The Data Stack Show - 203: From Data Dreams to Practical Marketing Outcomes with Spencer Burke of Braze

Episode Date: August 21, 2024

Highlights from this week’s conversation include:Spencer's Background at Braze (1:54)The Early Days of Braze (2:41)Finding Product-Market Fit (4:44)First Major Customer (6:33)Unique Aspects of Braze's Growth Team (8:07)Startup Culture Experience (10:40)Data and Marketing Perspectives (12:50)Common Marketing Data Challenges (15:50)Changing Dynamics in Marketing Tech (18:12)Evaluating Marketing Tools (19:38)Transformation of Marketing Tools (22:18)Marketers Becoming More Technical (24:10)API Utilization in Marketing (25:46)Connecting Customer Experience (29:09)Flexibility in Data Integration (32:05)Pushing vs. Pulling Data (34:35)Anomaly Detection in Data Reporting (37:02)Understanding the Importance of Core KPIs (39:09)Making Data More Consumable (42:38)Final Thoughts and Takeaways (44:51)The Data Stack Show is a weekly podcast powered by RudderStack, the CDP for developers. Each week we’ll talk to data engineers, analysts, and data scientists about their experience around building and maintaining data infrastructure, delivering data and data products, and driving better outcomes across their businesses with data.RudderStack helps businesses make the most out of their customer data while ensuring data privacy and security. To learn more about RudderStack visit rudderstack.com.

Hi, I'm Eric Dotz. And I'm John Wessel. Welcome to the Data Stack Show. The Data Stack Show is a podcast where we talk about the technical, business, and human challenges involved in data 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. Thanks, Eric. Thanks, John. It's great to be here. All right. Well, give us just a brief background.

Yeah, 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 messages across WhatsApp, email, push notifications, all the relevant channels. And we're really big fans of data here at Braze too. Awesome. So I can tell you're a marketer at heart because when we talked 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've already got this nice headline. What other topics do you want to cover? 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, there are a 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 persona. 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. Nice. Awesome. Well, let's dig in and chat. Yeah, great. Spencer, I already know this episode is just going to completely fly by, but we have to start

with some history because you have been at Braze for is it 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 tenure at a SaaS company. So congratulations. Yeah, in this space, it's truly incredible.

Yeah. Pretty awesome. It's interesting. It's a little bit unusual these days, but... Very unusual, I would say. Yeah, I know you guys have been early at some companies as well. Yeah, yeah, yeah. Maybe trade some more stories. Yeah, I know you guys have been early at some companies as well. Yeah, yeah, yeah. Maybe trade some more stories. Yeah, yeah, yeah. Well, give us just a brief overview of your

journey at Braze over those 13 years. 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, I've gone much deeper on my technical side. At the time, though, 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. Wow. This is back in 2011.

You have to remember back then, the state of the app store and the app economy. Oh, yeah. So nascent. So nascent. Totally. And like skeuomorphic. Was that the wooden shelves era?

Oh, sure. Yeah. You remember that? Yeah. Because the first iPhone came out in 2007 and the App Store was a couple of years later in 2009. So two years in the App Store. It's all apps, calculator apps, soundboards. And we were really excited about the space. We're based in New York City and the tech ecosystem there was also really just starting to get going.

So that was me 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 it was a lot of guys who were going to these mobile developer meetups looking for something called their app so yeah i got to see that whole thing oh man so yeah 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 like feature offering that people got excited about? Yeah, it took a minute. I think if you're working in B2B 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 three years of building and iterating in the space. I think what started to really resonate, though, was not too far from where we are today, which is like, how do you send the right message to the right person at the right time? 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 and early days of red and things like that but more more importantly for us, from a consumer perspective, everyone's expectations on how personal marketing interactions were, how real-time it was, how relevant it was for them based on the circumstances and just expectations of what

the brand or the app knew about you just going through the roof. And so it was just in confluence of technology and consumer expectations changing. Yeah all right we only got the first the very first step you have to finish you gotta finish the rest of the story on the edge of my seat yeah well this is so then we want to win our first big customer we're like oh man now we've got a customer we got a onboard service lab so i was one of the first hire in our customer success team and eric i think you spent some time building can you can you say

who your first like big customer was we so one of the first customers that was really important for us was working with urban outfitters and yeah that you know when you're a customer that you can tell your parents who it is and they know. Like they've heard of them. Yeah. Yeah. Yeah. It's a real company.

I work at a real company. Exactly. This is a real job. I promise. Yep. So then I spent five, six years building out, scaling CS, support, some of those customer facing functions. Yeah. And what do you currently do in the SVP growth role?

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. Yeah, very cool.

So that sounds like it might be a little bit different than other growth orgs I'm familiar with. So like, because you work with the customers a lot. So what do you think, 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-functionalness

that maybe if I had a growth leader that like won every two or three years, like you't do yeah I think it is unique I've been trying to evangelize it and getting some limited traction on that because I think more b2b 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 in 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 yeah yeah yeah yeah okay well there's so many other topics we need to cover but one question so second employee braze 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 I mean, we had a couple real moments like that. There is a deeper psychological undercurrent, though, where you're always kind of thinking that like, yeah, totally, in those early days. Totally. Yeah. And you're just like, you're trying to figure it out, and constantly iterating and working through things. I mean, it was funny for me because prior to Braze, I'd been working at a large consultancy. And so the first day I showed up, you know, I'm there right at 9 a.m. There's only five of us at the time and no one's there because it's basically everyone else's engineers. There's show times a little bit later. I'm like, oh, man, what?

What did I do? And eventually everyone, you know, I'm messaging oh man what well what did I do and eventually everyone you know I'm messaging the 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 be there eventually yeah then and that first week all they had stocked in the fridge was Red Bull of course and yeah so great 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 in how much caffeine I could take. That was my trial by fire with my introduction to startup life.

Yeah. That reminds me of a story. This was not exactly a startup, but a smaller founder-led company. And my first day, I get there and I was coming from a really big company, several billion dollars, several billion dollar company. And the first day, you're looking around trying to figure out where I'm supposed to sit. And they're like, oh, yeah, glad you're here. Your desk is in that box over there. So if you can put that desk together, that'd be great. And then everybody's running around like they were having some kind of net system issues that day.

So I was like, okay, cool. I guess I'll put a desk together. I'm here. Startup world. 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 world, they're working together, right? But they place like really different weights or even have different perspectives on data. Can you just speak to that from what you've seen at Braze,

both from your customers and then also just yourself? Because you do a lot of this. Yeah, should we start from the customers and then also just yourself? Cause you do a lot of this. Yeah. Should we start from the data perspective or the marketing perspective? Your choice. Yeah. Yeah. Dealer's choice.

I mean, from, so I think from the marketing perspective is, it is really interesting. I think especially because a lot of the modern tooling that exists both the braze 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 it's you can't really survive as a marketer right now being totally ignorant of what exists in terms of tooling in the data ecosystem. At the same time, for a data engineer, data analyst,

analytics engineer, whatever role on 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 in 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 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 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 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. But then you lose that institutional knowledge on 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. And then you start to, you blame the data, you blame the data team, or you blame and have to hit reset at some point.

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 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 marketer's day-to-day? Yeah, they probably send an email to the wrong people or a push notification to the wrong people. Which didn't make their boss happy. Even worse, their boss was probably

in that segment of people and got that. Oh man, that's so true actually. That is so true. Eric's over here trying to develop some sympathy amongst the data audience for the marketing team. I think that's what we're doing here. I respect that.

I can respect that. Yeah. I knew a team that took this so far that when they were testing new messaging for some of their marketing, they would set up a geo-targeter right around their HQ and exclude everyone. Is that the...

Oh, totally. The push campaigns. 100%. I have absolutely seen that before. That's so funny. Yeah. 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 tens of millions of people totally even when it's smaller that fear still exists and i think that it's healthy to some degree it's you respecting 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 yeah 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 i i think of like like 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 you know like in the back end doing engineering or audio engineering or like the like that's my best like 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 yeah yeah one of the things i think that's changing though is as marketing technology is 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 the tools that the marketing team chose led to some of the frustration, I think. Yeah, oh, definitely. 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 it 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 yeah it's like i mean yeah we probably shouldn't be calling out names yeah sorry mailchimp's great no i mean mailchimp's great but another classic example is like marketo right It's like, you buy it because it's like a super powerful tool, but it's like, it's really hard to do like 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 work 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 braise's api documentation like multiple times over but we were doing consulting and we were doing evals right like when we did that you know for your company or 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 right it was like, within 10 minutes, within 10 minutes, we knew like whether this was a tool that was like going to work

or it wasn't, right? It's 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 there, but. Yeah. No, I mean that, that can be extended to a lot of user journey, like, or it's not, you know? And I know obviously there's a spectrum there, but. Yeah, no, I mean, that can be extended to a lot of SaaS tools, honestly. Yeah, yeah. As far as evaluating. Also, 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 is serious about data. 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 summit we're talking to the technical audience yeah we're partnering with you guys with snowflake with router stack with awr with aws with databricks

and i think like inviting them to And I think inviting data people to be part of the process, especially when it comes to integrations into the cloud data warehouse, that's just a game changer because then we can start to talk about things that are part of their workflow. 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'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 part should be more ongoing versus you do it once then right not shit 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 some changes that 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 where 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 where they have that capability. It's just a better way to work together, better division of labor. Can 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, that is, I'm sure. I mean, that is... That is so exciting. Compared to where we were. Yeah, yeah, yeah. Well, and especially compared to 2011, right? Yeah. Especially compared to 2011. I mean, can you imagine, 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 connect this to our data warehouse. You're like, just... Just let's picture that conversation for a minute, right? you got to talk to your data and your IT team. Hey, I want to connect this to our data warehouse. Just let's picture that conversation for a minute, right? Yeah, at the time it would have been, can we connect this to Hadoop? Yeah, can we connect this to Hadoop?

Or they're calling your manager and being like, I think you need to check on this person. Right, yeah. Yeah, well, it's no SQL somehow. Yeah, I mean, and that check on this person. Right, yeah. Use NoSQL somehow. Yeah. I mean, and that was a lot, 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 zoomed once a day or once a week.

And so it's a huge paradigm shift from that. And, you know, there's been steps on the way and I'm kind of making fun of some of the API docs. But, you know, even like you said, like there's degrees there as well. And that's been some huge improvements over how marketing technology had to interface with data in the past. And so the fact that we're getting more real time, we're getting closer to the data tools and it's just such a huge improvement over something like dropping a file. Yeah. So we talked about the big dreams, boring marketing outcomes. I feel like this is the perfect segue to that. Yeah. Yeah. I want to know, you have seen marketers use Braze for over a decade.

Are they becoming more technical? Give us some intel on that. Yeah, definitely. We released not too long ago some capabilities for Braze dashboard users to be able to write SQL, to build their ports, 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 knew enough to be dangerous in SQL. One of the things that, that I took away from that was, you know,

if you gave them a data engineering, like interview question, like they're probably not going to do great on that, but they are experts in their data. They understand marketing data. They understand 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. We have some capabilities when we're sending a message to hit an API endpoint. The JSON data payload that returns, you can include in email or push notification or any marketing channel.

When you teach marketers how to do that, we use the same templating language called Liquid that Shopify built. It's so nice. God, it's so nice. And because that's 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 other parts of the marketing landscape, there's all these interesting tools and capabilities that are pushing marketers to be more technical. Even if you're just a couple people trying to stand up and e-commerce shop, Shopify is making that easier for you. And so to kind of piggyback on that wave of more and more professions becoming technical is a lot of fun for us.

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 Braze journey, pull the JSON payload and then pull like nested properties

into a message body with Liquid, it is like paradigm changing for what you can do. It's pretty freaking sweet. And you just put yourself in the top one percent you know a good way in a good way yeah yeah if you're not certified we'll have to get you a certification eric yeah you really need to should be an award a data stack show award if you know yeah yeah do that yeah yeah yeah yeah

yeah guys i do think this is part of the same theme though one of the places we saw with 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 they're 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 he's just lived in this product engineering data space. And kind of similar to what we're talking about, right?

Okay, the marketing tools can just connect into the technology you're 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 Yeah. It pushes the marketer, but again, I think that's good for them to start to develop those skills. Yeah. 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 say this, what this is going to 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 Braze. 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,

well, 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? Yeah, we recently announced the phrase data platform. And a part of that for us is 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 possible way. And I think for us, every customer is a little bit different in terms of how they want to architect how their tools work together. And I think the more you try to force them down one paradigm, the harder it is to get the end that you want. And so for us in the Braze ecosystem, on 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 from the warehouse. If you already have that being routed through a partner, could get it from Rutterstock, from someone else. We also have a capability called data transformation.

So if the support platform can webhook data out, we can take it and webhook and consume it. Yeah, so I think as a data person myself, I think the warehouse is a great way to do that because then you can join in to some of the other data that you have. You're already building 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 it so that the market can have more productive conversations, we hope, with technical counterparts. Yeah. Yeah, that makes sense. Yeah. John, you looked like you were... Yeah.

Yeah, I'm 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 some of these tools like it's been over 20 years we've got these you know sass based email and now like multi-channel marketing tools so 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 um so i

think so you mentioned like multiple connections 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 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, know right message at the right time now and implement this more of a i don't want to say traditional but like implement this as more of a standalone thing yet will 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 tooling like your braze 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? Totally fair. It's actually really important to us. We talk a lot about start anywhere, go everywhere as this idea that we want to be on-ramp for customers or they can also be on the highway for us, with us for a while. But there's a lot of on-ramps. We don't want to only sell to the most sophisticated,

most technical marketers out there. But if 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 yeah they're changing like yeah they're bringing over all the data they're not trying to make big changes to the architecture and then over time they're starting to like modernize more completely, bring things together,

change some of the ETLs, change how they're piping that data back out. And so I think even if you have a customer who's started that, they're still going to be growing with you and their maturity 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. Yeah. Spencer, I want to shift a little bit and just ask, you work with data a ton. And so I want to ask you as, I mean, you have this interesting 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. Ooh, this is a great question. So let me share some of the, happy to take this 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 is for dashboards. And I feel like every data analyst has this. Everything's a data. The dashboard is like the classic whole mechanism. Someone has to go in, refresh that dashboard, go get the data themselves. And I think that can be useful in a lot of cases where you have maybe an operational dashboard.

Someone's going to log in every Monday or every day, and they're going to look at they're running their business off that in some way but 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 the marketing it's like the right data at the right time yeah and and so the dashboards aren't your solution to everything and you know i'm no i'm not that's not 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 i'm so glad you brought this up because this is like one of my smiling 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'm i've cut some like devops data ops background and i'm used to using tools like like derelict 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, 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 report 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

yeah these are they're related to this thing to this is i see people asking for dashboards and 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 like 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 trying to attach actions to those. So we'll alert on certain things in the Slack. And for example, if we wanted a salesperson at Braze to go update something in the CRM, can we just give them that dropdown right there? And if we have some context on what we think they they should select make that the default instead of something else yeah

thinking about data more is it 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 it 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, the business day to day. And 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, 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, 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 was transformative 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 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 some aspects of the 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 we're trying to understand the business when you get when the analysis gets too complicated or 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 well analysis and he can pick that apart just as easily. That's tough. Yeah. Yeah. Brutal. When we're, when we're, yeah, these, these MIT engineers, they're a tough crowd. When we're trying to really influence the business in a way that we can socialize something across all the leadership. I think 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 like what options 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 OK, is this an enterprise or more of a mid-market account? Sure. And 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 are there 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 undervalued in a lot of places. Do you just have a firm understanding of some of the business fundamentals? And when you're trying to, especially up to exact, trying to persuade or influence the business. Can you boil that down to something a little bit more straightforward? John, how about you? 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 askew 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 and actionable right and i feel like i learned a lot from that. And I've, you know, other experiences where just make it like, you know, today, like on, you know, this day, like this amount, this queue, like done.

Like it's not more than that. And that was a big lesson for me. I love it. Yeah, I think related to that and some of the alerting conversation as well is we've been thinking about the place where can we 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 you know we have something yeah maybe it's in an e-commerce context it's like instead of feeling like the percent of inventory you have especially it's like if it's to the percent of inventory you have especially it's

like if it's to a non-technical versus you just need to say like hey like restock this now yeah totally yeah yeah like order five today like yeah exactly right yeah boil it down to the action put it into something that they can clearly understand i think we see a lot of the 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 that it's you might show something to the team it's like okay what's yellow why what should i do right yeah yeah or a yeah abc or yeah yeah and it's like well why is it c and and then you i've i mean i've actually been in meetings where we like constant discussion about like what should c be what should b be and then all this like and then you just end up with like lots of complexity right and you're like and then nobody remembers

what you even decided yeah yeah yeah when you try to abstract complexity too far then it becomes meaningless and it's very easy for people to stop trusting 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 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 at 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, and you were going to go start a company, what problem would you try

to go tackle? Yeah, 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 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 times it ends up being things that feel kind of rudimentary, but I think add a lot of business value. 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 post-revenue record. Oh, yeah, wow. Yeah, that's a gnarly subset, yeah. And 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 sass company that's just a pricing calculator for sales teams that plugs into you and crm and

lots of new acronyms i suppose like okay that's a pain point i've seen that 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 I've had and maybe started to, we've done something internally, but I feel like it could be more funny. Yeah. Yeah. Awesome. Well, Spencer, this time has flown by. We can keep going for another hour, but Brooks won't let us. So thank you so much for giving time to us. I've learned a ton and this has just been awesome. Yeah, thanks guys.

This has been a lot of fun and I agree. I think we could keep talking. So maybe we'll have to do it again soon. Yeah, let's do it again. The Data Stack Show is brought to you by Rudderstack, the warehouse native customer data platform. Rudderstack is purpose-built to help data teams turn customer data into competitive

advantage. Learn more at RutterStack.com.