The Data Stack Show - 164: How The GTM and Data Teams at Snowflake Work Together with Travis Henry and Hillary Carpio

Episode Date: November 15, 2023

Highlights from this week’s conversation include:The Unique Perspective of Practitioners (2:10)Account-based Marketing (6:30)Sales Development Representatives (SDR) (8:05)Descriptive, People, and Engagement Data (11:38)Data Overload and Actionable Data (14:20)Working with Data Teams and Internal Data (17:52)The relationship between business and data teams (22:27)The importance of collaboration between marketing and data teams (24:17)Travis and Hillary writing a book (25:33)The taxonomy of personas (34:23)Bucketing and grouping people in data systems (35:37)Account-based marketing and sales alignment (39:00)The data-driven approach and reliance on technology (44:25)Managing complexity in data and account-based marketing (45:35)Adapting to change and evolving data artifacts (51:58)The importance of understanding the business (54:58)Collaboration between data and go-to-market teams (55:56)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.

Welcome to the Data Stack Show. Each week we explore the world of data by talking to the people shaping its future. You'll learn about new data technology and trends and how data teams and processes are run at top companies. The Data Stack Show is brought to you by Rudderstack, the CDP for developers. You can learn more at rudderstack.com. Welcome back to the Data Stack Show. Costas, today we have a treat. And that's what we're going to talk with some very data-driven people who are not data practitioners or data professionals in their day job. So we're going to talk with Hillary and Travis, who work at Snowflake on the go-to-market side.

Hillary runs their account-based marketing function, which I can't wait to dig into. And then Travis runs their sales development representative function. And after we had Brendan on the show, who talked with us about being a data consumer, we thought, man, we need to get more people on the show. talked with us about sort of being a data consumer, we thought, man, we need to get more people on the show. And Brendan introduced us to Travis and Hillary. And they're amazing. Amazing relationship with their data team. Pretty amazing how they developed that relationship. So definitely something for either on the go-to-market side or the data side, definitely a must listen.

And I think the things that I'm most interested to dig into are to hear about their views on data. So if you're going to run an account-based marketing campaign or you have a sales development representative trying to understand all these data points about an account, on the data side, we send data to those teams often and then they consume it, but we don't really get to see their consumption of that data. And so I think being able to sort of go over the wall and get more insight into the specific practical things there would be really great. So that's what I want to dig into.

How about you? Yeah, 100%. I think we have a very unique opportunity here to hear from practitioners how actually data drives value for them. And do that in an organization that does it at scale. And not only that, but we're talking about

an organization that's also a vendor, a solution that is used for that kind of problems. There's some, I think, very unique perspectives here, both in terms of the scale, how innovative they are in terms of their marketing or sales, but also how they, in a way, they use what

they evangelize out there to sell their product, but they do it internally. I think it's a very unique perspective and a very unique opportunity to learn things that if we had someone who was only a vendor or someone who was only a user, I wouldn't be able

to do that. I think it's going to be a very interesting conversation and we'll try to do like a couple of different things with them. Like obviously like try to learn about how the data is used and why. Do like a little bit of data modeling, maybe like see how you represent all this information that marketing and sales

need for their day-to-day job and learn a lot about the people, actually, which is also important because it's not just the people we'll be talking about with dates, it's also all the SDRs or the marketing people have to execute, right?

And they have to use information rather than to do their job effectively. And see how this can happen at scale and correctly. So yeah, I think it's going to be a very interesting conversation. Let's do it. Every data engineer and analyst's dream is live data modeling with a business user. So let's dig in and do it. Yeah, let's do it. It'll be fun. Hilary, Travis, welcome to the Data Stack Show. We are very excited to chat with you. Excited to be here.

Excited to be here. Thanks for having us. All right. Well, we, both of you are at Snowfl, and we've talked tons about Snowflake throughout the life of the show. But this is really exciting because you both work on the go-to-market side of the house at Snowflake, and we're going to talk about the ways that you use data. So why don't we start out? Can each of you just give a brief background and introduction and tell us what you do at Snowflake? Hil Hillary, let's start with you. Yeah, so my name is Hillary Carpio. I lead the account-based marketing function at Snowflake in addition to customer growth marketing. Started in 2019 and have helped grow the team from five individuals to 20 in North America and 30 globally. So really been part of the scale and

have enjoyed the wild ride that Snowflake has been from private company through IPO. So really been part of the scale and have enjoyed the wild ride that Snowflake has been from private company through IPO. So excited to continue talking about how we use data and it's everything to our business, as you could imagine. Yeah. And Travis Henry here. I joined Snowflake just after Hillary, right at the tail end of it being a private company. So already a very big success story. But my role is sales operations and enablement just for our sales development team. So a bit of a unique role, but one that's catching on, especially with really high growth B2B software companies to enhance and elevate the role of sales development. So the journey here, it's been about 70 some odd SDRs when I joined over three years ago to over 250 SDRs

today. So very much a study in scale and using data to go on that journey. Awesome. Well, let's start out. I want to cover just a couple definitions that each of you mentioned. So Hillary, you mentioned account-based marketing. And for those listeners who may not be familiar with exactly what that means, could you just give us a high-level overview of account-based marketing. And for those listeners who may not be familiar with exactly what that means, could you just give us a high-level overview of account-based marketing? Sure. Yeah. Traditional marketing goes out to a very broad audience, kind of like fishing with a net and catching those fish that swim into the net and going down from there and qualifying and doing outbound, etc. Account-based marketing is more like spearfishing. So my team is responsible for identifying with the sales team, which

accounts are most ripe to go after at any given time. And then doing very customized and personalized marketing campaigns to those accounts in order to get them in the door on the marketing side, and then coordinate with Travis's team to get the SDR on the sales development side, going on the outbound piece so that we can bring in the SDR on the sales development side going on the outbound piece so we can bring in the companies that have the highest potential for spend, the most fit, and you have most likely the fastest deal cycle based off of timing. Yeah. So an example of that would be instead of sort of just launching a campaign maybe around Snowpark and sort of capturing general interest and ML stuff, you may go after

a specific, you know, FinTech company and like the ML team at that company with personalized type marketing. Correct. And we do have a broader demand generation team that does the more broad based marketing and addresses our workloads. And we're able to do what we do because we have their support and their content generation and their broader coverage. So we're a complement to that broader marketing function. Very helpful. All right. And then Travis, define SDR for us.

And so define it. And then can you just give us a sense of what does, you know, what does an SDR do every day as part of their job? Absolutely. So SDR do every day as part of their job? Absolutely. So SDR stands for Sales Development Representative. And you can think about it like a specialized role that's focused on what we call the top of the funnel or finding new business opportunities for Snowflake. So contrasting it to the B2C world that some listeners may be familiar with, or we all

know, you we all know, you know, Amazon, I get that great, cool pair of shoes coming in my Instagram feed, I do a couple clicks, and I'm checking out of Amazon, and I get a box to my door a few days later, it's very low consideration purchase, not a lot of complexity, probably don't need any humans involved in that. When you start to think about selling a product like Snowflake or really any B2B sales motion that's expensive, highly considered, requires a lot of people in the organization to say yes and for no one to say no, and takes a long time to do, you start to get into a place where it makes a lot of sense to specialize the sales motion. So if you think about the campaigns that Hillary's team's running from an account-based perspective, we'll start to gather interest. We'll start to see that there's maybe some traction in this account, some awareness in this company for a solution like Snowflake.

The SDR role is really purpose-built to monitor those signals, to understand them, to put a story together and to engage buyers in that account to ultimately compel what we call the hardest part of closing any deal. And that's finding the deal in the first place. So that's really the SDR team comes into play. And what that allows you to do is not only does Hillary get more conversion and return on investment from her campaigns, our closing sales executives, you know, folks who were 10, 20, 30 years in the data industry,

they're focused on working deals and working deals to close and presenting and negotiating. We're really that in-between kind of force multiplier function. Very interesting. Okay. So it sounds like each of your functions, you know, there's in some ways, it sounds like there's a collaboration. But when we think about a campaign, you know, sort of like pushing, in some ways, pushing information out to this account, right? And the SDRs are sort of digging into these accounts. In each case, though, each of you seem like you need a lot of data on accounts, right? And so when we say account, I'm kind of referring to that as a business or a company. Is that accurate? Yep. So when you think about all this data that you need about an account, I mean, what data is that?

Travis, you mentioned there are multiple people involved in this decision, you know, so maybe you need information on different roles that are associated with this business. But can you just paint us a picture of, you know, in your ideal world, what's the data set that you can use to do your job well? Absolutely. Yeah. And I'll start maybe with the broad buckets and then maybe pass it to Hillary for some examples of what we used in the past. But it's pretty cool to be in go-to-market in 2023 because it's a much more data-driven motion and data is that much more important of an asset for our teams. And to give you some general categories here, first, if you just think about accounts or the businesses, the entities out in the world you want to go sell to, having data about those entities is super important for your business.

Think all the way back to pitch deck to a venture capitalist for your great startup idea, right? You're trying to put together data about, you know, there's this many companies in the world with revenue above this size and these industries that we think we can go sell to, and you can kind of size a market using data. So there's kind of, in my mind, that descriptive element. So firmographic data, what are the companies? What do they look like?

How many employees do they have? Those basic elements of data. Then there's the people data aspect. So who are those people in the businesses? And how big is that data science team or that data engineering team? And what does that relationship map sort of look like? And what do those buying groups look like? And I think the

third broad bucket is what I would call engagement data. This is definitely the noisiest data set, the one with the most rapid change over time. But examples of that would be, you know, how many individuals from a company are registering for Snowflake Summit during the summer in Las Vegas, right? That's a level engagement that we can measure from an account. You can think all the way into very technical details like product telemetry. If you have a free trial, people are getting in your product, they're taking action in the product, monitoring that kind of engagement. Hilary, anything to add to that? Yeah. So on the account-based side, our job is

to take all of those different pieces of data and make them make sense in a single signal. So from the product telemetry side, that's a goal of ours from the third-party data side. So what are people doing related to our business outside of our property, first-party side on our property? We work with our data science team and our marketing intelligence team specifically in order to bring those into that score that helps the sales team understand who to go after and helps us help them as well dissect what their accounts are interested in. Okay. So even just talking about those sort of three buckets, and we covered a lot there. So different sources of data, internal and external. We talked about marketing intelligence. We talked about data science. I mean, you're touching a lot of different data sources and even different data teams, it sounds like. So, Hillary, what is it like for you as a data consumer? I mean, how do you go get this data? Do you define what you need and then sort of

have all these, you know, meetings with, you know, these sort of data producers? What is that experience like for you? Yeah, I mean, we live in a world right now in marketing where we're in data overload from a commercial standpoint. So I can open my email any given day and there's 10 different emails, 20 different emails from providers with why their intent data is different, why their contact data is different, et cetera. So there is no shortage of input in terms of what data is out there. The challenge is in the accuracy of the data for your specific business, because one data provider might be great for a different type of business, a different set of personas,

but not for us. And so my time is spent distilling what is the data source? What is the methodology and the forward-looking plan with the data from the vendor? And how does that relate to what we're trying to do? Is it relevant? And then I'm constantly asking myself the question and Travis as well as is it actionable data. So we have plenty of data sources available to us that we choose not to use because there's no action to be taken from it. And it puts sales and marketing both in like an analysis paralysis to just have too much data out there as a list of statistics, as opposed to a meaningful insight and what to do with it. So that's kind of where I'm spending my time and my discretion. And then we're very fortunate to work with an excellent marketing intelligence team and sales operations

and sales intelligence team that can help really guide us, consult us and tell us what's possible. So I do the dreaming and the ideating and they keep us in reality and then also do a ton of innovation to make our data dreams come true. On the third party side, and this is a really specific question, but really curious. So when you procure data from a vendor, which is common, right? So there's tons of data vendors out there. When you procure data from a vendor, do you work with your data team on sort of what that looks like in terms of ingestion or like, where do you, I mean, some companies just like send a giant CSV or FTP it, but increasingly we see companies collecting their data into a single sort of repository

in Snowflake. For you as someone who's procuring that data, how do you work with a data team in terms of actually receiving it and sort of, you know, operationalizing it from a data perspective upstream of you? Yeah. So I'll kind of set the business case of what data we need and why and what we're trying to do with it. And then the intelligence teams will help us understand how they map together and how to ingest them. Our preference is always going to be to consume data through our marketplace and do a data share using Snowflake

so that there's no FTP or anything along those lines. So we'll work toward that if it's not available or if it is available, that would be our preference to use our own product. Yeah, yeah, it makes total sense. The marketplace is really sweet for sure. Travis, can you speak a little bit to the internal data? So let's just use like product telemetry data as an example. So that can actually be quite complex from a data standpoint to get to aggregate. We do some of this at Ruddersack, of course, on a much smaller scale than Snowflake. But I just know from

experience, those data teams are collecting that data. That's a non-trivial exercise. But you as a consumer, what does that look like? I mean, what data points do you want when you're trying to look at an account, you get to too much data pretty quickly in terms of what's useful. And the challenge is actually for our data teams to distill down the signals that matter or specifically the moments that matter. Think about in a customer's journey using our product or going through the trial. So it's more an exercise in reduction and focus than it is give me as much data as possible about everything everyone at this company is doing with the product. And for us, you know, that is kind of an interesting open-ended conversation that we have with our data teams and with marketing, which is like, if I are always trying to put yourself in the customer's shoes, right? If I'm using the product and maybe I've

signed up for it, but I haven't started to use any credits inside of the trial, that's probably a moment that matters to us in the sense that I kind of abandoned my journey there. And for some reason, I continue on to use this resources. So maybe is that because there's a knowledge gap and now we can have SDRs provide very targeted, you know, helpful information or use cases or some queries to get started with? Or is it because that individual's, you know, maybe no longer interested in going with a competitor?

So it's very much around, you know, may be no longer interested in going with a competitor. So it's very much around, you know, feature adoption, as well as, I would say, credit consumption, specifically in that product example. But I think the even more interesting part of how we work with data teams is that dialectic of educating the data teams on the business process and giving them the definitions around how do our teams operate? Because if you think about internal data, you know, outside of the product example, definitely SDR teams, Hillary's team,

pretty much all go-to-market functions now are using like a crazy tech stack to do their work, whether that's, you know, end user facing or it's behind the scenes, kind of capturing the exhaustive activity. That internal database, right? That internal set of data of what users are doing has only become more valuable and robust.

So just to give you a concrete, simple example, most companies will track statuses on individuals at companies that we're interacting with. So, hey, is anyone communicating with this individual? Are we actively trying to reach out to this person? Have they responded to us or have we disqualified them as not a good fit? You know, things as simple as that. You start to multiply. Hey, we have seven statuses for all the people we're engaging with, and we have 2 million people in our database,

and we have them segmented by account. If you can educate your data teams on what those statuses really mean and how they fit into the process, you can start to really build some interesting value-added, business-relevant models and insights from a data team side. And I think that's the opportunity for data teams is to move from purely order takers, for lack of a better phrase, into strategic partners who understand the business and can recommend, hey, have you thought about a contact propensity score to predict who's most likely to take a meeting? Because it seems like that's your choke point in the customer journey, things like that. Can you talk about how that relationship

has evolved? Because it sounds like it's running really well. And I think for a lot of our listeners, unfortunately, in many companies, it's sort of a, you know, here's a Jira ticket, I want, you know, a list of active users, you know, or, you know, people who have, you know, started using, you know, X feature in the last however many days, right? And you sort of get into this back and forth hell of like, I'm asking you for something, you know, and then it's like, what does an active user mean? Right? I mean, what kind of user is it? Is it an admin? Is it a, you know, I mean, that's like, they're different definitions. How did you form that relationship with the data team? And what

does that collaboration look like? Yeah, I mean, hats off to our data team. I think they're great. So it starts with really good talent. But I think if I was to characterize the two sides of that relationship, on the business side for me and Hillary, I think what's made it work is not being afraid of data just in general, and actually getting really curious about data. Like, tell me more about how that works. And how are our tables structured in snowflake and you know how would we shot this thing over time and measure change and really be curious and open to a data conversation rather than shying away from it which i think a lot of go-to-market teams do on the flip side of that relationship I think our data teams have done a great job of not just being curious, but pushing back and kind of calling bullshit, if I can say that on the show. You can. what we need and we think we know how it should work and it's just a list of requirements and we

hand it over and instead of going and building our data teams are like let's get a meeting on the books let's talk about what this means hey if you don't you know normalize these different populations of contacts and you're weighing everyone against each other you know asia pacific's never going to get a high score and everyone in North America is going to get all the good scores. And they start to push back and educate the business on some of the pitfalls there, and they're not afraid to recommend alternatives. And I think that it is a very human kind of work through it, jump on calls, give each other feedback and commentary type of relationship. It's not that advanced or technical. It's actually very human in terms of how that

relationship has evolved over time. Love it. Hillary, anything to add from your end? I'll just reiterate what Travis said is it has to be a partnership, right? You can have a marketer who has all of these ideas, can really make a difference on the go-to-market side and a marketing intelligence or data team that isn't on board, and that's not going to go. And you can have the opposite with a data team that's really on board and a marketing team that's not bought in, and that's not going to go anywhere. So you really need two innovative minds or two innovative teams to come together and share the same vision and be willing to push the boundaries and be willing to try things. I think we're really fortunate. Our counterparts, like I said, receive our ideas and are willing to go figure out how to help make

them happen. And so it has to be both sides. Love it. Well, I know Kostas has a ton of questions, but I have another question, which is a little bit of a detour. But both of you wrote a book, which congratulations, that's a huge accomplishment. What was it like to write a book? And I mean, really, this is, you know, a lot of what we're talking about is covered in the book, right? On how you built these relationships inside of the company to accomplish some pretty great outcomes. But what was it like to write a book? Who had the idea to start with? So we were presented with the option or idea to write a book by our CMO. So after several QBRs of Travis and I presenting where we were heading, what we were building

in conjunction with, of course, the rest of the team. She was like, hey, this is really unique. I think the market needs to know about it. And we jumped on the opportunity to share our ideas with the world. So cool. Did you ever think that you would be an author? It's always been on my bucket list, but I never had gotten to the point of thinking about what the topic might be or where to go. I of a background in journalism or more so the degree in journalism. I've always loved to write, so I wasn't sure if it was going to be on the fiction side or the business side or what. So, appreciate her helping me check that off.

I absolutely did not anticipate myself ever being a published author, but here I am, of course, getting to share that burden, that journey and accomplishment with Hillary, which was a really cool opportunity. And just to answer the question directly for anyone thinking about it, you just got to put in, it's like working out. You know, you got to set out your gym shorts. You got to go do your reps every morning you've gotta take it little by little over a long period of time so and i think the journey was probably eight months of just heads down writing rewriting stylizing your ideas but the cool part about it is you know you have a lot of good stuff to say, or you think you do. And that's why you're writing the book. But it also is a challenging exercise, because not only

do you have to crystallize the thoughts in your head or what you've done into words on a page, you also need to make those clear and valuable and understood by an audience as well, which is a big challenge. And I think, for me, that was the biggest surprise, which was just it helped me think about concepts and the work that I did in a much more clear and structured way. And so it's kind of a forcing function to go through that. So, you know, if you don't have a publishing deal or the funds to go do that, you know, maybe write a little book

or get your thoughts out there and publish stuff. Because I think just going through the writing exercise and discipline is a really good exercise, no matter what field you're in. Totally. Did y'all have some sort of editor to give feedback? Oh yeah, totally. And that was the other cool part, which was complete non-industry, non-subject matter expert, right? So weekly meetings and your reviews, they're like, hold on, back up. You've used seven acronyms in the last 30 minutes, and I have no idea what the hell you're talking about. Like, okay, yes, I need to get out of my bubble and unpack them right very cool well congratulations on the book that in itself is a huge accomplishment costas i've been monopolizing the conversation yeah that's fine because you're asking some very interesting questions and i'm really enjoying what i'm learning here and there's a lot to learn for me okay so i want to start with the concept of the account and I'll ask you to do a little bit of

data modeling, actually, and try to model an account, right? So, what's under an account? If I'm, let's say, we bring a data engineer or DB admin today and you ask them to

start creating the schema there to represent the account, right? What you would ask them to put in the table there? And we can start with Hillary first. Put on the spot. I mean, we're going to start with our basic demographics and firmographics, company size. We want to know what industry they're in. We want to know sometimes how long they've been around, all those sorts of pieces, where their headquarters are, how many offices they have, the elements that help us understand if they could be a good fit. And then once we have those basic

firmographics and demographics, we also want to know what they're interested in. And that's where that intent or third-party data comes in. So first side fit, we have a score that's based just off of that. Second side timing, are they consuming information on our site and across the internet about the things that we are selling to help us understand if they're in market? And then I guess there's another side too, which is technographic. So are they using tools that either complement a Snowflake tech stack or compete with a Snowflake tech stack that can help indicate whether they would be a ripe customer for us?

What's your take on this, Travis, like from the sales perspective, like what you would add there? Come on, we have to make the model like really complex here. It's super complex complex bulletproof okay you definitely need a unique identifier because one of the things in sales and every operation is you look like an entity like berkshire hathaway right that's a holding company that owns a bunch of other companies or a global corporate hierarchy like Alphabet now, right? That owns Google and Waymo and all of these other entities. And then you think about, wow, if you're selling to retail,

you know, there's Walmart in Huntsville, but how do you go out and map all of the different Walmart locations and start to build out corporate hierarchies? So that's the big data challenge and kind of schema challenge of how do you deduplicate those different entities and how do you put parent-child relationships around those entities? Because ultimately, that's kind of fundamental for a go-to-market team

because it relates to questions like, how many customers do we have? Well, if we've sold the japanese arm of honda and we have not sold to the jap or the american arm of honda is honda a customer or a potential customer and you start to get into these kind of strategic board level questions with the schema so i think hillary nailed it. That's what I would add on. And then I would also talk about all the complexities where what accounts become, especially in an account-based world, which we are obviously big advocates of. So zooming out, you know, there's account-based go-to-market, there's product-led growth, there's B2C, you know, there's all these different go-to-market motions. But we're very big fans of looking at everything in the world through the lens of that account.

So under that account umbrella, you have to build out all of your sales opportunities, your potential deals. How much revenue do we think we can get from this company or how much have we gotten? You have to build out the people. So who are all the people. So who are all the people that work at that business? What are their relationships to each other? And then you can start to continue building on that account model where, hey, well, we're not just selling to them. We're also servicing and supporting these customers. So what about things like support tickets and customer satisfaction? How do those fold in? And then you think about marketing campaigns and marketing lead generation and how are those driving the

account? So it's actually a real big pivot point or core or whatever the correct data term is for that model and understanding your business's relationship to that business. It really all falls under that account and you can build that out to a very you know extreme degree yeah yeah 100 and okay like usually in in companies you have many different people right and it doesn't have like to get really big to get to that like even like medium-sized companies or even small companies, you have many different, let's say, personas in there. They interact with something that is as fundamental as infrastructure.

Because at the end, Snowflake is a piece of infrastructure for the company. So how do you distinguish? What types of people do you distinguish? What types of people you have there? I can think, for example, there's probably a distinction between a buyer and a user, right? Maybe. You will tell me. But I have a feeling

that's probably very crude. There's probably more into that, let's say, taxonomy of people. And I would like to hear from you how you see this taxonomy. You try from the sales perspective, and then you hear from the marketing perspective and try to see if there are differences or contact points there. Yeah, I think we'll probably have more contact points

than differences in our answer because what we do is we do that exercise, right? We sell to and work with probably most of the listeners of this podcast, right? And those are different distinct groups and functions that have different interests and pain points and use cases. And so we do a lot of work and specifically our product marketing teams and our go-to-market teams think about what are those logical personas and how are they different from each other? What do they look like and care about?

And we group them together. So data engineers for us, you know, very distinct from, let's say, a VP of analytics, right? Different drivers and pain points and different ways we would provide value to those folks. The interesting part just on the data level is how do you go about bucketing people and grouping them in your systems and in your data warehouse it's actually kind of hard not that straightforward because we collect titles from people and there's many creative titles in the world people really like to have fun with that so you have to account for all those different permutations of title and then make sense out of them and normalize them. And that's actually a really helpful project that our data teams have done

to support us is, hey, we actually have two axes of the people in our accounts. So we understand function. So are they data science? Are they data engineering? Are they analytics? Are they data science? Are they data engineering? Are they analytics? Are they developers or software engineers? And then the other axis is that seniority kind of level. Where are they in the hierarchy? Because the conversation with an end user data engineer is going to be very different from global head of data engineering inside an organization. So we've actually normalized those. And then we track our engagement inside of accounts

against those kind of squares or quadrants of buying groups. How high or low are we in the account? And also, which groups are we most engaged with functionally? And before we go to Sheila and Travis, is there, let's is there a natural bias towards a certain subgroup that sales care more about?

And while I'm trying to say it here, okay, I can't think of many people signing up and trying to use Snowflake. It's not necessarily the people who are going to sign the check to buy. Yeah, right. Probably in the bigger organizations, the people who sign

the checks are like never show like the user the face or like snowflake. So like sales let's say focusing more like on trying like to communicate, specifically from the SDR point of view, right? Like more into the buyer, like let's like to the person who's going to like to write the check or it doesn't matter it's a great question so that's really you hit it with the sdr piece which the sdr plays a big role in translating a lot of that end user education discovery you know folks playing with the product and attending events and learning who, like you mentioned, typically not people signing checks and making big budget decisions inside the organization.

And so one feeds into the other. We're reading all those signals. We're probably having conversations with some of those end users. We're understanding what's not working in the organization. And we're starting to map out a story about this business and about, you know, their data journey and their data challenges. And then what that does is that empowers us to go have conversations with the people who do sign the checks, which are ultimately who we need to get in front of.

So we can provide a much more educated, relevant, resonant point of view to those decision makers because of those end users. Cool. That makes total sense. And I'll move to Hillary now. So what about the marketing side of like this, right? Do you, from your side, like focusing more, let's say, on the user instead of the buyer or it doesn't matter. And then we can get a little bit like a little bit more tactical and like trying to understand what's the difference between the two,

like from the perspective of like a marketeer. Yeah, so there's two different angles. We do sell to full buying centers and we market to full buying centers. So an account-based marketing at any given time in our larger accounts is not just going to be one campaign to one account. We're going to have a message going to the CIO, a different message to the CDO, a different message to the DBAs, a different message to the marketing analysts. It's going to become different depending on who they are. And the goal is that when Travis and the SDR organization make their phone calls, that's not the first time the teams have heard of us. And so when the DBA goes up to their boss or when the CIO gets brought in or the CFO, they're not going, what's no click and why do we need it? We've already hit them with a message and a value add that's super relevant to their job in the buying committee. So we're tackling most of them. Now, the other side of that is that we follow job in the buying committee. So we're tackling most

of them. Now, the other side of that is that we follow the sales team's lead. So there might be an account where they're like, hey, ABM, we have great engagement at the practitioner level, but we are stuck in finance. So we might go heavier into that finance department. We might go heavier into a different role depending on what the sales team needs uh on their sales cycle and where they're seeing traction and whether or not okay that's super interesting and sounds like very complicated to be honest so hey i okay let's start from the i'm trying like to and again like consider me like like a person who knows, like, absolutely nothing about, like, the good market motions here that are in play. So, what's the hierarchy of, like, all these different functions inside, like, an organization like Snowflake? So, we have, like, demand generation, for example, right?

Which I would assume that they are, like, on the top of the funnel. Like, let's say someone has never has never like heard about Snowflake. They are probably going like to hear about Snowflake for the very first time from like an activity that comes from them, right? Yeah. What comes next is like account-based marketing that like after like someone qualifies,

like an account qualifies, you get there. And then at some point there's another qualification that's happening in sales starts or things are like more in their mix like how like the account moves into let's say like the funnel and who is responsible for each part of the funnel there yeah so i'll share an example that we added to the book that i think describes this really well which is if somebody goes to a marketing event that the sales team invited the people to, the field marketing team, which is the events team, set up the agenda and got their room for, the demand generation did advertising for, and ABM helped with the advertising as well.

And that person at the event ate the croissant and listened to the speech. Who gets credit? And at the end of the day, we just care that they ate the croissant and got the information. We don't necessarily care which of those teams gets credit. And when you think about it that way, there's multiple touch points from different pieces and parts of the organization into those accounts that are contributing at any given time.

So it's not a direct like one-two punch like it is. ABM is typically ABM first, then SDR one-two punch. The thing that I'll share though, is that our demand generation team goes and targets our entire account base, our entire database, right? So they're going to get all of the named accounts that are owned by sales. They're going to get accounts that aren't owned by sales that our corporate account executive team will follow up on. And then my team and account base is only targeting up to 30% of those. So they're going after a much, much broader audience and they're going to get a lot more touch points intentionally than my team is. And then same thing with field marketing. They're going to be throwing events and they can be

massive 12,000 people at a company conference, or they can be a boutique event that has 10 people that are all high level and they're doing an executive event of some kind. So they're going to touch a different number as well. And so we're really looking at between all of these different functions, how are we cohesively touching our addressable market and how are they getting through the funnel together? So it's not quite as linear as one team goes first. I would say in general, a demand generation team is going to touch them first naturally and ABM would come after.

Okay, that's awesome. And I always had like the impression and that comes like probably more like from the more traditional kind of marketing. Like marketing is always always a little bit of spray and pray practice, right? Especially when we're talking about demand generation. But ABM actually feels to me as the opposite of that. It has to be very focused, almost laser focused to even the people that they are getting targeted, right? Yeah.

And the content obviously or like whatever like you are doing there. How is this done? Because like traditionally let's say all the tooling that most people are like exposed to when it comes to marketing are like more into the first category than like the second. So how do you choose RadarStack, for example, and make sure that Eric is going to read the right type of blog post or email, or I don't know, like what kind of like channels you are using there, like to deliver like the message. But how do you do that? Because like, to me, it feels like magic, to be honest. So I'd love to hear that. Yeah, so both of these are very data-driven,

but we rely on technology, right? So we work with different vendors that enable the capabilities to target individuals with display advertising, for example. So the little banner ads that follow you around the internet that we can do it at when we put in our audience, it can be the company name, it can be the function, the title, the seniority, they provide us with these parameters that we can choose from, and we can build that audience. We also use a vendor that allows us to put a person's

email address in as the targeting. And so when we can get to that people level personalization, we can deliver a hyper relevant message. And then these vendors are starting to be able to reveal who actually received it as well. So did they view it? Did they click it, etc. So we rely on them. And then we also rely on our data team internally to take all of those data points and help make sense of it at a bigger level, because we can't look at individual people all the time for everything. But that's what it comes down to is innovative technology. And we tend to prefer to work with emerging organizations that are really building out the newest, best, most innovative technology for us to leverage.

That's okay. That makes a lot of sense. And then, especially listening to Travis and what comes also from my experience, like with sales, sales tends like to get a lot of signal, right? Through the interactions that they have that are also like a very interesting signal because it tends to be, because it is personal, tends to be like very high, like let's say bandwidth kind of signal, but it's not signal that you can easily quantify. It's more qualitative in the most cases, or it's really hard to add in a system and make it

electronically available. And let's not get into the LLMs and all that craziness. But how do you deal with like the fusion of so much information coming like from so many different directions that in many cases, it's also like not quantitative information, right? It's not like the number of signups or like how many times someone clicked the button or how much data they processed, right?

How do you do that? Yeah, there's multiple sides. So on one hand, we have a lot of anecdotal feedback from sales that, hey, I was stuck and this data or this campaign or whatever it might be is the first time I've been able to get a conversation in the last two years with this account. That type of feedback or quote data that's anecdotal

is difficult to quantify in any way. And so we'll capture that feedback and share it in our QBRs and other formats. And then the data points that are harder to normalize that are still data points at the end of the day, we kind of funnel into an account engagement number. And so we'll look at in an account, how many people are responding, what are their levels, and then create. There's a very sophisticated propensity model that our data team creates. And then we also have a more simplified version that if there's no campaign

responders, no SDR meetings, no ABM page visits, then they're unaware. If there's X campaign responders or ABM visits or SDR engagement, then they're aware. And then that goes down to engaged, highly engaged, etc. And so we do our best to create parameters to make sense of the data, regardless of what format it is in, because going back to what I said earlier, it's useless if you can't action on it. So our job is to do our best to simplify it with the intelligence teams to make sure we're not, you know, doing anything that doesn't make sense. Yeah, that makes total sense.

And how do you deal with the complexity of having to address like so many different like types of like professionals in there, right? It's like a data engineer and data scientist, they both one way or another, they are going to interact with Snowflake. They are completely different users. They are using... I mean, they think of the world in a very different way, obviously.

They have other priorities. They even might have, from a marketing perspective, completely different channels that you can reach out to them. Yeah. And from what it seems like things just get more and more complex than getting simplified when it comes to like data infrastructure. So how do you manage this complexity, like from a marketing perspective and from an account based like marketing where your job is actually like to deliver the right message to the right person at the right time and do it for all of them. So how do you do that? Yeah, it's a great question because I think that complexity is actually our superpower.

So you think about more traditional marketing and persona-based marketing, you're putting them in those silos that are specific to these different roles. And the reality is humans are complex, roles aren't the same at every company. And so reality is humans are complex. Roles aren't the same at every company. And so what we're doing in account-based marketing is saying this person, based off of what we know, likely cares about X, Y, and Z. And if it overlaps a little bit into other categories, that's fine. Our goal is to get them what they care about. And what I always tell my team is, our job is to help them get a promotion. So it has to resonate with what's important to them in their role at their company at that time, or everyone's too busy to pay attention otherwise.

So it has to be that hyper relevancy. In order to be hyper relevant, you have to be complex. You can't simplify them down into a single profile. Yeah, that makes a lot of sense. Like, it's very interesting what you said about like how you, like they help them to get a promotion. Yeah, that makes a lot of sense. It's very interesting what you said about how you help them to get a promotion. I think that's

very important for everyone who is one way or another involved in getting to markets like a technical product. I think they need to understand that at the end we have people who regardless of how much like excited they are about the technology at the end they are professionals

and they have targets they have their problems there and at the end they need to make a living right so that's that's a great insight um cool one last question from me and this is going to be like a little bit more related to data and the complexity there. We talked a little bit about that also with Travis about how complex modeling just the account can get.

But I would also assume that as time passes, these concepts that you are tracking or even how the semantics like these same, the concepts that you are tracking, or even how like the semantics of these concepts, right. They evolve, they change even because like in the simplest case, let's say you add more product lines. So yeah, like when you started with Snowflake, it was a data warehouse and now it's a data warehouse it's a complete like platform that you can do

so many different things right so things change to change rapidly and also like more importantly like that's my like my opinion is that the the business understanding of the world out there like changes through the experience that it gets by growing? How do you codify that and also make sure that you can evolve, let's say, your data artifacts without breaking anything, right? Because to me, at least like at first, that's hard. That's not easy. Yeah. I mean, at the surface, accepting that things are going to change, I think is the hardest part. I think there's a lot of people in organizations that have worked so stinking hard to get to where they are that it's hard to accept that might not be relevant in whatever amount of time that's coming up. So first accepting that things are going to change. The second is we work with great data

teams. I am not capable of explaining how they continue to evolve our data and how they continue to change it other than never settling. And our CEO has a phrase that he uses about never being satisfied, right? Always going for that next thing, always trying harder, always looking for more. And I think it's that mentality that drives the ability for the organization to come together to drive that change. And then we use machine learning models and other more technical components that that team could speak a lot better to. But it starts with just accepting and knowing that things are going to change.

And we have to prepare for that change from the beginning. So when we're building our models, building our dashboards, building these different things, we have to make them flexible for the future as more data points come in and other data points go out. Yeah, that makes total sense.

And that's like from the, let's say, technology perspective. What about like the human point of view, right? Because all these people like in the sales organization or like in the marketing organization, they need like to have a common understanding of what, let's say, an account is, which is the simplest. But like how, as that involves like all these people needs to be trained in a way like that's right so from and i'm asking you because you have like an experience of this like at scale and like that's like pretty unique how hard that is and like

what are like some good let's say tips like how to make sure as a manager of these teams like to do it? Yeah. Eliminate assumptions, right? If you assume that somebody knows what you're doing or knows how to do the thing you're asking them to do, assume it's not going to get done. Everyone has so many different inputs and asks coming from every direction that it's constant enablement. It's reaching back out. You have what you need. Here's the messaging. Here's the content. What is your priority? Have your priorities changed? So it's a lot of asking questions, especially with our sales team to understand what they're focused on and make sure it's aligned to what we are. At the end of the day, sales are compensated financially, right? Based on quotas. And so you can motivate change if it's aligned with their

quota. If you are looking for change misaligned, you're going to have a mismatch. Yeah, makes a lot of sense. All right, Eric, I'll give the microphone back to you. Okay. I was trying to think of a good question to sort of land the plane on.

What I guess I would say for the listener out there who is on a data team, who is saying, man, that sounds like such a good working relationship. I wish we had that at my company. What would you say to that person? Yeah, I would say you have to learn the business, right? So the things that you give back and the conversations you have to learn the business, right? So the things that you give back in the conversations, you have to be relevant to the business that you're trying to drive. And part of that is that no, or this data doesn't work, isn't really an answer that a go-to-market team can do anything with. And so figuring out how do you help make sense of the data and share the caveats of where

it might be, have some hangups where things might need to change, but you have to help the go-to-market team present that data back to the business, back to the sales team. A lot of times these requests come from sales leaders. They come from marketing leaders. And if the data team, and I've had this experience says it's not, we can't do anything with it. Right. Like you can't just go back with that. So if you can help that business leader to the data, put all the caveats on the table. So there is no, you know, hiding anything, then that's going to help establish that relationship that you're on the same team and trying to drive the same results for the business. Yep. I love it. Well, Hillary, this has been such a fun show. Just so thankful to have you and

Travis on to teach us so many things. And congrats again on your success and writing the book. Thank you. It's been great to be here. Thanks for having us. We hope you enjoyed this episode of the Data Stack Show. Be sure to subscribe on your favorite podcast app to get notified about new episodes every week. We'd also love your feedback. You can email me, ericdodds, at eric at datastackshow.com. That's E-R-I-C at datastackshow.com. The show is brought to you by Rudderstack, the CDP for developers. Learn how to build a CDP on your data warehouse at rudderstack.com.