they're in. I think what's happened, what we found is, you know, marketing, I think, is often just automated sales. It's just sales at scale. It's probably a pretty good analogy. Right? We found that it's like, yes, Klaviyo is obvious in a B2C context. What happens is we're finding now, and this is probably through AI, you get these really high value conversations. Hey, I have this one consumer, but they spend a lot. They're a very frequent customer. They're a VIP.
I really almost want to provide them a more tailored experience. I think what we're finding, Klaviyo interoperates well with CRM. We can escalate to an account manager or a person or a human for now. This is actually one of the things I think is interesting about the Customer Agent we're building. We started out with text, so chat modality, and then audio. I think in short order, probably over the next year, we'll have to see how some of the foundational models develop. I think we'll be able to offer video as a modality as well. You start saying, like, what's the difference here? I think a lot of businesses are going to find it's like, what's their value add? It's sort of like the data set that they have that an LLM can think and train off of.
Then it's like, what is the brand and personality that you want to provide? How do you want to interoperate with the world? Once we get down that path of providing, I think, the kind of modality that people expect, they're more sales modalities because we're already doing that with marketing and sales. I mean, you could have a sales conversation with our Customer Agent. You think about more traditional sales where it's like, yeah, I'm looking at somebody in the face. I think we're going to get to the point where, okay, we now can provide that kind of experience. It's going to boil down to the depth. How good is your agent at understanding context behind the scenes? That's actually something we're using our Customer Agent on Klaviyo's own data set. It's fairly complex, right?
We have this fairly complex—people ask very specific questions about, hey, I built this marketing automation. It's not functioning this way. By the way, I have another question about, you know, this other feature over here that just came out. Like, should I use that or not? Is it applicable to my business? We need our agents to be able to handle that. We're already getting into some of these more complex queries that we tend to think of like, oh, only humans can answer those. I think in pretty short order, we're going to find that, you know, maybe it can't replace—maybe it can't be an augmentation or replacement for all of these. I think we're going to find it gets pretty good, pretty quickly. That's something we're very bullish on.
I think there's a natural progression for us from doing marketing at scale service, and then that starts to bleed into things that might look like more traditional sales use cases.
Yeah. Fantastic. We spoke a little bit about this at the beginning, but we've talked a lot about Klaviyo's infrastructure over the years. You talk about the speed and also having the completeness as well. How do you do that? How is that kind of different than how you see others in the space trying to solve that problem?
Yeah. This concept of if you want to replicate a person, you'd better build some data structures that are as good and as fast as this guy. I think it's very important to us. Our approach has been this: we look at it as, like, look, the way you store data, there's not one sort of database, data format, you know, indexing scheme that works. Instead, what our data platform does is when you push data into it, structured data, unstructured data, we actually index it in a variety of different ways. We have a couple of routers that will look at the data and intuit how it's likely to get used downstream. Just as an example, when a consumer takes an action, they buy something, they have a conversation with our Customer Agent, they browse a website, they go visit an event that you ran.
We take that data and we store it in a bunch of different ways so that you can access facts about that exact information. Hey, what did this person buy in this transaction? You can look that up in milliseconds. We also then go aggregate that with other things that they did. Hey, help me understand the attribution of what kinds of things this person responds to. For instance, maybe they prefer getting messaging over email versus text messaging, or maybe it's, hey, it was a result of that Customer Agent query. We index some of the attribution data. Then we aggregate it up with other data we have about that end consumer. Hey, how many transactions have they had recently? Can I query that also in very low latency? Obviously, larger across the entire business. Hey, how is this customer similar or different to other customers in my business?
Because we do this kind of multiplexing, what it means is when it comes to query time, when it's time to think and take an action, our database is very fast. That's our goal, that behind the scenes, you could replicate what we're doing if you had a large, say, data infrastructure team. That's a good idea. We've pattern matched our data systems, our data platform, the product we provide, with a lot of the big at-scale consumer companies and their data infrastructure team they provide to themselves where they're their own customer. Oftentimes, we swap notes and they're like, wow. This is really great. We could use a lot of the stuff that you're doing. I think over time, if we build our data platform right, people should opt to use this versus roll their own set of data warehouses and databases together.
That, you know, it's a little bit outside of scope because I know we're mostly talking about software applications, but because infrastructure is so core to what we do, I often really like looking at who are the data warehousing, data lake, data tech companies, and how much are they thinking about this multi-index, sort of multi-data backend future. There are a couple of interesting projects there, but I think we're, if not the only company, one of the first companies to really try to commercialize that. We haven't commercialized it directly. We do a little bit through our data platform, Klaviyo data platform SKU. We mostly just use it to power our applications. I do think over time, that's another really interesting one. Can we just expose this brain to our customers? I used to think that was going to be more important.
I actually think now it's more likely that that's going to be a competitive advantage for our AI because it just has a fast link to look up a lot of information that otherwise you'd have to go invest a whole bunch of infrastructure to basically get the same results, to get the same latency.
As you embed more AI in your platform and you think about the potential for maybe commoditization and some of the AI functionality and your ability to charge for AI eventually, how do you see that playing out over time?
Yeah. That was all, I'll give you an example. Some of the things we're doing with marketing and embedding AI right now. We've taken it kind of a dual approach. It's probably more biased towards baked things into our products to, you know, to just grow market share. I think there's such a moment here where you build these wow experiences that just so level up what people should come to expect. I mean, we've got these interfaces inside of Klaviyo now that, like, you know, that we'll be sharing with all of our customers very soon. It's just dramatically different. It's very different when you log in. You can tell what software you log into, and there's no to-do list. It's just a, like, choose your own adventure. You just click around everywhere because, you know, no idea where you're supposed to go.
AI software is going to tell you, no, no, no. I need you to review these things because if you weren't here, I would just take these actions. I think, as we move towards that new AI future, you know, that's going to become the default. You want to get that to everybody. We don't actually want to put that behind a paywall. Now, what we are finding is there's some functionality that's maybe more applicable to our larger customers, or we believe that some of the things that we're going to build, we actually have price, you know, there's room and customers will understand, price and leverage over time. For example, our Customer Agent, we're deliberately pricing at a price point that we want to encourage people to get it out in front of consumers.
We don't want you to hide it on a page where it feels very hard to get to. We also know there's different types of conversations. There's some that are cheaper, and there's some that are actually higher, you know, they're cheaper to compute, cheaper to serve, but also, you know, lower value. There's also some that are higher value. I expect over time we'll see some pricing discrimination even on the cost of those conversations.
Yeah. On the consumer side of things, I think how consumers interact with technology could look different, very different potentially 5 to 10 years from now. How do you think about investing in your product stack to prepare for that when there probably feels like a lot of unknowns right now?
Yeah. It's all about, like, this is a very fun time for our product engineering team, myself, the whole company, because things go very fast. I just gotta give you an example from, like, I think it was a week or two ago. The models are improving so quickly, and it's so changing, you know, internal functionality. We'll talk about the consumer side. There were some new models that made image editing much easier. It was an Achilles heel of a lot of AI models. You'd say, hey, take this person and, you know, put them in a, you know, a white shirt. It's like it would totally change who the person is. That problem is just like they solved it or almost solved it. It's like really, really good. Right?
You can imagine it has lots of applications for us where we're like, hey, we have these, like, staged, you know, product inventory where it's like, hey, here's we did this, like, photo shoot. We've actually code named a product internally, photo shoot, where we're like, why can't you take that product and put it on anything? Right? Put it on a model of your choosing. Right? Or put it in any kind of scene. Literally, we took that model, and we, you know, I think it shipped on a Tuesday. By Friday, we put it into our product. We watched the traffic over the weekend. Already, we had thousands of customers. We're not, when the, you know, on the weekend working through these things, using this. Velocity, I think, is the key thing.
I think, as it relates to both, I mean, consumers, but whether it's our customers or end consumers, I think you have to iterate a lot on the types of experiences, you know, like, what that could look like. For instance, I'm very bullish on we have this new product we're shipping called Customer Hub. You go visit a website. Not only will it rewrite the web page, but it gives you kind of this pane of glass when you go visit a retailer that's, you know, not named Amazon, where you can see your entire experience with that customer. We're iterating on the little modules you put in there like crazy.
One of the things we added the other week was we put in the ability that if you're a customer and you come to a website and you, like, just remind us who you are, we'll show you all of the discounts and promotions that you're eligible for that you might have missed because you missed that email or text message. We're finding already people are going in there just to hunt for deals because they think they might have missed it. Brands love this because the whole reason they ran that promotion in the first place was to drive engagement. We've just given them another surface. That's something that we just had to play with and then see what the data says.
Your comment on the data warehousing companies is really interesting. Maybe just contextualize for us. We know that Salesforce is Data Cloud. We know that Snowflake talks about marketing as an end use application. We know there's a CDP layer in there somewhere. Where do you think the value accrues? You're talking a little bit about there's some displacement opportunity there, it sounds like, with your multi-infrastructure end.
Yeah.
The database.
For us, I think so much of the value is actually one of the reasons we got into marketing after building this, you know, kind of data platform first. I've always felt that, like, if you choose to be infrastructure, then you go wide, right?
Yeah.
With probably lower margins, right? That's a great business model. Lots of companies have done that. If you do applications, the more those tend to be higher margin, higher value because they're closer to the end outcome. It's just a question of how widely applicable are they. In general, we've looked at our data platform as a means to an end to power our application. Our belief is that we should build the core applications that we think are going to be ubiquitous, everybody needs, but also, we want to open it up to third-party developers. I've actually done a lot in the last 12 months to invest in our developer program. I think we now have, I think there's, like, hundreds of applications in our version of our app store, our app directory, and we're investing a lot in getting people to build more there. Why?
Because if we can't build it, we would love for somebody else to do it and at least do it on top of this shared data platform. We know businesses want this because they say, look, I get requests all the time. Part of the reason we got into our Klaviyo Customer Agent and Klaviyo Customer Service is people say, why can't you unify this part of it? We know we can't get to all of it. We would actually like people to build more applications. This is also helping us as we branch out beyond retail and commerce. There are a lot of bespoke applications that we're finding in other industries or something that if I could just go do this with Klaviyo, this gives those folks access to do that.
Yeah. Yeah. Makes sense. Okay, I wanna ask you about the R&D headline from a couple weeks ago.
Mhmm.
Tell us a little bit about what your framework was for doing the reduction in force, and then how you're reinvesting those resources.
Sure. Yeah. I mean, that's, I think it's, we've all, I have a lot of respect for everybody that signs up to put on, as we say, puts on the Klaviyo jersey. At the same time, we very much believe that, you know, we already drive very hard. I think of, you know, we talk about careers at Klaviyo as a little bit like joining a professional sports franchise. People don't spend 30 years in the NFL or the NBA or whatever, and I think it's fine if it's like not everybody's there, but we respect the work that folks do. This for us is kind of normal course of business.
I will tell you the one thing that's maybe a little different, or something that we're thinking a lot about right now that's not entirely related, but there's some overlap, is this idea of, like, I mentioned the AI-native companies are the most interesting. This idea of thinking AI first and LLM first is so important to us as we think about talent. I think in the SaaS era, you could get away with a little bit of, like, yeah, I've seen this before. Let me take my playbook. I'm going to rerun it here. It'll all be good. I think that's just totally broken. We talk a lot about what are the advantages of scale, and being a company where it's like, yeah, you've got, you know, 100, 200,000 customers, partners. Right? We've obviously built a business in certain size, cash flows.
We have a ton of amazingly talented people, but there's some biases that come with that too. One of the big ones is, and I think about this every single day, is what got us here probably is not right for the next era. One of the things that we're checking for both folks that work at Klaviyo now and folks as they come in is not just are you into, you know, are you into AI? We have to use this analogy of, like, if you were in the late 1990s and say you were, you know, you were at Amazon or Google and you were like, hey, you want to go work there? I mean, if you showed up for an interview and they're like, cool. Tell me about how you're into the internet. Somebody's like, well, I've used Amazon before. I'm like, yeah. Yeah.
So, like, you and, like, tens of millions of other people. I'm like, I've tried Google. Right? That doesn't mean anything. What you want are the people that have these side projects. They're like, yeah, I built my own website. Well, okay. Now we're talking because that was hard and people figured it out. I think the exact, and you can imagine, it's like all these things. If you were at Google and you're like, hey, I've run a data center, but I run it. I don't think internet scale. It's like you're just totally the wrong fit for what we're doing. It might have made a ton of sense in the 1980s or the early 1990s. It just doesn't make any sense anymore. We think about the same thing for AI.
It's like if you're not the kind of person that's going and coding against LLMs or, my gosh, now with all the tools, you don't even have to. Coding is taking on a whole new world. It's not about semicolons and braces anymore. Now it's just about can you type in text and iterating on that. If you're not the kind of person that's just proactively going and doing that, I don't think you're the right fit. Now it's a little tough and people gotta go figure that out themselves. Thankfully, I think we have thousands of people that are very leaned into this. If you're proactively in the AI, that's great. There's a little bit of that that we're also testing for as well.
Yeah. Fascinating. The other thing that we wanted to spend a little bit of time on is we've talked about the different product modules. At the same time, you're also going from being primarily mid-market focused to going further off market.
Mhmm.
There are plenty of companies in the SaaS world that have really struggled with that. Tell us a little bit more about what you think the limiting factor to the type of customer or the size of customer is that you can mandate. How do you continue that kind of one size fits most approach while also layering some of the best in brave functionality that you need to go up market?
Yeah. The first is, like, it's my ambition. It's our ambition that, like, I think we should be working with businesses of all sizes. I have a soft place in my heart for every entrepreneur. I know how hard that is to build from the bottom up, and we really want to help amplify those businesses. I know there's a lot of other great companies out there that want to do the same. I actually started my career working with the Fortune 50, and it's very fun when you're working with brands like McDonald's and Starbucks and CVS. These are great companies, right? Huge scale. We saw what those patterns look like, and I'm eager to bring those, like, working with those businesses and some of the, like, how to help them be successful at their scale with their complexity, to Klaviyo. We're very much invested in that.
I remember a couple years ago, I got this advice from somebody, and I said, like, how do you think we should go about it? I have a little bit of experience here, but it's a couple years old. I got two pieces of advice from somebody I really respect. They said, one, find some people that are very, very curious but have seen this before. They'll help you with some of the patterns and help you pattern match on this. The second is, don't wait. Just go get your butt kicked, right? Just dig in there, take that mentality you have of digging in with customers, and just get in there and help them solve some problems. I think in the enterprise, that's one of the things I learned back from my stint doing that before we started Klaviyo.
You have to go in there and really understand what problems matter most, where are you going to start, how are you going to get in there, and then you grow and expand from there. That's very much our mentality. As we think about Klaviyo, we're finding there's opportunities with our marketing stack where we're starting out in some regions, some business units, and then working our way across the organization. We've gotten a lot better at that. I think with our Customer Agent, we've now actually given ourselves another angle. We're getting introductions now into the operating teams that are thinking through, you know, hey, what does this look like to have an agent present that represents us? There's a lot more to do there, but I think it's like go idea, get your butt kicked. We've taken our bruises on that, and we continue to learn.
Thankfully, I think you go talk to our customers. We review every single one of them every month and make sure that they love us because everybody talks. On the building product side, I think about good engineering. I think it's like you build systems that can scale. You give people concepts that are abstract that they can work with. We've thought about Klaviyo, this idea of, like, well, what is a customer? We've thought about that deeply. What does it mean? What does it mean to represent them in all of their actions? What does it mean to do marketing at scale when you have thousands or millions of marketing campaigns? We've designed our system from the ground up to think that way. Honestly, we actually almost, like, with our smaller customers, our entrepreneurs, SMBs, we almost try to reduce all that complexity down.
We hide it behind a ton of UX. It's all there, but they just can't see it. It's great engineering, I think, and flexibility gives you what you need in the enterprise. Give people the building blocks, the Lego set. We use the analogy of, like, yeah. For the entrepreneurs, the SMBs, either give them the book on how to build the pirate ship or whatever. Now with AI, I think we can actually just build them the damn thing because a lot of them are like, I don't even have time to build this thing. Just do it for me.
Yeah. Yeah. I remember at the time of the IPO, you had this beautiful chart that had essentially a backend infrastructure.
Mhmm.
It was a bunch of different flavors of database technology. The whole point, or one of the points, was you can swap in new technologies as they come down the curve to keep your backend best in class, dynamic, nimble, etcetera. Maybe share with us, is there one technical change you've made or one technical upgrade that you've made in the last six months that levels you up?
Oh, yeah. There certainly are new data stores. I mean, people have talked a lot about, like, vector database and things like that as it relates to AI.
I think that that's very important for interacting with. Actually, one of the most interesting things with enterprise that we've learned is a lot of folks, those are, you know, they're somewhat standardized at this point, like, the kinds of different formats. I think what's unique about Klaviyo is the fact that we can scale such a breadth of them, like, different ways to store data.
Mhmm.
We're smart about how to store things. What's actually more interesting is I've got a lot of enterprises. They're happy to put their data with you, but what they really want is a place where they can build models outside of Klaviyo because they look at the models themselves. I'm not talking about LLMs here. I've seen models where they're clustering their customers. They look at that as a big competitive advantage. One of the things that we've tried to upgrade is the ability for people to train directly on data sets in Klaviyo and do that quickly without having to import and export data all the time. I think that's going to be very key for us.
Those data science teams don't actually really want the data they want to clean and normalize, but they really want the ability to go build and train and put their own intelligence on top.
Fantastic. I think this is a good place to leave it.
Please join me in thanking Abe for his time. Abe, thank you.