From Data Dreams to Practical Marketing Outcomes with Spencer Burke of Braze

Eric Dodds 00:06
Welcome to the data stack show.

John Wessel 00:07
The data stack show is a podcast where we talk about the technical, business and human challenges involved in data

Eric Dodds 00:13
work. Join our casual conversations with innovators and data professionals to learn about new data technologies and how data teams are run at top companies. Welcome back to the show, everyone. We are here with Spencer Burke from Braze. Spencer, we’re so excited to have you on the show.

Spencer Burke 00:34
Thanks, Eric, thanks, John, it’s great to be here.

Eric Dodds 00:37
All right. Well, give us just a brief background.

Spencer Burke 00:39
I’m Spencer Burke, the Senior Vice President of growth at Braze. If you don’t know Braze, we’re a customer engagement platform. Work with some of the world’s leading brands and the most innovative brands, helping them send marketing chat messages across WhatsApp, email push notifications, all the relevant channels. And we’re really big fans of data here at bridge, too awesome.

John Wessel 01:02
So I can tell you’re a marketer at heart, because when we talk before the show, you already have a really nice headline for one of our segments. We’re going to talk about big data dreams and boring marketing outcomes. So we already, like, got this nice headline. What other topics do you want to cover?

Spencer Burke 01:19
Oh, man, I’m excited to talk to you guys as someone who’s worked in the marketing technology space for a long time, but also manages the data team and the lot of great topics for us to talk about, yeah, how to think about working with data people and balancing that versus an internal customer marketing person, how we can help support data savvy marketers get even better at marketing and at data at the same time, I think a lot of stuff that both of our companies and our personal backgrounds really resonate with.

Eric Dodds 01:50
Nice, awesome. Well, let’s begin in chat. Yeah, great. Spencer, I already know this episode is just gonna completely fly by, but I have to start with some history, because you have been at Braze for 13 years? 13 years next month? That’s right, wow. Okay, and so how did you like, okay, and you were. Employee number two, Yep, that’s right, Eric. Employee Number two, I mean, that is amazing. And I mean, I want to say, what is it like, 200 episodes now? I think we’ve only had one or two other people who have had, like, that long of a 10 year at a SaaS company.

John Wessel 02:31
Yeah, in this space, it’s truly incredible. Yeah, pretty awesome. It’s

Spencer Burke 02:34
interesting. It’s a little bit unusual these days,

John Wessel 02:37
but very unusual, I would say, Yeah, I

Spencer Burke 02:42
I know you guys have been early at some companies as well. Yeah, yeah, maybe trade some more stories, yeah.

Eric Dodds 02:47
Well, give us just a brief overview of your journey at Braze over those 13 years.

Spencer Burke 02:52
Oh, man, let me give you the very quick version. So I started was the second employee, first really non technical, although, you know, over the 13 years have gone much deeper on my technical side, sure, at the time, though, was we had no product, no revenue, no customers, so we were right in the thick of trying to build find product market fit. So I spent a lot of time building our early beta list, trying to find folks that would test our first SDK when we launched it. And, wow, yep, this is back in 2011 and it’s, you have to remember back then the state of the App Store and the app economy. Oh, yeah. So nascent.

Eric Dodds 03:38
So nascent, totally and like, skew morphic, was that the wooden shelves era?

John Wessel 03:45
Oh, sure, yeah, remember that? Yeah, because

Spencer Burke 03:48
the first iPhone came out, 2007 and the App Store was a couple years later, 2009 so yeah, App Store, it’s all apps, calculator apps, sound boards, yeah, and we were really excited about the space we’re based in New York City, and the tech ecosystem there was also, I think, really just starting to get going. So those were my early days, just hitting the streets, getting to know as many people as I could, talking to everyone, going to every single meetup, which was fun, because back then there were a lot of guys who were going to these mobile developer meetups looking for something to build their app. So yeah, I got to see that whole thing. Oh, man. Again.

John Wessel 04:33
What was the offering that like then, like, what like when you what was that first like moment where you’re like, okay, like, I think we have something. And what was the feature offering that people got excited about? Yeah,

Spencer Burke 04:45
It took a minute. I think if you’re working in B to B SaaS, you’ll understand this already. But I think a lot of people underestimate how long it takes to find product market fit. I would say, like it’s three years of building. And I. Iterating in space. I think what started to really resonate, though, was not too far from where we are today. How do you send the right message to the right person at the right time? The thing that actually really drove that change was on the data side, because of our mobile devices, like you have your phone with you everywhere, and so that means there’s constant opportunities to collect data do that in real time, which, you know, from an infrastructure and data perspective, really started to was kind of at the same time as there’s a lot of evolution on big data in the early days of red and things like that. But more importantly, for us, from a consumer perspective, everyone’s expectations on how personal marketing interactions were, how real time it was, how relevant was for them based on the circumstances and just expectations of what the brand or the app knew about, you’re just going through the roof. And so it was just in the confluence of, like, technology and consumer expectations changing. Yeah,

Eric Dodds 06:07
all right, we only got the first, the very first step I have to finish. You got to finish the rest of the story on the edge of my seat. Yeah?

Spencer Burke 06:15
Well, this is so then we want to when our first big customer, we’re like, oh, man, now we’ve got a customer. We got an onboard service lab. So I was one of the first hires in our customer success team. And Eric, I think you spent some time building Can you, can

John Wessel 06:30
you say, who your first, like, big customer was we? So one of

Spencer Burke 06:35
The first customers that were really important for us were working with Urban Outfitters fans. And, yeah, that you know when you’re a customer, that you can tell your parents who it is and they like you may have

John Wessel 06:47
heard

Eric Dodds 06:48
of them. Yeah, yeah. It’s a real company. I work for a real guy, right?

06:52
Exactly. This

John Wessel 06:52
is a real job, I promise. Yep.

Spencer Burke 06:55
So then I spent five, six years building out scaling CS support, some of those customer facing functions. Yeah.

Eric Dodds 07:03
And what do you currently do in the SVP growth role?

Spencer Burke 07:07
Yeah. Today we started the growth team inspired by some of our customers that were building growth practices. And for us, we saw that as they’re data driven, they’re experimental, they’re cross functional. So today, I’m overseeing aspects of our go to market strategy, our data teams, and to help support that, we also have an engineering team that’s helping build new tools, experiment with AI, build internal tools for automation, a lot of things that kind of help us outside of the product and engineering, or try to be a driver of innovation within the go to market teams.

Eric Dodds 07:45
Yeah, very cool.

John Wessel 07:47
So that sounds like it might be a little bit different than other growth orgs I’m familiar with, like that, because you work with the customers a lot. So what do you think, really, because you’ve been there so long, what are some unique things that your growth org can do because of your continuity and your like, cross functional Ness, that maybe if I had a growth leader, then, like, one every two or three years, like you couldn’t do.

Spencer Burke 08:07
Yeah, I think it is unique. I’ve been trying to evangelize it and getting some limited traction on that, because I think more B to B companies need to think about how they continue to drive innovation outside of the product and support their team scaling, and I think especially right now and the current software macro environment, like everyone, you still have to really emphasize growth, but you also have to do so in a way that’s efficient, as everyone’s thinking about profitability. So I think for us, and like for me, is like, I can just really dig into problems. I know all the stakeholders across the company, I can challenge some of the conventional wisdom on things. And I think there’s just a lot of trust to be able to go do that, yeah, yeah, yeah. And so yeah, I don’t take that for granted. I think it’s a unique position. But I think more companies need to have that innovative spirit that continues to drive some of that small company DNA continue to

Eric Dodds 09:06
Yeah, okay. There’s so many other topics we need to cover, but one question so second in play bras, was there ever, is there a time that sticks out where you thought like we might not make it as a company in those early days,

Spencer Burke 09:22
yeah. I mean, we have a couple real moments like that. There is a deeper psychological undercurrent though, where you’re always kind of thinking that like in

Eric Dodds 09:34
those early days, totally, yeah. And you’re just

Spencer Burke 09:37
like, you’re trying to figure it out, and constantly iterating and working through things. I mean, it was funny for me, because I, prior to raise, I’d been working at a large consultancy, and so the first day I showed up, you know, I’m there right at 9am there’s only five of us at the time, and no one’s there because it’s basically everyone else is engineers over. Later, I’m like, oh, man, what did I do? And eventually, everyone you know, I’m messaging one of the founders, and he was traveling out to San Francisco at that time, and he’s like, Oh, just wait around like someone will eventually get in. And that first week, all they had stocked in the fridge was Red Bull. Of course, I just wanted something to drink all the time. And so I’m just, I’m there, I’m working, going to the fridge, grabbing another drink. There’s only one thing to grab. And so that first week for me is an exercise and how much caffeine I could take. Yeah, that was my trial by fire with my introduction to startup life. Yeah,

John Wessel 10:40
That reminds me of when the story started. This was not exactly a startup, but a, like, smaller founder led company. And my first day, like, I got there and I was coming from a really big company, several billion dollars, you know, several billion dollar companies, and, like, the first day, it’s like, like, you’re looking around, trying to figure out where I’m supposed to sit. And they’re like, oh, yeah, yeah, glad you’re here. So your desk is in that box over there. So if you can put that desk together, that’d be great. And then, like, everybody’s running around, like they were having some kind of, like, net system issues that day. So I was like, okay, cool. I guess I’ll put a desk together

Eric Dodds 11:13
here, startup one. Yeah, well, I want to jump over to talk about data, because that’s obviously the purpose of the show. And one of the interesting things about your view into Braze is that you see a lot of the data side you as a company have launched a lot of data tooling and infrastructure and features around data recently, with the data platform stuff, but then your end user is trying to send messages right, get the right message to the right you know person at the right time. And so there’s this really interesting there’s this really interesting spectrum where it’s so critical for whoever is doing instrumentation or feeding the system with data to get it right, to make sure that it’s clean, so that the marketer who is building those journeys can do their job, right? And so it’s really important on that end. But then I think a lot of times to the marketer being one myself, you know, historically, like, Okay, I mean, the data is valuable to me to the extent that it helps me do my job really well, right? And so, you know, it’s kind of a means to an end. And so you kind of have these different perspectives, where these different personas in an ideal, where they’re working together, right? But they place really different weights, or even have different perspectives on data. Can you just speak to that from what you’ve seen at bars, both from your customers and then also just yourself, because you do a lot of this.

Spencer Burke 12:44
Yeah, should we start from the data perspective or the marketing perspective,

Eric Dodds 12:49
your choice?

John Wessel 12:50
The idea was a choice.

Spencer Burke 12:53
I mean from so I think from the marketing perspective is, is, is really interesting, I think, especially because a lot of the modern tooling that exists, both a bras rudder stack, you know, and you go down into the warehouse and think about that, there’s so much capability that exists for a marketer. You can’t really survive as a marketer right now, I’m being totally ignorant of what exists in terms of tooling in the data ecosystem. At the same time, for data engineer, data analyst, analytics engineer, whatever role in the data side that you’re in, you have potentially decades of professional experience and building on this. And so I think for a marketer right now, a lot of the challenge is you’re getting up to speed really fast on some of what’s possible in the data world, and trying to do that in your arena of expertise, which is then creating really effective marketing campaigns that are great for your customers, drive ROI for your business. And so I have a lot of empathy, actually, I think, for both sides and the intersection of how roles come together. I think for marketers, I like try to push them to really start with some of the basics in terms of mapping out and understanding your customer journey, having a clear understanding of what you what data you want to have at different stages of that journey, and then having some form of data dictionary if you know if your team has a data catalog or tool or something that’s helping you support that awesome if not just keeping track of what that is and helping enable your team on it. Because I do think there’s a lot of effort that can go into setting up a new tool or vendor, and there’s all this excitement you’re getting onboarded, their team’s helping you get going. You’ve got this brand new marketing platform, brand new CDP, and then over time, people turn over there’s a lot of great data and a lot of inspiration that started that. Then you lose that institutional knowledge. Down over time. That’s where a lot of people can get stuck, is like, they just, they don’t know how these systems work anymore, from a data perspective, sure, and then you start to, you blame the data, you blame the data team, or you blame and and have to hit reset at some point.

Eric Dodds 15:18
And what’s a typical like, can you just describe because, and actually, I’ll be even more specific here. So I want to speak to our listeners who are at the receiving end of that frustrated Slack message or that JIRA ticket where you’re like, oh, like, not again. You know, on the data side, what is the marketer experiencing? Like, how does that form in the marketers? Day to day? Yeah,

Spencer Burke 15:45
they probably sent an email to the wrong people, or people which didn’t

Eric Dodds 15:50
make their boss happy. Even worse,

Spencer Burke 15:53
their boss was probably in that segment of people and

Eric Dodds 15:55
got that. That’s so true.

John Wessel 15:57
Actually, that is so true. Eric over here, trying to develop some sympathy amongst the data audience for the marketing team, yeah, what we’re doing here, I respect that. I can respect it. Yeah.

Spencer Burke 16:09
I knew a team that took this so far that when they were testing new messaging or their for some of their marketing, they would set up a geo target right around their HQ and exclude everyone. Is that the, Oh, totally, the push campaigns. So 100%

Eric Dodds 16:27
I have absolutely seen that, yeah,

Spencer Burke 16:30
and I think that’s something that for data folks and for engineering teams generally, like to have an appreciation of, is the moment as a marketer, when you’re hitting send on a campaign, or you’re launching like a new piece of automation and a customer journey, no matter how much QA and testing you’ve done, can still feel a little scary when that is going out to 10s of millions of people. Totally, yeah, even when it’s smaller, that fear still exists, and I think that it’s healthy to some degree, you respect your audience. But I think on the data side, you can lose sight of that, because it’s like, oh, it’s, you know, it feels like, you know, either it wasn’t in the requirements or it wasn’t in the ticket. Or, yeah, yeah, like your part of the process worked well, that data pipeline from the product to that tool, or, yet, wherever it may be, like you feel like you did your part, but then the fact that broke and just that con, that everyday fear that they have of running that stuff is like, that’s why the data is so important, though, and you get it right, you it’s a huge asset to what the team’s doing. Yeah,

John Wessel 17:37
I think of like, like the music industry where, like, the marketers are the ones that are kind of on stage, and sales is on stage, and the data team is typically, like the, you know, like in the back end, doing engineering or audio engineering, or like the like. That’s my best analogy, and it is very hard to have an appreciation for either. But especially if you’re doing like, more back end stuff, to know what it feels like to be on stage like, you think, like, you’ve got that angle of like, and you think you know what it feels like, but until you do it, and I’ve, like, been on both sides of the ball, it’s just it’s different, yeah, I feel like there is that. Like, yeah, that’s super interesting. Yeah,

Spencer Burke 18:14
yeah. One of the things I think that’s changing, though, is marketing technologies getting better integrations into the data stack. I think it’s different, right? Because I think a lot of the frustration in the past was you’d have to go use some poorly documented API to go do something, and yeah, the tools that the marketing team chose led to some of the frustration that, I think, yeah, oh, definitely.

John Wessel 18:41
And in my past experience, I think Eric would agree, the marketing team would go find a tool and say, Oh, hey, team, we picked this tool, like we’re using it. And it was like, you know, it wasn’t like, a collaborative thing, because they didn’t want to get slowed down, and they didn’t want to, like, they didn’t care what the APIs were like, for example, oh, for so, so was this? Like, it would start adversarial sometimes, because it’s like, well, we picked MailChimp, and we’re using MailChimp, and then, like, on the tech side, it’s like, Well, did you even look at any other tool? Yeah, so yeah, it’s

Eric Dodds 19:14
like, I mean, yeah, we probably shouldn’t be calling out names, sorry, MailChimp is great. No, I mean, MailChimp is great. But another classic example is Marketo, right? It’s like, yeah, you buy it because it’s like, a super powerful tool. But it’s like, it’s really hard to do complex, like, data things, yeah, right. It’s just, like, the API support. You just end up having to do like, all these, like, interesting workouts or whatever, yeah, and yeah, there’s a bunch of them, yeah, when we were doing actually, this reminds me. Spencer, this is kind of funny, because I’m sure that I’ve read Brazes API documentation, like, multiple times over. But we were doing consulting and we were doing evals, right? Like, when we did, yeah, you know, for your company, whatever, yeah, like, the way that we evaluated marketing tools was literally, we wouldn’t go to the marketing site or anything. We. Would just go to the API documentation. It was like, Yeah, within 10 minutes, totally, within 10 minutes, we knew, like, whether this was a tool that was like, going to work or it wasn’t right, is because it’s like, okay, these people actually care about, like, building a tool that can like, is flexible enough to like map to a very complex user journey, like, or it’s not, you know, and I know, obviously there’s a spectrum

John Wessel 20:25
there, but yeah, no, I mean that that can be extended to a lot of SaaS tools. Honestly, yeah, I’m not far as evaluating, plus,

Eric Dodds 20:31
I’m not necessarily saying that’s the only way to evaluate a tool, but like, it is a telltale sign of, like, whether a company is serious about, you know, whether a marketing tool series about

Spencer Burke 20:41
data? Yeah, yeah, I agree. And I think if you look now, what we would do is we want to engage with the data teams when we’re selling into the marketing teams. And we, you know, we’re at snowflake, somehow, we’re talking to the technical we’re partnering with you guys with snowflake, with rudder stock, with AWS, with AWS, with Databricks, and I think like inviting them to be part of inviting data people be part of the process, yeah, especially when it comes to integrations into the Cloud Data Warehouse, it that’s just a game changer, because then we can start to talk about things that are part of their workflow. Okay, when you’re updating the data model, you’re adding a new column, or you’re changing how something works, the fact that it’s just sitting on top of that, hopefully there’s some automation in terms of the ingestion that’s sitting on top of those tables, then it’s not a new thing. They have to go do some API docs that they have to go read again, that they looked at when you did the implementation two years ago, but haven’t picked up. It’s just a continual part of your process, and that should be more ongoing, versus like you do it once. Then right? I should add every so often. And I think too, for the marketer who’s starting to get more technical, giving them some capability, so once they have the underlying data, if they need to do some light transformation, they have some power to do that. I think that’s also an unlock, where you push some of the more basic data work into the marketing team, or they have that capability. It’s just a better way to work together, better division of labor. Can

Eric Dodds 22:13
you speak to that a little bit? So you’ve seen, you know, sort of, I agree with you. It is transformational now that tools like Braze can plug directly into the data warehouse. I mean, that’s as, yeah, how crazy. So, so compared to where we were, yeah, well, and especially compared to 2011 right? Yeah, especially compared to, I mean, can you imagine,

John Wessel 22:34
like, say it’s 2011 and you’ve got your email tool, and then you go, like, you bring your email tool, you go talk to your data and your IT team, hey, I want to collect, connect this to our data warehouse. Like, just, just, let’s picture that conversation for a minute, right? Like it’s

Spencer Burke 22:49
Yeah, at the time it would have been, yeah, can we connect this to Hadoop, and then Yeah, can

John Wessel 22:55
Do we connect this to Hadoop? Or,

Eric Dodds 22:57
uh, or they’re calling your manager and being like, I think you need to check on this person, right? Yeah, yeah. Well, it was no sequel

John Wessel 23:04
somehow, yeah, I

Spencer Burke 23:06
meaning, and that was a lot of especially from email marketing, which has been around for decades now, a lot of the data pipelines were dropping CSVs onto an FTP server to be totally assumed once a day or once a week. And so it’s a huge paradigm shift from that. And there’s been steps on the way, and kind of making fun of some of the API docs, but yeah, even, like you said, like there’s degrees there as well, and that’s been some huge improvements over how marketing technology had interface with data in the past. And so the fact that we’re getting more real time or getting closer to the data tools is just such a huge improvement over something like dropping a file. Yeah?

John Wessel 23:49
So we talked about big dreams, boring marketing outcomes. I feel like this is the perfect segue to that. Yeah,

Eric Dodds 23:55
yeah. Is. I want to know if you have seen marketers use Braze for over a decade? Are they becoming more technical? What? What do you like? Give us some intel on that. Yeah,

Spencer Burke 24:09
definitely. We. We’ve released, not too long ago, some capabilities for Braze dashboard users to be able to write SQL to build reports to do some segmentation, and at first our we started to test this, and we thought it would be a pretty niche feature in the market, just with what we’d expected from for marketers and how they wanted to interface with data. And what we found was really surprising. There’s a lot of marketers who know enough to be dangerous in SQL. One of the things that I took away from that was, you know, if you gave them a data engineering interview question, like they’re probably not going to do great on that, but they are experts in their data. They understand. And marketing data, the data that’s being used to power personalization and to build the segments like that’s their job. They’re in that every day. And so when you give them a data model that they know inside and out, and a little bit of instruction on SQL, they can get quite a bit done. And I think that, I think it’s a really healthy thing for our ecosystem, but there’s other ways they’re technical too. I think being able to work with an API like we have some capabilities when we’re sending a message to hit an API endpoint, the JSON data payload that returns you can include an email or push notification for any marketing channel. When you teach marketers how to do that, you know, we use the same template language called liquid that Shopify. It’s so nice. God, so nice. So it’s like, and because that’s what Shopify built that they open source it. You’re seeing that technology pop up all over the place. So not just in our part of the marketing ecosystem, but I think in other parts of the marketing landscape, there’s all these interesting tools and capabilities that are pushing marketers so you have to be more technical. You know, even if you’re just like a couple people trying to stand up an E commerce shop, Shopify is making that easier for you, and so to kind of piggyback on that wave of I think more and more professions becoming technical is a lot of fun for us,

Eric Dodds 26:21
yeah, well, for anyone out there who cares, which maybe this audience won’t. But I will say, if you figure out how to hit an API in a Brazen journey, pull the JSON payload and then pull like nested properties into a message body with liquid, it is like a paradigm changing for what, for what you can do, yeah, it’s pretty freaking sweet. And

John Wessel 26:45
you just put yourself in the top 1%

Spencer Burke 26:49
You know, yeah, if you’re not certified, we’ll have to get you a certification. Eric, yeah, you

John Wessel 26:55
I really need it. Should be an award, a data stack show award, if you know, yeah, do that, yeah? I think they should get awards, yeah, yeah.

Spencer Burke 27:03
I do think this is part of the same theme. Though, one of the places we saw something like that is a lot of companies will have an API to serve a product recommendation on their website, for example. And so there’s all this investment that’s happening where, yeah, you have a data science scientist that is building a recommendation service, maybe that’s in the warehouse. You have the engineering team that’s building an API that sits on top of that, it’s serving the website or the app. And these just lived in this, like, product engineering data space, and kind of similar to what we’re talking about, we’re like, okay, if the marketing tools can just use, connect into the technology you already using, like a data warehouse, the same thing, but just, hey, you already have an API, or you know how to build APIs, let’s just plug that API and so instead of you having to figure out all these other complex ways to get that into the email using the tech you are already building, yeah, is a way, I think, from a marketing technology perspective, to Just be more friendly to your data and engineering. Yeah, it pushes the marketer. But again, I think that’s good for them to start to develop those skills. Yeah, yeah.

Eric Dodds 28:09
Can we talk about the warehouse a little bit? And where I’d like to start is just your perspective on what this is going to unlock. And I want to, how do I say this, what this is gonna unlock for customer experiences, so I’ll give you just one example, right? Like you have a customer support ticketing system, right, that some company is running and they’re running bras, and then you also have data in the data warehouse, and even though there are sort of direct integrations, like one data engineering challenge traditionally has been, how do we share data between these two systems so that the experience on both ends can be better, right? Because it’s really helpful for me to know when to send a message or when not to send a message based on support tickets or whatever, right? And those sorts of things. Do you see the warehouse as the way to sort of truly sort of connect that experience across these different functions of a company and the tools that they use.

Spencer Burke 29:09
Yeah, we recently announced the phrase data platform, and a part of that for us was positioning some of our data capabilities around the idea of composability and how to connect into different systems and tools that you have. And I think the warehouse is important, but it’s also one, one possible way. And I think for us, every customer is a little bit different in terms of how they want to architect how all their tools work together. I think the more you try to force them down one paradigm, the harder it is to get in that you want. And so like for us, in the Braze ecosystem and a data platform, you could, let’s say, from a support tool, you could, if that data is already in the warehouse, could ingest it in the warehouse, if you already have that being routed through a partner. Or could get it from rudderstack, from someone else, if we also have capability called data transformation. So if they, if the Support Platform, can web hook data out, we can, Oh, interesting, really? Yeah. So it’s, I think, as a data person myself, I think the warehouse is a great way to do that, because then you can join in some of the other data that you have. You’re already moving a customer profile that then you’re pushing out into other systems. So the warehouse is a powerful place to do it, because it lets you centralize that view of the customer. But I think we all have to we live in this reality where there’s so many different enterprise architectures that yeah, or you can give different options, the more likely you are into to be able to, especially for us, to have that marketing team, be able to go to their teams and get to a solution that’s going to work, versus having to say, Okay, there’s only one way, and Braze is making us do it this way, Yeah, makes us that the market can have more productive conversations, we hope, with technical counterparts,

John Wessel 31:06
yeah, yeah. That makes sense, yeah, John, you

Eric Dodds 31:09
looked like you were, yeah, yeah. I’m

John Wessel 31:11
just, I’m still thinking, you know, kind of about this, you know, working between the different teams, the marketing and the data teams, and, you know, moving from that era of like, and some of this, I think, is just maturity and development. And some of these tools, like, it’s been over 20 years we’ve got these, you know, SaaS based email and now, like, multi channel marketing tools. Some of it’s just maturity over time. Sure, where, when you’re then integrating with data, it’s like, Oh, great. Like, this is a mature thing that, like, you know, like, you like, we’ve been talking about has all these API endpoints that are mature, they’re somewhat documented, right? Yep. So I think so you mentioned, like, multiple, you know, so, multiple connections. So one would be, like, a warehouse centric. Another would be, hey, like, we’re not really at that place where, I think there’s still a lot of companies that they don’t have any sort of warehouse, like, maybe they’re thinking about it. I’m specifically thinking e com right now. Like, there are definitely short e com companies that it’s a great that are there, but there’s a lot that are just like, like, yeah, we’ve been hearing a lot about that. We’ve been thinking about that. So I imagine, like, to your point, with the flexibility that you have a company like that, and like, we want to do that in the future. And you’re like, All right, look, we can get started today. You can get all the personalization and, you know, right message at the right time now, and implement this more of a, I don’t wanna say traditional, but like, implement this is more of a standalone thing, yet we’ll be very compatible with whatever data, you know, data cloud, data warehousing solution you pick in the future. So I, you know, I think that’s a real positive thing, because then you’re doing like your bras or whoever isn’t waiting on like warehouse maturity or data maturity, but you’re also compatible with a data maturity journey. Is that a fair characterization?

Spencer Burke 32:55
Totally fair. It’s actually really important to us. We talk a lot about, start anywhere, go everywhere. Is this idea, like, we want to be on the ramp for customers, or they can also, like, on the highway for us, with us for the ramp, right? But there’s a lot of on-ramps, so you don’t have, yeah, we want to, we don’t want to only sell to the most sophisticated, most technical marketers out there, but we think it’s the right aspiration to build toward. It’s funny you say that, because I’ve seen this even within customers who are in the first stage of, let’s say, moving some workloads or data into their warehouse, where it’s like, the v1 is just lift and shift everything off of the old whatever they’re changing, like, yeah, they’re bringing over all the data. They’re not trying to make big changes to architecture, and then over time, they’re starting to modernize that more completely, bring things together, change some of the ETL, change how they’re piping that data back out. And so I think even if you have a customer who’s started, they’re still going to be growing with you and their maturity within, you know, even within that warehouse. So I think that there’s a lot of levels to this. And if you Yeah, if you only try to position something for the most sophisticated teams, you send such a small market, hey, yeah. Spencer,

Eric Dodds 34:15
I want to shift a little bit and just ask you to work with data a ton. And so I want to ask you, as I mean, you have this interesting, you know, role, where you do work with data and you manage a data team, what are some of the top things that you’ve learned about working with data yourself to do your job? Yeah,

Spencer Burke 34:35
ooh, this is a great question. So one, let me share some of the happiness to take this where, wherever you guys want to go. Let me share some of the things I’ve been talking with my team a lot right now. I think the first thing we’ve been looking at is where we want to push data, and where we want people to pull data and push pull for us is. I think a lot of requests that we’ve gotten through the data team are for dashboards. And every data analyst has this like everything’s dashboard is like the classic pull mechanism. Someone wants to go in, refresh that dashboard, go get the data itself. And I think that can be useful in a lot of cases where you have maybe an operational dashboard, someone’s gonna log in every Monday or every day and they’re gonna look at they’re running their business off that in some way. But in a lot of cases, people want data. It’s an alert, it’s an anomaly, it’s something else. Really, you need to push them that data and try to do that a little bit more in real time, give them just the data that they need. So it’s actually more like how we think about marketing, where it’s like the right data at the right time. Yeah, and so the dashboards aren’t your solution to everything. And you know, I know that’s a new thought, but I still think, as a data team, a lot of business stakeholders come and they’re leading with the dashboard, rather than leading with their goal and letting us help them solve it with the data team.

John Wessel 36:03
I’m so glad you brought this up, because this is, like, one of my links. This is one of my things where I’ve been. I’ve had multiple conversations recently around, specifically around bi tooling of like, so I’ve cut some like, dev ops, data ops, background, and I’m used to using tools like relic or data dog or things like that. And it’s all exception based. It is all alert based. Like you don’t, I mean, they have dashboards, but like, people don’t use them that way. Like they alert, they route them through ops, Genie, pager, Genie, whatever, to get alerts about when things are anomalous or just clearly, like, wrong, right? And that’s the whole ecosystem. And bi world, like power, bi Tableau, like all of the major ones, it is the exact opposite. And there is usually, like, a fringe feature of, like, database alert, or, like, whatever. But it is, I don’t know any of them where it’s like a first class thing, and I’m just fascinated, like, like, my whole career, like, I’ve had tons of conversations about exception based reporting and, like, you know, anomalous based reporting, like, all those things conceptually, but I’ve yet to see a tool where, like, that’s really a first class, like, core component to it. So, yeah,

Spencer Burke 37:15
yeah, these, they’re related to this thing, to this is, I see people asking for dashboards when really they’re just looking for a one off piece of analysis. Sure, yeah, and it’s, I think it’s kind of the same thing. I mean, it all rolls back to, I think our job is to make sure people know what we can do, what our capabilities are, and then move conversations to talking about goals. But yeah, for us, we’ve invested in trying to have these different capabilities, so we can push alerts in different places and where we can to try to attach actions to those. So we’ll alert you to certain things on Slack. And for example, if we wanted a sales person at bras to go update something in the CRM, can we just give them that drop down right there, and if we have some context on what the what we think they should select, make that the default instead of something else, yeah, thinking about data more tied into that overall business process, helping get some kind of outcome rather than just Yeah. Data sake is something that really the teams love, because they can more clearly see the impact they’re driving versus, you know, some higher level analysis or dashboarding. Sometimes you just end up feeling so far removed from how that actually affects some of your business day to day. And

John Wessel 38:34
I think a lot of those topics are around that data product management thing too, right? Like thinking about more, more like a product and less just like a, you know, a feature request. Spencer,

Eric Dodds 38:45
when we were chatting before we hit record, one of the things you mentioned offhand was talking about the utility of having a deep understanding of one simple data point, as opposed to just having, let’s, I mean, just to go for the jugular, you know, like 20 dashboards, right? Is there a specific example of that you’ve experienced where, you know, just keeping it really simple was transformative?

Spencer Burke 39:11
Yeah, I’d be curious for you guys too. I mean, I think back to a lot of the places I’ve used data to influence decision making at the company, maybe there’s some sophisticated analysis that sat behind digging in or understanding part of the business, but especially when you’re communicating to execs and you’re trying to drive investment or change where the company’s focused, or some aspect of this strategy, I would say, a lot of the time we’re boiling it down, back to really core business KPIs, that can understand for us, that’s looking at revenue growth, that’s looking at customer retention and renewal rates, churn, like things like that. So when you’re trying to understand the business, when you get when the analysis gets too complicated. Esoteric. And actually our CEO is great. He’s super technical. He was a CTO before he was a CEO, and so we can actually show him a really slow analysis, and he can pick that apart just as easily.

Eric Dodds 40:11
That’s tough, yeah, brutal when we’re back.

Spencer Burke 40:15
Yeah, these, these MIT engineers. They’re a tough crowd when we’re trying to really influence the business in a way we can socialize something across all the leadership. I think a lot of times for us, it just boils down to some simple metric, and maybe the the hard work comes in, what’s the right way to segment or slice that data, chance to pivot the data on and I think that’s like where a lot of you know, like we were looking at trying to understand part of our customer base. And one of the ways we think about our customer segments is by how many employees they have, and that helps us. But okay, is this an enterprise or more of a mid market account? Sure, and you know, so we did a sensitivity analysis, and we’re just starting to break this down. It’s like smaller and smaller buckets and look at behaviors, because some of the buckets had ended up pretty big, and like, okay, are we over generalizing here and behaviors within them? And then how small is too small? And yeah, things like that, where we ended up finding some really interesting insight that helped us evolve how we were thinking about segmentation, were really useful, and it wasn’t the most complex analysis, but it really got down to something simple the business could understand. So I think that’s under-valued in a lot of places, like do you just have a firm understanding of some of the business fundamentals, and when you’re trying to, especially up to, trying to persuade or influence the business. Can you boil that down to something a little bit more straight

Eric Dodds 41:47
forward? John, how about you?

John Wessel 41:50
And the one that came to mind was, we worked with a phenomenal partner on this at my last company, but it was such a simple output from a very calm there’s a lot of complexity, like going on in the back end, but the simple output for the team, this was for a procurement team, was how much quantity for ASQ to buy today, like, it was just like, three columns. Now, lots of like, lots went into that, tons of inputs into that, tons of like, forecasting and fairly sophisticated stuff. But the output was so simple and actionable, right? And I feel like I learned a lot from that, like that. And I’ve, you know, other experiences where just make it like to, you know, to today, like, on, you know, this day, like, this amount, this queue, like, done, like this, not more than that. Yeah, it was a big lesson for me.

Spencer Burke 42:38
I love it, yeah. I think, related to that and some of the alerting conversation as well, we’ve been thinking about the place where we can make the data more consumable, and maybe by making it not data anymore? It’s like, should we represent this as a string instead of as a number? And it’s because we have something, yeah, yeah. Maybe it’s an E-commerce context. It’s like, instead of showing like, the percent of inventory you have, especially, it’s like, if it’s doing nonsense, you seem to say, like, hey, like, restock this now.

John Wessel 43:08
Yeah, totally Yeah. Like, order five today. Like, yeah, exactly right, yeah.

Spencer Burke 43:16
Boil it down to the action. Put it into something that they can clearly understand. I think we see a lot that probably the most common version of this that everyone does is, like, some kind of red, yellow, green, scoring. Or, Yeah, sure. I think even if you do, you might show something to the team. It’s like, okay, what’s yellow? Why? What should I

John Wessel 43:35
Right? Yeah, yeah, or Yeah, ABC, or, yeah, yeah. And it’s like, Well, why is it C? And then you, I mean, I’ve actually been to meetings where we have, like, constant discussion about, like, what should C be, what should be be, and then all this, like, and then you just end up with, like, lots of complexity, right? You’re like, and then nobody remembers what you even decide, yeah,

Eric Dodds 43:53
yeah. When you try to abstract complexity

Spencer Burke 43:56
too far, then it becomes meaningless and it very easy for people to stop trust and that trust just, yeah, exactly, and they find one data point that doesn’t match what you’re telling them, like how that algorithm should work, or how it should work, then, then they just don’t trust the whole thing anymore. Yeah,

Eric Dodds 44:15
yeah, okay, well, we’re at the buzzer. One more question for you, so you have helped build a customer engagement platform for a very long time now, and I hope that you have many more great years of Braze. But I’m just interested if I gave you a blank check, which I am not going to do, because I don’t have one like you and you are going to start a company. What like, what problem would you try to go tackle,

Spencer Burke 44:41
ooh, yeah, it’s, you know, it’s funny. I spend a little bit of time on this, doing a little bit of angel investing, trying to find companies that are solving problems that are near and dear to my heart. Yeah, and that’s kind of where a lot of times my head has gone to is, what are the challenges I’ve had building. It and building great and a lot of times it ends up being things that, like, feel kind of rudimentary, but I think add a lot of business value. Like, we went through a lot of work with the data team, especially leading into the IPO, working on how we track consumption data, how that turns into billing, how that’s a new record. Oh, yeah.

Eric Dodds 45:18
Well, yeah, that’s a gnarly subset, yeah. And

Spencer Burke 45:22
so when we’re in the thick of that, that would have been at the top of my list. I actually just recently came across this company that has, it’s a SaaS company that’s just a pricing calculator for sales teams that plugs into you and CRM and new acronyms out. And I suppose, like, okay, that’s the pain point I’ve seen. Like, that’s cool. I don’t know that I would go work on that, but that’s the way that I kind of look at the world right now, is like, I’d want to build things that are problems that that I’ve had, and maybe artists, we’ve done something internally, but I feel like it Sure,

Eric Dodds 45:57
yeah, awesome. Well, Spencer, this time has flown by. We keep going for another hour, but Brooks won’t let us. So thank you so much for giving crime to us. I’ve learned a ton, and this has just been awesome.

Spencer Burke 46:11
This has been a lot of fun, and I agree. I think we could keep talking, so maybe we’ll fit in again soon.

Eric Dodds 46:15
Yeah, let’s do it again. The data stack show is brought to you by rudderstack, the warehouse, native customer data platform, rudder stack is purpose built to help data teams turn customer data into competitive advantage. Learn more at rudderstack.com.