The Data Stack Show - 151: How To Unlock the Data Warehouse for Marketing with Chris Sell of GrowthLoop

Episode Date: August 16, 2023

Highlights from this week’s conversation include:The need for reverse ETL in marketing (2:24)Closing the gap between engineering, data, and marketing teams (8:37)The analytics persona’s opportunity (11:53)Interface layer (13:06)Approach to messy warehouse data (15:57)The need for a complicated infrastructure (28:43)Challenges in data integration for marketers (29:26)The evolution of the analytics stack (31:53)Orchestration of the data warehouse (38:39)The role of marketing tools (40:35)Generating custom assets (46:27)The shift towards making data processes easier (48:13)Final thoughts and takeaways (49:23)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. Kostas, today we're going to talk with Chris Sell. He's one of the co-founders and co-CEOs and leads product at a company called Growth Loop. And I'm so excited about this conversation because we talk a lot about

sort of concepts, old and new and trends and hot terminology in the data space. And reverse ETL is something that's come up on the show multiple times, but Chris runs a company that sort of skipped the whole reverse ETL hype and just went straight for a use case, right? So they have a tool that enables marketers to sort of build customer journeys and do segmentation, et cetera, straight on the data warehouse, right? So it just runs right on the data cloud. And they don't refer to it as reverse ETL. That's a really interesting take. They also are five years old. And so a lot older than a lot of the current reverse ETL vendors, which is really fascinating. So tons of stuff to talk about.

One of the big questions I want to ask him is that in terms of data engineering, I mean, you and I both know that data engineers have been writing pipelines that get data from the warehouse to some API downstream forever. That's not a new concept. And it makes a lot of sense to me that they're going after a really specific use case. And you actually are starting to see this among the more recent reverse ETL vendors,

really going after specific use cases. I want to know if Chris thinks that's inevitable. They sort of started there, but we're also saying that happened in the industry. But what do you want to ask him? Yeah, I want to have a conversation with him about reverse ETL in the context of marketing. Why we need to move all this data back and forth. And I think it's, I mean, it might feel obvious like to people why we want to do that, but I think that someone tries like to get into the details of that and give

like a specific use case where this needs to happen, it's not that easy. And I think we have the right person to do that and the right use case because marketing is one of these parts of like the lifecycle of a company where you really have to move the data around. Like the needs is like very clear there. Yeah. And I think that's one of the reasons that reverse ETL pretty much started

from there. I think most of the reverse ETL vendors that we know pretty much started with a marketing use case anyway. I think it's going to be great to hear about the whole lifecycle of data in this use case. Why we need, first of all,

to take the data into the data warehouse and would come just like live in Marketo. And why then again, we need like to take data out and put it back to Marketo. And I think especially like data professionals will find this like very enlightening about why we built at the end,

like all these complex infrastructure and pipelines. Awesome. I am interested in those things as well. So let's dig in and chat with Chris. Let's do it. Chris, welcome to the Data Stack Show. We have so many things to talk about. So thanks for giving us the time.

Yeah, thank you for having me, Eric. And Kostas, very excited to be here. All right, well, let's start where we always do. So tell us about your background and what led you to starting Growth Loop. Absolutely. So by the way, a quick intro, I'm the co-CEO and co-founder of Growth Loop. But long story short, what led me to this point was I actually started in marketing over at Google. So I was a direct response marketer

dealing with the problems of CSVs and data silos that we all know and love if you've ever been a direct response marketer. And basically realizing that I had about 50 ideas and only five of those would ever reach any of our customers. And most of that difficulty was because I did not have access to the data that I needed to run my campaigns. So I was dealing with email marketing in my case. But as I started to grow in marketing, I started to see it in other channels as well. And so the reason we started Growth Loop was we noticed that where a lot of the data in organizations was being aggregated was the analytics stack. So in the BigQuery, Snowflake, Redshifts of the world. And they were using that for BI. And we said, well, why can't marketers use that same data

for marketing and create customer journeys? So we created a customer journey platform directly on the modern data stack to help marketers. Very cool. Now, one thing I want to just dig into right out of the gate here is that what you described a lot of people now call reverse ETL. You know, that's sort of like the hot terminology for, it's kind of funny. It describes like a general direction of data movement. So it's not actually very descriptive relative to, you know, all the tooling, but you intentionally said that, you know, you built a sort of marketing automation,

marketing journey platform on top of the warehouse. So would you say that you were around, you were doing reverse ETL before reverse ETL was reverse ETL? Yes. I mean, like we kind of all were. I mean, if you were a marketer too, Eric, if we were talking about the podcast here

and like we were all sending CSVs to our end channels to target audiences. And what I saw teams starting to do was then they go to their analytics or eng team and say, hey, can you set up an airflow pipeline or a cron job on your computer and just automate CSV for me? And yeah, at the time there was no name for what that was. Now it's been given the name Reverse ETL, but it is just a movement of data. Essentially what you're saying to your marketers is, I know you're going to use 20 tools. I'm going to send the data to every single one of them. And yeah, it makes sense that now with

Reverse ETL, there's a more reliable infrastructure to do that. But what we started to realize too was I was a business user at the end of the day. So to me, the only thing that mattered was, does this thing drive revenue or an impact on my customers in a positive way? If it doesn't, I can't justify it. And so as we were starting to think about growth loop, we thought about it in terms of, yes, reverse ETL going from the data warehouse to the end channels, but how do you actually get somebody to measure value and actually produce campaigns at the same time instead of just transferring data over?

Yep. And so this is really interesting because, you know, what is it, three or four years ago that you started Growth Loop? It was almost five years ago. Almost five years ago that you started Growth Loop? It was almost five years ago. Almost five years ago. Okay, so five years ago. Man, okay, I'm thinking back to five years ago and thinking about, for most companies out there, thinking about sort of connecting to the data warehouse

and directly using data from there in some sort of downstream tool was a pretty provocative idea, right? And I would even say, you know, I'm not going to make up numbers because who knows, but I think a lot of marketers wouldn't even necessarily think about that as a possibility because of the traditional sort of schism between, you know, engineering teams, data teams, and marketing. But you sort of assumed that, you know, based on your experience, can you speak to that a little bit? Like, does that divide closing? Like, you're on the front lines? Yeah, I think, well, one, nobody knew what we were talking about at first. So that

happened, where I think for us, it wasn't obvious. Like now I think business applications will come to the modern data stack and be built on top of it. But if you would have asked me five years ago, I would have never made that statement. Instead, I was at the level of trying to solve a problem for a marketer. And actually, one of the first problems we saw was we were working with customers. They were leveraging Snowflake and BigQuery were the big two at the time. And they would be using Looker typically to do BI visualizations. And then they'd have a data science team producing some machine learning model to predict churn. Predicting churn was actually the first one we saw. And they were like, they did this big presentation to their marketing teams and said, hey, look

what we did. Look at how smart our analytics stack is that we can actually predict when our customers are going to churn. And the marketers were like, cool. Like, that's great. Now we know that. And it looks like we have several that are going to churn. What are we going to do about it?

And they were like, I don't know. Go to this BigQuery table. We'll do some SQL. And so we actually started with just a pipeline that would actually, hey, we had a very simple UI where a marketer could go and select those people likely to churn based on that one field in BigQuery or Snowflake. And we sent that data to Marketo and that was it. And so it was like just solving those specific problems over time. And then marketers at first, they didn't understand. I mean, they still don't really understand reverse ETL or the difference between a marketing

cloud or what we're doing on the journey platform. All they care about is do they have the data they need to run their campaigns faster? Yeah. This is an interesting challenge that I think will grow in size and scope as the data warehouse just to both the data persona and the marketing persona. Yeah. How do you as a, I mean, curious, you know, as a, as kind of a business nerd and a marketer, that's a difficult line to, to walk, you know, in an ideal world, there's a great relationship there, but, you know, practically day to day, like, you know know it's not always that way yeah i think i'll answer that question i just wanted to go back to is the gap you asked me on the last one i missed it is the gap closing are people starting to understand it and they are because of what you just said when you were saying that the day-to-day warehouse is expanding in terms of the data sets that it

holds as it gets more gravity in organizations and more companies are actually adopting it, now they're starting to ask us, why can't this just operate off the data stack? So that's the first time I've seen that. That's been about the last 18 months, though. Interesting. So we had a good three and a half years where everybody was just like...

Well, thanks for educating the market for us. That's a really hard time. Yeah. You see these lines on my forehead? Yeah. But getting your question about both personas. So if you look at it, there's the analytics persona and the conversations I'm having with folks. they saw the opportunity first because they were the ones that were pitching their organization on building an analytics stack and then putting Power BI Looker or Tableau on it because it could

solve their insights and decision-making problems for their executive teams. So they've already gone in internally and pitched this as, hey, this is the future of how we get insights on our business. And so as they reach the end of that curve, they're like, well, what can I do next with all of this magic, this goldmine I've started to aggregate? And you're starting to see the IT analytics leader look around the business and be like, hey, I have all this data and intelligence about the customers. What else can I solve? Now, so they're excited to go talk to the marketing users. Now, the translation layer between the marketers and their use cases and the value and an analytics stack, there's a translation that needs to happen. So a lot of our job really is once that analytics leader is excited, bringing the marketer in the room and talking brass tacks use cases.

Right. And you're playing, you know, you're trying to develop the relationship if they don't have one or make it more trustable if they already do. Yeah. Do you think that this is kind of a question about your philosophy? So there's sort of a couple of ways that let's just still let down because that's hopefully that's even spicier. There's sort of two ways to think about this problem. You obfuscate technical things for the marketer, right? And you just, you're essentially building an interface layer on top of the warehouse. Yeah. Or you expose technical things to the marketer, you know, which means that you sort of give them like deeper access and maybe the ability to use, you know, features that are closer

to the warehouse, more native to the warehouse. Do you have a philosophy on that? Yeah. Generally, the way I think about it is most marketers, there's a couple of personas in marketing. There's the data-driven marketers that want to get in there. And then there's the 80% of other marketers that are like, hey, if you could wrap this up with a bow, that'd be fantastic.

Yeah. So my leaning is our interface's job is to work for that 80%. Yeah. And to make it as easy as possible for them to build audiences and journeys. And I don't even want them to care what data warehouse is running

because they don't frankly need to know. I don't even want them to understand the data schema if they don't have to. So like audience templates, things like that. How do you wrap this up with a bow for that audience? But I think where typically in business tools, what's been the problem today, if that's a SaaS tool and it's only focused on that 80%, that means that's your only users and the technical team ends up hating it because there's no interface for the technical team. Right.

Difference is when you bring SaaS to the data cloud, the cool part is you can service this 80% of marketers, but then I can talk to the data teams and saying like hey did you know all these audience artifacts and everything's written back to the warehouse so you can do analysis and you can use still use power bi or looker whatever you guys want to use and so they still have control by virtue of you resting on the stack they already love to use so like if i'm when i'm talking to other entrepreneurs and talk to them about like where I build an app, my pitch on building it on directly on the stack is like, you kind of get to unlock both personas and you don't even have to create one of the UIs. Fascinating. No, that is super interesting. Okay. Question for you on leveraging warehouse data because you know the idea is great but anyone

who works in a business knows that the warehouse is generally super messy yeah you know and there's filling out there that that helps that but how do you approach the issue of like okay well someone really there's you know marketers who want to use this warehouse data, but it's kind of messy. And so I would guess for you that creates this challenge of like, well, do we try to help solve that problem? You know, you know, the value that your tool sees, how do you approach that? Yeah. So there's two, like two tactics we typically we've taken on. One is we kind of take this approach of meet you where the data is at right now because if you have to do a bunch of manipulation to get started and activate that's going to take time and you want fast time to value if you're in marketing usually

they're coming to you too late in the campaign already and they want it to go and so like what we've done is we have a flexible scheme under the hood. And the idea is that as long as you have a customer's table and some transactions tables with a unique key on them and events tables, I don't care what columns are on them. I don't care what they're named. All you have to tell me is how you want to join these tables together. And that's it. And so one, I don't want you to manipulate your tables if you don't have to.

Right. One, I don't want you to manipulate your tables if you don't have to, right? But two, we're also seeing some things where you may have a common identifier. For example, like email address, and you're using Marketo and Salesforce, right? You want to join those two sources together. That's a great when it's exact match. But we also see cases where it's first name, last name, and they want to join those data sets together and handle misspellings or a shortened last name. There are interesting providers coming into the space there around identity resolution directly in the warehouse. important. But what you can do is actually just get started with that Salesforce data today, start building marketing audiences to expedite opportunities in your funnel. And then you can engage one of the identity providers and they can help you match in the Marketo data set,

which allows you to create more complex customer journeys. So there's always a route to get started. I think that's actually what people miss is they try to be, hey, can I develop this perfect customer 360 data model that all the CDPs have promised me, but do it in my warehouse, spend two years on it, eventually produce business value. And the truth is your executive team's never going to allow you to get two years. So you're going to be cut off and lose your funding before that happens. So the better approach is get the data sets that are there and start activating. Yep. Makes total sense. Okay. Last question for me, although that's usually a lie when I say that, do you think that in many ways it's refreshing for you not to refer to

yourself as a reverse ETL solution, but just to say, Hey, we're building a customer journey to marketers that runs on the warehouse, right? Because it's going straight for the jugular on the use case that you're building for with these certain data sets. But do you think that that's inevitable for reverse ETL, right? I mean, pipelines generally get commoditized over time. If it, if it's just a, you know, sort of unidirectional data flow that is taking data in one structure and sort of translating it to, you know, an API.

It's just a data integration, data pipeline problem. And so, yeah, I just, do you think that the approach that you've taken is actually inevitable for all these pipeline vendors? So this is going to be an interesting debate because the answer is, I don't know. I think so, but I don't know. And so we've obviously gone in a certain direction. And a part of that is because of our background. We were like, we want to justify driving revenue and growth. That's what our business is supposed to do. It just so happens we're on the warehouse.

This is how we're going to do it. And so we knew we wanted to go for marketing use cases and as we get into specific industries yeah that's what a lot of our team does we're talking use cases so like in financial services i'm talking to them about switching people from checking into savings certificates of deposit upselling them into a mortgage that's the level we're talking about. It's like, where are they at in the customer journey? How are you going to use your data in the data cloud to orchestrate that journey? I'm not talking about, hey, do you want to get this data to Marketo? That's not the conversation. So for other players in the space, I think this

is where the debate comes in is like, I don't know, can there just be a strong generic reverse ETL player that doesn't go down the use case path? And for some reason that becomes, you know, that's valuable as an ETL tool. I think they're going to want to push towards value anyways, because that's going to be better for the business model. But can they subsist? I always think of like the ELT tools that still are still there and doing well, like Five Trans, a fantastic partner of ours, and they're still growing. And I know they're doing ELT, but I don't know how far they've gone down the business use case path. So honestly, I'd be curious to hear your and Costas' thoughts on that space, because I think where I'm leaning is, yes, they will have to go to business value, but I see other examples in the market where maybe that's not the case. Yeah.

Costas, what do you think? I mean, you built an ETL company. Would you have started going, like, what do you think? I think the next year or so is going to be very interesting, primarily because, I mean, many of the things that we see, like the industry out there also is going to be very interesting. Primarily because many of the things that we see,

like the industry out there, is also shaped by the market dynamics. Obviously, you have the data warehouses that need the data to deliver value. A data warehouse without data

is nothing, right? And having like a technology and the vendor like Fivetran together with something like Snowflake worked like really well in the past, but that was like a completely different world. Like it's going to be very interesting to see how Snowflake, for example, is going to react when their growth is going to start slowing down. And they will need to figure out ways to keep growing. So why not introduce some of these functionalities, for example, as part of the core solution, just like AWS is doing. So the reason I'm saying that is because

there is a lot of fragmentation, I think, in the modern data stack. Not necessarily with the pipelines, maybe with some other tooling out there, but I think we will start seeing what people are calling consolidation. I'm talking more on the product level. I think we will start seeing, there will be reasons for companies to start getting into the stuff that like their partners were doing in the past.

Right. So quick follow up question. So like as they hit their growth late, slow, they're going to be looking left and right and left is ingestion ELT, which is getting data to my platform and reverse ETL is getting data out of my platform. So I guess I'm curious to get your perspective since you built an ELT company.

Which way do you see them going first, if any? That's a good question. Probably they will go to the ELT side first. And again, I don't think that they will just do everything. I don't think they will go and be, okay, let's replicate exactly what Fiverr is doing on top of BigQuery or Snowflake. It's going to be more use case driven, especially because this company is, I think, a big part of their strategy is to go after the enterprise, and in the

enterprise, you have to be about the use case. That's what we are saying. It's exactly what you said. You can't go to Bank of America and sell like, okay, I'll sell you reverse ETL to Marketo. What does this even mean? It's a much more value-driven conversation that you need to have there. Right. So I think the narrative is going to be a little bit different compared to how companies like Python might not like to grow through this PLG approach where it's more of like the tool is out there, get on it and like figure it out, right? We're not going to be talking that much about like use cases. It's about the product and the it out. We're not going to be talking that much about use cases. It's about

the product and the tech itself. And I think the ELT or the ETL is going to be the first part, primarily because there's more obvious value for the data warehouse vendors there. Data warehousing is all about processing, right? So you need

the data to get in, right? So especially for cases where there's potentially a lot of data that can come in, I think that's the first thing that we will see these companies going after. The other thing, the reverse ETL where the data has already been reduced into some kind of substance and sent it back, I think it requires much more work to figure out exactly how to connect it with the core value that a data warehouse delivers. So that's at least like my opinion.

We'll see. The question is also like, are they going to be building that stuff or they are going to go out shopping? Yeah, that's a good... Yeah, it's interesting to think about which way they'll go first to your question, Chris. But one other interesting thing I would say in terms of them going for the ELT

side first is that the long tail of integrations for reverse ETL is a much more fragmented problem because you're going from like a standardized format, say, to like a bunch of different APIs, whereas ELT is sort of the inverse, right? You are ingesting it into your own system. But the other thing is that I think that there's... I mean, if I was planning market expansion at Snowflake, which I'm completely unqualified to do, they have a huge opportunity to get those vendors to do that work for them, right? I mean, when you think about this is already happening, actually, right? So vendors are already saying like, okay, let's just plug Braze directly on top of Snowflake, right?

Now, how good does that work? Like, you know, for these, you know, varies vendor by vendor. But I think you'll also see a lot of that where the big players are just going to be like, create us, you know, an app, put it in the marketplace. And like you manage this. And they offload that to the vendor, but they still have the benefit of sort of, you know, some level of control through the marketplace. And so I think pushing that long tail fragmented, you know, API integration nightmare off onto those vendors is probably wise of them as well, because they won't spend as many resources trying to manage all of those integrations without that driving, you know, it's like, what's the return on how much compute that's actually going to drive?

Yeah, that makes sense. And if they, I mean, if you do think about the data warehouse, if that does end up being this enterprise, the single enterprise data layer, and then all the SaaS apps start coming to it. Yeah. They're going to want to eventually the ELT side, whether it's through partnerships or white labeling, they're going to want to make that part disappear because they're going to want the vendors that SaaS applications like Grossloop to just sell the value prop that drives compute on Snowflake. And it just so happens it's on all of this data, but you don't want Grossloop to go into a situation where I'm pitching on top of BigQuery yet they only have half the data there. I just want to hide ELT and make it happen. It's got

to be there. So it'll be interesting. I think they're going to try to wrap ELT and event streaming with a bow, but I think they're going to... I bet it's going to be with partners as well. That would be my guess. Yeah. All right, Costas. The mic

is yours. Oh, mine? Well, I guess it's always is yours. All mine? Wow. Well, I guess it's always been yours because you came up with the idea of the show. Yeah, true. You just linked it out. It's a long time. All right.

Let's go back to the basics a little bit. And like, let's talk about like the data that is needed for marketing. And why today we are talking about like such like a complicated infrastructure that is needed for marketeers like to do their job, right? And let's assume we have, we don't have like marketeers right now, like in our audience, but we have like these Gramby data engineers that they get like all these requests about, I want this data from there and this pipeline doing this and that. And let's try like to help them understand why like all these things are like at the end useful and required yeah i think that the the dirty little secret is probably some of them most or maybe most of them aren't required part of this is a problem of marketers don't even don't know what to ask for

because the data warehouse is a black box so they're like feeling around the edges of what this thing is which is their customer data and so they're coming to the analyst team and just saying like, hey, I think this is what I want for my business use case. Good luck world, you don't have to worry about any of this complicated stack. What they would want is like you look at when you're a smaller company, usually what companies end up doing is they go through different stages. Now, I would advocate all companies. I think it's going to get a lot easier earlier on to have a modern data stack. Right now, it's usually large companies, but I think it will get easier. Now, where people what they're actually doing today, though,

is marketers are starting with a HubSpot. And they just start jamming their customer data in there. They start doing emails, and they might use it for the sales team for their CRM. And what starts to happen is they have their customer data and some profile attributes, some basic things like total purchase count, or total amount spent, or last purchase date, some attribute data. And then they start saying like, well, actually, I want to track all the mobile interactions we have with these customers and actually link it together. And then, oh, actually, we're running surveys to our customer through Qualtrics. Can we get the survey response data? Because I want to run a marketing email campaign on that. And then people are like, well, that's not in HubSpot. They're like, well, how do you get it to HubSpot? And they're like, well, are we asking the right question? Maybe we should have an analytics stack. And they're like, yeah, I've actually

been thinking about using Attentive for push notification instead of HubSpot. So I also want you to get that data over there. So could you go get all three of those data sets over to Attentive as well as HubSpot now? So you've had channel splintering, but then you've also had the marketer asking for additional sets of data. And eventually that breaks. So the marketers become very unsatisfied with like an all-in-one solution. And then the analytics team actually says, hey, the proper way to approach this is to have our own analytics stack. And so that gives birth to the warehouse in an organization. And first, all they use it for is for visualization of insights. So then the marketer can ask questions.

They can see how campaigns are performing. And what we're seeing companies, that's when there's a lot of friction though, because you're starting to build at stage two. You have two centers of gravity. You have a data stack and you have HubSpot, let's say. You have your marketing stack and attentive at this point. So at that middle stage, that's where the grumpiest of grumpy analysts are born,

including myself when I wrote SQL and people come to me and ask for these audiences. Because this is the point where the marketer only has access to the customer attributes that are in HubSpot. They don't have access to any of that mobile interaction data. They don't have access to any of the Qualtrics data. So their requests start to be on the idea of Qualtrics or the idea of their mobile application. And they say, hey, I really want to run a campaign to everybody that hasn't logged in to this section of the mobile app in the last 90 days.

And they're like, well, then the data team's like, okay, let me go look if that's there. By the way, we don't even track that marketer. Let's have several conversations, follow-ups about who you can target. Then I'm going to write the SQL for you, pull a list, and why don't you go manually load that into a tentative or HubSpot.

And so that's the stage two where there's the biggest friction is because you have a strong data asset and you're starting to build out a splintered marketing stack. And then the analytics folks are caught in between. And so to unlock data analysts, like really when I talk to data analysts and data scientists, what they want to work on is making that analytics stack the best data layer possible for their business, for insights as well as activation. But then they

also want to work on data science. They want to be predicting churn or LTV or propensity models. They don't want to be working on pulling that latest SQL query because we're stuck in stage two. And so eventually they will graduate to, they consider two things. They say, the marketers come in and say, well, if we have all this problem and I have to keep asking you these questions,

should I buy a CDP? And the data team's like, well, start an RFP process. And they go start talking to all these CDPs that say, we'll centralize the data for you in your stage three. And then the analytics team comes in and is like, are you kidding me? I spent all this time building this analytics stack. Now you're saying our stage three. So you have a unified customer review and another ETL out to the CDP player so you can send your data to HubSpot and Attentive. And so the analytics team starts to stand up to it and say, there's new solutions being built on the warehouse that we can actually activate this stuff through HubSpot,

Attentive. And if you change your mind tomorrow and you want to go with Braze, we can get you over to Braze because you're going to be able to build audiences there. And so I call that the stage three alternative that we're seeing a lot of analytics teams push for because they're kind of tired of, they built up this great stack and then it doesn't go used and they have to actually just pull SQL queries or consider a CDP, which is just another ETL to a island system. So that's a long answer to your question, but that's why I think this stack exists. It started with good intentions. They started with HubSpot, and here we are.

Yeah, 100%. And why is, I mean, okay, let's think a little bit from the perspective of the marketeer. What would be the ideal, let's say, situation for the marketeer? What would be the ideal, let's say, situation for the marketeer? My assumption, and I'd love to hear your opinion on that, is that at the end, for the marketeer to be happy, they would love to live only inside

HubSpot, right? That's where they are doing their work. They don't want to go and use a BI tool or a data warehouse or I don't know, whatever else is required for them to go and create their audiences and then these audiences are going to be moved back and all these things. Are we there today? Do you think it's possible for the marketeers to live only in

their marketing tool of choice and do their work there and always have like somehow the data that they need and the data teams live in their own world without having to mess with whatever is happening like in the marketing world or we are far from that and what do you see out there in the market yeah so the ideal state for a marketer i think you're right is like they have one place to go right and they can run their journeys for a turn wind back program or whatever it is and the channel wouldn't matter the reality is much more splintered than that. So the issue, like even imagine you reverse ETL the data out of your data warehouse straight to HubSpot. Okay, then your marketing team started to use it too. So then are you just going to start replicating your database across all your marketing tools and keep those things in place?

Maybe, but you're also multiplying your data spend by 10 because you're recreating your customer database and all the transactions and 10 different SaaS platforms. So the thing preventing it is marketers should choose different tools. Sometimes they even do it by tea. So you'll have, especially in enterprise organizations, product one and product two, one uses Marketo, the other uses Braze. It's like, good luck, right? So when you have that proliferation, what starts to happen and what I'm starting to see organizations go to on the marketing side is they want their marketing teams to stop thinking as like channel marketers. So like email or I only do Facebook ads or I only do push notification and attempt it and actually think about the customer journey.

Now, the issue with that is typically they have a splintered MarTech stack to be able to do that. So it means to orchestrate the data across all those channels, they have to enter like five different tools. And so what I'm actually starting to see for those organizations is they move more customer journey focus instead of channel focus, is they need an easy way to use all the customer data and go across channels. So my opinion is, essentially, I don't see it going all the one end destination channel for marketers anytime soon. And it's likely never going to happen because of the splintering. I think there will be this CDP

like layer that is centralized off the data warehouse where marketers will go to orchestrate across the channels as they move more towards journey thinking than channel thinking. And how does the orchestration work? Because, okay, I totally get like how the data think can work, right? Like we get all the data, we put it into the data warehouse, we have governance, blah, blah, blah, whatever we do, who are magic there with the data. We have the data that we need right how is the orchestration happening on the other side though because that sounds

not very straightforward especially if you have complicated journeys right yeah so from the marketer's standpoint like in in our platform and growth loop, it's a journey builder tool, like a workflow tool that you typically see where you're saying, I'll give you an example, like account based marketing. Right. So let's say you're targeting your SaaS company, you're targeting 10, 20,000 target customers that you deemed as these are great accounts. And I want them to know about my business. You're probably going to start with paid media, right? On LinkedIn ads or Google ads. So if I can orchestrate that audience of the people at those companies to LinkedIn ads and start advertising to those businesses, that's great. Let's say I do that for seven days. I'm then going to check in my warehouse, well, how many signups or signup events have I received

with interest, right? So I can then say, based on that, I want to trigger a nurture campaign in Marketo. That's a seven day campaign that sends three message sequence to those folks based on the landing page that they went to. Then let's say they actually sign up for a webinar. If I have that event attendance data in my data warehouse, I can then say, hey, that's an intent signal. I'm going to route that as a lead in my Salesforce. All of that can be done. Now, the key is that you have good ELT and you're bringing those data sets in through a five trans stitch. Even like Rudderstack is for the event stream key to bringing that data in. So

you have that mobile event data so you can orchestrate more powerful journeys. The orchestration can be done off the warehouse now as that single location. To date, there hasn't been an easy-to-use interface, and that's what we're building for marketers. If we take the data into the data warehouse,

we also create this orchestration layer on top of the data warehouse, right? What is left in the marketing tools? What is the value that like HubSpot is delivering at the end? Creative and delivery. So I actually think the creative and delivery are challenging problems. Now, what's interesting to me about creative is typically it's a totally different process, a different set of brand marketers doing the guidelines, and they love to use their end tool to do it, right? So I don't see that going away anytime soon. They're going to use those tools to load their HTML files, do their QA testing processes, like that's going

to happen. What's going to be interesting, though, is if Gen AI is brought to the data cloud, which is where a lot of these services are launched, like Google Cloud with the Palm, and now they have ImageGen, and they're going to be trying to, like, you can generate creative or subject lines now from your data warehouse for specific users. That is very possible today while we're talking. So which pieces of content are generated off the user data from your data stack versus in the platform? I think that's going to happen over time. I think some of that's going to get pulled in actually towards the data stack. But I still do think most of the process lives in that end tool. And then of course, the delivery, right? There may be tools

that end up becoming experts on not delivering spam and deliverability at a certain time of day like these end platforms are that come directly to the data warehouse, but I think that still will remain in the channels, right? I've had customers come to us that say, hey, I'd like to use SendGrid. Just trigger the emails through there. All I need to do is load my templates there

and the rest of the intelligence is going to live elsewhere. Does it go that far? I don't know, but it's going to be somewhere in there. Yeah, that's interesting. And so with all these new things that are happening with Gen AI, how do you see

marketing changing? Yeah, so at first, some of the people on my team can tell you I did not think it was going to change much at all. I thought it was a hype cycle. So I like being wrong, I guess. I do think it's actually the real deal. And specifically, I think what we're dealing with is you're trying to explain data to a business user. and typically that's very hard because some of us are technical and speak in sql and database schema design other of us others like on the business side speak in terms of personas and what they want the life cycle journey to look like i think it's a translation layer and if you look at like even our product today we're trying to use a UI as a translation layer. We're trying to make it easy to use the data, right?

What's even better, though, is an LLM saying, actually, here's what this data field means based on the data in it. I'm explaining it to a marketing persona. So I think the way I view it is Gen AI is going to attack each phase of marketing. So one is the targeting and segmentation. How do I explain the data schema to marketers and make it understandable so they can self-service in an easy to use UI? I think it's also going to start to be more of a chat assistant of, hey, what type of journey or sequence of messages across channels should I be sending to churn win back users that is most

likely to win them back? I think it will become an actual guide to how do you do that as a marketer. I still think there will be a human in the loop there for a long time. And then measuring outcomes, how do I see what works best? So which channel performs best for my churned users on my mobile app? I'm going to be able to just ask that question. And if I have a proper data stack, I'm going to get the answer as a marketer. So each of those three, I think it is going to start to infuse itself. And the way I look at it is if Gen AI and the LLM models are all going to be in your data cloud, and I think a lot of enterprise organizations are going to end up training their own localized models on their own data.

When that happens, you're going to want the predictions coming from that. And that's going to be, that's just another accelerator of bringing this stuff, these business apps, towards the data cloud. Otherwise, you're stuck with some random LLM in your SaaS tool and you have no idea how it was trained or where your data is going. Yeah, yeah, 100%. That's one of the things that I also find very interesting

because you see, and it's not just in marketing, right? Even with tools like Notion, for example, where, okay, they bring their own AI assistance there, but it starts to feel like, I don't know, like it doesn't feel right yet. Like, as you said, like being able able to optimize the model with your own data, because the data is going to become a much more important mode than it was before with all these systems. I think it's going to be very important. And I think that's going to be another force that is going to increase the gravity towards the data warehouses and the data stack, whatever this data stack is going to be, right?

I don't know how it's going to look like, but there's going to be much more gravity towards the data, exactly because of this whole AI thing. Well, I think you're right. And one of the unlocks I've seen recently is, so some customers out there will start, they start experimenting faster once the marketers can self-serve, create audiences and measure them really quickly. They start just throwing more shots on goal, essentially. And when that starts to happen,

the question is, how am I going to generate all this creative? You start moving so fast, outstrip creative generation. And so I don't think anybody expected this, but generative AI basically is about to change the name and creative content generation and how you personalize it. And that's why you're seeing like Imogen, Dali. But now I'm even seeing companies actually train the LLMs

on your brand assets and say, actually, you know, that's not really useful to generate a random space photo mixed with a, you know, Dali painting, although it's cool. That is very cool. But now I can actually generate a Coke product image on a beach circa 1922. I don't know. Whatever. But now I can do that and actually have that asset ready to go. That, I think, is going to open up something I didn't see coming, quite frankly.

And I think it does make the data warehouse more valuable, like you said. Yeah, 100%. All right, Eric, the microphone is back to your hands. Well, the first thing I need to say is that I think there will be great rejoicing in the marketing community when you can get sort of LLM speed turnaround on every size of ad that you need for this campaign. I mean, goodness gracious, it's still so painful to do that. Well, we were just doing a product launch

working through on the marketing side. And it was like, that was like the most, the hardest part was all the different asset sizes that you need to go out to these different channels. It's crazy. Yeah, that's wild. I i mean it'd actually be interesting for the ad platforms to just productize that right i just saw google ads just launched something like two weeks ago is basically they took their responsive ads products and then i think integrated palm and

image and their image models and so like am i feeling lucky you're creative based on your goal so awesome so good okay last question for the show we've talked a lot about sort of reverse etl pipelines you know layers for the marketer, et cetera. Outside of all that stuff and LLMs, which you talked about as well, what excites you in the data space around the data cloud that you're seeing out there? Yeah. I think what gets me most excited is it's starting to reach this phase of everybody has the architecture diagram of ingest data warehouse with intelligent models and then activation out to all these channels like i've seen that everywhere and so it's no longer educating about what are we trying to do together it's now about making it easy to do and that's the big shift so like what's going

to end up happening is you got to hide all this stuff like you got to make it easier where the technical teams can get in and get this stuff going easily. But the marketers, business teams, they don't care. And if they do need to care, they're going to keep going with the Salesforce marketing cloud for in perpetuity or go explore CDPs.

And the analytics teams don't want that. So I think what we're about to see a lot of innovation on in the product space and what gets me excited as a product manager is, what's the easy button for doing this? Love it. Well, Chris, thanks again for giving us some of your time. Conversation flew by, which always means it's a good one. And we'd love to have you back sometime. Yeah. Thank you, Eric. Thank you, Costas. It was great joining you all today. And hopefully I didn't talk your ear off. Fastest fascinating conversation with Chris Sell from Growth Loop. And I keep wanting to call them

reverse ETL, but they don't call themselves that. They're sort of a marketing activation tool, which I love, by the way, which we talked about a ton on the show. I think one of my big takeaways from the show that was really interesting was we kind of had a three-way conversation about whether or not the big data cloud vendors will get into the ETL space or sort of the reverse ETL space, right? Like, are they, you know, once they sort of start to plateau on growth, they're going to start looking at other market opportunities. That was a fascinating conversation. Chris had some interesting thoughts.

You had some interesting thoughts. So I loved that one. And I think listeners will love it as well. Yeah. Yeah, 100%. I think as we said, and without revealing too much of what the discussion was,

the next couple of months are going to be very interesting. And I think we are going to see movement in the market. So I think our audience definitely has to pay attention to that part of the conversation. And the other part of the conversation that I think was fascinating was when we started

talking about AI and why AI doesn't look like it's going to be a hype, just a hype. There is a lot of substance there. And most importantly, I've read some very interesting things from Chris about how AI is changing marketing, which is very fascinating. Yes. Again, it's one of these things where like you can see like exactly like the impact that a new technology has in something that like pretty much everyone can understand right like everyone can understand like the marketing fun okay not knowing in

complexity but it's much easier like to communicate that compared like to like vector databases blah blah blah like all that stuff we usually talk about. So it's an amazing conversation about like the real impact that AI has both to the industry, to the marketing industry, and also how that is going to affect the data industry. 100%. Also, a really good show for anyone interested in

a lot of good thinking around the interaction of data teams and marketing teams, and then also the interfaces that need to exist for both if you're building on the warehouse or data cloud. So, great episode. Definitely take a listen.

Tell a friend about it. Subscribe if you haven't, and we will catch you on the next one. 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.