This week on The Data Stack Show, Eric and Kostas chat with Brendan Short, the Co-Founder and CEO at Groundswell. During the episode, Brendan discusses his journey in being at Zoom during the high-growth stage in 2020 and his journey in founding Groundswell. They also discuss data models and how they create moats for startups, particularly in the sales space. The conversation also covers Brendan’s background in B2B SaaS go-to-market, the challenges of Salesforce’s data model, the potential of data warehouses as an alternative to Salesforce and the role of generative AI in improving user interfaces and sales processes, the future of data ownership, building trust in data-driven tools, and more.
Highlights from this week’s conversation include:
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Eric Dodds 00:05
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, we have a really fun show. We talk with all kinds of people in the data world. And today we’re going to talk with Brendan Short. He’s a founder of a company called Groundswell. But he spent time at zoom. He’s been a founder and has exited startups, many of which use data, but all sorts of focus in the sales space. And he has some really strong opinions about data models, and how they create moats for startups. And his perspective is really going to center on sort of Salesforce and sales tooling, which is interesting. But he’s also the first guest that we add, who is building a company entirely based on generative AI. So his startup is leveraging MLMs to generate actual materials that are sent on behalf of salespeople. Fascinating. So a couple topics we haven’t covered before, which I’m super excited about, I really want to learn about how he is a go to market person and is thinking about building a company on generative AI. Because we’ve talked a lot about ML ops, we’ve talked a lot about MLMs. But he’s taking your company to market. And so I want to hear from his perspective how he is thinking about that. So that’s my burning question. How about you?
Kostas Pardalis 01:52
Yeah, we are going to have a unique opportunity to talk about how data can be molded. But in a very surprising way. I think like, Okay, everyone says that day like I’m being molded, right. But the first thing that everyone thinks when we’re talking about data modes is having some unique secret data that no one else has access to. Right. But with Brennan, we are going to talk about something a little bit different, a little bit more meta. And, but equally, and even, like, maybe even more important and much stronger as a mold. And it has to do with Salesforce. I don’t think to say more right now, but I’m really looking forward to having like this conversation with him about data moves
Eric Dodds 02:43
are well, let’s dig in and talk with Brendan short from groundswell is good friend, and welcome to the datasets show. So exciting to happen.
Brendan Short 02:52
Thanks for having me. Appreciate it.
Eric Dodds 02:54
All right. Well, we’ve known each other a long time actually through many startups, you know, trials, travails and successes. And so because I know you, I’m actually going to ask for you to tell your history in sort of two phases. And tell me if this doesn’t make sense. But you have a lot of your career that has been focused on going to market, which I’m super excited about, because we haven’t had that many people on the show that focus on that. But you also recently founded a company that’s sort of being built on AI. Which is really interesting, because so can you sort of tell your story and maybe those two phases or give us the breakdown? That makes most sense?
Brendan Short 03:35
Yeah, absolutely. So yeah, I’ve spent the better part of a decade in b2b SaaS going to market so squarely, you know, in b2b SaaS and literally started off as an SDR 10 years ago or so. I didn’t really know what that role was for about a week, and I realized what it was in quite the shock. Cold calling cold, emailing. Brutal. As Right, exactly. So I didn’t understand this role. And yeah, it basically is just knocking on doors digitally all day long, and being told no 99 out of 100 times. And that’s what success looks like. And yeah, it was, you know, early SaaS days, I guess. And was lucky enough to join a company, my second company out in San Francisco, which was the first employee and we grew to about 100 employees. We went from basically zero in revenue when I joined to about 10 million in revenue in just shy of four years. And then I left, did some consulting, ended up joining or actually started a company. My first company SAS Company, which was acquired, is a good base hit after a couple of years. Then I left in just trying to figure out like, do I want to start another company or not? And ended up joining Zoom Back in 2020, and so we doubled headcount that year. We can talk a little bit more about that there’s a bunch of challenges, a bunch of fun things, crazy things that are happening in 2020. At zoom, of course, lots of things were breaking, lots of problems to solve. And I was leading operations and enablement for the BDR. Team, they’re the BDR that Zoom rolled up to marketing. So it’s people that are doing net new sales, but also doing upsells and cross sells. So we can come back to that at some point as well. And then, yeah, most recently, you know, I started my second company, SAS, that leverages generative AI, and I’m definitely in the camp of like, I think generative AI is the biggest technology shift since the internet itself. I think it is actually kind of bigger than mobile, I think it’s, I think it’ll, I think it’ll touch basically every company, and certainly will change SAS. And I think it’ll change a lot of the data world. So I’m sure we’ll get into that. But I think it’ll also just generally change product building, and how people are building products, and their interfaces of how people are interacting with those. And for my lens to go to market. Rep. Historically, or I’m still, I’m still rep as a CEO, of a founder of a company today. I think also, for sales reps, the way that they interact with software, with data is going to be very different. As we continue to see the AI craze unfold in the coming years. So that’s where I’m at today. Yeah.
Eric Dodds 06:40
And what’s the name of the company you found? Just so everyone knows?
Brendan Short 06:44
Yes, it’s called groundswell. Thank you for helping me plug my company.
Eric Dodds 06:48
Oh, yeah, totally. Your humble guy. Okay, I am really sad that I am going to save dessert for Kostas. Because I think that he has a bunch of questions about AI. I want to cover maybe two things. So first of all, I would actually like to go back to the days that Zoom during the pandemic, when they were experiencing sort of, ironically, explosive growth in a time when, you know, many things were in a state of chaos or implosion. And you were leading a team, and you had mentioned operations in your title. And so in any company, the size of zoom operations is going to require a lot of data to actually run your organization. You describe your experience as a data consumer, this is something that we don’t often get to hear about on the show. You were a data consumer, you were trying to manage explosive growth. And I’m sure running a large team that you had all sorts of data needs. What was that experience? Like? What were the limitations you faced? And then I think this is three questions. But I’m bad about doing this. How did that influence your decision to start groundswell?
Yeah, yeah, so all I’ll take the angle here of being the go to market person that is frustrated with data engineers or with the data people that own the data. I would say that as a data consumer. Another way that I would put it is like black box. I think it was unclear to me what data we had, what data we could use, what data we didn’t have, but we were able to use. I think a lot of these questions are probably top of mind more for going to market, folks. I would also say that some of it falls into the category of like, as the go to market person. Even in operations, like I don’t know what I don’t know, to some extent, I need education, even within my own organization at zoom, of what data we have. You know, in our world, we are using Salesforce at zoom, as many companies do Salesforce or HubSpot, that’s like the primary way that go to market folks are consuming data in a lot of cases. And that’s an interface in which reps you know, sales reps, sure, or act actioning the data. And so if it’s not in Salesforce, like it basically doesn’t exist, maybe there’s third party datasets that we’re looking at. But we don’t have access to this magical place called the data warehouse. We don’t have access to other tools, you know, like RudderStack, and others that maybe product people have the data engineers have, we just have, you know, what gets pushed to us into our interfaces. And so zoom, I think that was an interesting moment for me where when I joined zoom from the outside, it’s like, the magic of zoom is this free product, right? It’s this product led growth motion. And when I joined, and I started like researching Dang, like within the team doing interviews with, you know, reps there, and with leaders and ops people is like, okay, you know, let’s roll up the sleeves, like, show me this treasure trove of data that is there for users. And it turned out it was actually basically not being used. It was a little bit, but very little of that data was being exposed to salespeople. Some of it was by design, but a lot of it was actually just unintentional and oversight. And so I think that was, for me, a very big, aha moment, that probably only came from the outside, was it possible for me to see with kind of fresh eyes to say like, we have to be using this data like this is actually a huge component of what makes zoom magical. And what makes any plg company magical is being able to actually leverage that data and activate that data within the go to market motion.
Eric Dodds 10:52
For sure, no, okay. So my first question is, did you have a good idea of the data that you wanted? Because, you know, I come from a marketing background, not necessarily a sales background. And so, marketers are notorious for saying, Well, I want this data, but it’s a very ambiguous request. What’s interesting to me about your experiences, Zoom, is you’re probably using it every day and your reps are literally using the product you’re selling every day. And so any friction point or any sort of experience, my guess would be that you actually had a ton of intelligence about the data that you wanted to use to empower your team with. Is that accurate?
Brendan Short 11:34
I think so. Yeah, it’s an interesting thing to think about. I mean, I think as a consumer of the product, dogfooding, our own product definitely helped. There. We also thought a lot of products at zoom that I didn’t even know existed until I joined full time, other products within the Zoom suite that I had no idea existed. And so that’s probably the case with a lot of companies, you know, as a rep, you’re selling something that you’ve never actually done the job, or may not know what data is relevant, you’re not actually using the product yourself. So I definitely think that that can be a challenge. I would say that this is a bit of a tangent, but as an ops person at zoom, I was doing operations and enablement. So basically, at the core, I was trying to build out the playbook for the BDR. Team. And again, these are, you know, salespeople that are opening conversations primarily. So they’re setting up meetings for accountants’ API’s for salespeople. I think because I had been a rep myself previously, I knew to some extent, like what would be helpful in terms of data, I think you also get into this interesting place that I’ve seen time and time again, and we definitely have this at zoom, where, you know, I see this often with lead scoring, where somebody technical, maybe it’s an analyst or some team, maybe it’s even a marketing function that builds a lead score to help the sales team, what I’ve found is like, actually, salespeople do have fairly strong opinions on what they want to see in a lead score on what a good lead looks like. But they don’t know if that did exist, how to get that data, or where to look for that data even. versus just having a lead score sent to them. And it’s like, okay, this person has an 88. I don’t really even know what that means. But I don’t know if it’s some part of the score or something. And the marketing team threw it over to me, and I don’t have any other options. So I’m just gonna reach out to this company, because apparently, it’s the highest propensity to buy lead, in my book of business. Yep.
Eric Dodds 13:36
Yeah. Yeah, super interesting. Yeah. Can we talk about the interface a little bit? So you said that, if it’s not in Salesforce, it doesn’t exist? And that, to me, is a very weighty phrase on a number of levels. And, you know, of course, I think anyone you know, anyone who is at a company that uses Salesforce, especially our listeners who, you know, have to deal with Salesforce data, it’s probably coming into the warehouse, or they’re dealing with a Salesforce integration, right? It’s just sort of, it’s the behemoth, right, like any company that’s dealing with leads or accounts is using Salesforce. Salesforce, in many ways, is like a pain point for so many data teams, because it’s kind of inflexible, right, like getting that data into Salesforce is not actually easy, right? The data you were talking about? Yep. You know, okay, well, we want a lead score, right? And it shows up as a number in Salesforce without any context, right. And so from the data side, that’s actually a challenge because it’s like, well, it’s actually pretty hard for me to send you the context for that number because my only option is to send you a you know, 88 and it’s going into a field that’s like a number of fields Salesforce, right. How do you give that context? What’s so interesting to me though, is that Almost the entire world runs off of the Salesforce data model, right? I mean, we can, Salesforce has done a lot of interesting things from a marketing standpoint. And, you know, there are a lot of things there. But really at the core, at least my conviction, and I’d love to know, actually, this is a question for you, Brandon, and for you cost us like, I think Salesforce is data model dominance is actually sort of the underlying foundation of their success, right. So you sort of have leads, accounts, opportunities, you have phases. I mean, every company runs off of this, and they can make it as Frankenstein as they want. But like, it all relates back to the same like three or four objects in Salesforce that actually comprise like, what it means to run a business.
Brendan Short 15:49
Yeah, totally. I completely agree. I mean, I think that, you know, if you ask most sales people, and you’re Frank, frankly, like, yeah, Salesforce is kind of a necessary evil. And, you know, it’s become this massive company, I would say, because of the ecosystem, which I think is very interesting. Their data model then is used by all of the companies that plug into them. This is like one of the biggest, you know, people, every company wants to become a platform. Salesforce is the one that blaze that trail for the last decade and a half, right, they are the platform in b2b that everybody looks to to try to replicate. And what that means then is all of the companies that plug into Salesforce and into that ecosystem, groundswell, my company, for instance, has to do the same thing, right? They have to fit into the data model that is existing in Salesforce, right, which is leads, accounts and contacts, which we all know now is like, not really actually a great data model. It is what it is. And it’s, you know, it’s Yeah, the other phrase for Salesforce is like, it’s the carpet, it’s the first thing to be bought, it’s the last thing to go at a company. And like, it’s there, it’s not going anywhere. There is no alternative, maybe HubSpot, hopefully, hopefully, something else anyway, probably an AI first CRM. But I do think that to your point, it’s the blessing and the curse of Salesforce, right is they had such an opinionated data model that anyone can understand it. And when I leave one company and go to a new company, it’s the exact same format. And I’ve personally spun up probably six or eight different Salesforce instances. And it’s easy to spin up like it takes a couple of hours. And you’re basically, you know, working, you have a working instance. But I’ve talked to, you know, hundreds of companies and dug very deeply into Salesforce and sold companies that integrate with Salesforce and literally 10 out of 10 say our Salesforce is a nightmare for Salesforce is a mess. So I think it’s easy to get started, which is the blessing and then the curse is like it kind of breaks at a certain point, it’s actually not good once you get to a certain scale, and then it’s you’re stuck, like you can’t do anything about it. So yeah, that’s, that’s my
Kostas Pardalis 18:02
brother. And I have a question. Do you think that’s the reason that sinks break? Is it because of the data model? Or because of how the data model is exposed out there?
Brendan Short 18:15
Yeah, that’s a billion dollar question. I don’t know the answer. I mean, I can tell you like, at zoom, for instance, you know, we had a dozen different tools for the team that I was supporting. And, you know, reps were supposed to work out of all of those different tools. You know, it is a pretty standard b2b SaaS sales stack. All of them, including a tableau, for instance, is a good example. zoominfo, LinkedIn Sales Navigator, whatever. This is all technically integrated into Salesforce, right? So it’s like, okay, we don’t want reps working out of a, another interface. Salesforce is the quote unquote, source of truth. Great, we have a Salesforce integration, okay, let’s buy that software. But actually, from an operational perspective, when you look at it, like that data is not flowing into the contact object or the Account object. It’s in most cases, just iframed in it’s just some image that’s, it’s in Salesforce, but it’s not actually on the contact level. And so then what happens is, you’re basically left with a bunch of different iframes inside of a login in Salesforce, but reps are still looking at, effectively, multiple different products within this login of Salesforce. And so to me, that’s like, you know, at least in part, the data model or something is broken where I can’t just have it all Rolling up to a single account object or a single contact object. It actually doesn’t really work that way once you kind of get under the hood.
Kostas Pardalis 19:59
Yeah, yeah. I don’t know, maybe it makes sense. I mean, for me, because, okay, like, you also asked me about the data model. And for me, it is like a chicken egg kind of question. I don’t know, like if the data model is, let’s say, what caused the success, or the success of Salesforce made the data model dominant, right, like, I don’t know. But what I know is that the data model right now is a kind of mold for Salesforce, right? Like, whoever decides to go and start a new CRM, one way or another, they will create something very similar at the beginning. That was like, I was very obvious, like, even like 10 years ago, like, when I was working like Blendo, and started integrating with other CRMs, it was like all CRMs were following the same data model. At the end, it was more of like, okay, how we can build, let’s say, a different user interface. And like, whatever experience we can deliver on top of that, right, compared to Salesforce, because Salesforce had, and still has like, its own, let’s say, rough edges when it comes to the user interface, but at the end, yeah, we still have Salesforce, right? I don’t think like the rest of these long tail CRM models like to do something. Something crazy. So I don’t know what goes on. But I do know that if you manage to reach a point where you like your data model, let’s say pretty much defines a whole category out there. Yeah, you’ve done something great. So I mean, this is going to be super, super helpful for you. That’s what he’s my thing.
Eric Dodds 21:49
I don’t know. I don’t disagree, Costas. But I think that’s Okay, so here’s the next question. Actually, this is great. We’re turning this into a panel for you. And Brendan. Yep. This is my next question. So I agree. I mean, I don’t think anyone disagrees that Salesforce has accomplished something pretty incredible with their data model. The problem is that everyone’s Salesforce is a nightmare. Because they’re creating sort of Frankenstein iterations and custom objects, and all this sort of stuff in Salesforce, which, you know, is such a nightmare for data teams. And actually go to market teams. I mean, like, all the crazy customization is happening. This is what’s interesting. So the data warehouse is sort of becoming the central source of truth, right? And in many ways, like, actually, I think, the data warehouse, what they need to displace, is Salesforce, that may sound like a pretty direct statement, but like, Salesforce is the source of truth. It’s so many companies, right? And so in some ways, the data warehouse is competing with Salesforce, the problem with a data warehouse competing with Salesforce, at least in the context that we’re talking about, is that you have unlimited options and data models, right. And that means that there is no opinion and no opinion means that every company is going to do something custom. In the end, that becomes a very costly skill, right? And so, of course, like Salesforce is a data model, being opinionated has been a part of their success. It’s reached its limit in terms of its utility in terms of like, how do we serve contexts, like we talked about the lead score of 88%? And right, like, how can we serve that in Salesforce? With contexts? That’s not a crappy iframe that includes all of this other metadata? That’s really important. That’s so difficult in Salesforce, and like, there’s such a large company, can they overcome that? Right? But the alternative is like, Okay, well, you can build any data model that you want in the warehouse, but then you have fragmentation you have, you know, people building whatever they want. That doesn’t necessarily mean it’s hard for a company, or even a business to sort of wrangle that, right? Like, unlimited customization isn’t good, either. So I guess the question is, if the data model is what made Salesforce dominant, but the warehouse opens up unlimited opportunity, like what does the future look like? From that standpoint?
Kostas Pardalis 24:26
I kind of disagree with what you’re saying, Eric, give out. Explain away. Okay. I don’t think I don’t think I don’t think the problem that Salesforce has is that it’s not flexible enough. Actually. You can go and create whatever custom objects you want there. It is like a database behind the scenes, right? Is this database, let’s say exposed in the same way that a Postgres database, where you connect directly with a sequel client, the ease No and for good reason, because I mean, they’re not selling like a database that you can do whatever, right? Like they are a selling platform where you build applications around sales. Right? So there has to be some kind of guardrails there like, yeah, like in the guardrails, in a way is the core data model, right? Like there has to be some connection there like to connect it with the context of sales. You can’t avoid that. Like, even if you go and build this thing, like on the data warehouse, like you’re going to replicate something similar, right? Now, I don’t think that the problem with Salesforce is necessarily, let’s say, the lack of flexibility, like adding new tables or like new columns, or whatever, right, you can do that. Let’s say what do you think like, for example, that’s like a question back to you already. Right, let’s say, magically, we could put like the de la warehouse as the back end, for Snowflake. Right? Do you think this would have changed anything outside of like, making Snowflake extremely happy, I guess,
Eric Dodds 26:11
made that they might be trying to do that? Maybe? That’s a great question, I think. Yeah, that’s a great point. Maybe it’s an interface question. Maybe it’s more of an interface question. Right. Like,
Kostas Pardalis 26:23
and to add something here. When I’m using the term interface, I’m not talking necessarily about the user interface. Right? There faces like, there are many different layers of interfaces on top of something like Salesforce that end up giving you at the ends an iframe. Right? So I don’t know exactly what the UI is? I don’t think so to be honest. But I think we are lost, like in all these different layers of interfaces there between the different systems and like how we are building platforms on the end on top of it, while at the same time we keep control over our market. Right?
Eric Dodds 27:09
I guess maybe what I would say is, I think that’s a really good point. I think the challenge is that, for most companies, when you think about a data team, interfacing with a go to market team and trying to get stuff into Salesforce, Salesforce certainly has unlimited possibilities for customization. The problem is, it’s extremely expensive, and it’s very difficult to maintain. And there tend to be like just a few people who can understand these custom things that are built on top of it. And so I think that’s why the warehouse is very appealing in terms of creating more flexibility where you don’t have the limitations necessary. So I guess that’s what’s interesting to me about the warehouse and the data most like, basically, if you confine yourself to what’s possible, with the Salesforce objects, everyone ends up defaulting to the main, even if you build something really wild, you still end up defaulting to the main because that doesn’t actually work.
Kostas Pardalis 28:14
Yeah, I want to quickly add something and give the microphone to bring it on, because he’s the guest, by the way, he should be talking more. But I would like to add something. And it is inspired from something that Brendon said, like Brian mentioned at some point that if it doesn’t exist in Salesforce, it doesn’t exist at all. And I totally agree with him. And although I’m coming from the data infrastructure world, I’ll wear my product hat now. And I’ll say something important here. I understand the pain of the data team. Okay, like I totally understand. But when we are talking about building something that’s going to be used by salespeople, we primarily have to serve the salespeople. Is it the same also with the marketing people right? Now, one thing is how we can build tooling for the data teams to achieve what they need without, like, guessing and shaping their lives. That’s one thing, right? But the fact that we are building for like salespeople, to me at least means that like, we can’t replace what the salespeople are used to working with, with whatever we think is better, although we’re coming from a completely different world, right? And I’ll finish here by saying something that I noticed, like the conversation was better. She used terms like our BDR AES. I’m pretty sure that if you go to the people that will listen to our podcasts, and that’s why I’m not not saying something bad about them. They won’t know what these roles are. And that is just an indication of what a complex domain sells Ah, and we have to respect that. I’ll stop here and give the microphone back to Brenda.
Brendan Short 30:08
Yeah, let me just say, I mean, I think from my perspective, this is a, you know, I would say before I started groundswell, but certainly through the experience of starting groundswell. I think that salespeople don’t care too much how they interface with this data, they do need a source of truths that they can trust. And so from my non-technical, I would say, perspective, Salesforce is the underlying database, right, fundamentally, the database and then on top, there’s a UI where you can build workflows on top of it. Right? I think the, where we’re seeing kind of this source of truth, shifting from Salesforce to the data warehouse. Is Okay, so maybe there’s more, especially in the plg world, for instance, zoom, every sign up did not go into Salesforce. I know that’s the case with you know, companies that have millions of signups. You know, without naming names, like you can think of these plg companies, they don’t have them all automatically created as leads or as contacts in Salesforce, right? Because it’s expensive. And because there’s only so much you can do and like a lot of those sales people aren’t actually going to do anything with them. However, those are like very interesting things for the marketing team and for the sales team to know about. And so I think that the question then becomes like, Okay, if, again, if Salesforce has a database, and then let’s just call it a UI and workflows that you can build on top of that. If the source of truth, the database, does shift to Snowflake, or a data warehouse, what does that then mean for the UI on top of it? Can there be this thing that emerges? What is a UI on top of the data warehouse? Where salespeople and marketers interface with the data without being technical people? And I think that’s like literally a UI like how do I as a sales rep, able to understand who are the people I’m supposed to go after today? But then also building out automated workflows on top of that data? And I think that is an interesting question. I mean, just to go back to it, Eric, I think like, it’s confusing to me why Snowflake, when it tried to go after Salesforce, I don’t know, they have some partnership. It’s above my paygrade. But like, to me, it’s very obvious, like, that’s a very big opportunity. If I were Snowflake, like, I would just build a UI on top of my database. And like, I don’t know why you need Salesforce, then, like, if all the billing data is going back to the data warehouse, if all of the you know, whether it’s a customer or not customer, you know, at basically every, you know, previous activity, marketing activities, that’s all going back to Snowflake, I’m kind of now left with like this clunky thing called Salesforce that serves basically no purpose. And so I think that, I don’t know, maybe I’m just not smart enough. But to me, like, I would be very threatened. If I was Salesforce, I would be threatened by Snowflake and other data warehouses. Because at the end of the day, it’s easy to build a UI on top of that database. I think the database and where we are at the center of gravity is, by far, the most important question. And by far, the biggest moat for a company to build. And so I think that the interesting thing for me is like, what does that look like? You know, five years out, if more and more companies are putting more and more data into the data warehouse? I think at a certain point, Salesforce gets squeezed. Yeah. And it’s hard for me to imagine a world where that doesn’t exist. And then we’ve talked about generative AI, but I think that also kind of breaks things. Yeah,
Eric Dodds 33:47
Let’s talk about AI a little bit. So. So I’m gonna, I’m gonna continue with a panel thing here. So this is a concept that has interested me for a long time. If you think about going to market in general, right, that generally includes things like marketing and sales in whatever forms. They exist, right? If you think about the warehouse becoming the source of truth for all this information, right, and in many ways, like what removes the most from Salesforce is the ability to pull their data model into the warehouse and then build things on top of it, right. I mean, that’s, you know, ultimately, sort of the big threat, right, is that you have this but we can pull it in, we can sort of build on top of it. What becomes interesting to me is that you have this central source of truth that is not dependent on the Salesforce data model. And then you can almost imagine SAS tools are a set of endpoints or interfaces that are actually just consuming input or output from this very large data set and ideally, models that are sort of Making decisions on a layer that someone’s configured logic in, right? Like, that’s really interesting, right? Like, even if you think about, okay, when does a rep need to reach out to someone, or when does a, you know, a nurturing email need to be sent? There are entire publicly traded companies, huge fortune 500 companies built on like building email campaigns, right? When you think about all that data living in the warehouse like that, those companies actually in the future just become an API endpoint, where they’re being sent a signal that says, you need to send this user this message at this time, right? Or that’s going to a rep or something. Right? How far away? Do you think we are from that? Or do you think that’s likely? I mean, I think that’s sort of where things are going, where the delivery mechanism is an endpoint. And the logic is mainly managed on sort of a layer on top of sort of the central repository.
Brendan Short 35:56
Yeah, I mean, I’ll get my tape really quickly on that, like, I’m seeing this already. Like, there’s companies, I think that like, you know, the guys over at inflection, you know, them, they’re like building, basically Marketo, but on top of the data warehouse, I think that’s like a very obvious trend, that, again, like you don’t need Salesforce or HubSpot in that world. And so I think that’s definitely going to happen. I also think that like where the logic lives is where the value will accrue. And so I think that’s actually a very big question that I don’t hear a lot of people talking about, but where do you actually build the logic? So as an operations person, I’m building logic in Salesforce, I’m building reports to send to my sales team, I’m building automations, to trigger emails, whether that’s in, you know, outreach and sales tools, or Marketo, and marketing tools. And I think that logic is, you know, that logic is the stickiness of the platform as well. And it’s, and so I think that then the question becomes, okay, if I’m not building that logic, in Salesforce, even if it’s kicking off, you know, a workflow that’s in an external tool, let’s say, you know, Marketo, for instance, where’s that logic going to be built in the future? And can that logic be built in, you know, something that is in the modern data stack somewhere, that is outside of the Salesforce ecosystem. And I think that also, in an AI first world, which I believe is going to, is an inevitable future. I think the other thing is like, okay, logic is kind of 1.0, the 2.0 version of that is machine learning models, right, is actually a feedback loop into, okay, we had logic, it kicked off a marketing email, or it surfaced a lead to a sales rep, what happened, what was the outcome of that email, and then feeding that back into the model. And I think this is where there’s going to be the most value over the next five years is in those machine learning models. And I think if Salesforce doesn’t have that, it’s going to be somewhere in the modern data stack. Or maybe there’s some third party tool that can own those kinds of machine learning models that, again, are ingesting data from all of the different places, and then kicking off workflows are kicking off actions in third party tools. And I think that’s like, where that logic lives and where those machine learning models are housed. is, I think, up for grabs, as I see it right now.
Eric Dodds 38:37
I agree. Kostas?
Kostas Pardalis 38:38
Yeah, it’s, it’s interesting. I mean, there are a couple of different things. A lot going on here. First of all, there is a very, how to have like, strong force in this whole conversation. Okay. And that’s the data itself, like, who owns the data? Like, I think, with all these craziness that’s going on, like right now with AI and all the conversation about where like, that’s how GPT was trained, what data was used, like how we can use like data to do that stuff, like seeing like Reddit or Twitter, like suddenly being like, we’re not going to be as open as we were, because we’re scared, right? Like, we build all this data, like we created all this data at the end and yeah, now it gets scary, right. And I think we are entering like a phase but probably would have entered this phase already where they call I think, like the war like, like the battle like, what’s the best term to use, they’re like in the market right now. We’ll be around data ownership right now. Salesforce. It’s kind of a little bit sad, to be honest, because Salesforce had way too many opportunities. Is to dominate in many different ways. And somehow the miners like to not do it. And that’s not to say anything about the people who run the company, right? Like, it’s just like, extremely hard to do that. But if you think about the Heroku acquisition, for example, they have an amazing opportunity to become like a cloud provider, okay, it didn’t happen. Or like, go into build like to developers and provide, like, developers, like with the tooling to go and build on top of this platform instead of the scales that it is right now. Right. And then at some point, they came out, like with Einstein, or whatever, like the name of these things was right. I didn’t know what he was doing. But I, in my mind, it’s like next to Watson from IBM, you know, it’s like Einstein, and welcome, they talk to each other. And they pretty much say nothing, right? And they’ve had, for decades of like, crazy access to crazy amounts of data to go and build. And like, we can see, like, what’s the value out of these, like, see, like what Microsoft did with the data from GitHub, right? So I think there’s going to be a lot of fighting around who’s going to own the data, I think there’s going to be also a lot of like, how to like, it’s not only market dynamics that are important here. It’s also what the state is going to say, like, what legal implications will be around that, like we are meeting, you know, like, the legal frameworks around these things. And things might change, like, a lot when this came out. So one thing that is super important, and that’s why Brennan, like what you said, like why, like Snowflake is not building this UI on top of it. While I would say that, obviously, sales, Snowflake is after the data, like that’s what they want, right? Like they want the data to be hosted on them. And actually what they say like yes, or like, let’s let us host the data and then Salesforce operate on top of us Marketo the same thing right now, I think that like people then Salesforce right now they realize that no, we don’t want like to we have to safeguards, like these data rights, if we want to survive, like in the long term. And the weapons that they have, like they really like, how powerful, like there are two weapons that they have. One is like the platform, what you said all these integrations with all these systems out there, we’re like 99% of them will like us, we don’t even know about them, like call center software, like crazy stuff that we don’t even know that like markets exist around, but they are huge. And the other one, of course, is like what we’re talking about, like the mode of the data model, which is more of a cultural thing. And in a way like we have a whole army, like a whole generation of salespeople out there being educated around that.
Eric Dodds 43:03
Yeah, man, that is fascinating. Okay, so yes, so many thoughts there. Brendan, do you have thoughts? Because I have another question. But I want you to, I want you to respond to what Kostas said.
Brendan Short 43:17
I guess the question is, like, what this is maybe a dumb question probably is, but by the way, I totally agree. I don’t know what Einstein is, but it seems like it should have been the AI play for Salesforce to have a bunch of data. I guess, technically, though, like, they have a bunch of data. Have customer data, for sure. But I don’t know that they have access to third party data flowing in the same way that Snowflake does into data tables sitting in the same place, multiple different data sources coming into the same data table. So I don’t know, technically, that they could have trained models off of data that flows into Salesforce. Is that right? Or, or, technically? Could they?
Kostas Pardalis 44:02
I mean, I don’t know. But I would assume that if they wanted to extend their platform in a way that it could accommodate that, like they have the luxury of time, and yeah, you seem to go and do that. Right. So it’s, yeah, like, do they have it? Probably not? Should they have it? Today? We say yes, maybe five years ago, we would say something different. I don’t know. Like, it seems to be, you know, like it. Like afterwards, I like to have an opinion on that stuff. But they were already in. That’s like a big part of like, the power that they have, right like is the platform itself, so why not extend outside of the service also like to the data and there is like a lot of data that is generated in there, right? Like when you have, like I remember, for example, when we started syncing data from Salesforce, there were like all these hundreds of like tables that we couldn’t figure out what they were, and they were actually The tables that were generated by applications are like, people were like, installing, like, old center software, for example, right? The call center software was directly like adding data there. Now, obviously, it wasn’t like the whole spectrum of data that could be generated there. But in a way it was happening. It was happening, like to accommodate the interoperability needs of the platform and make it work better, right. So what technically could happen with training on top of this data? I don’t know. That’s something that I would like to ask you now with, like you’ve experienced, you have like with AI, and like what this data can do for the salespeople, if you combine it with, with AI?
Brendan Short 45:45
Yeah, I mean, I think that’s, I think, I don’t know, what is the analogy here, like, if you know, whatever, 10 years ago, five years ago, data is the new oil. I think that’s like, we’re at an inflection point where it’s like, more data is better for sure. But I think we’re at an inflection point where it’s like, actually, you don’t just want to get as much data as possible, you need to figure out how to act on that data, right? And the people that make money on oil are, you know, the oil doesn’t make the money. It’s like the refineries that do something with the oil that actually make the money. And I think that’s where we’re at. Now. That’s what we’re trying to do with groundswell is like, okay, there is all this data in all of these disparate data sources, whether it’s Snowflake, Salesforce, third party tools, like zoom info, you know, and many LinkedIn Sales Navigator, many other tools that go to market folks are looking at marketing automation, etc. I think now is the question of, okay, how do we actually do something interesting with this data? And technically, it’s quite difficult to do that. It’s hard to make sense of this, all of this data that lives in these disparate, siloed places. And so then the question is, okay, can you point, these new tools, right, at the end of the day, like generative AI is just a tool? Can we just use this tool to point to a bunch of these different currently siloed systems, to not necessarily pull it all into a central place, and action it but can we point and say, Hey, go collect data, if it looks like this, or go kick off a workflow, kick off an email campaign, if that certain action takes place in that siloed system. And I think that’s one of the big unlocks that I believe we’ll see in the coming years. And I actually think that, interestingly, I think that there’s maybe a world where you don’t have to centralize all of the data into Snowflake. So this is kind of a little bit of a different point. But I think that there’s a world where generative AI actually unlocks the ability for these decentralized systems to exist, but you bet your ability to actually take action against them at scale, using these new tools. So I think that’s also an interesting trend, where maybe you don’t need to just be spending tons and tons of time and resources, you know, building out and dialing in data tables in your data warehouse, and ingesting all of the data from all these different places into a single place. I think maybe it’s actually you can just have these autonomous agents going out and fetching data from these decentralized sources. And then there becomes an interesting question from like, the business side of like, what is the value of those decentralized places? I think then it does come back to data, I think it is like, okay, to the places with the most interesting data and data exhaust are going to be, you know, that the value is going to accrue there. It’s less about the interface of those, you know, software’s?
Kostas Pardalis 48:47
Yeah, there’s that. I have a question with like, a little bit of like, a fundamental question, I think. So at the end of the day, what is the needs of the salesperson, right? Because that’s why you build all that stuff, right? It’s not like for the sake of technology itself. Like it’s to deliver value. It’s like Shell sales, performing better, or like, whatever. But you go in, I’m sure. You’re doing love, like, go and talk like with the artists, the RSA is all there today. Right? Like, what is the pain that they have? That can be solved by data? And like all these technologies?
Brendan Short 49:24
Yeah, so the pain is, as a salesperson, you’re responsible for trying to bring on board customers, right? And so you have, say, 100 companies assigned to you. And you’re literally handed, you know, 100 logos and they say, okay, you’ve got the next year to go land as many of these as you can and your quota is a million dollars. The question then as a salesperson is like out of 100, who am I picking today? Who am I going to go reach out to and have a sales conversation with and what data unlocks is focus right data unlocks the already for you to say, I’m going to focus on these three companies this month, for certain reasons. And I’m not going to focus on the other 97 companies, because these three companies are the most likely to have a sales conversation with me and the most likely to buy, because of literally hundreds of different reasons. There’s lots of different reasons. And those reasons sit in these siloed tools. And so the way that it works today is sales reps are just literally manually jumping into these different tools, researching, trying to figure out, you know, out of my 100 accounts, who just raised funding out of my 100 accounts, you know, who just got a new VP of it, and that’s who I sell to, and maybe it’s a good time to reach out to them, or my accounts, you know, who used to be a customer and went to a new company. And now they’re not a customer yet, but I should reach out to them. There’s all of these different kinds of sales plays that salespeople are running, to try to figure out what is the highest propensity to buy leads in my book of business? And so I think that the technology, what technology enables is the efficiency, right, it’s the ability for you to get in front of the right customers at the right time with the right message. So that you’re not like a salesperson having to do this, you know, mundane, monotonous, painful work of like, manually researching across a bunch of different tools. Yeah,
Kostas Pardalis 51:25
I think that’s one of the best like, this group shows. I have an explanation, as I have heard about, like, what is the value of sales from technology? To be honest, thank you for that. What was awesome? And what kind of, okay, sounds like to help also, like our audience understands a little bit? What are the shortages? Like all these different tools that you’re talking about? Right? Like, what are they like, what kind of data they’re like offering?
Brendan Short 51:58
Yeah, so it’s a bunch of different data, I’d say there’s a few different categories, some of the data is sitting. So you can kind of categorize it into different buckets, first party data, third party data. So first party data being data in your own system. So that’s your CRM data, data warehouse data, marketing, automation, data, sales, engagement tools. So these are like email sending tools specifically for salespeople, companies like outreach and sales, loft, and Apollo, then you have third party data tools. So these are tools that have contact information, Zoom info is the biggest company in that space. They’re a publicly traded company, a multibillion dollar company, they’ve been around for a decade plus, you know, other companies in that space, Apollo, there’s many companies in that space. And to varying degrees, there’s also companies, those companies and others that are tracking other signals that are relevant for the business. So this might be technographic, or firmographic data. So if I’m a company that integrates with Marketo, let’s say, I want to track cross all of the potential customers for me, which of those companies have recently purchased Marketo, it’s a good time for me to go reach out to them and say, hey, my product integrates with Marketo, we’d love to show you how we can make, you know, Marketo better for you. So you basically want to see all of these different types of I call these signals, basically signals that indicate that it’s a good time to go reach out to have a sales conversation. Yeah, there’s hundreds of these companies that are tracking all of these sorts of signals for sales teams.
Kostas Pardalis 53:39
Yeah, let’s let’s Okay, that’s great. And how is blg? Related to all that stuff? Like, what’s the difference when we’re talking about a product like growth motion?
Brendan Short 53:50
Yeah, so product lead growth is really, I think, the tip of the Iceberg, or the tip of the spear around, you know, where the source of truth is moving. At the highest level, like zoom, every sign up did not go into Salesforce. So if somebody signs up and starts using zoom, they might be at a fortune 500 company that signup actually may not literally the sales rep that owns that account is responsible for reaching out to that account, may not know that person even signed up, much less what they’ve done in the product, or that multiple people have signed up at that same company, right? If 25 people at the same company signed up for a zoom instance in the last week, right? That’s probably an interesting company for you to go reach out to and have a sales conversation and try to serve that customer and see if they’re, you know, potential products that they may be interested in, in using or buying. And so, not because it’s just such a high volume game for these plg companies. All of the users are not going into the quote unquote source of truth for the sales team, which is a big problem. And then the second level of that is what they’re actually doing in the product. Right. So are they using the product more this week versus last week? Are they adding more users? are they connecting to a data source? Are they doing something interesting? What are the events that are happening within that usage, and those are sitting in, you know, kind of traditional product analytics tools, right, whether it’s amplitude, Pendo, Heap, whatever. But again, salespeople don’t have access to those tools either. And that’s kind of overkill anyway, for what a salesperson needs. So I think that again, like the plg motion, just means that there’s a lot more data, a lot more users, and that it’s just too expensive. For all of that to be housed in Salesforce, frankly. And it just breaks the architecture of Salesforce. At a certain point you need, you know, things like Time Series events, you need at zoom, we care about week over week, minutes spent on Zoom, that’s a good indicator that they’re trending up, it’s a pretty difficult thing to actually build into Salesforce. And then by the time you get it built into Salesforce, they’re like, actually, I think I want month over month usage, right, you have to go back and do it all over again. So I think that’s where the problem is really exacerbated in the plg world, as it relates to Salesforce being a source of truth.
Kostas Pardalis 56:24
So while I hear from us that just another huge source of like more signals for salespeople out there. So my next question, then my last one, before I give the microphone back to, to Eric, how is AI? Helping with the fusion of all these signals? Right?
Brendan Short 56:46
Yeah, so I think there’s two two primary ways where it’s helping, as far as I can tell so far, and, again, I think we’re very early days here. So in five years, I think there’s gonna be a lot of things that we’re not clear right now. I think that the first way is what I talked about a little bit earlier, which is kind of these autonomous agents that are going out scanning different data sources and coming back with information, right. So it’s basically what humans are doing, you know, the sales development role. For instance, a lot of what they’re doing is researching text, reasoning through that text, and then writing a message based on that text. That motion is fundamentally what an LLM is very good at doing, searching through text based content, reasoning through that content, and then generating some output in the case of sales, probably an email, or maybe a call script, based on what was found in that contact. So that’s number one. And they can just do this at scale, they don’t sleep, they don’t take vacation, they’re not hung over, they’re much better at doing this, they’re not going to get annoyed after, you know, two weeks of doing it. I think the second one, which is I would say maybe more fundamental to software as a service specifically, is kind of just product building. In general, I think that the UI of Salesforce if you think about that UI is and how salespeople are interfacing with it. You know, as a sales rep, like you are looking at a list, you’re sorting that list, you’re filtering that list, doing all these clicks and drags and drops, to get to somebody to reach out to them. And I think in the generative AI world, what you’re really going to be able to do is, you know, chat UX is a good example of this, although it’s just one. But I think you’re going to be able to just ask the system the question that you want to know. And it’s going to come back with the answer. Versus again, today, the way that it works in Salesforce is I have to click and drag and drop and filter and sort and then I get my answer. And I think in the future world as it relates to building products, there’s going to be products that just interface with a chatbot. Or maybe the chatbot just presents you with information every morning and says these are the interesting things for you to care about, versus having to actually go, you know, find that data is going to proactively send you information that is relevant for your role.
Kostas Pardalis 59:03
Yeah, that makes total sense. All right. That’s all from my side. Eric,
Eric Dodds 59:09
my, you’re lying, you have more questions, but guess we’re getting close to time. Actually. So Brendan, my question is, how do you build trust with the person who is receiving the signals? And I’ll get very specific here. As a data team, we have sort of a tiger team that runs data RudderStack. And, you know, I’m involved in that team, and we send a lot of signals into a tool called six cents that sort of collects, you know, our sort of comprehensive like marketing website, usage data product usage data. We send it into the sixth sense, they combine it with other intent data and They can create composite scores. And, you know, other interesting things, it’s a very powerful tool, actually, it’s amazing, you know how accurate they are in terms of accounts that are interested in maybe like a certain product or whatever. But not all of our sales reps trusted me. And so when I think about the level of detail that the data team at RudderStack went to, to send data into sixth sense, and sort of like, my personal level of trust, have their like, heat score, say for like an account or a lead or whatever, I actually have a pretty high level of trust, because I think the fidelity is pretty strong. How are we going to deal with a world where like, even if the rev ops person and the internal data team are saying, like, guys, like you have got, like, this account is good. Like, you need to pounce on this or whatever. And that’s a struggle in this world, right. But then you have generative AI, obfuscating that even more, whereas like, I feel like we make data driven cases all the time to reps, and this isn’t a dig on, this isn’t a dig on salespeople, I’m just saying, this is the natural course of a lot of things like sales, people want to know that what they’re dealing with, is solid, right? Like, the worst thing they can do is waste their time. That’s the absolute worst thing they can do. And so when you have a generative AI tool, making recommendations, you kind of have like, one, maybe two chances to sort of get it right. How do you think about that, in terms of this tool becoming a useful thing? Because it’s hard, even if you show everyone all the data and prove to them where this is 100%? Or 80? No?
Brendan Short 1:02:02
Yeah, I mean, I do think that it’s important. So. So two things, one, I think as a go to market person, it’s my duty to anyone listening, like involve your go to market team, don’t build that in a silo, ensure that you’re bringing in a couple of stakeholders from the go to market side, including the end users who are going to consume this data, right. So that might be a sales rep. Go get one or two of your best performing sales reps, and pull them into this conversation. And let them kind of help you. At least towards the end of the build of this thing. Or maybe at the very beginning, I don’t know, help you think through like what would and would not be interesting data for them to have access to? I think that’s super important. I do think the data has to be transparent. Like I think that these black box algorithms I’m actually quite bearish on, I think they’re not a good thing, especially in this context. Again, I’ll go back to lead scoring, and it’s similar to the example you just gave, we have this zoom. So we had a lead score that was quite sophisticated, actually. And, and it was built by very smart people, you know, the data science team at zoom, built it. And when I joined Zoom, I was like, okay, cool. There’s this lead score that I’ve seen in Salesforce, like, it’s just a score, but like, how is this made up? Right? There was no context. So that’s number one is like, you need the context of why something becomes an ADA. Okay. That’s, hopefully most people are getting that, to me. Anyway, that’s like table stakes. But number two is like, how do I go back and look at this kind of cookie crumb trail, and understand what is that score comprised of, and it actually took me, I got pinged around and multiple people until I finally got to some Google Doc that was like, here’s what the score is made up of. And what I realized is, and again, it was very sophisticated, it was good, it had, you know, multiple different variables and weighted averages against them. And they were pretty good. The problem was, by the time it finally got in front of the salespeople, there were a couple of missed early days, they kind of were like, I don’t know, if I fully trust this, then it wasn’t used. And then it just dropped off. And so very quickly, there is no feedback loop to improve it. Because of course, like your V one is not going to be great in the same way that a product isn’t going to be perfect. You’re going to iterate against it and a lead score should be the same thing. But you’re exactly right. If the trust is broken early on, like that’s it as a salesperson, I don’t trust that score. I don’t trust that little field and sales go back. I’m done. And then it’s never going to improve because I’ve already lost that trust. And so I think that it’s actually super critical in the same way that we when you first test the software like that first mile product experience is so critical. So if you drop off like I’m just done with that software, I’m probably not going to go back to that software. And so I do think that again, if you want to be open, it can’t be a black box algorithm, there needs to be context sent along with whatever it is where the data is sent. And then you need to involve the end consumer of it. And then the fourth one is you need to be able to actually get buy-in from them, and then improve it over time. So there needs to be that feedback loop, which this is not, you know, I say that about zoom. I’ve seen this time and time again, where some marketing team or some data team builds a lead score, and then it just isn’t used by the sales team. So I think that it’s, yeah, it’s a very common problem. And I do think that generative AI has to become a little bit more transparent, I think you’re going to have to, I think these open models are going to have to exist for people to be able to trust like a chat UX, in the way that that you need to, especially when you’re interfacing with actual customers.
Eric Dodds 1:05:54
A lot of the wall Brendon, we are, we’re over time I told Brooks, this is going to happen, because I knew it would. But honestly, I feel like we scratched the surface, I think we need to get much more into generative AI. And so we’d love to have you back on soon, for part two, just to dig into that piece of it, in particular. But this has been really amazing. I think we’ve learned a ton about going to the market. And I love the conversation about data models, and just sort of how the warehouse is gonna impact things. So thanks for joining us.
Brendan Short 1:06:30
Yeah, appreciate you having me on the blast.
Eric Dodds 1:06:33
What a fascinating show with Brendon Short from Groundswell, Kostas, I believe that’s the first guests that we’ve had that’s building a company that is sort of a bet wholly on generative AI. Is that right? Am I thinking, Am I remembering correctly?
Kostas Pardalis 1:06:52
I think show Yeah, yeah, yeah.
Eric Dodds 1:06:55
We’ve talked a lot about AI and ML workflows, and ML ops. But it was really cool to have Brendan there. He’s a go to market guy, actually. So he’s the founder and CEO, and has a long history on the sales side, believe it or not, I mean, I guess, maybe making up for making fun of salespeople so many times on the show. But it was fascinating. I think there’s two big takeaways for me, because we covered a lot with Brendon, two big takeaways. One was certainly on the AI side, where, you know, his vision for what he believes to be possible for his company, using generative AI was really cool to hear about, it’s easy to get caught up in the technical side of it, you and I talked about the ethics of it, you know, recently, the infrastructure sides really interesting. But he really believes that he can make things drastically better for salespeople using generative AI, which is interesting. But perhaps the even more interesting conversation was around the supremacy of Salesforce as a data model. And that is really one of the topics. I think that on the show I thought about it every single day, the week after the show, because you can say a lot of things that you want about Salesforce, but the lead contact account opportunity model route rules the entire world. And we had a fascinating conversation about whether it’s possible to dethrone the Salesforce data model that has become king. So that was fascinating. Definitely one of the more intellectually stimulating shows that we’ve had, in terms of sort of broad market stuff in a while.
Kostas Pardalis 1:08:56
Yeah, 100% I think it is like a super interesting conversation. Well, we’re having not only because of generative AI, which obviously is a very fascinating topic. And it’s very interesting to see people trying to build in this space today, but also because Brendon has a very interesting background. She is not like a first time intrapreneurial founder. He has done this before. He has done it during, like the previous, let’s say, duration of innovation that had to do with SAS, and cloud. And it’s very interesting to hear all the commonalities between, let’s say, the two eras in terms of innovation back then. And now, what is similar? What is different? And there’s a lot to learn, actually, from all the stories that he shared about Excel. was forced about SaaS, how Salesforce became like, such a dominant product out there. And we also talked a little bit about what can happen like to what can potentially dethrone them, right? How this can happen. What are the threats against the Salesforce show? It’s a very interesting show. Joe about data again, but in some very surprising way, I think everyone’s going to find a very interesting episode to listen to.
Eric Dodds 1:10:43
I agree. So do you think that Snowflake can become the new de facto? Can they create a data model for CRM? We discuss that on maybe that’s the word that sounds like a shock jock episode.
Kostas Pardalis 1:10:58
Yeah, don’t disclose. It’s not mass. Let’s Okay.
Eric Dodds 1:11:02
I won’t disclose. I just wanted to just drop a juicy enticing nugget of it. Yeah, definitely listen to this one. Really fun to talk to someone from the go to market side who has studied data and is building a startup on generative AI and using a ton of data. So definitely take a listen. Subscribe if you haven’t told a friend and we will catch you on the next one. We hope you enjoyed this episode of The Data Stack Show. Be sure to subscribe to your favorite podcast app to get notified about new episodes every week. We’d also love your feedback. You can email me, Eric Dodds, at firstname.lastname@example.org. 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.