Speaker 1Welcome to the Revenue Leadership Podcast. And this is a special episode because this was a conversation that happened behind closed doors at the Clay CRO Summit. And to the best of my knowledge, this is the first time it's being shared with a broader audience. It's also a bit unusual because it's actually Kyle Norton, who's the host of this show and the CRO at Owner.com, who is getting interviewed by Clay's head of GTM engineering, Everett Berry, who has some awesome questions for Kyle. They get into how Kyle's team is using AI tooling to generate more pipeline. They also get into Owner.com's approach to AI experimentation, right down to the org structure and how headcount is distributed, but also where they're intentionally focused on human involvement in order to get the best results. If you've ever wondered what CROs are talking about behind closed doors, this is literally a recording of that from an exclusive CRO-only event.
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Speaker 3If you could talk us through maybe actually what we were just talking about in the small group discussion about what you're doing with your SDRs that is kind of resulting in the ability to sell to what I consider to be some of the toughest customers in the world, which are basically your mom and pop restaurants and so on.
Speaker 2Similar to Stevie, we pretty quickly figured out how much time is being wasted doing non-revenue generating activities. We have this slogan, RGAs over everything. And so I had first done a time series. I started doing a time series study in like 10, 12 years ago now, where I started to understand how many bad leads my reps were calling, how much time they were spending on stuff. And I had super rudimentary solutions to that back in the day, like just sales force reports with trigger words that would go into groups that I would go screenshot and say, hey, don't call these. And so we've really applied most of our AI efforts to pipeline generation. It's my opinion that if you can just generate way more pipeline, everything else gets better. And it's much easier to generate pipeline than it is to move conversion rates. And so we've largely had years of effort going after pipe gen. And so from the top of the funnel, we have a series of machine learning scores that power how big is this deal going to be? What's our estimated win rate? We've got this thing called eConnect now. What is our estimation of whether or not that person will actually pick up the phone call, which led to like a 2.3% win rate. And so we've been doing a lot of work on that. And so we're starting to see a 3x lift in decision maker connects. And so from scoring to enrichment to AI pre-call research, just delivering on a platter what the sales rep should say on the cold call. We have a pretty rigid cold call structure.
Speaker 3And can you actually share what that structure is? I think it's quite good.
Speaker 2It's Pekrios, the Greek goddess of cold calling. We joke. And so it's pattern interrupt, credibility, relevance, intrigue, offer of value steps. So some of those components have some degree of personalization. So the pattern interrupt is what we call a local name drop. So basically, we've taken every single prospect that we're going to call, we take their zip code, we find the customer that is closest to them, and it gets enriched into a Salesforce field. And then in their sales loft experience, we found a way to pump in like our own little window in the sales loft experience that is AI PCR, AI pre-call research.
Speaker 3I mean, sales loft, not easy, by the way.
Speaker 2Yeah. We're thinking about ripping out sales loft and building natively on top of Salesforce. It seems like a little redundant now, but we've got this window that we've injected into the sales loft experience. So the pattern interrupt is like, "Hey, Everett, it's got an owner. Hey, wanted to give you a call because I actually worked with Stevie's Pizza over on Fifth Street. Do you know Stevie?" And they don't know if it's a sales call yet. They just know that they're like this person. They very likely know these communities are small. They probably know who that person is or them personally. Oh, and we work with them because we work with them to help them grow their online ordering. And I noticed, and this is the second thing that's personalized out of the AI PCR, the I noticed is basically we hoover in everything from their digital presence, and then this workflow ranks. What is the thing that's most broken about their experience today? It's like, "I noticed that you use off-domain online ordering and has," whoever their current vendor is, "Has pot menu ever explained what that does to search rank?" "No." "Okay, cool." So this was the same as Stevie, and then that's basically the cold call structure. And we're creating intrigue by this mysterious, "Do you know what that damage that does?" So that is the structure of this AI pre-call research. And then we've got follow-up workflows and sell-betweens. So to in between the call and the meeting happening, some automation. So send these videos, do this thing, AI cold E, email. We're basically moving all emailing away from the BDR and the SDR. That's all done centrally. And then the BDRs are just told to call what leads based on the orchestration that we're doing centrally. So my BDRs make 150 to 200 calls a day now, and they talk to 20 to 30 decision makers. And that is probably 5x what it was when we first started where they were just pressing the button.
Speaker 3I was just going to say, you didn't start with that level of productivity, which is an insane level of productivity. What was the experimentation framework that you ran to arrive at this?
Speaker 2Early on, there wasn't much of a framework. It was just me and the VP of biz ops and data cooking up weird stuff. And then we're like, "All right, let's give them this list today. Let's do this thing." And we would just YOLO it to the whole entire team and see what happened. Now we have a more rigorous, but still a work in progress approach where we work as a team. So my VP of rev ops, me, the VP of data, and our applied AI leader brainstorming and basically scheming, "All right, where is the most lift that we can find in the stack?" And this is a weekly meeting or a- No, once a month maybe. We know the next six things that we want to do, and sometimes new things come up and we're trying to think of a way to actually bring more ideas from the team up. Because we're very centralized right now. And so now YG, the applied AI lead, will have his idea. He'll build a prototype for it. He's in New York and the sales team's in Toronto. He'll fly in for three days and sit beside the team. So we'll pick two or three reps with one manager who, this is their only non-hit-the-number project at a time. You're just doing the E-Connect pilot. We're doing this time shift thing now where we took four of the reps and we have them start their day at 10:00 AM instead of 8:00 AM because our data said that the best call window was actually five o'clock to 6:30. And that'd give us a big lift. And so we do these things in now these small batches. The data team pre-sets up what we're tracking. The manager is running the team cadence to make sure that they're doing the thing. And then he writes the update at the end of every single day with the data that the team has given him. And then if it's a winner and their stat sig, then we'll roll it out. Then it goes, it's an enablement ticket. The enablement takes it and we figure out how to roll it out more broadly. And so that machine sort of hums now.
Speaker 3Okay. And YG, your applied AI lead, reports into you.
Speaker 2No, no. He applies into the VP of data in BizOps. It was my headcount. This was inspired by Stevie, actually. She was telling me about her applied AI team. I was like, "Man, I want that. That sounds awesome." And so it was the first headcount I put into the plan after we raised the Series C. Steve: And, but we were hiring for the role and it was clearly like this person was very technical. I wasn't going to be a particularly awesome manager for them to be a sounding board. And the VP of data is quite technical and has enough business acumen to not make me worried about them getting too in the weeds. And so I just gave him the headcount with the rule that he can only work on sales stuff for the first 12 months. Yeah. And now forever, right? Steve: Yeah. Yeah. So basically, he's going to do sales ongoing until he builds a team under him, but we have another applied AI leader for growth marketing. Almost everybody in that team is data science and applied AI or data engineer and applied AI. Everybody has to be massively AI-pilled to be in that org.
Speaker 3Perfect. Okay. So following Varun's lead with the heat here, I've had some leaders recently, and actually some folks in this room as well, tell me that they're they're actually planning on getting rid of their SMB sales team or their Velocity sales team. And I think OJ was kind of hinting at this. The idea is that basically highly transactional sales are very close to or it's actually possible to fully automate those. Considering you guys mostly do nothing but SMB sales, what's kind of your reaction to that?
Speaker 2If you can, you should. We're already doing this. Like there's a lot of things that we say, oh, AI is going to let us automate SMB. But like PLG is that. There's massive companies that have a fully self-serve motion at the bottom of the market. So this just enables that experience to be better and maybe creep farther up market. It's not as much a reality for us. We sell to mom and pop restaurant owners who like didn't go to college, struggle to get on a Google Meet. Like the BDR has to help them get on the Google Meet because that's sort of their technical proficiency. So we're trying to give an agentic experience for the bottom of the market with like mediocre success so far. Yeah. The bar is just extremely. Extremely high for how easy this experience needs to be for our customers to do it. I think many of you who sell to technical audiences, it's like way easier. They would rather talk to a robot than a human anyways. So it's more possible. But yeah, I think it's certainly coming and we're going to do a lot of it.
Speaker 3And are there like ACV thresholds that you think about for that where below a certain threshold it has to be agentic fully or?
Speaker 2ACV is one input to your unit economics. So yeah, you want really attractive LTV CAC and you want to have as much go-to-market efficiency as you can. But if your ACV is 10K and you're closing at 80%, that's like way better than if you were at 12K closing at 40%. So the ACV is like one input. But that's largely our lens. And I'm looking at LATAM reps to do the bottom of the market. We're a quarter of the cost of our North American reps. And we have people in my launch team who have enough sales. They're salesy enough that I think could do it. We tried to hire LATAM sales talent. It was really tough. There's not very good sales craft there, unfortunately. So there's other options. Like I think people, one side tangent here. I think people too often, it's a hammer looking for a nail. And it's like, what can I do with AI? Right. It's fundamentally sort of the wrong approach. It's like, what are the most important problems for you to solve in your business? What are the different ways to do that? And some of those options might be with AI. But you can have a deterministic flow for some of this stuff. If it looks like this, then do these things. You don't need an agent at every single step of the way. Oftentimes, that leads to sloppy outputs. So it's more about what's the business challenge? What are the ways that we can solve that particular challenge? And is AI the right solution? And that usually means, do I have to ingest a bunch of data and make a reasoned decision that is not deterministic? OK, AI is awesome there. Is it pattern recognition? Awesome. But jumping to it for everything is not really what we're trying to do.
Speaker 3OK, perfect. So one thought that we're maybe vision that we kind of have is a lot of roles are collapsing and converging, especially within go-to-market. Right. Yeah. A version of this might be that basically the entire go-to-market team, at least at the IC level, converges into kind of two roles. You have GTM engineers that build automations and scale the best ideas and that reps run direct human interventions, like you just talked about, and also feedback into the automation's understanding of the world. Is that kind of the end state feedback loop that we're headed towards?
Speaker 2I think it's a little reductive because there's still like so many other parts. There's so many other parts of go-to-market. Like, are your agents creating your core product positioning and creating campaigns? Like, they're important parts of that flow and ideating. Like, all of us, like, we ideate a lot with our LLMs. I think that those will be much bigger parts of a go-to-market org. We call it applied AI. Some people say GTME, like tomato-tomato. But I think you need way more of those people than most of us have today. Like, we're trying to hire. We're trying to hire them as fast as we can. In terms of like, oh, is there only one other role? There's an equal argument to, like, have more specialization. Like, the reason that we have this big SDR team when a lot of people are moving to more full cycle is if you can strip, if you can unbundle that job description into the discrete tasks, move all of those discrete tasks to whatever place, maybe an agent or a workflow, or you centralize it, then, like, what is left? And there is no way that any of my AEs could cold call the way my BDRs call now. Just, like, 200 dials to the face every day, like, doing the grind, like, just being so good at the knee-jerk and the early interruptions. Like, you could argue that more specialization is where we'll end up because you strip all the other stuff out. Then you have one person who is awesome at, you know, demo close, whatever, and then a launch person. And then see it, like, post-sales. I see the job functions at a leadership level converging more than I do see the, like, the frontline role.
Speaker 3Okay. So, in a post-AGI world, there's maybe a few things that are still protected. One of these is personal brand. I think you, of a lot of revenue leaders I know, have done an amazing job of building this for yourself. And you're well-known on LinkedIn. And. And at different events. What, maybe talk me through, like, how purposeful was that? How much, how many resources do you kind of dedicate to that? And then what are some of the outcomes that you're trying to drive for the business when it comes to, you know, having tons of LinkedIn followers or having a strong point of view on where a lot of this is headed?
Speaker 2Semi-purposeful. I think it was because of Naval, Naval Ravikant, talking about, like, compounding assets and, like, you know, everybody needs to be a brand. And sort of owning something that is uniquely you. It was, like, him and Seth Godin that originally, like, got me thinking. I was like, man, yeah, I should really just, like, start to do this and, like, build it up over time. And it takes a long-ass time to, like, get the ball rolling. And the purpose was always recruiting to start. I've typically done, like, you know, big builds. And so I wanted an easier way to attract talent. And so at Owner, like, we've never had outsourced recruiters. We've never really done outbound recruiting even in-house. It's all been, like, Kyle writes a LinkedIn post and then 1,600 people apply. And then we just wade through it. And that was awesome. Like, we saved hundreds and hundreds of thousands of dollars because of it. And then, like, I got all these, like, other benefits. Founders reach out, want advice. And I'm like, it's an easy way to do advisory. And I get to do cool stuff like this and hang out with super smart people. And that's, like, a great side benefit. And more and more becomes the more important. That's the reason for me. But from a recruiting perspective, it's huge. I don't monetize it for Owner because our customers aren't on LinkedIn at all. Unlike Clay. Yeah, exactly. And the question is, like, well, should I do that? And, like, my feedback is always, like, only do it if you're excited about it. Because you can solve recruiting through some other avenue. I like to write. I like to, like, get my ideas on paper. It helps me crystallize things. I enjoy trading ideas. It's fun to have people. It's fun to have people, like, argue with me or email me back after a newsletter goes out. And so I enjoy it. So this is, like, a thing. Like, going back to Naval, this uniquely works for me because it doesn't feel like work. I'm, like, excited to do it. And now that I've got all these AI workflows that help me, it's super easy to do. But if, like, that's not fun for you, then I, like, don't think it's a requirement.
Speaker 3Maybe you could talk a little bit about those AI workflows or, like, what resources you have in the background behind this. Because you've got a newsletter. You've got a podcast. You've got a revenue. You've got a new org to run. And I think you actually are writing all these LinkedIn posts or maybe you have. Not much anymore. So, yeah, maybe you can tell us a little bit about the structure there. And I do think for folks in the room, and we're actually focusing on this at Clay, leveraging your kind of exec presence to generate pipeline or do recruiting has been a major new unlock for us this year. So, yeah, whatever you can share on the tooling or structure that you have behind this I think would be great to hear.
Speaker 2Yeah, so the baseline is I have a bunch of writing skills. So, I fed, like, I had 150 pages of my, because I used to draft everything in one big Google doc just to keep it all there because it gets lost in the LinkedIn abyss. So, I had this 150-page Google doc of, like, all the LinkedIn posts I basically ever written. And I just dumped this into a model. I'm like, hey, so it's a meta prompt. I was like, okay, write me a prompt that will be able to take my writing and turn it into, like, a skill. And then it wrote the prompt and I took the prompt into another thing and gave it all the writing. And it describes. It described in, like, excruciating detail how I write. The, like, tone of voice and the patterns and all this stuff. And so, I've got a newsletter writing skill, a LinkedIn writing skill. I've got an email one, which is hilarious. It's like, never say hello. Just, like, only write one line because I gave it a bunch of emails. I was like, oh, I guess that is how I write. So, that's sort of the baseline. And then I just have workflows for other stuff. So, like, I have a clay, like, a big clay table for the podcast where when somebody is going to join me, I in my in my invite template in superhuman i've received these yeah yeah you go to the tally intake form and you fill in your information your linkedin and the topics that you think are interesting and i asked for your contrarian views and so that goes into the clay table and then it does a bunch of research and so every column has like a big research prompt tell me everything that ever has ever said online basically and then like then that then it goes into the next column which is like break it down into some themes what is what is like different and unique about his views and then it categorizes it into like how he how the guest thinks about leadership and then um systems building and then it and then the last one is it pulls in your answers all the research and then usually i read it at that point and then i'll pick the three topics that go into the last column and then then and when the last column is filled out the very last one goes and then it writes me my like research my docket and the docket is all the research a summary an introduction a bunch of questions and then basically my job is to spend 10 to 15 minutes just like editing the questions because usually as much as i freaking try yeah there's still like like really obvious stuff and i like really try with the podcast not to like talk about the things that are just like cliched or obvious i really want to like dig into the dig in deep to things um and so then that just gives me the whole thing and that's what i go off of the podcast and then the then i get the transcript transcript goes into the newsletter writer i almost don't touch the newsletter output at all yeah because it's trained to not like great ai uh so there's a wikipedia page called like ai writing giveaways and there's like 15 things like the m dash the the like it's not just this it's that whatever sentence structure that is and so i that whole i just copy pasted that into the into the writing file and so it just kicks out the newsletter i now just do a light edit and then it writes a linkedin post at the end uh so it's pretty pretty seamless yeah it's it is
Speaker 3really interesting to hear about you know where the human intervention is in that process you know selecting the big three topics for example because i think that actually is where i see a lot of people get things a little bit wrong is they they pick the wrong point in the flow to kind of you know no point right or no point
Speaker 2people are just like i put in the thing and then i like use the output so even the newsletter one of the things i've done differently about that with the newsletter skill is um it will give me uh five different options for hooks because the hooks would be like sort of cringe and i'm like oh like i never for years i did this wrong and i was like i didn't actually do that wrong but it's like and so it gives me five different options and then i pick one and it gives me 10 i want to write about the five key takeaways and so it'll give me 10 key takeaways stack ranked and then sometimes i'll go like i don't like three use seven and then it drafts so now that i have the hook the intro the the t takeaways now it basically one shots the output um because it's got the right like anchor theme from from the hook and so like that that having that intervention is really is is really
Speaker 3helpful okay a couple of minutes left here any questions for kyle uh yeah go ahead where do you
Speaker 4find the time to do this like what is your personal operating system to dedicate focus time to what you're doing and what you're working on and what you're trying to do and you're working on what you're working on and you're trying to do what you're trying to do and what you're trying to do outside of your core
Speaker 2job i now have like a lot of workflows for anything that i do repeatedly like my the weekly update i write is like basically an ai workflow and that's why i was asking oj about the context thing that's the thing that i'm still like figuring out but the guest outreach i just have templates for anything guest outreach the research the writing like and so you know i record the podcast for 90 minutes i probably spend like another 30 to 45 on it every single week and then i'm like okay i'm gonna do this and i'm gonna do this and i'm gonna do this so it's like pretty seamless the hard thing is like finding time to learn and so i just like you know after the kids go to sleep and my wife and i are sitting on the couch uh watching a show like i'm usually got an air but air pod in and i'm like watching stuff on youtube or on twitter to be honest and now that i've built up this base of knowledge i find it's like much easier to like add the new thing to it and and uh you just like you know i was lucky i just was so interested in it so early that i've like rode the wave uh and if you but if you were starting at a cold start now you gotta carve out a lot of time to like tinker and spend and basically waste like 20 hours building a vibe coded app that like never really works and it's sort of frustrating but then you actually sort of start like building an intuition i think with a lot of this stuff you have to just build up like a i hate the word taste but like you have to build like a taste and an intuition with it like what is the job that an ai is going to be like pretty good at like what's the thing that it's going to like give you a slop slop output or what's the thing that's like overkill for it and it's just like repetitions and and spending the time and finding like one simple thing to go use as the way you're going to learn that like skill so like okay i wanted to learn i spent a bunch of time trying to learn make.com and like i don't use it at all anymore and so it's like sort of a waste but it made me learn to like break things down now if i'm using cloud code i like know how to break things down into the little components and it'll spit out a prd like usually if i'm building something i talk yap away ask it for a prd then like in the user story i'll like break it i know to like break it down more discreetly and you just have to like keep doing stuff and and develop the like feel for it yeah i think i
Speaker 3think that's kind of the lesson for gtm engineering teams as well as the it's sort of in the iteration and the experimentation that the taste making occurs and then that's actually where you get some of the the gtm alpha as we say and i might be
Speaker 2overkill like that's the thing like i i don't know uh i'm really interested in in all of it i think you need to understand how the how these things work at a decent enough level that you can understand how to build your system but like you probably don't need to be in cloud code terminal and like you know have a github repo and like all this like other stuff um that that's like not a prerequisite but where the line is i'm like i'm not totally sure
Speaker 3yet got time for one more question for kyle if anybody has one here stevie go ahead if you could start over today how would you build your stack and your team differently this is going to seem
Speaker 2like a really shameless plug because we've talked about this a couple times like we built waterfall enrichment in late 2022 before clay was a thing before i knew what it was and we've talked about we've talked a bunch like should we just rip it all out and and so i tell they reach out to me people reach out to me like hey where should i start i'm like it's got to be first party and third party data get those foundations right and i'm like we do it like this but i would just use clay because right now the way we've done it because it's all like we internally built it only two people can really interact with these systems and like rev ops doesn't really get good access my my bdr leader can't go in and do anything but in clay you could easily democratize access a lot more i don't think down to the rep level we have a different approach there but um that would be one uh i pushed my managers really hard to learn a bunch of ai stuff and now i'm like i don't actually need them to know that much they just need to know what problems they want to solve and then the applied ai team is going to solve them so i probably had them waste a bunch of cycles like i sent everybody these two rj karpathy videos that are like six hours long this is required watching by monday i wrote this big ai memo sort of like toby did and the duolingo guy did and that probably uh was overkill it was my enthusiasm so like i wouldn't have pushed so hard there and taken them away from the craft that they need to be good at which is like hiring coaching managing to the number like i've like changed my my stance there actually
Speaker 3we've seen that as well where we used to test people in interviews for uh like knowing where to kind of click in clay to some degree knowing how to build clay tables of course now in clay you can just chat with it and more or less build what you need and and it's more about having the sort of problems the right problems to to talk
Speaker 2about here's one good piece of design feedback or like if you're trying to learn any of this stuff just ask claude what to do it's like hey i want to do this thing like can you break it down and so when i'm building something i ask it to explain it to me as i go i was like hey i want to do this thing like i don't know how to do it so you guide me through the process of breaking down the problem set and coming up with a solution but then i'm not technical so explain what we're doing as we go and so or else it'll be like okay now just like set up vercell for the front end and your super base is going to be whatever i'm like whoa hold on like why do i need a super base database why like what's what what is a front like to explain like what is vercell why like why can't you just do it and and so i've learned so much from like having the model walk me through the process of breaking down the problem set and coming up with a solution but then i'm not through the problems i'm trying to solve and why it's solving this problem in this way that arguably is where i learned more now than like like even youtube because i'll like watch a little bit of a youtube thing and i like get frustrated that i'm like it's a bunch of shit like subscribe to my channel and later in this video i'm gonna tell you all about and i'm like skipping through i'm like god damn it and but like you can just ask the model it's like teach this
Speaker 3to me okay amazing thank you so much kyle thank you for
Speaker 2listening 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 five-star review share it with your network and please join me next Wednesday for another great conversation
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