(87) E45: The Inside Scoop on GTM Engineering with Clay’s Everett Berry - YouTube https://www.youtube.com/watch?v=kuG-BR7uBtY

Transcript: (00:01) [Music] Hello and welcome to the Revenue Leadership Podcast by Topline. I'm your host, Kyle Norton, CROO at owner.com. And every Wednesday, we dive deep into the strategies and tactics being used to drive success by the best revenue leaders in the world. So, join me as I sit down with revenue operators to discuss actionable frameworks that you can implement in your business today with no fluff, no sales pitches, and no platitudes. (00:37) If you're measured on pipeline, win rates, and forecast accuracy, listen up. When your product doesn't show up until the second or third sales call, you lose momentum and deals. Walnut fixes that. Gives your reps interactive demos they can personalize and use early. (00:55) So every buyer sees a consistent story every time. The result, faster cycles, higher conversion from demo to OP, and better forecasting based on real product engagement, not guesswork. You ramp reps faster and close more without adding SE headcount. Go to walnut. (01:14) io and hit the get started button to see how you can turn revenue targets into reality with a team you already have. Today's guest is Everert Barry, who is the head of GTM engineering at Clay. Uh everybody listening should know what and who Clay is. They are the uh vanguards of the GTMAI movement in many many ways. Massively successful in the space, growing like crazy with uh marquee investors like Maritech. The best of the best. I say that because there we go. (01:44) Uh Kurlin's on our board too and we're big fans. Um, and Everett, uh, who's been at the company for a little over a year, comes from a pretty unique background, having started his career as a software engineer, a CTO, CEO, then he was a growth leader, uh, and now is running sales and revenue. You can explain what a head of GTM engineering is, uh, Clay, but appreciate you joining me today, and I'm excited for this one. (02:10) Thanks, Kyle. It's super nice to uh, to be on. I I feel like my career is a bit uh varied in terms of background, but actually a lot of it leads into this GTM engineering thing. So started out with my own company. We were basically analyzing foot traffic in retail stores. (02:28) We were actually building cameras and installing them in the ceilings of of of retail stores. Uh and so very kind of heavy technical work. Um unfortunately I learned the hard way that if you have no go to market plan, you uh really cannot make progress no matter how cool your tech is. So um so that's kind of what what happened with with Perceive. (02:48) Um but after that I had the good fortune to begin to work in these different um operator roles. And in fact what what actually happened and this is kind of I like telling the story for folks that are too worried about career planning is I joined my friend's company to help him basically close out his seed round and do the sort of paperwork for it and some of the operations around hiring people and setting up a company. And I was doing that while I was shutting down my previous company. (03:13) And we had hired this great growth guy um named named Derek who was great. He wrote a lot of the initial content for us. He was successful in terms of driving the numbers up. And then a couple months into the role, he just like left. And we didn't hear from him for a long time. In fact, I was trying to text him to like roll over his uh his 401k and so forth. (03:31) Turned out he became a contestant on the CBS reality show Big Brother. Uh, and the reason he wasn't uh the reason he wasn't responding to me was because they take your phone and then um on that show he met his now wife and the two of them won the amazing race like two years later and so um and so that is why I am in growth um and um and at archype it was actually successful. We we grew the user base quite a bit. company was acquired by Clickhouse. (04:00) Um, and then I joined a cloud cost company called Vantage where we were trying to target engineering leaders to help them save money on AWS effectively. And I learned a lot of great techniques doing that. One of them was Clay. So I found Clay. I heard about it um at this like Brooklyn growth meetup in like early 2022 and I used it to enrich GitHub repositories to figure out what companies were using specific kinds of Kubernetes technologies and then we would outbound those companies and the leader their engineering leadership very targeted messaging and that worked insanely well. Um in fact that was like a hugely successful uh set of campaigns for us. I (04:37) ran those continuously, automated big parts of it, and in doing that, I got to know the Clay team really well, um, and eventually ended up sort of leading what is effectively our sales team at Clay. Although to kind of, um, to kind of speak to your initial point, we follow the same like forward deployed AI engineer model that a lot of AI companies follow. (05:03) And so my team of goto market engineers are people that go into customers and help them basically scope and implement clay. Um, and because clay is a consumption product, it's a noode product and it's a product for basically ops folks and expert level users, that has turned out to be the right setup for us. Um, but we didn't know that a year and a half ago. (05:22) Um, and so when I joined it was very much like, hey, let's just go help people build stuff in clay, which uh, since I was already doing that for two years was a really good fit for me. So never led a sales team before you become the head of this one at Clay. That's right. Yeah. (05:40) And in fact, um, in fact, Clay is actually hiring a real sales leader who starts very soon, which I'm quite excited about because as we've gotten to a certain scale, there's you you could probably know this better than most. Uh, there's these thorny organizational problems that come up that I could really use someone to uh to help me help me solve. (05:56) So, um, so so we are we are kind of like headed in that direction, but the core of it still remains like you you got to help users build stuff in clay. You got to scope them correctly on the number of credits that they need. And then, you know, when they get in there, they they got to see results. You got to see pipeline numbers move up. (06:14) You've got to see, you know, data data costs go down, systems get simplified. If those things aren't happening, then you've then then we haven't really succeeded as a as a team. Yeah. And this is the palunteer model you know like embed embed an engineer in the customer have them build things and and uh create value and good things good things will happen. Yes. (06:37) In fact we have go to market engineers at clay who are kind of real gotomarket engineers that are internal and they build our scaled out pipeline generation systems. So we considered calling our team for deployed go to market engineers but then we kind of thought that was getting a little lengthy. So, uh, so yeah. Yeah, it's, uh, back in the day, Shopify chose to call it sales reps, sales hackers, and, uh, that was cute and funny for a while and then we're like, let's just call them account executives. Yeah. (07:09) Yeah. And, you know, I could see I could see a couple evolutions of of what we have today, but 30 30 people later and five go to market engineering teams later, it is working for customers. So we've um if anything we're thinking about how we can scale this model more for longer. Yeah. (07:28) Yeah. And I mean for a for a product with such depth and and you know it's no code but still quite complex. There's just you know you can go as deep and as sophisticated as you want with it. that model is makes a lot of sense and I think where sales is going in general you know that that is um that is a very sensible approach if it's consumption based and if it it it you know the success of the company is driven by uh the success of your customers like it's a it's a very modern modern take on it which is awesome I I do think there's a lot of work in the sales process that is more or less um things like asking for updates and (08:10) and it should be possible in the near future to have more of like a expert level saleserson that is just surrounded by automation which does a lot of that grunt work for them. Um in fact today we do things like we do pre-all research for all of our accounts. We we automate the followup. We even automate the Salesforce stage update. (08:29) Um which is a little bit of an interesting interesting thing. Um, but we don't do all of the kind of routine stuff we could do. Um, but I think we'll we I think I think we might actually get there soon. And um, and you know, if you're just kind of proactively suggesting next steps to your sales reps and and it's defined based on the process that you run and you know how that works, then actually the sales rep maybe doesn't need to be as sort of like medic oriented. (08:54) They can really focus on deep deep product knowledge. um that that's a hypothesis that still needs to be kind of tested broadly in the market, but it does feel like that's where things might be headed. Yeah, it's it's interesting because it feels like it's going to go one of two ways. (09:11) Either you automate a bunch of that stuff and then you know the product expertise and the subject matter expertise matters or you end up delivering the subject matter expertise to people through you know an AI solutions engineer somebody who knows everything you could possibly know about the product and the customer base and and which is sort of impossible to replicate and then the job of the rep becomes more sales centric and more relationship driven and and uh I don't have a strong sense on where we're going. We're doing similar things, automating as much of the grunt work as humanly possible um through a set of (09:47) different tools. So, I think we see the world fairly similarly. Yeah, there is a yeah, there is a counternarrative here. Uh for example, Jason Lenin would tell you that actually what you just said is is the right vision of the future. Um that there will be these these like sales engineer companions on sales calls with just friendly um you know helpful helpful sales reps. (10:12) I'm I'm probably a little more in in my camp and and your camp on more of an expert person. Um but I do see I do see some companies actually for example um working on hiring SDRs once they have exhausted their sort of like autooutbound channels. Um you can think of some companies that are famous for outbound that are actually doing that. Um and so and so it is um it is a space where I think like the further removed you are from what's actually happening on the ground with the field the the it's much harder to make like projections about what's going to what's going to play out. I actually think (10:45) something like engineering and writing code is easier to kind of reason about with where AI capabilities are than something like something like sales. Yeah, very true. Um, so one of the one of the things that was flying around Twitter and LinkedIn last year or last week was this MIT study that said, you know, 95% of AI deployments are failing to produce ROI. Yeah. (11:12) and you you sort of like start to pull it apart and and you know it's a pretty selective sample and you know they're they're trying to grab a headline and a lot of what was written about it was was um clickbaity in nature and I you know you and I both know a bunch of people that are getting tremendous amount of value out of their AI deployments both of our companies uh being being good examples of that. Yeah. (11:41) where why do you think companies are in this place where some of their deployments and we can talk specifically about the the GTM um deployments are are failing to maybe produce what was anticipated. Yeah. Well, a great example of this is the is the AISDR. Um you know that's not something that um most most folks are having success with. It is it is working for some companies actually. (12:04) Most often what happens is it works for a a while and then it and then actually you end up in a place where you're you're worse off than you were before because you you've like crunched your domain and so forth. Um but you've burned your market, you've you've you've tired them out, you know. Yeah. (12:23) But there's a couple things about AISDR that it's like worth actually unpacking that because I think that speaks broadly to um to challenges that folks are facing on other types of AI deployments. So one of them is um in my opinion giving too much control and work to the AI. Um I actually think today's AIS you have to prompt them incredibly carefully for them to kind of really capture all the context. You have to use these tricks like you have to tell them hey think about this for a while. (12:50) You have to do stuff like this in order to make sure the model like really doesn't skip any steps. And so, um, a full endtoend ASDR, I think, is a very tricky thing because because typically there is a lot of hidden context in what an SDR is doing that, um, that is hard to capture. (13:14) And it's much especially hard to capture if all you're doing is giving this kind of agent thing a a rough description of your ICP and a target account list and just telling it to to go email folks, send emails, which right, which which to me is is a recipe for disaster, basically. Um, and like to give you an example of this, at my previous company, we we learned a couple kind of subtle things about our customers that our SDRs were trained on that that it's not clear to me that a it would have to be a very carefully attuned SDR to capture some of this. (13:40) So for example um we learned that when we were outbounding engineering leaders if the engineering leader came up through the kind of backend ops part of the organization they were much more receptive to our pitch than if they came up from kind of like the the front end or like the app development side of the organization. (14:04) if that was the single engineering leader, then we would actually go one level down and look for the kind of like back-end DevOps leader and um and not worry about the the VP of bench. And so um and so that if you think about the kind of like reasoning and steps that a model would have to take to to go through that, you would have to prompt it very specifically in order to do that. (14:21) And you'd almost end up in a situation that looks more like I think what Clay is where it's kind of this like human in the loop guess and check type setup where where each step is actually like a you know there's more detail that's put into it versus kind of this like give an agent a task and have it and have it go do it. (14:42) And so um that's that's sort of like one thing that I think is happening with these AI pilots is they're given they're given too broad a scope um which is a classic problem that uh that even humans encounter. The the other thing that that I've observed is that um people are expecting the AIS to do the wrong things. So, um I talked to customers who are like, "Okay, great. I'm sending all this messaging out from Clay. (15:06) Um now I want Clay to actually monitor the like replies and responses to this and adjust the messaging based on um on the you know what's when messaging is working." And my feedback to those folks is that I think that is actually putting AI in the wrong place. Um I think you should use AI to generate the message to um use the agents to help find data points in your accounts and contacts. (15:33) Um, but you should not use agents to kind of like analyze a messaging campaign and then make adjustments to prompts to generate different types of messaging because there's so many factors that go into um that go into that messaging that a that an AI is probably not aware of including things like you know brand perception, product efficacy um in many cases like even things like history of the account might get lost and So, a really professional marketing demand genen person will do a much better job of taking an initial campaign and adjusting the copy on that than an (16:08) AI. And so, um, and so anytime you're kind of introducing an AI into like a system that has a feedback loop, I actually think the the back part of that should involve a human versus an AI. And I think that's another reason that like a number of these a number of these pilots are failing. Um, and so yeah. (16:27) So, so maybe this is kind of on us as an industry, but I think people are actually overhyped on what AI can do and they're actually missing some of the very basic like data labeling, data cleaning, message drafting tasks that AI is really good at, they kind of don't seem like big picture high value, but when you start to stack up all the wins on those things, you actually get to a successful place versus some of these like agentic things that just are not just are not fully baked yet. Yeah. Yeah. and the the agentic and we're going to get deep into this, but (16:56) the agentic stuff doesn't work without great data underlying it, right? And and so I have a half-written Substack post on this and so it's it's very topical. I'm curious to get your opinion. I think one of the main problems that people have is is this issue of like much too broad of a problem to go solve. (17:22) And so you're actually giving an AI multiple problems to solve at the same time without an intervening human in the loop or like deterministic step. And so I think the problem and so in in the more technical language people call it lossiness. And so if you're taking like one set of prompts and handing it to another set of prompts or workflows and another one, well, every layer of that stack has some lossiness. (17:52) You know, your your ability to control and to direct and to be specific because this is generative technology. It's it's it's uh it can go many different directions, which is strength and weakness. I I find that the that when I see a demo of a really cool product that doesn't work in production, oftentimes it's because there's no deterministic checks and balances. (18:18) There's no human in the loop in a chain of like multiple workflows or agents. And so a good example is like you see a product like oh all you have to do is like enter in your domain and we'll we'll find your target market and we'll come up with your uh positioning and we'll do this and we'll do this and we'll do this. (18:37) Well, all of a sudden there's like slight you know there's there's slight hallucination in variance at each step and then what you get out of it is just like slop. That's where the slop comes down in my mind versus like we are so thoughtful about what our positioning is is at owner with specific language and specific words and we can be very semantic about it but but like the specificity around language matters. (18:58) So I would never trust an AI tool to be like oh yeah just like type in your website and then give me the customer and I'll give you a a target like a target account plan. It's like whoa whoa whoa whoa whoa. You know like I want to say in the prompt here's the spec like you know this is the context engineering thing. (19:21) I want to stuff that very specific context of about how we position the words we use the words we don't use the tone of voice and and so that you're sort of engineering it that way is and this is like my observation. I'm curious to get your reaction though. Yeah. Well, I think that's true. And I think I think one thing that one reason this happens is because the models are confidently wrong as well. (19:40) Um, and so if you get a confidently wrong output from one model and you feed that into another model which kind of takes the prompt as um, you know, as gospel, then you end up with like a doubly confidently wrong um, output down the line. And it's I think a lot of us have had this experience where you're using a um, like chat GPT and you can just ask it like, "Hey, is this right?" and it will actually be like, "Oh, no, you're correct. (20:03) " Like, this Yeah, I actually had this experience. I was on the way um flew into Chicago to do a demo for a customer. I was in the the Uber from from the airport. I was trying to get this Gone Engage API to work. Gone has a brand new API for injecting um custom messaging into Gone Engage, which we're all super excited about. (20:22) And the first pass of the um you know, generate the the post request, like half the fields were just hallucinated. Uh they were they just didn't exist. And I actually had to point it to the URL of the docs where that happened and I'd be like, "Please inspect this URL to actually do this correctly." And and then it did it correctly. (20:40) And so um and so you know when you're dealing with a system like that where not only can you um not tell sometimes if the answer is correct. Sometimes the model will like really convincingly tell you this answer is correct and as a human you're sort of like a trusting person. You believe it and then you chain multiple of that together. That is that is a recipe for for disaster. (21:01) And I I give this advice to customers all the time which um a lot like one thing people try to do in clay is they stuff a lot of context into one prompt. And I I say first of all you know break it up and then as you're breaking it up in clay there's you know the ability to to create these web research agents but then you can sort of like gut check them against the databases of data that Clay has. (21:25) So you can gut check like you know revenue number from clear bit against the revenue number that an agent kind of pulls from the web. And so this is definitely a best practice to kind of like get these models to um to to perform correctly. There's also a ton of prompt engineering in here. If if you look at some of the prompt templates inside Clay, there's like multiple like checks and validations directives and and things like this. (21:49) And and you know this comes back to my core argument of like if you have a system where you really do have to constrain it quite strongly um in order to kind of make it perform right then you really should wonder about products which hide some of that customization from you um because because you know you just don't know if all that is in the product or in you know more commonly you really can't say that that it's being tuned to like your actual thing that you're trying to do. in my experience is like is like you know the vest prompts take multiple iteration loops and that's just the (22:20) state of the technology today. Um the the kind of the fun thing that we've done with this is we now have this metaprompter in clay where you can give it this very general directive and then it will generate a prompt but I advise customers not to take that prompt for for gospel. (22:39) I advise them to go through it and actually read it and almost always when I do that I fix uh you know a couple of things. Yeah. Yeah, even for for my podcast, you you went through this flow that people fill out a tally intake form which web hooks into my personal clay research table. And even with that, you know, it took a bunch of different playing around with, you know, one person who I know well, go and and sending it through a series of of the agents, one that looks at their background and summarizes stuff and one that looks at content and then I put my own questions in and your answers (23:12) go into it. But the it took so many rounds of like no I like I don't want it to sound like that and I go back and fix the the prompt in the column and then like run it again and run it again and just this I I felt like I was doing something silly. I was like man like I wish I was better at this stuff because I'm just like trial and error trial and error and then I talk to AI people and they're like no that's that's sort of how you do it. (23:42) It's and yeah, you know, people call it the the fancy word to be like eval like do you have an eval for the output to measure if it's good, but it it does feel like there is a lot more of this um artistry that's needed and like a a taste for it. somebody uh I was interviewing an AI lead candidate yesterday and he goes, you know, you just develop like a there's like a mouth feel to these things and like you just have a sense for like where Claude is good and where OpenAI is good or where TRP is good and and like a sense for like their relative strengths and weaknesses that you you just have to be (24:19) in the tools. Yeah. Yeah. That that's totally correct. And like a great example of this is my my favorite sort of like how should I get started thing is industry classification. So you know most companies have these like boutique industries that they think about when they think about their market. (24:38) So for Clay, we take the entire tech market and we segment it into like a dozen different things and then we take basically the whole rest of the business world and we kind of like, you know, there might be like financial services or real estate, but it's very broad. And that's because Clay's ICP right now is a lot of, you know, growth stage growth stage tech companies and um and so most customers they they just have the industries that they get from you know at a data provider D&B or whatever D&B or LinkedIn. (25:02) It's like telecommunications or telecommunications and internet and it like doesn't really mean anything. Um, and so what a great initial starter task for a lot of teams is just to correctly segment all the accounts in your CRM. And that's that's an easy prompt too because it's just like description of the company plus visit the website plus here are the categories of industries that we think about and how we how we define them. And so I always recommend customers use chat GPT for that. for whatever reason, I (25:27) always find chat GBT in terms of just like let me bucket something into something is is really good. But then at the back end of that, you might actually want to um generate a message or something or or actually write some content. And I always I tend to prefer anthropic models for that. They seem to be Yeah, same. (25:48) They seem to be, you know, um a more natural in many ways. They seem to kind of like be a little bit more like liberal arts chatbt is like math and science. And so lots of breath is what I say. Right. Right. And so what you say is is totally correct and and um and it's it's kind of the like it's kind of why I think this go to market engineer role is a legitimate role because this detail is just not captured in most other roles. (26:17) Most revops, most sales ops folks don't aren't thinking about this and in many cases don't care to think about it. you kind of need these like automation people that are in these tools and keeping up with the models and understand how to prompt GPT5 differently versus GPT4. (26:34) And those are the people that get a lot of these like 10x results that that you kind of hear about. Um but it you know for leaders or others listening to to this um it's it's just important to understand the details on these things and we all know in go to market the like percentage point or like basis point differences and and campaigns and throughout your funnel like those actually are where that the difference is in your entire business. (26:58) Are you ready to level up your go-to market strategy for 2026? Then don't miss GTM 2025, the only B2B tech conference exclusively for go to market executives. Join a thousand other revenue leaders this September 23rd to 25th in Washington DC for an exclusive executive onlyly experience. This 3-day event is hyperfocused on connection, strategy, and execution. (27:22) Expect hands-on workshops, in-depth strategy sessions, and curated opportunities to build relationships with VPs, CXOs, and founders facing the same challenges you are. You'll stress test your GTM approach, align your team, and leave with actionable insights from top performing leaders. (27:41) Don't miss the mustattend conference designed to help you boost your go-to market results in 2026. Visit attendgtm.com. That's attegtm.com to secure your spot today. And don't forget to use code topline for 10% off your GA ticket. Yeah. So, so I want to talk about the foundations. So, like if I'm a if I'm a CRO and I want to go on this like AI transformation journey, everybody's talking about AI. (28:10) I'm feeling left behind and I I need to get started. What? And maybe we can break these into three categories like infrastructure being one, people being one, and maybe strategy being the third. And and if there's like another pillar of like GTMA Foundation to throw it out there. (28:32) So how just at a high level, if you're talking to a CRO who's just starting on this on this journey, uh they've been under a rock for the last uh 24 months. How would you tell them to think about building this thing? is like job one hire a GTM engineer or what do what the people people in and strategy the the um I think job one is to get AI in the hands of um as many people in the company as you can um because because if you just hire a GTM engineer some of the things they're going to need to do are going to need to require kind of buyin from others and in my opinion the easiest way to get that buy in is to just buy you know a 100 chat BT or (29:08) claude or or you know whatever licenses for for the sales reps for the ops folks um and then for the CRO themselves they should uh try to kind of use this for like meeting prep and quick research and so forth. Everybody should kind of gain a base level understanding of some of the some of the capabilities. (29:27) And then, you know, sales reps get uh a lot of flack these days, but but usually within every organization, you can find a couple of just insanely creative um really on top of it on top of it sales reps and they will actually come up with um cloud projects or custom GPTs or things that you will actually want to look to for inspiration for things that you should think about centralizing. (29:51) And so that's kind of like the next step. So step two in my opinion is once everybody's got their hands on AI and you have some real usage going and you have some folks raising their hands saying wow you know I just did this thing with chat GPT that uh I used to spend four hours a week you know preparing for whatever this was or um you know like pipeline reviews I used to spend an hour preparing for my pipeline reviews and now I can just quickly get all the data that I need um through through some like MCP connection or something like that. Um, that's where (30:20) you start to say, "Okay, my best people are doing this. Some of my most creative people, some of my most forward thinking people, but what if I brought what they're doing and I actually just gave it to everybody and they didn't but they didn't need to use the tools. They could just have it done for them. (30:36) " And that begins kind of the centralization journey where the goto market engineers get involved. And so that's where you start to buy or you know not to pitch Clay so aggressively but you start to think about uh like orchestration tools or or um AI agent tools or places where you can kind of stick one or two people there in there and they can do things on behalf of everybody else. (31:03) Um, and of course, like the most basic way to to do this would be you could, you know, if you've got a contract with OpenAI for chatpt, you could add on an API key and have your organization do it. The problem with doing that is, you know, there's there's really not a lot of tooling that is offered by the model companies for like um for this. And so clay in my opinion is actually kind of like this go to market development environment where you can plug in these models. (31:25) You can try the different models and you can get all the data you need and then begin to automate things on behalf of everybody else. And when that happens you should see some like organizational shifts in in metrics in my opinion. Um one of them should be just like number of leads worked. (31:42) This is a classic one where if you're able to do research for everybody, then they should be able to work more leads and and you should be able to look at that and say, okay, you know, we started with step one, which was getting AI in everybody's hands. We then moved to step two, which was centralizing this. Now, we're kind of seeing the impact of step two, which is we're starting to see some actual business metric changes. (32:02) Of course, you could also look at things like pipeline. You could also look at headcount. you might see some some headcount curves start to bend down if folks are able to work more leads and be more productive. And a company like ClickUp is a great example of this. Um I I talked to their chief business officer about this just just recently. (32:20) And then and then step three is to say, okay, the existing stuff that we're doing is better and automated and centralized and we're serving that that up for people. Now let's actually do some new stuff. And so this is where the like agentic workflows come in. (32:40) This is where you might start to say, um, okay, we're going to build, you guys already heard my my thoughts on ASDRs, but we're going to build a sort of like a gentic driven u messaging system for inbound or we're going to build this like signal driven um uh alerting system or we're going to build a like early alert um uh deal is like trending negative system uh using data and salesforce and and activity data and stuff like that. (33:04) Those are net new things, net new capabilities that you can begin to add in. But I think where folks might make a strategic error is trying to add those on top of a bunch of manual processes with a bunch of people that actually don't understand the AI technology and haven't really been exposed to it in like a work setting. (33:22) Um, and so that would be kind of my order of operations for for a CRO starting from scratch. M and and so it's really about eat like eating your vegetables in in many ways like I think so and a lot of my work that I do at Chachi Clay is just like customers like you know um um hey I'm trying to I'm trying to get my Six Sense data into Clay and it's like well okay how are you how are you planning to do that? Well, Six Sense doesn't actually have an API that allows you to pull your list of accounts out of Six Sense. (33:57) And when they sync to Salesforce, they actually sync to it in an iframe. And so, and so there's a lot of detail around this working versus this not working uh in this kind of go to market engineering world. And the same with AI around these details of like what the prompts do and what the models do and what they're good at. (34:16) And if if very few people at the org are kind of tuned into that um then I think it's going to be it's going to be something that is that is a challenge. I would actually compare it to like maybe like APIs, you know, like I think if you reversed 15 years ago, not everybody really understood what an API was. A lot of services were still weren't hooked up that way. Stripe didn't exist yet. (34:36) All of this. Now everybody knows what an API is. Everybody's familiar with it. Everybody kind of imagines that, oh, if I want to get some data from somewhere, I can probably plug into it. A lot of customers always ask me, what's your API connection like? they they actually have internal programs around building APIs for all their systems and having all their data in a central place and and so but that understanding took a while and and and AI I think is is the same where you there sort of have to be a a common cultural understanding of like what it can do before you start to automate (35:06) things and then especially before you start to do net new things that only AIs can do. Yeah. The one of the pieces of advice I give the most most commonly is you just have to start with the data quality and you need a lot of buyin from your data team because you know in the six sense example it's like okay so if it iframes into Salesforce then does that mean we need to go six cents to snowflake snowflake to clay is that you know and like that's not really the realm of revops anymore and so I'm I'm gonna ask (35:39) a question about like where where some of this should But um it feels like everybody wants to go to the exciting fun stuff like carpet bombing their market with AISR before they do the hard things which is like dude you just got to spend four to six months on making sure the baseline data quality is good and using AI to do that. (36:04) like you gave the example of of industry categorization, but it feels like that might be the best place to orient people. Is it is is that a fair argument? Well, I well, I totally agree because that's Clay's pitch as well. And so, and you know, when we go into a conversation with folks, we say, "Hey, like um let us you know, send us a sample of your data. (36:27) We'll send it back and we'll we'll prove it's better and then we'll do that same thing for your entire for your entire CRM." But the the the reason that that's um that that's important is because you need you're going to end up in a place where you need to kind of have the AI like um make do some logical things like make some decisions like qualify a customer, qualify a lead um or you know put together a reasonable piece of copy. (36:47) If you have a bunch of contacts in your CRM that don't work at the places that they're listed at anymore or you have a bunch of accounts which have, you know, employee account employs where you can see the kind of fill rates across your CRM. (37:08) If that's your state of the world, it's just going to be really tough to to do a lot of the more interesting things. Um, and because like we talked about, the more you can constrain these models and these prompts with information, the better they perform. So, if you're able to say, hey, here are like 50 filledin data fields, um, and you give that to a model and then you give it some instructions, that's going to be a better situation than here are like 10 filledin data fields, and then in the next record there's 12 and the next record there's six. And then by the way, of the ones that are filled in, some of them are out of date. Some of those companies have (37:40) merged. Some of them are out of business. Some of those contacts are like at a different company or retired or been promoted or which is just really nasty. And and and if you go down the sort of like rabbit hole of this, I I think most most folks if they were begin to click through their their CRM, they could almost do this like Wikipedia thing where they could just click between objects and they would see bad data like everywhere. (38:06) Um, and so yeah, the the consequences of layering AI on top of a setup like that are are not good because because like we said, the model is not really going to discriminate of like it's it's not really going to make a judgment about whether the information it's being given is correct. (38:24) It's going to assume it is and then if that's connected to other systems that that send to employees or send to customers, you're asking for kind of like a like a like a a bad setup. Yeah. Yeah. AI is a pattern recognition machine and so it's just going off of what what it is has access to and it's guessing what the next appropriate thing is and you know if the input data is not good obviously you're you're going to have problems. (38:47) This is so I'm I'm giving this presentation next month at at GTM25 and and the argument I'm really trying to leave people with is like do the boring stuff first. You have to get first-party data in a good spot and and I'm always recommending momentum to like gather all of this insight from across your customer journey and then you get need to get thirdparty data in the right place and my generic recommendation is always Clay. (39:10) I'm like look we built a bunch of this in-house because it was before Clay like exploded into the zeitgeist but we're as you know talking to your team about potentially like uh converging on Clay to do a bunch of it. But I'm just like you these two things like these two products are like 80% of your AI roadmap you know and instead people are like go ahead. (39:35) Oh well I was just going to say you know the good news on this is like is like we're we're trying to do a lot of the actual boring tedious work for you. So, you know, when you pull data points from Clay, we've done a lot of work in the back end to like actually assess that quality. And we've done a lot of work to assess the quality of the 150 odd data providers in the marketplace. (39:54) And then we've ranked our waterfalls by that. And we've set up the the commercial agreements with those folks. And the sort of like um now we're working on actually a lot more like compliance related stuff, which has become more of a more of a thing as we've moved a little a little bit more up market. (40:10) But you know there is so much beyond even just actually um pulling data from somewhere and updating into your CRM which is a admittedly somewhat somewhat routine task what that what the data source that you're pulling from itself needs to be kind of like cleaned and made made accessible and all of this and and so yeah so I I completely agree with you the the the plumbing to to do that is um is not fun and like that's that's what that's what people pay clay for the the The prior world of this was you'd have four or five different often conflicting (40:41) data sources connected directly to your CRM and then and then rubs could look at it and pick the employee account that's, you know, most favorable for them. If it's if they want the account, well, they'll pick the one that's in their territory. If not, they pick the one that's out of their territory. And it's it's it's um it's a difficult thing. (41:00) And so, um and so yeah, the there's so much like picks and shovels type work around data that um that is necessary. And then and then you know the good news is once you come out the other side of that the a level of automation of these systems today means that you you can often just kind of like leave it alone. (41:19) Um and and that's a great that's a great new thing versus um versus kind of the prior world at least that I grew up in from the rev ops side. Yeah. And and is that the domain of revops of a data team? Where do you most frequently see that live within your customers? So it depends on kind of how the customer views their market. The more volume that exists in the market, I think the more it lives with the data team. (41:51) Um so you know as as an example of that um we have some a lot of the major AI companies are click customers and the volumes that they deal with are absolutely insane. Frankly never seen anything like it. And um you know, you almost don't even want to like have a lot of that data live in Salesforce um because you need an actual production grade data warehouse, an ETL setup, data team that is managing some of that some of that feedback. (42:19) Um and so and so um the way that um the way that we kind of like um see that in clay is we we work with the data teams when they have pretty high volumes and also when they have kind of a PLG motion because another type of like large data is product air. So at Clay, for example, we've got people using credits and and doing things like this. (42:46) Um, and most of that lives in Snowflake, but the aggregations of those things live in Salesforce. So people can kind of see the see the work on that. The models that power that, the DBT models and things like that that actually power the aggregations and the and the consistent updates from our production database into our warehouse into Salesforce, all of that is the data team. (43:09) And you can imagine how there are some data teams that take it further where you know they actually want to kind of sync their accounts to their warehouse and then they want to have the enriched data live in their warehouse too. And so those teams are definitely involved with um with Clay. And then a lot of our customers, those teams are kind of like producers for the downstream revops consumers that take what they have and carve up territories and think about pipeline coverage and things like that. (43:33) We encounter customers that have a smaller volume in their market, which by the way doesn't mean their market is smaller. It might just mean they target the top, you know, the Fortune 100 or something and they have eight figure contracts. Um, that usually does live more with ops. And a lot of the problems here are more about can I build kind of like a a virtual copy of my customer um in my in my CRM. (43:58) So, can I have like when someone changes jobs, do I have that recorded? When an account um reaches a new employee mile account milestone, do I have that recorded? When they launch new AI initiatives and things happen in their business, do I kind of have that have that recorded? Oftentimes in those types of companies, we do see more ops and demand genen folks involved with that. (44:17) Um partially because, you know, you don't really need a warehouse or like a DBT pipeline to manage that stuff. Um you can do it all in Clay and and in Salesforce. And so um and so that would be sort of my my general like like cut on the market. You know data teams exist where there is like a lot of data to sort through. (44:36) Um and but for many companies there's still you know 10,000 plus accounts but not like millions of accounts. Yeah. Oh, that's a really interesting framework because uh for us we have a big really big market hundreds of thousands of of target accounts and so this is fairly exclusively the domain of our data team. (45:00) They think about acquiring managing uh the data that the the GTM org uses. We our system of record is Snowflake not Salesforce. Yeah. And and we push into Salesforce but the scoring is built in on top of Snowflake. So this resonates a lot. Um I want to ask so where should a GTME live and maybe before that like what are the roles? So I want to be so I'm like committed to fixing my data foundations. (45:27) I have a path there. Um what are the specific roles that you think need to be in an org that is serious about pursuing an AI roadmap and where do those people live? Yeah. So you at a minimum you need someone thinking about strategy that is aligned with um the executives and understands the business very well and someone thinking about implementation that actually may not understand some of those things but understands the systems that they're dealing with and classically you know you've got your you've got kind of your revops sales ops role on the strategy align with executive side and you've got your GTM (46:04) engineer that is hands- on keyboard doing stuff. I think it is interesting that today the GTM engineer really is more of a like IC IC role and the and the the best way to utilize GTM engineers is just to have them doing stuff you know maybe in clay or maybe in other tools all day. Um I I I don't for example see a lot of like VPs of TTM engineering right so I don't see like these like big org structures that are built around this whereas you do see you know pretty substantial sales ops or rev ops organizations and that's because (46:37) they have a lot of internal alignment business strategy account coverage pipeline you know issues and things to sort out and they need they need you know really strong context from the CRO and so forth in in order to do that. I think when you get down to the GTM engineer, they are kind of like burning down the list of things that are most likely readily automatable. (47:03) Um, or they're burning down the list of data problems and like solving them in interesting ways, but they're really focused on solving them and they're they're focused on the sort of like building aspect. Um now in terms of where in the organization that lives because the GTM engineer has a big impact typically on pipeline generation and rep productivity and so forth um they they will in the best orgs in my opinion straddle sales and marketing. (47:32) And so if you have a CRO that um that owns both sales and marketing, then you ought to have a centralized GTM engineering team within that CRO or that serves both of those functions because um direct into the CRO or through RevOps. I um through web revops, sorry, but within the within the CRO or I actually have the opinion that if marketing if there's a CMO and marketing um marketing reports to you know up to that person, you might consider having um uh GTM engineers live next to marketing ops inside the CMO organization. Um, and that's because today I would say that, (48:11) um, you're gonna get the most bang for your buck with GTM engineers when you're working on automating like top offunnel activities, when you're working on automating somewhat routine sales tasks. But as you get into organizations where they sell more upmarket and so forth, I think you actually see a like fewer routine sales tasks that that sales reps are doing, but you do still see a lot of routine stuff in top of funnel. (48:36) Um and then finally you have the consideration around like data and systems and so this is like a third organizational option would be to have the GTM engineers actually live somewhere in like the CIO or maybe CTO organization. This is a situation where you might have a setup where you have like a business systems team that actually owns Salesforce um Zoom Info Clay all the tools. They own the spend and the budget and the implementation of those tools. (49:03) um and and the GTM engineers should probably sit very close to th to those folks um as as long as they are able to kind of like take requests and so forth. So you you have some options but the sort of through line is that it's a centralized centralized team and I think the best setup is actually to just funnel basically like the implementation of the gotomarket AI initiatives through the go to market engineering team in whatever way that's kind of set up in the organization. (49:31) Okay, that makes a lot of sense. So, so it is specific to the business and their market and segmentation, you know, like where what SMB versus enterprise. Yeah. And and you could hire different types of go to market engineers, too. (49:50) I've actually kind of thought that we should have like full stack go to market engineers and like backend go to market, but we don't we don't have to we don't have to go down that rabbit hole. Um, like if you have a very experiment-driven thing, you know, you're in marketing, you're trying to launch new experiments all the time, you might consider hiring more of a like experiment GTme that is going to like quickly hack together hack together new campaigns and launch them. (50:15) Whereas um you might also have a different set of problems around data quality where you actually want to hire kind of like a deeper system data scientist data scientist type thing who is hands- on keyboard and clay. then they're probably going to live closer to the chief data officer, CIO, CTO, something like that. Um, and so and so yeah, there's not like a um uh offtheshelf GTM engineering or design thing um that that I generally tell customers. (50:46) I generally kind of give them this readout and ask them, you know, what's your what's your setup today and where you trying to get to? Yeah. And you could even have multiple of those roles. You could have the data person and the experimentation top offunnel person in the CMOS's team and the person in the revops team that's doing the sales automation stuff. So it doesn't necessarily need to be the same person. I guess that's right. That's right. (51:08) I do think it's like um it's almost like having like a staff engineer maybe that can kind of jump around different product teams and help out. you you could consider the GTM engineer as like one of the most technical people within the go to market org that can also that can also do that and I think that's a um that's a fine model too when you have that setup that's where you probably would just have a GTM engineering team that is kind of loaned out um and um and I've I've actually I've seen that model too you will need a a manager for that team like a head of GM engineering or something like that. Yeah. uh and uh and (51:44) um you know that team really ought to be able to push the boundaries and kind of say hey this is the stuff that we've done and and look the impact that it's had. Yeah. So when you think about the jobs to be done, how how quickly do you go out and find a GTM GTME? Is is this something that people should be looking at at like 5 million AR, 10 million AR? Is it like now the third role that you hire in the sales or like how do you think about staging? So if I was starting over, I would build out an organization where I've got I've got my revops, salesops role kind of combined (52:19) that's thinking about um you know pipeline coverage, some of the strategy things around sales. Then I would have a go to market engineer attached to that um like sits right next to that person doing some of the stuff. Then I would also have an agency that probably running outbound for me and also probably providing some like services level work around some of the data quality stuff that's that's happening. (52:44) Um and so I actually like at Clay people are often surprised to hear that we use a clay agency for our outbound. Um and that's because outbound is this kind of servicesoriented work that is um that is like always changing. There's always things with inbox health and domain health and things like that. (53:01) Um, and then there's always kind of like constant constant iteration. And so, um, and so starting from scratch, I would try to have those three things kind of attached to my to my go to market engineering, uh, or to my go to market organization. Um, and then what I would do is over time I would start to make some decisions about what do I keep in house, what do I farm out. (53:21) Uh, maybe I pull more of the experiment driven work inside from my agency and and let them just kind of operate existing campaigns that are working. Um, maybe I move my GTM engineer actually into more of a revops role and I farm more of the work out to the agency around some of the systems maintenance. (53:46) Um, and so those are decisions you can make, but but I think like you're not talking about an overly costly op structure here when you're if you just do those two or three things. Um, and then the benefit you get is, you know, if you're at 3 million, you're at 5 million, the kind of book isn't written on how you're going to grow yet. (54:05) And so if you need to make pivots, you can kind of like take those two roles and actually morph them into other things and and create, you know, still retain those employees. And my whole thing is I always think it's sad when go to market organizations have low retention because the tribal knowledge and in go to market is sometimes like deeper than the tribal knowledge in like an engineering organization. And so um I would I would start up with a setup like that. (54:22) And I think founders should not be afraid to like look external. There's so many thirdparty agencies that are really good that will just help you skip like several steps in the whole journey of like learning about your go to market. Yeah, that makes a ton of sense. (54:42) Um where should people go to hire this these GTM? This I think is one of the hardest things. So we have some resources for this now. So we have a Java. Um yeah. So right now it lives for unknown reasons. Uh well I guess it's good reasons in our in our series C announcement. So clay.comseries-c you'll see a jobs board where you can actually post your job. Um, and I think we also have clay.com/jobboard. (55:08) Um, and we can post your role and that will be surfaced to uh to our community of go to market engineers. We also now have a talent um a talent board. So you can actually submit um you like your LinkedIn profile and we'll enrich it of course um into our into our organization and then we'll do the ma we'll do some of the matching as well. (55:29) So, we'll surface talent for our customers and folks that um that are looking for GTM engineers can kind of get into that pool. And then we also have a Slack. It's about 20,000 um 20,000 folks in there. Not all of them GTMEs of course, but actually a good number of them of them are. And so, I think usually between all of those three people are are seeming to be able to at least begin interviewing. (55:52) Um although I will still say I kind of will make recommendations to folks and people can reach out to me on LinkedIn as well to kind of like get um reviews or feedbacks on job postings or to get an initial list of of candidates. And you know I've hiring for Clay. I've interviewed like thousands of great people that for whatever reason often look location-based uh couldn't couldn't join Clay and I kind of have them at my disposal to refer to to organizations to. (56:20) Um, so, so that's that's kind of one thing I will clay.comjob-board. Yeah. Unless Okay, great. Yeah. So, so you can post your job posting there and then we we do have this talent directory which I don't know if we've exposed I think we will like send that to people if they ask, but it's not like exposed to the general the general web. Okay. Um, and then we have our Slack as well, which actually has its own channel for for hiring and and recruiting. (56:45) So, um, those are options and then you can also DM me on LinkedIn and I'll I'll usually, uh, try to try to help. One more question before we get into QuickFire. Um, so we talked about data as the foundation for for everything. Um, how should folks think about the right use cases to start with? So, I' I've now got like a a good data foundation. I've done the enrichment. I've got a plan in place. (57:10) when and we've talked about automating some of the low value tasks, but how should people think about the biggest areas of opportunity? Everybody wants that step change ROI. Like how should people hunt for that? And what have you seen as maybe the the rocks to look under? Yeah. Um I I think the biggest thing is actually uh like account scoring and segmentation. (57:36) I I think I think there's a there's a there's a massive problem with probably more than half the time you're going after accounts or talking to accounts which are not qualified, not in market. You're missing out on accounts that are actually like ready to buy or actually like very hot or are smaller than your existing criteria, but for whatever XYZ reason they're they're actually going to purchase a large a large contract. (57:57) Um it's it's simply aligning the the accounts and the folks that are you know great fits for the business with like ready and willing reps to talk to them and sell to them I think is like a real challenge and and I one analogy on this is um is you always have these kind of good logos right like like you always have these companies um we were chasing Figma for like a long time they eventually became a customer, but we're chasing them for a long time. (58:28) And so, you know, when you're in seat as a rep, you're always like, "What can I do to break into Figma? What can I do to get into Figma? What can I do to um, you know, um, research Figma to some to some greater degree?" When in reality, probably the best thing is actually to go ahead and just like let them let them lie for a while and um, spend time on the accounts that are actively going to buy right now. (58:51) And so I think businesses still have this kind of like matching of demand issue that actually AI can help really resolve. And um you can now pull like most of the data that you need in order to um in order to get that information about an account and um uh and I think that is like probably one of the biggest opportunities. (59:13) Do I just have incredible books of business for my reps? Are they real- time updated? Do I have all the signals that I need on my accounts? And are my people talking to like literally the right accounts and contacts? That I think is is um probably one of the biggest opportunities that that folks can can go after here. (59:31) Doing that well is downstream of a lot of the other things we've talked about on this on this on this podcast. Uh but the yeah I I just consistently observe that um that the right people don't uh get talked to even if they're even if they're ready to buy. (59:49) And by the way, this this still happens at Clay and I I always get get super frustrated when I see it. Um, but we've made really good progress in kind of matching our demand and supply and um, and I think most companies are still kind of in the early endings of this, especially large organizations is where this this really um, this really, you know, if you're if large organizations are looking for like 1% revenue list in different places, this is probably one of the places. Yeah, for sure. (1:00:14) Okay, let's get into rapid fire and I'll let you get out of here because you've got US Open tennis to watch, which I don't want to get in the way of. Um, what do you think separates a good CRO from a truly great one? Um, systems thinking. Yeah, def definitely. Um, systems thinking. Um, uh, I I think you can be you can be pretty math oriented in terms of like dashboards and so forth, but if you're not clued into the systems and the flow of leads and deals and and um, what actually happens in the in the belly of the beast to make all that work, um, you you probably are not tuned in to some of (1:00:48) the things that going to cause trouble for you and also some of the opportunities that you can go after. So yeah, I think systems thinkers are are some of the best CRO that I meet especially today. Um, what's the most common advice you give to newer leaders outside of AI? Just like what what advice do you give to leaders that are taking their first big like VP sales job or um like second line role? Yeah, spend time recruiting. Uh this is actually something that I've haven't done well in my role. Uh and I've I've really tried to work work harder on, but (1:01:20) um you know, recruiting is kind of like a drag in some ways. Um it's uh pretty hard. It's um the consequences for failure are immense, especially if you're hiring, you know, first or second line leaders. Um and it's really hard to systematize. you you when you're hiring at scale, when you're trying to hire, you know, 50 reps in a quarter, it's like, man, how do I get even 30 good people to fill these seats and and then maybe turn out 20. So, recruiting is super super tough. (1:01:51) There's a lot of art to it. Um, I actually think Verun, one of the co-founders of Clay, is an incredible recruiter. I've learned a lot from him. Um, but yeah, appreciating and diving deep into recruiting, you basically cannot go wrong if if you do that well. And you can go extremely wrong if you if you don't. Yeah. (1:02:12) What's the hardest lesson you've had to learn as as you're figuring out how to lead salespeople? Um, I I think it's that, you know, people um people don't just kind of like take your word for it. I think I've tried really hard at Clay to be in the weeds and to and to lead by example. (1:02:39) Um but but even still I I catch flack for just kind of firing off these like things and these policies and not really explaining why or not really demonstrating why we're doing things or or um or or sort of like catching people off guard or catching people by surprise and and you know folks even if the reality is that it's it's kind of a a show as you're scaling a startup, folks kind of want to work in a place where there's stability and there's thoughtfulness and there's rigor and and even if you have all those things internally. If you don't kind of like show people that that's happening or just or show them the way, then then (1:03:10) they kind of think it's not happening and they start to kind of like mistrust. And so I've I've kind of had bad moments on that. I've had good moments on that and it's something that is all everpresent in my mind. Um especially now that Clay is is much larger than it was. Yeah, that is a good one. Uh last question. (1:03:31) What's the best thing you've read in the last 12 months? best thing I've read in the last um the last 12 months. Um um there is a book that I love. I actually said this on a on a previous podcast, but it's still true. Uh it's called Stoner um by uh by by John Williams. Um it is a fantastic book. It's about this like um literature professor in the Midwest who's sort of it goes through his entire life from almost like childhood and to death and um and he sort sort of like he sort of like never from an external sense has like finds happiness like he never gets external validation and people are often like mean to him and stuff like that but (1:04:13) he he ultimately does find kind of internal validation in his work. Uh, and I thought I thought it was a good analog for sort of like scaling startups and sales because let's be honest, most of the time you sort of like especially as a leader are dealing with escalations on things. You're eating glass. Eating glass. (1:04:32) And in fact, in fact, I just said the phrase eating glass in an email to a customer today or I think I said chewing glass. Um, and so you must be able to kind of find internal validation in the work that you're doing. and and oftentimes the external stuff might come much later or or be expressed differently than than when you're an IC and your manager is like, "Hey, great job on on that thing. (1:04:51) " So, yeah. So, Stoner by John, I think, fantastic book and a good uh tie into my last episode with Dr. Michael Derase about purpose- driven leadership. So, yeah, that's a good good plug and a good place to leave it. Well, every this was awesome, man. I really appreciate it. (1:05:10) I think uh we got to a lot of great answers for folks on how they should think about this journey because it's the thing that everybody is talking about. So uh appreciate you joining me and have to do it again. Amazing, Kyle. Yeah, thanks. Thanks so much. And um yeah, you can you can talk to me about this for another several hours. I uh I won't get bored. So appreciate it. (1:05:28) I have a bunch of questions we didn't hit so I could do the same. Thank you for listening to the Revenue Leadership Podcast. If you enjoyed it, don't forget to subscribe and you can find a link in the show notes. And be sure to leave a fivestar review, share it with your network, and please join me next Wednesday for another great conversation. [Music]

E45: The Inside Scoop on GTM Engineering with Clay’s Everett Berry