Durable Growth in the Age of AI: Data & Marketing Lessons From Scaling Braze

Speaker A: That ultimately is what led to our ability to continue to compound growth at the scale required to eventually go public and keep building to, you know, the position that we're in today. Uh, and I highlight that just because I think it's so relevant, uh, to right now with the AI disruption, which is like there's a lot of independent high velocity change happening and then there's a lot of compounding effects that are happening because of all of those. Uh, and you know, the, if you're just kind of right on the leading edge, it's, it's interesting and it's exciting, etc. But you really need to think carefully around like what the composed or compounding effects as well as the second order effects of those of all that change is going to be, to find the change that will be durable so that you can really work on building a business that's going to be able to compound value creation around that durable change.

Speaker B: Welcome to the Data Table, our monthly podcast on the news, trends and technology transforming data. Today, we're your hosts, Arman Petrosian and Satish Jayanthi, the co founders of Coalesce. Let's get started. Welcome everybody to another episode of the Data Tea. My name's Arman Petrosian. Um, I'm the CEO and co founder of Coalesce. I've also got my co founder here, Satish Jayanthi, our chief product and technology officer with really special guests sitting down together in their office. Bill Magnuson, Spencer Burke. Uh, welcome you two. It's uh, it's a pleasure to have you both. I'm really excited about this conversation. I've got a ton of questions before I jump into any of them. Would love just a quick intro on both of your sides before we jump into the episode today.

Speaker A: Yeah, sure. Spencer, you want to go first?

Speaker C: Yeah. I'm Spencer Burke, the senior vice president of growth at Braze. Been at braze over 14 years. So I was lucky enough to join Bill a couple of months after he founded Bracelets.

Speaker A: Yeah, and I, uh, like to say I've been here the whole time, but Spencer's basically been here the whole time as well. Uh, we founded Braze in July of 2011. Uh, I was the CTO at the beginning and uh, co founder, took over the CEO role in 2017 and you know, have, have seen a lot of evolution in the space, a lot of it driven by uh, data and data technology trends and so really excited to have this conversation today.

Speaker B: Awesome. Welcome to both uct. Sharon, if you want to add anything, quick Intro and then I'll start firing away.

Speaker D: Uh, yeah, I'm Satish, uh, Jayanthi, as I said, um, I oversee the product and technology and um, my background has always been in the building data foundation players, which is where systems like yourselves, ah, likely to consume data for serving the end user.

Speaker B: Awesome. Well, let's jump right into it. I did see, uh, Spencer, that you have been at Braze pretty much since the very beginning. So I'm looking forward to asking you some of the questions around your experience. But before I do, Bill, just, just starting with you, I mean both Satish and I as co founders, we've gone through the inception stages of starting a business and all the fun times that come along with it. But I would just be curious what inspired you or triggered you to actually start Braze in the first place. Like, can you give us a little bit of the founding story?

Speaker A: Yeah, I mean I think uh, a few things, uh, all came together in that 2011 time period. But the most important thing was just the promise, uh, and opportunity of mobile entering the sc. You know, I had worked at Google in 2009 and 2010, uh, on a team in Google research associated with Android. We actually, the team was called App Inventor for Android and we built a visual programming language, uh, for building Android applications that'll become relevant as we chat later and talk about Canvas, which is Braze's visual programing language that marketers use, uh, to implement and design customer engagement programs. But was there for you know, the earliest days of Android and mobile Cupcake, uh, launch that summer that I was out there, I remember seeing the big fiberglass cupcake get rolled out on the front lawn and just having that front row seat for mobile, uh, for me built this tremendous conviction in really two stages. You know, first was that huge businesses would be born and built to be mobile first. And that was certainly not the case yet in July of 2011. And we can kind of talk through some, uh, some of that why now question that I think is so important in venture financing. If you guys want to dig into that later. Um, but uh, the other one was that the wide scale adoption of mobile by consumers would force change in business, you know, in the enterprises. And um, of course we've seen that happen over time as our relationships with technology, with each other, with institutions, with knowledge, et cetera, um, have all, you know, changed over time. And what we really wanted to do and what I was really motivated by was just being at the forefront of that wave of massive disruption and wanting to work on a hard Business problem, um, that I thought, you know, where I thought new opportunities would arise because of that technology change. And so when you look at what Brace does, you know, we're a customer engagement platform. We help brands build, ah and you know, nurture and strengthen uh, relationships with their customers over time. Uh, we want them to do so with having a strong data oriented, um, understanding of the context of their customer, what they care about, what makes them tick, where they are in their journey, um, be able to use that context combined with sophisticated technology and intelligence to drive the right interactions that are going to then build value in those relationships over time. Um, but that all boils down to actually some very human things which are like, if you want to build a strong business, we believe you should do so by having strong relationships with your customers who are, who are humans. And if you want, if we as humans want to build strong relationships with each other, you know, it starts with paying attention when we first meet, you know, looking for those cues of interest, the body language, um, you know, tone of voice, et cetera, uh, really paying attention to the details in the context so that we can use that understanding to just have more enriching conversations and better shared experiences over time. And if we do that, those basic things, we'll build strong relationships uh, with each other. And what we wanted to do was use technology and the disruption that was coming about because of uh, mobile to just create that opportunity for more sustainable businesses to be created. And, and that really has been know, the driving force behind BRAZE across all of these 14 years. And um, you know, a lot of different aspects to talk about. But you um, know, I think it all started with just that tremendous conviction in mobile as a disruptive technology and seeing the opportunity to build a business that was uh, focused in the fundamentals of how new, uh, you know, new companies and old companies adapting to new consumer behaviors, uh, would be able to strengthen themselves as businesses by really looking at the opportunities that that created.

Speaker B: That's incredible. And I certainly remember the platform shift of mobile and how big of a shift it was, disruption, whatever you want to call it, I think we're obviously going through something similar on the AI side and I'm sure there's going to

Speaker C: be a lot of talking points about

Speaker B: that here later, uh, on this webcast and podcast, uh, when you did see that firsthand, when you did see that shift and you mentioned you had a ton of conviction around it, did you, did you think that it would be to the magnitude that it is today or bigger? Like what was your Perspective on it as far as like any surprises from leaving, starting braze and actually seeing that come to life.

Speaker A: Yeah, I mean I think my conviction in mobile that it would be an extremely meaningful part of every human's you know, future with technology was uh, perhaps a little bit early and maybe didn't have all the right supporting data in 2011 but uh, I would also say ended up being true uh as well. Right. And so, so uh, that part of the opening thesis, I think that conviction was there from the very beginning. I think the hard part is really navigating um, both uh, the way that markets change over time where value is created in the value chain, also with respect to business priorities and all the skill sets that get built um, within organizations over time as they evolve and adapt to the uh, new realities of these technologies and how do you build for enduring areas of value creation in the face of disruption. And you know I think that that's uh, that's an area where this wave of disruption that's happening now uh, with you know, new gen AI capabilities is being supercharged by the fact that we in the intervening time put you know, really advanced technology in the hands of every human, basically every human on the planet. Uh, and, and also you know, I think when we look at customer engagement as an example, it's a field that has evolved from you know merely like batch and blast campaign driven thinking to um, something that's really taking a holistic customer journey, being data driven, having you know, new insights derived from an experimental process and also by virtue of really the melding together of customer journeys and our digital experience with our lived experience and how we've really just had mobile technology permeate every aspect of our lives but um, both digital and physical. You know, what that means is that this problem of customer engagement is not a one that is like solely marketing oriented anymore. You need to have cross discipline collaboration across creative and marketing skill sets, data engineering and analysis as well as product, um, and engineering. And that also means that you need to have collaboration across that entire like data sphere, uh, I guess I'm data platform sphere, uh, and you know, and be able to have all the right customer touch points to deliver that coordinated experience that like actually helps you stand out of the noise and that your customer actually values um, and if you can achieve that you get that reciprocal value creation and that's what builds strong relationships. But um, all of that has really taken you know, an advance simultaneously of technology and skill set, um, skill sets, um, it's evolved a lot. The way that Teams, uh, get organized and businesses prioritize a lot of these things around first party data investment, uh, the building of direct and first party relationships, you know, and I think that the sheer uh, pervasity of like first party relationship building too, when you look at the fact that companies in all different parts of supply chains and in the economy across all different verticals, because of the entrance of aggregators and the way that um, you know, personal digital technology has created the opportunity for these touchpoints, it's become an imperative for brands of all kinds. And I think that's something that you know, I think we intuitively understood because we were seeing the importance of direct relationships in uh, mobile apps and in some of these early technologies. I think the extent to which that has now fully permeated are like so many aspects of our lives. Nearly every aspect of our life, um, was, was probably underappreciated in the early days but you know, became increasingly apparent as we continue to scale and grow.

Speaker B: Yeah, go ahead.

Speaker C: I was going to add to that. I think while mobile was the catalyst and one of the reasons it was a catalyst was all the data that it generated, a device, a computer that's with us all the time, generating all

Speaker A: different interactions and it's just ours. Right. It's a personal computing device kind of for the first, for the first time ever for most people. Yeah.

Speaker C: The actual computer.

Speaker A: Yep.

Speaker C: Behind that there is a whole wave of data technology that was changing, you know, around the time that we were getting started, was probably close to the peak of some of the big data movement and people building on Hadoop. And at the same time what you had was document based storage, you had stream processing on things like Kafka and then you know, just about on the precipice of the rise of the data warehouse. And Redshift was kind of the first mover there and the data technologies

Speaker B: really

Speaker C: enabled a lot of what we all experience now in terms of modern marketing technology. But those two things coming together, we saw it on mobile as the front end, but then it was data technology in so many ways on the back end.

Speaker B: Yeah, it's like convergence of 2, 2, 2 actually two major platform shifts.

Speaker A: Right.

Speaker B: One from mobile and then one from actual data engineering, analytics, data warehousing, moving to the cloud from on premise platforms like Hadoop. Bill, were you going to say something?

Speaker A: Yeah, yeah, that trifecta uh, of like capabilities really being pushed forward as well because there was a huge amount of investment in the skill sets and the underlying technologies that helped like you know, data science flourish over time and m moving into a lot, a lot of the data science that now drives machine learning and now bringing gen AI into that as well. And just like that entire craft and discipline, you know, exploded in capability. Um, similarly mobile and a lot of the um, demands of the customer engagement space required like real time understanding of data in a way that you didn't have before either. Because, you know, in addition to these devices being personal and therefore we have an expectation that the interactions that we have feel unique and ours and individually tailored. Um, we also are using this technology in a lot of fleeting moments in our life. And so those opportunities actually engage with people are like here today or you know, here right now and gone the next second. Um, as I like quickly background the app and move into, you know, back into the Instagram feed or whatever and you know, that, that demand to be able to satisfy the customer expectation, stay competitive in like an incredibly intense like competitive landscape across mobile, um, and for attention and mindshare and wallet, etc. Um, as well as the real time and interact demands. Like, we try to really use the concept of interactivity when we talk about responsiveness for data at Braze because real time is kind of so bastardized, um, in the uh, in the space. But that concept of interactivity still feels very visceral. Right. Like when you're interacting with a mobile device, it needs to maintain that liveness or else it might as well not be happening. Uh, and so skill sets, technology and like customer demands all really come together in that trifecta to really push the whole field forward quickly.

Speaker B: Yeah, I mean, I love, personally, I'll speak on behalf of myself. I love getting advertisements that are targeted to me that I look at. I'm like, oh, this is actually what I want to buy. And so for anybody that's listening to this, you can just thank Braze for that. But basically you go through and you're a consumer, you're leveraging all the data from yourself or the consumer themselves and being able to provide them what they actually care about, which I think is a huge value add for all consumers and people are people in general. And so having that holistic approach makes a ton of sense.

Speaker A: And I think also really important to remember that, uh, you know, it's so far outside of retail. Like so much of what we talk about in marketing technology was very focused on like the consumption and purchasing of kind of like individual things. Um, and certainly like retail and consumer goods is our largest vertical as it is for almost all marketing technology companies. But the other it's actually a little bit over 20% of our business. And when you look at the other 80%, there's also a whole bunch of other categories in there, um, where you know, that concept of like, like discovery and knowledge and like habit building are like directly aligned with people's goals for their, for how to like live their lives. You know, when you look at things like um, healthcare, fitness, uh, learning applications like personal finance, like these are all places where the um, the role of customer engagement and the craft and the creativity of marketing combined together with product delivery to really help people form health habits that they actually want to, you know, have in their lives and, and that deliver. If you can really understand people and what they care about, what they're passionate about, what they connect with, um, and deliver that connection to them, you can really enrich their lives. You know, it's not just like, oh, hey, I saw a product that I, you know, that I wouldn't have discovered on my own via uh, like a really good reinforcement learning model that put the right ad in front of me. Um, it's really like how do I look at how mobile technology and in a lot of these kind of digital products and services and a lot of the first party experiences that brands have invested in, uh, in particular over the last decade and the way that they become a part of my life, the value that I get out of that and you know, the role, the customer engagement and a really data driven, real time understanding of the customer, uh, can you know, really move the ball forward in terms of delivering value to them?

Speaker B: Yeah, that makes total sense. I'm curious, you've had a ton of success as a company and as a uh, founder. As you were going through this journey, were there any gaps or problems that you were seeing back then that made you confident that building a company around solving them? Like was there certain issues that you had been seeing with digital advertising or marketing in general that gave you additional conviction outside of the mobile shift and some of these other aspects?

Speaker A: Yeah, I mean I think one of the earliest ones that we identified was just the mismatch in the amount of money that was being spent to acquire customers and how little attention was being paid at retaining them. Uh, like I actually remember conversations with prospects in the early days where they didn't even know how many monthly active users or daily active users they had. They like knew how many total downloads they had ever had. Um, but like understanding you know, their retention funnel, uh, and you know, how many people are still there after 7 days and 30 days and what does that stickiness look like and like, what promotes or correlates with that stickiness and how do we drive that? Like, these were all brand new concepts to, you know, a lot of teams in the early days of braze and uh, but, you know, the, that mismatch in how much was being spent in acquisition and how much was being left on the table from a retention and ongoing engagement standpoint. I think that gap gave us a ton of conviction in the early days. Um, but, you know, even in that example, you can already see the importance of new skill sets and new technology coming together in order to, like, elucidate those opportunities for people have the right experimental mindset to be able to, you know, build insights to improve those curves and really understand those ROI functions and optimize, uh, you know, those aspects of the business. And, uh, and you know, that, that created a lot of opportunity for us, but there's a lot of timing dates on things like that too, as you need to wait for those skill sets to develop and the new technologies to arrive and get adopted.

Speaker B: Very cool. I, I've got another one for you before I switch it over to yourself, Spencer, but Bill, on your side, you. You've mentioned in numerous interviews that you had some fundraising challenges in the early days, and fundraising is typically always its own process for any company. Like, we've gone through, uh, several rounds ourselves as well, and every single one of them has had its own levels of chaos. But I'm curious, especially in today's environment, where funding tends to be concentrated really largely around AI tech, uh, any words of advice for founders out there or what got you through those times? I would just love to hear what that journey's been like for you and what your takeaway is.

Speaker A: Yeah, I mean, one thing that is a, a piece of encouragement and, and something that literally got us through is that one of the benefits of being a private company is like, you're, you're alluding to the anecdote I've shared in the past about how we, uh, only ever got one term sheet in each of our seed rounds. And all the way up through our Series C, actually, uh, it was nothing like, you know, you would have seen in Silicon Valley with people chasing us around with 10 term sheets, like, you know, falling over each other to try to give us money. It was like we had to, like, really scratch, uh, and claw our way to getting that one person to really believe in us. Now, each one of those one people that believed in us in those rounds did make a lot of money betting on us. One of the benefits of uh, being a private company is that you actually only do need one person to really believe, right? Or like a small, um, set of people. And you know, as you go public, your valuation is a little bit more of like a weighted average of everyone's opinions. Right? But as a private company, um, you know, you, you need to find those people who believe in you to give you the chance to make it to the next stage. Um, and you know, get more people to um, you know, to share that conviction and grow from there. Uh, but your problem of today is like find the people that will believe in you today to give you um, you know, the, the investment that you need to, to prove that next, you know, stage gate and get to the, the next challenge. And so, you know, some of the advice around that is also that you know, a lot of VCs are um, a lot of VCs are more comfortable uh, within, you know, certain areas of investment, analyzing certain business models, different verticals or industries, etc. Um, and you know, I think that one of the easiest things for a investor, especially in the early stage to get conviction on is if something looks like a win that they've had before. And what I mean by that is that like, you know, if you're in a new area of just if you're like harnessing a new wave of disruption to solve like a business problem and it's not a new business problem, there's probably like a bunch of companies from 10 years ago that solve in business problem and there's, there's a bunch of seed and like, you know, early um, venture uh, capitalists that made a lot of money betting on those companies. And so as you now are like in the next major wave of disruption, you're looking to solve similar business problems. The best place for you to go and find, you know, people that are going to have conviction in your space and be able to invest in it, you know, regardless of where it is, is to find the people that understand that space. They know that they can make money in it. They know that they can, you know, they can pattern, they can probably pattern match to um, you know, similar characteristics, uh, of bets that, that worked out really well for them in the past. Uh, and, and that's a place where you know, you don't need to boil the ocean. You don't need every venture capitalist to believe in you. You actually just need one or a handful of so what better place to go and look than people that have made money on, you know, a problem Space that looks like yours in the past.

Speaker B: Yeah, Yeah, I got great advice. I remember when I went to raise our seed round, it was my first time raising from venture capital. I just moved to San Francisco from Portland, Oregon, and an awesome advisor of mine, his name's Ozon, he was like, look, man, you're going to get some no's and that's okay. It's like selling a house. You just need one offer, and as long as that's the case, you're good to go. So you just got to find one person in the midst of hundreds of potential investors. And so I've, uh, shared similar feedback to other founders that were in the same stage. And I couldn't agree with you more. It's all about finding that right person with that level of conviction that believes in you and believes in the vision of the business. So, super helpful. Uh, Spencer, as promised, coming over to you. Uh, you were incredibly early employee. So you've basically been there since the very, very beginning. I think just a couple months in after Graze had started. What were those early days like? What were those early days, months, years like for you as an employee? Not necessarily the founder, but I don't know, maybe effectively on the founding team. What, what was it like, uh, those first, first few months and years?

Speaker C: Oh, man. Ah, well, first, I love hearing Bill retell some of these war stories. Like going back into the early days of the company is. It's a lot of fun to, to sit in the time machine and go back to it. Uh, I mean, it's. Those early months are some of the hardest time in company building when you really have this deep existential crisis, when you don't have product market fit, you don't know if the company will continue to persist. Obviously, from where we sit today, there's, uh, a lot that we need to focus on and we have ambitious goals to go do that, but from a much more comfortable place than, you know, are we going to be around in a year or two years? And at the same time, there's something incredibly inspiring and fun about, you know, being deep in the trenches with some of your friends and with colleagues and smart people you really respect to go do that for. For us, I think it was, like Bill said, it was a really exciting time. And as a, uh, young tech company in New York in 2011, that was also really interesting. The tech scene was pretty nascent here, and I think for us that was, it was an advantage in many ways because it insulated us from some of the noise and there's also a deep marketing community in New York City. And so I think for people that we were focusing on as our customer and as we're evolving the platform and understanding that, hey, this platform shift that's happening, it's not just about mobile. It's about all these other channels that marketers use all the time too. Email and SMS and other channels have emerged since then. New channels were created on desktop and on mobile. They want some of the same things. They want to be able to move faster, they want it to be more interactive, they want to take more advantage of data. Like if you would have talked to, uh, most marketers in 2011, and maybe a few too many after that, if you ask them about a database, it'd be their email database, their list of contacts. And now it's so inspiring because a lot of marketers understand the data warehouse. They understand the interconnectedness and the importance of what's happening upstream from, from everything else and being able to connect into that. So, yeah, I mean, those early days were, were fun and exciting, but it's also, it's one of the most challenging times in company building.

Speaker B: Yeah.

Speaker A: I also remember, uh, when Spencer started, like literally the first week that he started, I think we had run out of all drinks in the fridge except Red Bull. And, and he was just like, no problem. Just drank like eight of them a day. Uh, and I rehearse like noticing that a couple days in and was like, have you only been drinking Red Bull the last two days? Yeah, no, I mean, whatever. It's all we had. Yeah, that's right.

Speaker B: Waters for the week. It's, uh, that was your fuel. Sounds like a great early stage employee.

Speaker A: I poured an energy drink into this glass before we started.

Speaker B: All right, Clear energy drink. Uh, Spencer following up on that. So given you went through so many different stages of this business, is there, is there a single lesson that you think that's been most pivotal for you or something that you've discovered throughout this journey? That thinking back on was like a really critical moment for you either in your career or personally while you've been at praise.

Speaker C: I don't know how, how to generalize this for everyone. I mean, I think especially for early employees, like, everyone goes through their own journey. And one, one thing I've tried to spend more time on is connecting with folks who are at a similar place because I think there's a, there's a challenge that you run into of, uh, the company scaling really fast and founders you're sticking around for the length of the company.

Speaker A: Hopefully, uh, but a lot of find in blood. Yeah, I guess some say a lot

Speaker C: of early employees struggle with finding their way through a company. And you hear from people like, oh, yeah, I'm a great sales leader for series A and B companies. I'm a great marketing leader for this stage of company. And for me it's been about staying eternally curious and challenging myself and where I can, trying to think like, how do I evolve at the same pace of the company? And sometimes that means going into really uncomfortable places. Uh, again, I've been really lucky that that's led me to go deeper into technical aspects of my role that I love managing our data team, building a modern data stack, and architecting things that in the beginning of the company first, like, it didn't even make sense for us to invest in. We didn't have the scale. But weren't things that were.

Speaker A: Well, uh, a lot of those technologies didn't exist yet.

Speaker B: Yeah, exactly.

Speaker C: And they weren't things that were really my wheelhouse. So pushing into that comfortable space where you're like, uh, this doesn't feel like the obvious career path, but it's a thing that I think will inspire me. You have to kind of keep trying to find that because otherwise I think you can fall into this trap of this mental model of like, I'm good at this kind of company at this stage. And then you don't have the same longevity and you don't get to see what something looks like when you build it for 14 years.

Speaker B: Yeah, sounds like I often say to the team here at Coalesce, uh, growth and comfort almost never coexist. And so at a startup you're forced to be uncomfortable. And I think what you're talking about earlier about people sometimes find comfort along the way and then don't want to become uncomfortable again. But you've put yourself in a position where you stay uncomfortable learning new things, staying curious, which I think is. It's a really important skill not just for, uh, being at a startup that's growing quickly, but just for anybody looking to grow in their career and their own personal life in general. And so that's, uh, that's awesome. I think one last piece on the startup journey. But, you know, if either of you could go back in time, knowing what you know today, is there anything you would have done differently or anything that you would have done in a different way?

Speaker A: Yeah. Let me actually just add, uh, a little build on, on what you were just highlighting, uh, in a funny anecdote, is that Spencer has also Been referred uh, to as various forms of like the head instigator, um, around here because. And you know, I think it ended up being uh, very appropriate that he ended up then also building the data science set because a lot of times the, the people with the best uh, knowledge and the most unbiased viewpoints to be able to really instigate the right changes, um, are also the ones that have access to the data. Right. And so over the years I think that you know I can, I can highlight a number of places where um, Spencer identified, you know, some, some major like things that we should leave behind or areas that we you know, needed to systematically increase investment in um, or new perspectives that we had to take on. Uh, and a lot of times those insights actually derive from being either like closest to the customer problem because in the, the you know, in the early days your role was really like kind of all of post sales and like as we had early customers, you know, you don't, you don't start hiring specialized post sales people before you have any customers. So like as you're growing in the early days, you know, that's um, something that gets picked up along the way. And then over time was like being right in the middle of all the data insights. Uh, and so you know, I think also part of that curiosity and having that parlay into growth in a uh, career over time as a company scales, um also requires translating that curiosity into impact. And in, you know, in the middle there are often insights that you know, very often derive from taking uh a you know, re looking at places where maybe momentum exists. But uh, the you know, the foundations need to be rethought or you need to you um, know, look at a different lens. A lot of times data guides the way on that getting, you know, getting human bias or other sorts of um, whether they're rose tinted lenses or other. Or blind spots or what have you, um, out of the picture. Uh, and you know, I think that's been a really great role for um, for Spencer over the years. Um, you know, a more direct answer to your question would be I've alluded to this a few times but you know, I think timing is super important now. We did benefit from like we did find the right venture backers. Um, we therefore did survive. And by virtue of us being early in our market but staying focused on it the whole time, we were ahead of everyone. When it became obvious that this was a really important market. Um, you don't always get that opportunity though. Uh, you know, and, and you know we, we almost ran out of money twice. Like, you know, it's not like the path that I would recommend exactly. It did result in a lead for us. But um, but you know, I think that that's really important to look at both timing and making sure that you're really working toward change that's more durable. And one of my favorite examples of that in mobile is that I remember conversations in the early days about like, oh, there's a land grab out there right now, we need to get to every like big app or every app that we can. Um, you know, and that meant that would mean like less discipline in all these other areas just so we could get the reach, et cetera. And you know, you probably remember in the early days of mobile like a hundred thousand apps, and I'm making up these numbers, right, but a hundred thousand apps, app store in 2012 and 400,000 apps in the app store in 2014. And like, you know, see all these charts of just like the rapid growth of the App Store and like, you know, and those numbers are from 10 years ago. Braze today still has less than 3,000 customers. So like the fact that there were 600,000 apps in the App Store in 2015, totally irrelevant, right to the way that we needed to build our business. Um, what really mattered was that there was a much smaller number of applications that were starting to build sustainable businesses. And that was actually the change that was coming about that was going to be a lot more durable and we were building for that future. And that ultimately is what led to our ability to continue to compound growth at the scale required to eventually go public and keep building to, you know, the position that we're in today. And uh, I highlight that just because I think it's so relevant, uh, to right now with the AI disruption, which is like, there's a lot of independent, high, uh, velocity change happening and then there's a lot of compounding effects that are happening because of all of those. Uh, and you know, the, if you're just kind of right on the leading edge, it's interesting and it's exciting, etc. But you really need to think carefully around like what the composed or compounding effects as well as the second order effects of those of all that change is going to be to find the change that will be durable so that you can really work on building a business that's going to be able to compound value creation around that durable change.

Speaker D: Right.

Speaker B: You touched on this a little bit too. So, uh, you know, as you've been Leveraging data to build the company in many ways. Not just the product stealth, but also how you navigate the business. Are you seeing any trends or challenges in data right now? Whether it's with customers, product development, anything that's relevant or resonating right now?

Speaker C: Well, we've gone a surprisingly long time with talking about AI. So yeah, I think one of the things I'm seeing on our team is that as there's AI capabilities getting added throughout the data stack. I mean, even downs, people using cursor and quad code and things to support pipeline development, scripting and even into some of the data science work. And then obviously, uh, we use looker. There's conversational analytics capabilities that are being laid in there. So every part of the stack has some, um, AI component. Now Snowflake has Cortex. You know, you can find it everywhere. I think a lot of data people are asking themselves, am I, am I solving a data problem or an AI problem? And I know how to solve data problems. A lot of AI problems are new. And so I think there's this little bit of crisis of m confidence in that moment of uh, I know I want to move forward. I know I want to bring in AI into our capabilities, that it's going to have a lot of potential for what we go do. But it's pushing me again, kind of outside of my comfort zone. I'm not too worried about it. And to give everyone maybe a little bit of relief. I think what we're seeing is that the, some of the great products that we're using have abstracted some of that in a way and productized it to a sufficient degree that if you're really great at the data problem, and in Snowflake, for example, you know, to use Cortex really effectively, you have to start with data models and solid data models. And if you don't have that and you throw a bunch of unorganized data, especially with scale. Yeah. That it's just not going to do exactly what you want. And so we're finding that there's still like an 80% data skill that's required to take advantage of that and then 20% some new learning. But actually that's getting easier over time as the models get better, as the. It gets more productized into some of these platforms.

Speaker B: That's cool. One of our. We have a joint customer, one of my favorite customers, the RSG Group, they own Cole's Gym, McFitt, John Reed. They were mentioning that as a data engineering team. So they use coalesce to actually build out all the models that you were just describing earlier, they said that working, uh, with Braze was their favorite project that they've done in recent months. Was that implementation. And I would imagine it touches on what you were describing earlier, Bill, around that experience of the. The gym. Not the gym employees, but the consumers that come into the gyms and whatever advertisements come towards them or just the whole experience on how the gym's able to support them as a client. And so that was just a cool thing to hear as you all were coming to join us on this podcast. Um, Satish, asking a bunch of questions after Bill shares his thoughts there. I would love to pass it over to you to ask something for Bill and Spencer too.

Speaker D: I guess we already covered those. I, uh, was going to ask around some AI questions, but one thing that you probably, uh, Spencer already touched upon, which is how these workflows are changing with AI and humans. And you talked really, um, about that already. But, um, is it in data engineering, um, that you're seeing this a lot in your marketing, um, area as well? How does that impact the same workflows?

Speaker A: Yeah, so I think at Braze, we're focused on building for both composable data and composable intelligence within the platform. And you know, those come together in these customer engagement use cases because, um, when you, when you think about what drives really great interaction and you know, the um, it's great to bring up the shared customer example in the health and fitness space, you know, which is a place where, um, the, the more kind of smooth delivery of product and marketing and uh, you know, customer engagement to just help provide the right nudges to uh, you know, push people toward healthier lifesty and healthier habits and remove friction, um, as they're accessing, you know, different gym benefits and, and just becoming a more loyal customer. Um, those, those things are all just win, win, win.

Speaker B: Right.

Speaker A: Um, but they require the combination of like, knowing the, the. The right times, elucidating the moments that matter. And, and there's also a lot, um, there as well. We, um, you know, one of the, like a gym use case is like a lot of times people will show up at a gym and they're like, membership has expired or they used up all their classes, um, passes or whatever. Um, and sometimes that's enough friction for them to just decide to skip it and you know, like move on to drinks or something right away, um, or, you know, or miss out on that opportunity to take that class that might have developed the right habit or what have you. And so there's all these places where there's really positive feedback loops to being focused on um, the cost, a really seamless product experience and doing that along with you know, marketing and messaging and engagement overall. But um, in order to do that you need to have the understanding of the customer and what they really care about, which don't you know that some of those things are a little bit more permanent. And so the latency and liveness, uh, requirements on insights like those are not as, um, not as demanding as say like someone is literally at a turnstile right now and their pass is not working right, which is like a real time signal. Um, and then, but there's a lot of sophisticated modeling that can be put on top of the data that's also uh, changing a little bit. Uh, less like with, with less liveness if you will. Um, and then of course in the customer engagement moment you need to be able to bring those things together. So you need these um, these deep insights that have come about because of the first party data that you built over time, the insights that you've layered on top of that through um, the great data science and kind of ML modeling work or predictive work that people have done. Um, and then you need the, the kind of real time flow of data to be able to elucidate those moments and those opportunities to be able to engage and, and then bring that together in the moment to create compelling content, um, that's going to drive relevance and help actually identify the goal that you're trying to work for, work toward for either your business or the customer or in the perfect world where you've harmonized what the customer's goals are and what your business goals are, um, together and then you're making another decision, uh, you know, with the aid of AI, to be able to increase the performance or the conversion rate or the um, you know, the relevance or the resonance of that, what ultimately gets delivered to them as an experience. And so throughout that you've got, you need all this flexibility and you need all this flexibility with data. You need sophistication in the prediction and the modeling. Um, and then you also need the liveness of the data to elucidate those moments. You need a system that can orchestrate those things together. And then there's another layer of optimization that has to happen in order to increase that relevance, um, and that resonance and you know, AI and like really fantastic data tooling that can provide, you know, data flows quickly, completely and with a low total cost of ownership, uh, need to come together with the creativity from marketing and the interfaces from product to really bring all this to life. And I think that AI also has, you know, an incredible role to play in not just deepening those insights and enhancing uh, the relevance, but also in the day to day for like the marketer and the, or the customer engagement professional that's responsible for orchestrating all of this. Because you know, the goal of marketing is so often to like increase LTV or increase the strength of the relationship or what have you. But the reality of the day to day of a lot of marketers is still like going into a dashboard and clicking through you know, 18 campaigns and checking the click through rates and the funnel on that, like trying to make like tweaks and come up with like clever subject lines. Right. Um, and the, the gap between like I am orchestrating a strategy as like a conductor where I'm bringing together the business goals and the customer journey. I'm merging those together with insights that are being derived from you know, these units of intelligence that I had from models or predictive capabilities or um, bringing in alarms or what have you in order to make these like judgments and then combining those in the moments of delivery. You know, that's ah, a, that's a strategic conductor process. And the better job that we can do in delivering AI technology that can lift that abstraction layer then the marketer to work at, um, so that you know, they can, they can move from the drudge work of campaign by campaign, click through rates, etc. To like up at a high level of like, you know, how am I elucidating the right moments, how am I resonating with people, um, you know, where, what are my business goals, how do I make sure those are properly prioritized by the AI so that when they're finding these, they're elucidating these moments where they can actually have interaction with the customer. They're, they're combining together that customer's goals with my business goals in the most productive way, driving that forward automatically, optimizing, et cetera. There's like a whole bunch in that process where I think more capable AI that can, we can trust around at a higher layer of abstraction and to make more compound decisions autonomously. Uh, you know, that ultimately changes the role that the marketer is able to play, lets them be more strategic, lived up to, you know, being more of a conductor, uh, and we're really focused on not just speeding up what they've already been doing right at that drudge work of the, you know, campaign, uh, you know, management and babysitting, what have you. Um, but really lifting the level at which they work to be able to be that strategic conductor in their businesses.

Speaker B: Yeah, it's funny because I can't imagine, I mean we've got a very technical audience that listens into this podcast, but I feel like people have, a lot of people have no idea what goes on behind the scenes to lead to such great marketing or such great decisioning. But it truly is such a complex workflow and we apply a similar philosophy around taking all those complexities, simplifying it for our customers. Certainly for the data engineering processes, but on your end for the actual consumer experience, which is, it's so cool. There's a reason why you've had the success that you've had. We've got time for one or two last questions. Uh, first one is a simple one. It's just around every year we do some thought leadership on trends that we're seeing in the market a, uh, year in Advance. So for 2026, and the question is, uh, what if any, business defined trends do you see coming in 2026?

Speaker C: It's not terribly original, but I think agents are here to stay. One I think thing that's been interesting for us and especially during this conversation talking about some of the history of our company and platform trends that we've been through, I've just felt that it's such an interesting time and we've been able to learn so much from seeing how mobile disrupted the world. But then underneath that cloud computing and other big technology shifts that Bill and I have spent a lot of time talking about how similar this moment feels and how do we just take what we've learned from the past 14 years to help marketers and where we can to be a great partner to data people that work with marketing teams. So that in this world of technology disruption that we're in now, that we be that trusted partner for them going into 2026 and beyond. But uh, I think tightly coupled into that, we're investing quite a bit in agentic products. In September we launched a product called Agent Console. And so we're taking a lot of the things that Bill's talked about in terms of real time architecture, operating at scale, and we're designing systems that let marketers integrate that directly into these campaigns and into these journeys. So as people are going down a path and we could think about fitness and Gold Gym as an example, how can then how can they use LLMs to make those journeys even better? And that could be part of A more conversational experience, it could be trying to categorize them um, in a smarter way as a customer, maybe where they are in their fitness journey. And I think for marketers 2026 is going to be the year that they really crack the code on how they get value and prove value to their business out of AI, but especially out of agentic products. Yeah.

Speaker A: And I would just add to that, uh, that composability and combination I think are going to be increasingly important. You know, I referenced this before that, um, something that really characterizes a lot of the um, you know, the AI frontier right now is that we've got a lot of different independent technologies that are all racing forward at the same time. Uh, and I think that as a result there's actually a lot of under explored combinations, uh, and compositions of those technologies together, as well as integrations with existing, you know, high scale, reliable, you know, durable products, um, and other integration points in customer dirties and such that are so far underexplored in terms of where these technologies are going to be able to move the ball forward. And so I think that you know, the foundational capabilities have leaped ahead so far. Um, and now there's a lot of cross pollination of perspective and skill sets and technologies to be able to compose these things together and surprise us again.

Speaker B: Amazing. Gentlemen, it's been a ton of fun, I will leave it at that. But I will offer if you have any closing thoughts, anything around Braze, anything for the audience that's tuning into this. What would you like to leave them with before we let you back to your amazing Friday?

Speaker A: Yeah, I mean one thing I would say, I know that we've got uh, primarily a data data audience here uh today and you know, I would encourage you if your marketing teams or customer engagement teams use uh, Braze to learn a lot more about it. Because I think that um, the best, uh, the best brace customers are the ones where there's really fluid collaboration between the data teams, the engineering teams and the marketing teams. And when they can build together and get that experiment loop flowing of like, you know, great ideas come to life quickly through um, great, you know, great collaboration with builders. And then uh, those results get analyzed and advanced into new experiments through like the craft of uh, data science and data analysis. Uh, that that loop spinning and iterating is exactly how our uh, our best performing customers, you know, work together. Uh, and so you know, if, if that's not something you're doing today, uh, look into it.

Speaker B: Yeah, take from us too. We, we see it firsthand with our customers here at Coalesce and the success that they've had with both Coalesce, but also with.