Michael Hartmann: 0:23

Hello everyone. Welcome to another episode of OpsCast, brought to you by marketing ops.com, powered by the Mo Pros. I'm your host, Michael Hartmann. Joined today by one co-host. Today I've got Mike Rizzo by my side. So joining us today is George Xing, co-founder and CEO of Super Grain, a customer engagement platform. So prior to and founding Super Grain, George held multiple roles at Lift, including director, head of decision science products, and was a data scientist at Indie Gogo. Before that, he was a fixed income analyst list with Morgan Stanley. Uh, which I find fascinating, the combination of things here. George, thanks for joining us today.

George Xing: 1:00

Thanks, Michael. Thanks guys. Uh, super glad to be here and excited to chat. Marketing automation and marketing ops.

Michael Hartmann: 1:07

Awesome. Yeah, no, I know we talked a little bit ago, what I was actually looking back, it was, we're now late November recording this. It was late August I think. So it's been a while since we chatted, so I'm sure there's been even more movement, what you're doing. But before we get into sort of the novel way that Super Green is approaching, uh, marketing automation, and I think it goes beyond that, we'll get that in a little bit. Like, can you first maybe. Share a little more about, you know, your career and your journey and how it led to starting super grade as a beginning point. Let's start from there.

George Xing: 1:39

Yeah, as you mentioned, uh, thanks for that intro by the way. Uh, started my career actually in finance, which is like totally different on a fixed income trading floor. working at a bank in Hong Kong, so literally the other side of the world. Um, and I think the only common thread was that there was something numeric and something analytical about the work I was doing. Other than that, I hated working in finance. So after about a year I moved over, uh, you know, came back to the States, wanted to be in the Bay Area. I'm originally from the East coast. Super cold winters. Didn't wanna be in New Jersey, so came out. Joined a startup called Indigogo. And the only role that I was qualified to do was something, uh, that at the time was, uh, they call data analytics, which I wasn't familiar with, but turned out to be something that, uh, was useful for the business. And, uh, from there, the rest of my career has been in data and analytics. So spent a couple years at Indiegogo. Um, and then, uh, while I was in San Francisco, I started using. This ride sharing service called Lyft, um, ended up joining them as the, the first data hire there. And then, you know, uh, it just kind of as the company grew, uh, I started working on data science, business intelligence, data engineering, and built out a number of teams there. Um, and it was really at Lift where I saw a lot of the interactions between data and a lot of the stakeholders, um, marketing teams, growth. and a lot of the challenges that were involved. So a lot of my job was just sitting at the intersection of data decision making, figuring out how to get the right data, sets the right data points to the right people in order to make decisions. And whether that was, um, marketing decision makers, whether that was running email campaigns were, whether that was running automation. So as you can imagine, at Lift, we had a number of very, very complex, um, automations for everyth. Uh, from driver incentives to, uh, driver onboarding to new users and coupons and things like this. So that's when I really gained an appreciation for the role of data, um, and, and marketing and, and marketing technology. Uh, that was kind of my first introduction to that.

Michael Hartmann: 4:02

So I, I'm interested in a coup. Couple of follow ups, if you don't mind, indulge me here. So the. Of the, the work in finance. So if for our listeners, you may not be as familiar with George, I'm, I'm a big advocate that everybody should at least learn the basics of finance. So I'm, it's interesting that you say you were in finance, but just like it still applied to what you did, you're doing now, but not as directly. Do you, do you still find that your, your education, your knowledge of finance is still valuable at this point?

George Xing: 4:33

Yeah, I, I think for sure. I think if nothing else that became. Pretty proficient at Excel, uh, when I was working there. And I feel like that is almost the foundation for any kind of analytical number based job is you, you start with a spreadsheet and you figure out how models work and, and I think a lot of the foundational principles, you know, now that people talk about SQL and data warehouses and how you slice and dice numbers. Build bi dashboards. Uh, there's a lot of parallels between that and just looking at a spreadsheet at the end of the day. And so I think that gave us, gave me a lot of foundations. And again, I think it's probably for that reason that they hired me at Indiegogo cuz I wasn't qualified to do anything else.

Michael Hartmann: 5:19

Yeah. You were, you're sort of an Excel Jackie, right? Yeah. Yeah. And so it's interesting cuz I, um, although I wasn't the one doing a lot of the modeling, I worked at a, a. A consulting company, second job outta my, on my career that did a lot of work with real estate companies. So there was a lot of, um, cashflow modeling that went into that for different kinds of real estate, you know, portfolios and stuff like that. And I, so I was around it enough that I learned, that's how I learned. And by the way, they all hated Excel because they were so good at Lotus 1 23 that, that's how it dates me. You probably don't even, never even heard of it, right? Cuz you could all do it with keystrokes. So Mike's over there laughing at me.

Mike Rizzo: 6:00

I know, like I totally know the tool, but I never used it. Um, but like I got my foray into tech and. I always heard about it and never really saw it so.

Michael Hartmann: 6:10

So I think at one point in early versions of Excel, they like to try to get people to adopt it. I think they kept like there was a way you could turn on like. How to use some of the lotus like control keystrokes. Right. You could do slash whatever. Right. So that way you didn't have to take your hand off the keyboard and use the mouse. Right. It was inefficient. Anyway, I'm

Mike Rizzo: 6:32

so funny, there's like, there's like a whole email product around this idea of keystroke, uh, managing your inbox now too. So it's come full.

Michael Hartmann: 6:40

Yeah.

George Xing: 6:40

my, my biggest, uh, transition when I was moved into tech was, uh, moving from a, from a Windows machine to Mac and not knowing my Excel shortcuts and not having the right, so

Michael Hartmann: 6:52

I, I'm using a Mac right now and I love the Mac though. I always told you the one thing I do not like about it is that I can't use the shortcuts in Excel that I was so used to.

George Xing: 7:02

Yeah, totally.

Mike Rizzo: 7:03

funny.

Michael Hartmann: 7:04

it's, that's

Mike Rizzo: 7:05

It is a hard, I have to agree, it's a hard transition to make. Like my first role in a tech job like tech SAS company, I was issued a, you know, standard issue, least MacBook Pro or whatever, and I was like, I was a gamer man. I built my own computers, right? So I was a PC guy, like for sure, all the way. I was looking at this thing like, what do I do with this Like, like it's a whole new language. And then as a marketer, I began to value it greatly. Um, just some of the stuff, yeah. The Excel side of things. Oh,

Michael Hartmann: 7:38

All right, so let me get us back on track. So George, sorry I went a little bit sideways there, but, um, okay. So that experience with Indie Gogo and Lyft, um, said you exposed you to data and reports of the data for marketing go, I'll call it go to market or, or revenue functions, but so is what was the, was. The catalyst for you going, Hey, there's an opportunity to start Super Grain, is that kinda where it's, was that the seed that where it started from?

George Xing: 8:06

Yeah, I, I would say, you know, the, the general theme that I've been passionate about, and I think that, you know, really I cultivated while I was at LI was just kind of this desire to, um, help people make better decisions with data. And that was the, that was maybe the seed that. Wanted me to start something. I, I think I wanted to do something entrepreneurial for a long time. When I left Lift, it was right before the pandemic, and then shortly during the pandemic, I spent a lot of time just thinking about what I was gonna do next. Took some time off, and then with this idea of how do I, um, enable people within organizations to make better decisions with data and do what I was doing. but helping other companies do that as well. Um, eventually, I would say through a number of iterations, we ended up with Super Grain and, um, you know, the direction that we're building in now.

Michael Hartmann: 9:02

Got it. Okay. So let's talk a little bit about what you're doing. And I think this is, this was really fascinating. I know when Mike introduced us, so, uh, and I may get this wrong a little bit, but you, you just, super grains described as a warehouse native, warehouse, native solution for marketing and marketing ops, and maybe even goes beyond that customer experience. So, for our listeners, can you define what you mean by warehouse native solution and maybe even give some examples of, of what kind of scenarios that would mean, or, you know, use cases or maybe even compare, like today you would use. Something that's not warehouse native. And we don't have to pick any specific, say, marketing automation platform, but cuz I think they're all fairly similar. Um, versus what you, how you solve the kind of the challenges.

George Xing: 9:54

Yeah, yeah, for sure. And so, super Grain, uh, is a, is a warehouse native. We're also B2B focused marketing automation platform. And what we really mean by warehouse native is that we, uh, there, there's a couple things. One, we integrate directly with cloud data warehouses like snow. And big query. Um, I, I think the second principle is that, uh, we're using cloud data warehouses as the source of truth for customer data, uh, which is I think one of the big differences, uh, versus traditional, uh, platforms which ingest all the customer data themselves and presume to be the source of truth for customer data. And this includes CDPs and. Um, you know, traditional marketing automation solutions and a number of other customer engagement tools out there. Um, but the key, uh, you know, so, so that's the key thing. We integrate directly with, uh, the customer's cloud data warehouse. And to the extent that we can, we run compute and run a lot of the operations directly on top of their infrastructure. Um, maybe to talk about why we, we took this approach, I think. With the big trend that we saw, and I started to see this while was at Lyft as well, is that companies are, are really struggling to keep data across different platforms in sync. So they have customer data in a number of different places. It might be their crm, it might be a cloud data warehouse, it might be a customer engagement tool, a S A Cs, um, platform. And at the end of the day, it's all the same data, but different teams are making changes and updates to, uh, you know, the same data in different systems. And you have this complex web of pipelines to make sure that they stay in sync. Um, one of the things that we start seeing about how companies are solving this problem is centralizing all their customer data in a cloud data warehouse like Snowflake or big. And that becomes the source of truth that powers all their downstream systems of engagement with customers. And when we started to see that, we thought, Hey, naturally, instead of building yet another data platform that ingests customer data, why don't we just sit on top of the data platform that customers are ready, uh, converging around, which is their own cloud data warehouse.

Mike Rizzo: 12:20

I have so many thoughts on this

Michael Hartmann: 12:22

Uh, like I, I was like, I've got a couple of follow up questions too, Mike, but you go.

Mike Rizzo: 12:26

Um, you know, George, when you and I connected for our listeners like. we don't normally have founders come on and talk about products. Right. Like as, as a sort of a rule for the show. That's something, something that we just, we don't generally do. But the reason we wanted to talk to George was specifically on this show was because this concept of warehouse native is super new. Like it, I, I would argue you're like at sort of the cutting edge of like this new set of adoption that's coming down the pike. At least for me, when the first time I heard it, it was definitely an aha moment, right when I suddenly thought about, wow, if there's an organization that's mature enough to try to actually centralize their operations on a on snowflake or big query on a data warehouse scenario. I have not worked for any organization that has done that yet, However, for those that are doing this, um, gosh, it does make a lot of sense, uh, to potentially have these apps that just sit right on top of that, um, and allow you to interact with that data in a way that is effectively is wholly owned. Right? Like, you know, to some degree. My take on this in Georgia, I'd love to hear your thoughts. To some degree, Salesforce owns your. HubSpot owns your data, right? Like at the end of the day, they're sitting in their infrastructure, their server's like, yes, you own it, you own the rights to it. You can extract it and move it to somewhere else if you'd like. Um, but it's certainly not your own servers or anything like that. Um, you don't, you're not building around your own sort of infrastructure. You're, you're, you're subscribing to software as a service. Um, and. What I'm seeing in the market is, at least with stuff like this, is this sort of shift back to, well, we actually, especially with privacy and compliance laws and all these things coming together, we actually need to own our data more holistically on our own servers that, you know, maybe have other software tools that help enable our interactions with our data sets, uh, and potentially the enterprise that needs to now activate that data and needs tools like a Super Grain or something like. To sort of get back into the flow of, you know, doing a go to market motion. Is that sort of like, am I, am I off? Like is this like, is there a ton of companies doing this and I'm just not working at any of them and like, am I speaking around in circles or

George Xing: 14:54

No, no, no. You're, you're totally right. And, and I, and I think you, you pointed out some of, a couple of the key reasons that I think that the warehouse native approach, um, is compelling to a number of people that we talk to, which is, uh, pri you know, privacy and data, data ownership, right? So with a warehouse native approach, instead of moving your data into, um, another third party, Uh, and copying it there every single time. You need to use it for email automation or, or CRM purposes. You just kind of, it just sits on your own cloud, your own data cloud. And then you can imagine the same way that you have an iPhone and you can install applications that run on your iPhone. You can essentially have data applications that run on top of the data that you already own. Um, it's like if you have your iPhone, And you have all your customer data there. Instead of figuring out a way to plug in a USB stick or something like that and transfer it to another device, um, you just have, uh, all these applications that use the same underlying data and they can also share data, uh, and communicate with other apps that are also installed on your phone. Um, that's maybe kind of the analogy. Um, and that's certainly a few years, probably down the road before we get there, but that's certainly the vision that. I think a lot of this, uh, can potentially get to, um, in a few years once the technology is ready, once the ecosystem matures. And it makes a lot of sense for the end users because you don't have to pay the cost of moving data back and forth. You don't have to worry about data syncing issues or inconsistency between different applications and different platforms. Um, and at the end of the day, you have a single source of truth, uh, that, um, becomes a lot easier. Uh, activating and doing personalization and segmentation, which, you know, I think a lot of folks probably listen to this would agree. One of the hardest parts of, um, you know, using product data and other data that lives inside of, uh, a cloud data warehouse for messaging purposes is actually getting the data into those platforms. So it also solves that problem.

Michael Hartmann: 17:15

Yeah. So, um, I wanna make sure I understood this right. So it sounds like, is there, did I hear correctly? There's a sort of a supposition for this. Uh, warehouse native concept that the, the organizations that are gonna be successful already either have or are in process of building will keep it to customer a, a customer data warehouse. Is that, did I, I'm understanding that right.

George Xing: 17:44

Yeah, I, I would see, I would say that one of the things that we observe is that once companies get to a certain stage, certainly the startups that we work with, um, when they get to series A or series B, it becomes part of the natural evolution of how they think about their data maturity. So, um, around that time, they start to invest in, uh, many different systems. They start realizing the pain of, uh, having data in a bunch of different places. And there's a solution out of the box where now they will, um, invest in a data warehouse, start centralizing all that information there. Also start building out data capabilities to manage the data that lives inside of that warehouse, and then start thinking about how to leverage that single source of truth in downstream places. And so that evolution happens a little bit earlier for some companies, a little bit later for other companies, but as a whole, um, certainly because it's now easier than ever before to set up a data warehouse. It's happening, um, I would say earlier than, than, uh, it was as an industry, uh, even a couple years ago.

Michael Hartmann: 18:52

Interesting. Yeah. I mean my, my head went straight to like, I've, the idea of simply defining terminology that everyone agreed to is what is a customer, right? Seems like it should be an easy thing to define, but it's usually like I've only been at one place that's even come close to doing it. Um, so do, I mean, do you run into those challenges where these companies, um, are still struggling with. The, the taxonomy, so to speak, of what they're doing in terms of that data. So they have the, they have this like, Hey, this need to do, they realize it to to scale or whatever, to grow the way they need to. They need to be more deliberate and conscious about how they manage customer data. Have they gone through, are they typically still going through that process of DEF definitions or are they usually beyond that at that point?

George Xing: 19:44

Yeah, I, I would say it's a spectrum, you know, as with most things are, um, typically we see that, uh, the first use case is that there's a particular. Piece of product data or some product metric that is really important to the business that they wanna use in some type of email campaign or for some type of onboarding flow. So to, to make this really concrete, um, let's say they, you know, a customer wants to send, um, and kind of like a upsell message to the admins of. One of their customers every single time there's five, uh, new users on that account. Right? Uh, when, when that account reaches five total users, well that calculation, like the number of active users on that account is a calculation that is done inside of the, the cloud data warehouse. So one of the first things that'll see is they'll throw all the data that they need into their cloud data. W. In order to calculate that metric. And then the next step is getting that metric into, you know, the system of engagement for doing marketing automation to support that onboarding flow that they want to, um, want to do. And, you know, I, I think like over time they will expand and calculate more metrics. They will build more of like a cohesive taxonomy around all of this. But it really just starts with that first use. And then spans, expands from there.

Mike Rizzo: 21:23

I love, I love that you bring that up. Just earlier today I was on. Call with a community member, and we were talking about, um, how challenging that that exact problem is like in marketing operations. Uh, we are frequently asked, I think, at least in the B2B SAS space, right? Like, how do you do an onboarding nurture? How do you do an activation of some kind for all these users? How do you do. Feature use case usage based sort of marketing activities. And the answer is, is like it's really hard to do, like even understanding whether or not a feature is currently in use. Like a lot of, a lot of folks that haven't ever had a chance to talk to a product team or a developer, uh, they don't realize that, like, that, that isn't inherently built into the code base to just send that stuff around. Right. You know, the, the, the engineers are not sitting there going, wow, the marketer's gonna want to know when a user does this, so I'm gonna make sure I write a piece of code. Like, that's not what they're doing. They're there to build really good product, you know, and, and one of the reasons why I love the idea of pairing up, you know, marketing apps and marketers with, with, uh, product teams, is to try to get to those questions a little sooner rather than later. But that's fundamentally, it's really hard to do. And so, you know, I love that you're tackling. With like sort of a layer of, uh, of technology that can like, at least facilitate that right

George Xing: 22:49

Yeah. Yeah, for sure. And, and this is the exact kind of use case that we're trying to, trying to go for because we see it so frequently. Um, one of the other things I think that, uh, I just thought of as, as you were talking about kind of. This interplay between the product side and what the engineer's instrumenting and what the marketer's able to to use in terms of the data is also just that I, I think some of the lines that we see, at least, you know, from the conversations that we have between, uh, marketing, marketing ops and growth teams, which often have engineers and PMs, those start to blur because who really owns an onboarding flow really, you know, Is it the pm, the growth pm who's kind of, uh, sending in app notifications? Um, or is it, you know, the marketing team that's kind of creating this nurture series, uh, who are really hitting the same customers with very similar messaging just through different channels? Or is it, you know, the CS team? Is, uh, kind of reaching out to somebody who's going through a free trial and trying to get them to, uh, you know, debug their data connection because they have trouble adding out data source or getting really value outta the product. And, and so one of the things that we see a lot is, okay, you have a marketer that's actually sending out an email and signing that as like a CS manager, uh, in the byline, or same thing happens with sales. Um, and, and a lot of these lines start blurring, even organizationally. Uh, you just have kind of one person sometimes that smaller companies that's doing, doing everything, and maybe kind of, you know, one, one takeaway that I have is I wonder if we're headed for a future where things are much more aligned organizationally around the customer journey rather than, You know, strict handoffs between different parts of the customer funnel because, um, certainly one of the things we see with as more product data becomes, um, important in the B2B marketing journey is that, uh, it's much more about there's, there's no linear path from or hand off between one function to the other. Uh, it's all about kind of the customer journey, which is very business. So I'm curious if you guys see that in, in, um, in, in some of the things that Yeah.

Mike Rizzo: 25:27

I definitely do. And I, I think, I think that there's a chance for, you know, we're seeing more of this like, um, you know, adoption of like, what do we believe the customer journey should be? Um, and then how do we sort of implement that, right. And I think like one of the core challenges that I've come across is, The, the realization that like there isn't, there's no like, um, silver bullet that like solves all of the, the problems, right? Like we can't, there's no one answer to any of this thing

Michael Hartmann: 26:07

You wanna repeat that? Cause I wanna make sure everybody heard that.

Mike Rizzo: 26:10

right?

George Xing: 26:10

there's no silver

Mike Rizzo: 26:12

There's no silver bullet that, that solves this. And George, I think for you, like it's a dangerous hole as a product owner. You're probably, you're gonna hear from investors and, and people who are using your product, they're gonna be like, well, can you build AI to tell us what the user journey should be like? You know? And like the thing is, is for

George Xing: 26:32

don't worry. We've already gotten that. Yeah.

Mike Rizzo: 26:35

see, not

Michael Hartmann: 26:36

imagine. What's the build in the best practice?

Mike Rizzo: 26:39

Right. Right. I think, I think my As, as someone who. Is now a curator of like, programming that is community led, right? Like I am, I'm saying like, Hey, what, what does everybody really want? And how can we try to bring that to the, to this community? Um, I think if you can, to the best of your ability, try to try to work alongside your current market, your customers, to figure out what it is that they, what they believe their journey should be and how they want to adopt your product. But I think, don't be afraid to. Create, like carve your own sort of path, right? Um, imagine like an art gallery, right? When someone puts an art gallery together, they actually want to tell a story through that journey on your sort of trip down, this like one particular artist's like timeline or maybe a genre and a set of categor. That person who put that gallery together did it with intention. And the turns, the lefts and the rights and the decisions that you get to make are the story that they want you to experience. And so I think it's like on us to also try to figure out like, what do we believe the journey should be? And like let's hedge our bets and try that for a little while, and then take people down that path. But to your point earlier, George, like the lines are blurred and it very much depends on your business. Like maybe CS people are trying to do upsells through the PLG motion. Maybe sales people are doing it. Maybe the product people are doing it. Maybe the growth team is doing it.

George Xing: 28:13

Mm-hmm.

Mike Rizzo: 28:14

But all of that, no silver bullet one and two. You know what? Just come up with the journey You want to take them on, like curate the experience that you want them to have and focus on just that. And don't divert like,

Michael Hartmann: 28:28

you gotta give it a chance. Yeah, we've been, so I think it's interesting that you asked that question, George, because it Yeah. We've been doing some thinking about, um, re. Rethinking our whole go to market, particularly in the marketing side approach for our business to think around it in terms of a, a marketing funnel. And I have, honestly, I have mixed emotions about, or mixed feelings about it because I think it, it, on the one hand it does provide, uh, some amount of structure so that we're thinking. At least having common way of thinking about how we're going to market and so we can measure it and all that kinda stuff. At the same time, I just don't believe that's the way that people buy and we we're not even in a tech space. Right? I don't just don't think that's the way people buy even a B2B space. Right? They, when they're, and I think of that for my own stuff, like when I'm, when I have an opportunity to research something that I'm just maybe just curious about, I'll spend a fair amount of time doing that and then I might not do anything. For weeks or months. And then cuz I'm kind of still thinking about it, right? And it's not this very linear thing, it's very chaotic almost. So, um, I do, I think that's part of the challenge too, is that, but I think it does matter where, where it does matter is if you can put this information in front of anybody who might interact with that customer or potential customer, regardless of where they are in the organization. I think that's where you start to have the opportunity to really build trust with those people, which then builds loyalty and, and advocacy if you get to that point. But without that, it's really easy, you know, if your customer success or customer support teams don't have visibility into what's going on, marketing and sales, they can really kill a deal, right? Um, so, or a salesperson might come in at the wrong time when there's a pro a product issue, right?

George Xing: 30:16

Yeah, yeah, yeah. I mean, we hear this all the time. It's, it's, you know, People, people say, what's the classic thing where they say, um, your product reflects your org structure, right? Um, yeah. And, uh, or you ship your org structure and, and I think this is a case where, um, the tools that we use reflect kind of the org structures that have been kind of like set. As best practice, right? There's a CS team, there's a sales team, there's a marketing team. So of course, all those teams have their own dedicated tools to manage customer communications for their part of the funnel. But what happens when you know the funnel isn't quite so linear? And when those lines get blurred a little bit, well then those tools still kind of then, then those tools basically start overlapping and um, and it becomes kind of a coordination c. we see this all the time. Um, we talk to customers all the time who are, you know, large at scale companies that everyone's heard of, um, and they still struggle with, uh, this today. And, you know, even basic things like subscriptions and, uh, making sure that you don't receive an email from another system if you've unsubscribed, um, you know, from one. Um, those things are, are. Are, um, are hard to, to kind of manage, uh, which could be surprising. Um, I think at least, you know, as, as someone who doesn't come from a marketing background, um, that was surprising kind of to hear. But it just kind of shows kind of maybe the complexity of some of these challenges and also just the inherent nature and evolution of some of the things that we're seeing in the market.

Michael Hartmann: 32:07

Yeah, I, I think it's, it's really interesting and, but I can testify to the fact that there are definitely companies where multiple systems are not in sync when it comes to things like opt outs. So definitely think that's the case. So it, so what I wanna kind go back to a little bit, you, um, When you and I first talked, you know, and you mentioned it already, right? I, I immediately was thinking about CDPs and I think around the time we talked, we actually had a guest not long before that on talking about CDPs. Cause it was something that I'd been hearing about, didn't know much about. So how do you, like, what's the distinction between the, the. Kind of warehouse native solution concept and CDPs. Is it, are they competing things? Are they compat? Like are they complimentary? Like what's the, what's the difference there? How do you see, see those?

George Xing: 33:02

Yeah, I mean it, I think it really comes down to the source of truth distinction that I was talking about earlier. Um, what CDPs do is they ingest. Customer data from a number of different source systems. It could be your event collector, it could be third party tool, it could be your crm. And then they let you do, uh, they do identity resolution, lets you do segmentation on top of it and then, you know, send it downstream usually to other tools for ad targeting, emails, et cetera. And, you know, I, I. The big kind of shift in the warehouse native approach is, you know, you don't need a separate data store to do all this organization and centralization and identity stitching. You actually just want your cloud data warehouse to be the place where all that happens. And then you're accepting the, the, the cloud data warehouse as truth and, and so concretely what that means, You're putting the onus or, or giving the control of, um, the data modeling exercise, the identity resolution, how you want to calculate certain metrics to the customer rather than being opinionated about, okay, this is what an active user is, or, you know, this is exactly how you, you can choose between these two options for identity resolution and, and because every business is the. That is, is not the same. Rather, um, that means that, uh, you're just gonna be able to have a lot more flexibility and end up with data models, metrics, um, identity, uh, that is tailored to your business, uh, versus something that comes out more out of box that you don't have much control over. Um, that's, that's kind of like maybe just from a. Usability standpoint. And then the other thing I would say is that kind of goes hand in hand is that CDPs typically, again from from our conversations, take a long time to implement because you're migrating a lot of data, you're doing all this crunching, um, in like a separate system. If you can do all that calculation computation inside a cloud data warehouse. The customer is already operating, then it simplifies the process to implement from sometimes months to days. And you know, obviously that's a huge, uh, difference when it comes to business results and getting up and running.

Michael Hartmann: 35:42

Okay, so let me, I wanna see if I can play this back cuz I think I, something just hit me. So I think what I'm hearing, so CDP based on, not what I understand, what you described, it's sort of pulling in data and then pushing it back out. Or some version of it back out to these different systems. They all have their own databases and data structures. Um, and maybe it's doing some calculations, but maybe those applications are doing'em as well for their specific needs. Is that, uh, do I have that about right? For cdp?

George Xing: 36:15

Yeah.

Michael Hartmann: 36:16

Okay. All right. But, and so I think what I'm hearing differently with this warehouse native is that rather than, um, Sort of ingesting, pulling data in from all these different apps or, or, or solution platforms? The, the database is the database and those, these, if you have warehouse native platforms, they're basically just apps that are accessing, um, and maybe sending some data back, but it's based on probably transactional level kind of stuff as opposed to computational kinds of things. Is that the way, am I understanding that right?

George Xing: 36:56

Um, yeah, I, I would say the, the, the CDP is requiring kind of, it's trying to be the source of truth and, and in the absence of a cloud data warehouse, you basically have to do everything that a cloud data warehouse does and then more, and whereas, In a warehouse native approach is you're, you're saying, okay, you have a data warehouse, so I'm not gonna do all the backend processing, I'm not gonna do all the computation, the ingestion, and everything I would have to do as a cdp. Um, I'll just do kind of the, almost the UI layer or the orchestration layer, so the segmentation, the business logic and the actual activation. And so it's kind of decoupling the, the database part from. The interaction and the UI and the application side. A CDP kind of combines the two and one because in a world where you didn't have, you know, cloud data warehouses, you had to do both.

Michael Hartmann: 37:54

Yeah. Well, and so reason I was trying to clarify is where my head went to, cuz as I've already like shared, right? I'm old enough to remember back when it was like mainframes and terminals and client server was a thing like this sounds very much like that. I mean, it's. That's kind of was, it's not quite the same, uh, but certainly, definitely not mainframe terminal, but the client server. That concept of where you're sort of splitting some of the, the, um, processing efforts right between different sides of. The, the, it sounds something like that, which, so it's interesting to me to see kinda the evolution has been to go to all these sort of, um, specific applications with their own databases and their purposes to one that's more like kind of going back to old school with newer technology to support the ability to distribute the, the data and all that. So, To me that's interesting, right? Like I think I finally, that helped me click on how to differentiate this from things like cdp. So tell me if I'm I'll totally off here.

George Xing: 39:00

yeah. No, no. I think, no, I think that's exactly right. I mean, you, you need, um, you need one database at the, at the end of the day, right? And. Your customer data should be stored in, in one place. You don't need multiple copies of it. If you can have everything pointed to that single copy and, um, you know, in a world where you have a cloud data warehouse that is your copy of the data, uh, you don't need a CDP plus another tool, plus another tool that all have separate copies of your data. Um, and uh, and, and I think, you know, that's why. You know, you're starting to see a lot of companies move from, you know, their CDPs over to kind of, uh, adopting cloud data warehouses,

Mike Rizzo: 39:50

Makes a ton of sense. I'm just like, I don't know, heart man. You might have one more question and, and so we could go with yours.

Michael Hartmann: 39:58

I'm, I'm, I'm already realizing we, like, we could have, we, we may need to follow up on this one.

Mike Rizzo: 40:03

right. Um, my, my question is, is like, you know, do you feel. Who's responsible for this, like, transformation around how to think about the activation of this data and like, is it, is it a collective responsibility? Like, like, you know, people just need to get in a room and talk and like, and like episodes like this can help educate them on this is why we need to talk about it. Or is it it like, I don't, who's owning

Michael Hartmann: 40:36

Is it? Is it a business operations? Is it, yeah. No, I think that's actually a really, really good question. So I'm curious to hear what you're seeing out there, George.

George Xing: 40:45

Yeah, I like the short answer is where we think about this question a lot. Um, cause in our go to market, it obviously kind of ha has a big impact on. And where, you know, who we go after and who we talk to, um, and how we position ourselves. I think one thing that, what I'll say is, and I don't think I have like a silver bullet answer here either, but I, you know, I can share some things that we observe. Um, one is that I think, uh, you know, obviously the adoption of cloud, cloud data warehouses makes it easier for companies earlier. To get their data into a better shape. So what I mean by that is, uh, if you talk to me two years ago, right, the average series B company would probably not have their data in a good place. Like, um, they might not even have a data team. Whereas today, the average series a company that we talk. Has a cloud data warehouse and has at least one person who's managing it. So just kind of the shift of data maturity, um, you

Mike Rizzo: 41:56

that's awesome.

George Xing: 41:58

Yeah, yeah. And, and what that means is that, you know, um, the people who really benefit from this are the downstream stakeholders. So, uh, marketing teams go to market teams that consume. I think they're just gonna have access to higher quality data faster in the life cycles. So you're gonna start to see earlier stage startups have more sophisticated life cycle and marketing programs. I think that's, that's the direction that we're headed and that reflects a need generally in the market where companies are collecting more and more product data earlier, earlier in their life cycles and doing more personalization and targeting and workflows based on that product data. Um, I think on the other, talk to marketers. Um, you know, we also kind of see a similar type of convergence where, um, you know, I think there's this stereotype, uh, at least, um, and I think it's kind of wrongly placed that, you know, marketers are not technical or data savvy. I think it's actually the opposite. Um, I think marketers are actually very data savvy and we start. Marketers are actually quite technical, very proficient, able to speak about, um, technical concepts and understand the way that their data is structured and interface very directly with their data team and say, no, this is, I need the data in this format. Um, I need you to get me this data, point a certain way, talk to their product teams, talk to engineers, and be able to ask for what they need in a very specific way. That wasn't the case. Three, five years ago when I was working, you know, at Lift. And, and I think what that means is there's just a much tighter collaboration between those teams, and I think it means that they're able to get what they need faster. I think there's just better dialogue and better collaboration.

Mike Rizzo: 43:56

Mm-hmm.

George Xing: 43:56

Um, so those are two things that I see from my kind of like selfish standpoint. I hope that this trend acceler. Because it'll make our job a lot easier in terms of educating the respective people, um, getting data people to appreciate the go-to-market challenges and the, and the business use cases, but also getting kind of the, the go-to-market teams to appreciate the technology considerations. Um, that's a lot of where we sit and trying to kind of pull those two teams together and get them to talk to each other.

Michael Hartmann: 44:30

Yeah,

Mike Rizzo: 44:31

I really like that, um, George, and I appreciate your answer. Um, hopefully people tune into this episode and we can accelerate that, that learning as well, uh, with you. So thank

Michael Hartmann: 44:41

Well, I, I think what was really, really caught my attention when we first started talking about this was just, I think in the back of my mind and probably said it out loud and public too a few times. Like it feels like there's been, even with all the volume of new. Marketing, revenue technology companies out there. They seem to be really niche. There's not really been something that seemed really sort of a, a a, I hate to use this paradigm shift, but like a significant change in direction, um, like this. So that, um, it's, it's really interesting and I, I'm gonna be really curious to see how it plays out. So, George, thanks for joining us. Anything, anything last minute. And truly, like I have a whole set of new questions that weren't even like, we didn't get to everything we had planned on even here. So, um, anything like last bit of nuggets you wanna share with our listeners before we, we, we sign off here.

George Xing: 45:44

Um, I, you know, I think that the, the one thing that I, I'll say that I learned is, uh, or, or, you know, learned from working with our customers is that, um, Almost universally, you tend to kind of underestimate the benefits of, um, being able to use better data to run the same campaigns. It's like, you know, we talk to people that say, okay, yeah, like we have data that we're not using product data that we're not using, but we haven't really prioritized improving this onboarding flow because, you know, we got like 50 other things that we're working on. And I, and I say, okay, well you. We can run a really like lightweight, simple poc. It'll take like a couple hours to get started and then, you know, at that point, if you can tell, if you see no results or uh, then no harm, no foul. Right? But why not give it a try? And inevitably, you know, I think people under estimate, and it's not because we have a magical tool or anything, right? It's just because people already have data that they're not leveraging and. The impact of personalizing the same emails with just a little bit more, or, um, being able to do a little bit of more routing logic to send a specific segment of users different messages or messages that they wouldn't have received before, um, will drive conversion at like a very critical step of their activation funnel. I think especially for founders and, and people that haven't run a lot of these programs before, your intuition is that, hey, it might not matter that much in the beginning, but you know, uh, it turns out that in general people underestimate the impact that it could have. So, um, I would encourage people to give it a shot.

Michael Hartmann: 47:39

no, I think, I mean that's what's something I've been preaching for a while is to focus not on like these big, easy to measure conversion for things, but looking at. Micro conversions through a customer's journey and, and really focusing on improving those incrementally and they have a multiplier effect, right? Can really make a difference. So, fascinating stuff, George. Thanks for, thanks for joining us today. Um, if folks want to keep up with you, connect with you or learn more about Super Grain, what's the best way for them to do that?

George Xing: 48:07

You can go to super grand.com, which is our website. Uh, also feel free to shoot me a note at George Super Grand dot. Always, uh, excited to have conversations like this, chat, marketing, uh, chat data. So, um, don't hesitate to reach out. Thank you.

Michael Hartmann: 48:24

Yeah. Great. George has been a pleasure. I my mind is really now, Mike, thank you as always

Mike Rizzo: 48:30

Thank you, George.

Michael Hartmann: 48:31

we'll get, Naomi on next time as well, hopefully, and to all you out there listening and, uh, continuing to support us, we thank you and continue to give us your feedback and support and ideas and suggestions for topics and or. Guess so. Until next time, we'll talk to you later. Thanks everyone. Bye.

Mike Rizzo: 48:50

Bye.