Lenny's Podcast: Product | Career | Growth - What world-class GTM looks like in 2026 | Jeanne DeWitt Grosser (Vercel, Stripe, Google)

Episode Date: November 30, 2025

Jeanne DeWitt Grosser built world-class GTM teams at Stripe, Google, and, most recently, Vercel, where she serves as COO and oversees marketing, sales, customer success, revenue operations, and field engineering. She transformed Stripe’s early sales organization from the ground up and advises founders on GTM strategy.We discuss:1. Why GTM is becoming more strategically important in the AI era2. The rise of the GTM engineer3. A primer on segmentation4. How to build a sales org that engineers and product teams respect5. The changing calculus of build vs. buy for go-to-market tools in the AI era6. Why most customers buy to avoid pain rather than to gain upside—Brought to you by:Datadog—Now home to Eppo, the leading experimentation and feature flagging platform: https://www.datadoghq.com/lennyLovable—Build apps by simply chatting with AI: https://lovable.dev/Stripe—Helping companies of all sizes grow revenue: https://stripe.com/—Transcript: https://www.lennysnewsletter.com/p/what-the-best-gtm-teams-do-differently—My biggest takeaways (for paid newsletter subscribers): https://www.lennysnewsletter.com/i/179503137/my-biggest-takeaways-from-this-conversation—Where to find Jeanne DeWitt Grosser:• X: https://x.com/jdewitt29• LinkedIn: https://www.linkedin.com/in/jeannedewitt—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Introduction to Jeanne DeWitt Grosser(05:26) Defining go-to-market(08:43) The evolution of go-to-market roles(11:23) The rise of the go-to-market engineer(14:21) Implementing AI in sales processes(15:28) Optimizing sales with AI agents(23:47) Defining sales roles: SDRs and AEs(26:04) When to hire a GTM engineer(29:04) Hiring and scaling sales teams(30:50) The ideal go-to-market engineer(34:24) The go-to-market tool stack(40:39) Advice on building a great sales bot(44:34) Vercel’s unfair advantage(46:37) Go-to-market as a product(47:04) Innovative sales tactics at Stripe(52:38) Effective go-to-market tactics(01:00:37) Segmentation strategies(01:09:31) Building a sales org that engineers love(01:14:00) Thoughts on PLG and pricing(01:16:44) Sales compensation and hiring(01:19:24) Lightning round and final thoughts—Referenced:• Vercel: https://vercel.com• Stripe: https://stripe.com• Rosalind Franklin: https://en.wikipedia.org/wiki/Rosalind_Franklin• Ben Salzman on LinkedIn: https://www.linkedin.com/in/bensalzman• SDK: https://ai-sdk.dev/docs/introduction• Gong: https://www.gong.io• Lyft: https://www.lyft.com• Instacart: https://www.instacart.com• DoorDash: https://www.instacart.com• “Sell the alpha, not the feature”: The enterprise sales playbook for $1M to $10M ARR | Jen Abel: https://www.lennysnewsletter.com/p/the-enterprise-sales-playbook-1m-to-10m-arr• A step-by-step guide to crafting a sales pitch that wins | April Dunford (author of Obviously Awesome and Sales Pitch): https://www.lennysnewsletter.com/p/a-step-by-step-guide-to-crafting• Kate Jensen on LinkedIn: https://www.linkedin.com/in/kateearle• Lessons from scaling Stripe | Claire Hughes Johnson (former COO of Stripe): https://www.lennysnewsletter.com/p/lessons-from-scaling-stripe-tactics• Atlassian: atlassian.com—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [e-mail].—Lenny may be an investor in the companies discussed. To hear more, visit www.lennysnewsletter.com

I've been getting so many asks for go-to-market help. With AI, it's just intensified because you have 10 players pursuing the same market opportunity. And so your ability to actually bring the product to market, to differentiate yourself from the competition has become more strategically important than it was previously. I had Jenna Abel on the podcast recently. One of her tips is you don't want to be focusing on here's the pain and problem we're solving and instead focus on here's how you will be better than your competitors. 80% of customers buy to avoid pain or reduce risk as opposed to increase upside, which is a good thing for startup founders to understand. We all love to talk about the art of the possible, everything we're going to enable in the future. But that's often really a sale that's going to resonate with another founder.

For everybody else, particularly enterprises, you're avoiding the risk of not making your revenue target next quarter. I've heard a lot about how you think about go to market as a product. We buy a lot of things because of how we feel about them. The experience that you have of being sold to will increasingly actually differentiate a company and drive buying decisions if products are only different at the margin. And so then you really want to create a customer buying journey that feels like very unique experiences. Something I've heard from so many people you've worked with is that your superpower is building a sales org that doesn't feel like a salesorg to engineers. The litmus test I have always given my sales work.

team is if you are an account executive in my org and I put you in front of 10 engineers at our company, it should take them 10 minutes to figure out you aren't a product manager. Today my guest is Gene Grosser. Gene was chief business officer at Stripe, where she built their very early sales team from the ground up. She's currently C.O.O. at Versailles, where she oversees marketing, sales, customer success, revenue ops, and field engineering. Gene has built world-class go-to-market teams at multiple unicorns and has advised dozens of companies on doing the same. In our conversation, we go deep on what a world-class go-to-market team looks like, including what the heck is go-to-market, the rise of the go-to-market engineer,

and how this role is already enabling her team to operate 10 times faster, a bunch of very specific tactics to level up your go-to-market skills, a primer on segmentation, how to think about your go-to-market process like a product, her favorite go-to-market tools, her hot takes on PLG and sales comp and sales hiring and so much more. If you are looking to get smart on the latest and greatest in go-to-market thinking, this episode is for you. A huge thank you to Claire Hughes-Johnson, Kate Jensen, and James Diett for suggesting topics for this conversation and Kelly Schaefer for The Connection. If you enjoy this podcast, don't forget to subscribe and follow it in your favorite podcasting app or YouTube. It helps tremendously. And if you become

an annual subscriber of my newsletter, you get an entire year. free of a ton of incredible products, including Devon, lovable, replid, bold, N-8-N, linear, superhuman, D-script, whisper flow, gamma, perplexity, warp, granola, magic patterns, raycast, chip, B, mob, and hand, stripe, Atlas. Head on over to Lenny's newsletter.com and click product pass. With that, I bring you Gene Grosser, after a short word from our sponsors. This episode is brought to you by Datadog, now home to Epo, the leading experimentation and feature flagging platform. Product managers, at the world's best companies, use data The same platform their engineers rely on every day to connect product insights to product issues

like bugs, Ux friction, and business impact. It starts with product analytics, where PMs can watch replays, review funnels, dive into retention, and explore their growth metrics. Where other tools stop, data dog goes even further. It helps you actually diagnose the impact of funnel drop-offs and bugs and U-X-friction. Once you know where to focus, experiments proved what works. I saw this firsthand when I was at Airbnb, where our experimentation platform was critical for analyzing what work and where things went wrong.

And the same team that built experimentation at Airbnb built Epo. Datadog then lets you go beyond the numbers with Session Replay. Watch exactly how users interact with heat maps and scroll maps to truly understand their behavior. And all of this is powered by feature flags that are tied to real-time data so that you can roll out safely, target precisely, and learn continuously. Data Dog is more than engineering metrics. It's where great product teams learn faster, fix smarter, and ship with confidence. Request a demo at Datadoghqhq.com slash Lenny.

That's datadoghqq.com slash Lenny. This episode is brought to you by Lovable. Not only are they the fastest growing company in history, I use it regularly and I could not recommend it more highly. If you've ever had an idea for an app but didn't know where to start, Loveable is for you. Lovable lets you build working apps and websites by simply chatting with AI. Then you can customize it at automations and deploy it to live domain. It's perfect for marketers spinning up tools, product managers prototyping new ideas,

and founders launching their next business. Unlike NoCo tools, Lovable isn't about static pages. It builds full apps with real functionality, and it's fast. What used to take weeks, months, or years, you can now do over a weekend. So if you've been sitting on an idea, now is the time to bring. bring it to life. Get started for free at lovable.dev. That's lovable.com. Gene, thank you so much for being here and welcome to the podcast. Thanks for having me, Lenny. What I want to get out of this conversation by the end of this, to basically have this

conversation be the thing that we send people when they're like, I want to get better gooder market. I'm trying to figure out what to do and get a market. We send them this versus having to hire someone for a lot of money. And usually they can't find amazing people because they're all snatched up. Yep. So let me start with just the basics. When people are here at the term, go to market, what does that mean? What does that encompass? I think there are two answers to this. Often what people think of is sort of the tip of the spear of what drives revenue, which is marketing and sales. For me, I think of it as any function that is going to touch a customer or make a dollar. And actually, my remit at Bursal is that.

So that includes marketing, sales, all of your technical sales roles like sales engineers or post sales platform architects is what we call them at Bursale, its customer success, its support, its partnerships. And the reason I say that is my experience throughout my career has been that those functions often have this bend diagram strategy where marketing is pursuing one thing. It overlaps with what sales is pursuing, but not perfect. which also overlaps with what support is pursuing, but not perfectly. Examples of this would be slightly differing segmentation frameworks, et cetera.

And so one of the things I think you're going to want to see more in this particular moment is that that become a really integrated lifecycle. In particular, because I think we're going to see a lot of the functions of go-to-market get redefined. So we've gone through a period of like hyper-specialization in go-to-market. you know, depending on how you count them, there are, you know, I think somebody quoted like 17 different roles within go-to-market these days. And I hypothesize that a lot of those are going to start to collapse. And so if you think of go-to-market more holistically, I think

you can kind of go back to what are the jobs to be done from making a customer prospect aware of your product all the way through to, you know, high LTV, five years on the platform, platform fully wall to wall. And you're going to want to map that out and orchestrate it the way you would think about that within your own product. Awesome. We're going to go through that whole cycle of go to market. But so is it safe to say just for most companies that may that are especially starting out when they say go to market that mostly is sales and then there's marketing as a maybe a smaller fraction of that. And then as you become more advanced and grow, customer success plays into a tech sales, things like that. Yeah, that's probably where most start,

is getting sales, or frankly, just because a lot of companies also start PLG, you might actually start with marketing and then you're layering in sales when it's time to do the sales assistant and ultimately sales-led portions. So I think it can, depending on your product and your initial target market, it can either mean marketing or sales or a combination of those two. Awesome. So essentially it's like the term go-to-market tells you what we're talking about. It's how do you take your product to market, get people aware of it, using it, sticking with it? Yep, absolutely. What is most changed in the world of go-to-market of the last few years?

You've done this for a long time at Google, at Stripe, you built the first sales team, now you're doing that over-sell. What's changed most in the skill and art of go-to-market? There are a number of things. So when consumption-based business models started, I think you saw go-to-market shift into being meaningfully more consultative because often that first land was the very beginning of the journey and represented a very small percent of, what you were ultimately going to do with that customer.

And so you had to go from being transactional to a lot more relationship-based. You had to more deeply understand what that customer was trying to do, so you could align that ultimately to your product. I think that has played out that much more with an AI because right now, everyone knows they need to change, but they don't necessarily know exactly what they need to change to, whether that's their customer-facing product or their internal productivity and workflows. And so I think you're seeing a lot more of go-to-market orgs leaning into the art of the possible best practices,

helping you actually think things through as if they were a consultant. And so one of the things you see more of right now is for deployed engineering, which on some level is kind of a rebrand of professional services, but kind of not. And a big part of that is, hey, how do I actually get into your environment, ride alongside you, better understand what you're trying to do and then help you actually bring the technology to life and learn a lot along the way. Often, you're not only making that customer successful, but you're then taking all of that back to your product and engineering organization to figure out, okay, what was generalizable that we ought to build into our offering versus what is something that ultimately is going to be more of a professional service in the fullness of

time. So I think that has been a biggie is actually just like really getting embedded with your customer. And then, unsurprisingly, I think bringing AI to bear on the sales process is another big one. And so you've seen the rise in probably the last 18 to 24 months of the go-to-market engineer, which, you know, different folks defined slightly differently, but it's kind of bringing one technical prowess to bear on go-to-market in general. So you can have a lot better tooling, data use, et cetera, and then to increasingly bringing AI to bear as well to re-architect your workflows and also make it so that's easier to have a personalized experience with customers, but do so at scale.

Amazing. Okay. Let's follow the threat on this. Go-to-market engineer. Yep. So what was it like before? And what are these engineers doing at companies? So I think maybe like an interesting story.

to tell. When I was at Stripe, we went to launch an outbound SDR function, so outbound prospecting. And Stripe always ran lean. The company at that time had an operating principle, which was efficiency as leverage. And so if you looked at the sales organization, I was running. Most companies out there probably would have had 30 SDRs. And I was going to get four. So, you know, there's no way I was going to do the typical SCR. approach and be successful. And so we thought to ourselves, okay, what can we do? We'll be super data driven. And so we went and we started building Project Rosland. Roslund is the scientist who originally mapped DNA. And what this was was effectively a company universe. So you can think of this as like a massive database. Every row was a different company on the planet. And every column was an

attribute about that company that would help you sell to them in a more targeted fashion. So at Stripe, an example would be like knowing that their business model was a marketplace was super helpful because that would mean you wanted to sell Stripe Connect versus vanilla payments. And so the goal was basically, hey, can we create a MadLibs, you know, where I will come up with sort of a predefined email template, but 80% of it will be fill in the blank based on the different attributes of that customer, right? So if they're this industry or this business model, then pull this customer reference, this value prop, you know, send it to this persona, not that. And we were trying to do this in 2017. And it was very hard and didn't actually totally work.

Our ability to like the false positive rate, and we worked deeply with data science, like just, it just never really got there. And now that we're, literally redoing here at Varsela as we speak, and it actually works. And that's because you can bring AI to bear on it. And so what's different is we now, I have a data scientist, just like I did back in 2017, but I have a go-to-market engineer, whereas before I just had someone in systems that was helping me configure outreach or sales loft. And my go-to-market engineer is helping me build an agent where we're coming up with, okay, well, what's the human workflow that you have done and then how do you encode that using Brousel workflows as an example, you know,

in actual code that's both deterministic and less so. We're an agent's going out and trying to replicate what a human might have done to produce that fill in the blank mad lips. I love the ambition of that project. What is this like eight years ago? Yes. It's such a, I love the big thinking there. We're going to map the entire universe of companies and then here's how we sell to them and then just, I'm trying to picture doing it. that without AI. It's like crazy to imagine trying that without AI. And that's like so much simpler to imagine. Yeah. Well, the thing that's amazing about that, just to geek out on a second. So I was working on that with a bunch of folks at Stripe on my team, obviously, at a gentleman

named Ben Salzman, who went on to go to Zoom Info and actually recently just founded a go-to-market startup that is basically sort of productizing that concept of a company universe and then layering AI on it on top of it. And ultimately, his view is actually, you'll, AI will get to the point that you won't have to do outbound prospecting because it will just sort of company and product match. So it's, it's fun to sort of see back in 2017. Some of the folks doing that now work at OpenAI. They work in Anthropic. They also are doing GTMN. You've got him starting, you know, a totally AI native GTM company. And then, you know, here I am at Roussel trying to do the same. Okay, so what's cool is this is an emerging role and emerging skill that I don't think a lot of people have recognized as something that is happening.

So one example I'm hearing of what this role does is they automate outbound emails essentially in outbound outreach. They figure out they write workflows and agents that figure out here's the company to go after. Here's how we message them. Does that end up being kind of like an email that's custom designed and written for this prospect? That's one version. So it's broader than that really. Basically, the full remit of GTMNG will be to go through each of the different functions within GoTo Market and break down all the different workflows that they do and then turn those into agents where, you know, AI is better placed than a human to do that task. So right now we started with actually inbound and are now moving to outbound because that work.

workflow is most legible. And by legible, I mean you can basically write it down. It's relatively replicable, mostly deterministic. So it's more likely that AI will do it well. And we actually built the agent and then we keep a human in the loop. But from there, we're starting to look at outbound. And with an outbound, we're starting more at the lower end of the market where you tend to, you know, have slightly less customization because there's a single decision maker at the company. but I think it will take a while before we're able to really do that in a very large enterprise. There, we might use an agent for research, but maybe not all the way to actually send a message. And that's just within the prospecting function.

So other places that we're looking at this would be for install-based sales. So again, there it's a little bit more deterministic because you've got awesome internal data on what a customer is and isn't using. What's the next best action? What's the thing they should get most value from? So that's where we're starting to map, hey, what does that ideal workflow look like? But basically, you want to get to a state where as long as I've been in sales, they release these annual reports that help us all benchmark ourselves relative to one another. And one of the stats is what percent of time do your sellers actually spend in front of customers?

And for the 20 years I've been in sales, it's always been somewhere around 30 to 40 percent. So the minority of time is actually talking to other humans. And I think we're getting to a point where with layering in agents, ideally, we finally get salespeople to a point where they're actually spending 70% of their time interacting with humans. And we can get the research, the follow up, the things that are a little bit more, you know, wrote and don't use the entirety of your human capacity done by an agent and then sort of unleash you to go deeper with your customers. I love that this is such a great example of our AI is contributing in a very meaningful, high ROI way, taking on all this work that people, let you have to hire, say, 50 STRs, as you described, to do. And now you could do it with a lot more.

So it's a really cool example of leverage that AI gives you. One thing that I know a lot of people think about when they hear this is, okay, I'm going to get more of these really bad emails trying to pitch me on stuff and just like, this isn't going to work. I can tell this is AI. What have you learned about how to do this where people actually receive emails that actually convert and do well? Our processes all always have human in the loop.

And so basically where we'll start is we take a go-to-market engineer and we have them shadow the highest-performing individual in that function. And so you can go and you shadow an SDR and you can see, oh, wow, they've got seven tabs open. They're looking up the, you know, person on LinkedIn. They're reading about the company. They're doing chat GPT on this. they're, you know, looking in this database to get these sets of attributes. And so that's how you sort of inform the initial workflow. And then what we do is we let the agent make a call. So, and the specific example of with inbound, right, you have to determine whether or not you think

the lead is likely to be qualified, and then you have to determine what to say to it. And so we'll let the agent make those two calls. It ultimately then does some deep research, pulls in a bunch of information from our databases and crafts a response. But we have a human review all of those and actually hit send. Now, for us, we had 10 SDRs doing this inbound workflow. And now we just have one that is effectively cueing the agent. The other nine, we deployed on outbound. So we got to move them up the value chain. At some point, I think we'll get to a place where we feel like, hey, you know, the human reviewer is saying, yes, enough of the time that we feel confident that these will be on brand, targeted, et cetera. But right now, we're still trying to train the agent

and it, you know, it incorporates feedback on what we choose to reject, edit, et cetera. And you shared that it's already having a lot of impact. Like you said, you had, you said 10 STRs and now one can do the job of 10. Yes. Wow. Yeah. And we, so before we did that move, I mean, the other thing that's just incredible about this is the person who built the lead agent was a single GTM engineer. He spent maybe 25, 30 percent on his time of his time on this. It was six weeks before we felt confident going from 10 to one. So it wasn't like this was a multi-quarter process. It actually moved super quickly.

So, and then again, now we just sort of keep that agent manager sort of working with the agent to get it to a point where we say, hey, we're ready to roll. And actually throughout the process, we also tracked all of the KPIs that you typically would hold an SDR accountable to. So we were looking at our lead to opportunity conversion rate. We're looking at the number of touches it takes, the time to convert. And basically what we were able to do is hold that lead to opportunity conversion rate flat. So the agent is as good as our humans were. But it's actually condensed the number of touches as it takes to convert because it's so much quicker at responding relative to leads inevitably sitting in the queue or coming in at nighttime and no one can get to it, that type of deal.

So that's sort of, you know, when we knew it was ready to pull nine people off and shift them into outbound. That's incredible. Okay, that's interesting. So you shift them to outbound. What I love about this is this is this SDR that is now doing this is, as you said, doing the things they enjoy more. They're talking to customers more.

They're not doing all this kind of top of funnel road work. Yeah. I don't want to get into a whole like jobs AI discussion, but there's always been this talk about AI SDRs, basically replacing SDRs. It feels like that's one thing where everyone's like, this is 100% going to be AI in the future. What I'm hearing here is it gives one a steer a lot more leverage and obviously still need people running the show. Tate thoughts there just like, do you think AI will replace all this at some point? And then I don't know. You don't need salespeople? I think on prospecting, it can replace a fair amount because, the average SDR wasn't doing overly sophisticated research in the first place. So where I think the last part to go, as I mentioned, will be in deep enterprise prospecting where, you know, you can be at multiple layers in an org chart. You've got to pick between business lines. You've got to triangulate those. But I do think for the things that are more repetitive that often don't take that much time to

learn and get ramped, AI will be good at that. And in my view, no one, like, graduated from college and was like, yes, I just went to college for four years to become an SDR. It was more, okay, that's where you are forced to start. But I think the average SDR could have gone straight into outbound or straight into an S&B closing role. And so basically what we're just doing is shifting folks into something that uses more of their full capacity right out of the gates rather than sort of the forcing function of working your way up the totem pole. Awesome. Since a lot of people listening to this aren't salespeople, don't have a lot of background in

sales. We've used this term SDR. There's also the term A.E. Can you just help people understand what is an SDR, what do they do, what's an AE, and then what's kind of the role above? Sure. So SDR is typically in charge of generating pipelines. line. So they're meant to talk to prospective customers and get them to a point where it is

worth investing time to run them through a sales process. So you typically have two types of an SDR. You have an inbound one. So this is where people come to your website. They fill out contact sales. They'll be the first call to make sure that it's actually worth a more expensive account executive to go and run a sales process. Or you then have outbound. So this is where when you want to grow faster than your inbound demand, they will go out. And at this point, you probably have a point of view on where you think you have product market fit. And so they will target that part of the market and try to drum up interest from folks who weren't otherwise raising their hand saying, I'd like to talk to you. So that's sales development, basically pipeline generation.

Account executives are closers. So it's their job to take. somebody from, okay, hey, I'm interested in learning about your solution. I have a legitimate problem. I potentially could make a decision to I now believe that your product is the best in the market for me and I'm willing to pay for it. And then account executives, depending on the segments that your company sells into, e.g. small business, mid-market, enterprise, etc., they may work their way up the food chain from selling to a smaller company like an S&B or a startup. those tend to be a little bit more of a transactional sale. You often have a single decision maker to then going into a mid-market or a commercial role where now maybe you have an economic buyer like

somebody in finance and a technical buyer like somebody in engineering to getting into enterprise where, you know, you've not have procurement and you have committees and 10 people have to weigh in. And, you know, you've got to help them figure out how to de-risk the fact that they're probably migrating from something. So much more complicated coordination effort to sell. That was extremely helpful. So SDR pipeline generation AE closer. Such a simple way of thinking about it. Okay, this is great. Going back to the GDM engineer, a few questions for people that may want to try this at their company, what scale do you think it makes sense to start hiring for this role, having someone automate the go-to-market process? What's interesting about this is it will force

companies to be more rigorous about their sales process early. So often startups, when they go from founder-led sales to say, I'm going to have my first sales person, whether that's an actual, you know, account executive who has prior sales experience or your general athlete, wick and smart, who's going to go figure it out, you know, often founders will just say, okay, sales is showing up and talking to people. Isn't, you know, isn't that what I just did for last couple years, but actually sales is more than that as a skill, just like writing code as a skill or building a financial model as a skill. It's about discovery. So asking all the right questions that help you identify challenges in pain, willingness to pay, you know, etc. And then going through

a process to handle those objections and showcase, you know, where you add enough value such that somebody ultimately wants to hand over some money. So often, you know, startups will get, particularly ones with strong product market fit, to pretty significant scale without really having a replicable process. And you can't really apply go-to-market engineering unless you actually have a point of view on what best practice should look like. And so I think basically this is going to force folks to have more of a playbook out of the gates. What's working? What's not? Can I document it? Do I have content for the different parts of the sales process? And then, you know, once you do that, which you know, maybe 10 people is a good size and skill for that. Ostensibly, you know, a GTM engineer can come in and turn that into an agent. You could also argue that if, you know, you're a founder who wants to bring in a general athlete profile and that person is technically minded, that you could have a hybrid, a GTM engineer who figures out what their best practice is and then tries to turn that into an agent, you know, that's riding alongside them and making them more effective as well.

So, you know, I don't know that I have a point of view yet on what's the optimal size and scale, but I forever have given founders the advice that it's, you often want to bring in revenue operations, which is basically the analytical arm of sales earlier than you think, because having data, having process is actually what gives you insights as a founder into what is and isn't working. And so I would argue just like it's a good idea to have that sooner than later, increasingly, it'll probably be a good idea to have GTM engine and be looking to bring agents to bear on your process at the outset. While we're on this topic, just a quick tangent, the advice for hiring your first salesperson

that I usually hear is wait until you're around a million in ARR. When you have a repeatable process, you can teach someone. Anything there is that, does that seem right? What would you recommend? Yeah, I think that seems about right. I do think as a founder, you want to stay deeply connected to customers and get it to with scale and get it to a point where, you know, you use the word, there's some repeatability there. I think that's one of the things that not all founders get right is founders are incredible salespeople,

right? They convinced a VC angel investors to fork over a bunch of money. So clearly they're going to inspire people to buy. But if you're getting to a million in ARR and the set of customers you have look nothing like one another, you still have very much like an evangelist sale, very much founder-led sale, versus if you can say, hey, I now have an ICP here or ideal customer profile, e.g., something you can write down, you know, we are good. Our product fits with startups with less than 100 employees who are typically building SaaS applications, right? Something like that. Then you're probably ready to hand over the reins. And then what founders have to remember is to actually hand over the reins. So, you know, you've got to enable

the person who comes in. What is it that, you know, you're doing effectively? What's your content? What are the discovery questions you're asking? How are you handling objections? So you can transition that knowledge. But also, don't handle them over entirely, right? You want to stay connected to the customer because you still have a fair amount of R&D to do to figure out where are you, you know, where is the product next going to resonate? Where are you getting, you know, stock as you scale, etc. It's a close loop on the go-to-market engineer. What's the profile? of the ideal go-to-market engineer, maybe you're first? What we have found works really well is somebody who does have go-to-market experience.

So at Vursell, our first three go-to-market engineers were actually sales engineers. So Vursal hires very technical sales engineers. All of them were front-end developers before they decided they wanted to get into sales. And so we just said, hey, three of you, congrats here to how founding members of our GTN, M-Eng team, and the thing that works well there is, you know, you do understand aspects of what is good GTM, what does a process look like. It's been really interesting, actually. So the gentleman who runs GTM-NG for me, we were going through, you know, this lead agent and QAing it. And, you know, so I'm going and I'm looking at some of the responses that we've ultimately

had the lead agent send and realized, oh, I wouldn't have sent that. And that's because I have 20 years of sales experience and we modeled the lead agent off, you know, our best person, but our best person who has two years of sales experience. So it actually is important to understand the art and the science of sales and how you bring best practice to bear. So either you've done it and so you know some best practice or you're going to geek out on sales. read a bunch of books, learn a thing or two, you know, and try to incorporate some of those into your agent development. That is really interesting. So come from the sales side, not from the engineering side. And I imagine this is such a cool opportunity for salespeople to do something completely different and move closer to engineering. Yeah.

I mean, we're having a lot of fun with it at Roussel in particular. We basically get to be customer zero. So everything that we're building with agents, we're building on Roussel's AI cloud. So, you know, these agents are now have multiple steps that they go through. So we're using VERSEL's workflow SDK and workflow offering. We, you know, use the AI gateway to call the different models that we use to do deep research or other enrichment that we do. So for us, it's great because we basically sort of bang on everything. The engineering team is building and get to go be a discerning customer before we actually get it out the door to real customers.

What a fun time to be alive. Yes. I could tell the fun that you guys are having just from the way you describe it. Stripe handles the massive scale and complexity of many of the world's fastest-growing enterprises, including 78% of the Forbes AI-50 and more than half of the Fortune 100. Enterprises like Atlassian, Figma, and Urban Outfitters use Stripe to create fully branded and customized checkout pages

with access to more than 125 global payment methods. There's a reason I've had more leaders from Stripe on this podcast than any other company. They know how to build great products that scale and that people love. And Stripe is a lot more than payments. They've also got a category leading billing solution and a highly optimized checkout experience built specifically to increase your checkout conversion. Join the ranks of industry leaders like Salesforce, OpenAI, and Pepsi that are using Stripe to grow fast.

and to grow the world's GDP. Learn how Stripe can help your business grow at stripe.com. Zooming out a little bit, in terms of you mentioned tools, some tools that you use, I'm curious just what are kind of the state-of-the-art tools within the go-to-market stack that you love that you'd recommend? Well, so I'm going to have an interesting answer to this. So I'll give you one, and it's not state-of-the-art per se, although I don't mean that disparagingly.

It's just that it's been around for a while now and a lot. lot of folks use it, but I think Gong has gotten just meaningfully more interesting in the last year. And then second half of my question, I will get into, I think the calculus on build versus by is changing. So, all right, gong. Gong is incredible because you can run agents against it now. So we take all over gong transcripts and we dump them into an agent called the deal bot. and that deal bot then can do a bunch of things. So the first thing we had it do was a lost opportunity review. So we had just finished Q2. We had, you know, a list of our top losses for the quarter, sorted by deal size. And we ran it against that. And it was incredibly interesting. So the

biggest loss that quarter, according to the account executive, was lost on price. And when you ran the agent over every Slack interaction, every email, every gone call, it said, actually, you lost because you never really got in touch with the economic buyer. And when you talked to somebody about ROI and total cost of ownership, it was clear from their reaction that they didn't really buy your math. And so really the reason we lost was an inability to demonstrate value, which upon reflection, I've got work to do to build out how we quantify the value of Riesel, which actually is very easily quantifiable. It's one of the things I love about selling this product, but we got to codify that for

the go-to-market team. So that was incredibly interesting. And now we run it against all of our lost opportunities and actually do a much better job of categorizing why it was we really, really lost, and then either feeding that back into the engineering team or back in. to marketing, sales leadership on, hey, where are we falling short in the sales process? And so that was awesome. But then we're like, well, it's not very fun to lose. So why don't we pull that forward? And so we went from LostBot to DealBot. And now the DealBot is running in real time.

And we basically feed insights into Slack. Versal is incredibly heavy users of Slack. So we have a channel for every single customer, either opportunity, or existing one. And so now we're feeding insights into that Slack channel, which is, you know, hey, you're this far into the sales process and you haven't talked to an economic buyer. You should think about that. Or, hey, you just got off that call with an economic buyer. It didn't sound like it went that that well. You know, here's some things to consider and how you might follow up. And last thing before I pause, the other thing that's really interesting and how we're using this too is, you know, we are in this moment, right? We're like,

I have never seen an iteration velocity like exists now in my career. My 20-plus year career has all been in tech. And so for Go to Market Teams, that's really hard. If you are launching something every other day, the ability to be enabled on that is actually quite challenging. And so this bot agent is now also letting us, where we're starting to go with it, is we'll release something,

we'll do our best to enable the team. Then we'll go run the agent across calls, interactions, and we'll diagnose where we did a bad job of objection handling, where we're getting stuck. And then at the end of the week, we can have a huddle and say, okay, what are all the places that our agent would suggest we aren't selling effectively? And then almost like an engineering team will now run sprints,

which is like, those are just bugs. They're bugs in your go-to-market process. So you should not have them. And, you know, by the next week, we're going to add content to our objection handling to guide. We're going to add content to a discovery guide. We're going to figure out something we need to change about our demo, so on and so forth.

So that's early. That's a little bit of a preview. But that's where we're talking about taking things right now within our go-to-market org. Gene, you're blowing my mind. It's so many ways. It's just sounds so fun and just like you guys are going to win is what I'm feeling when I hear all this.

Incredible. What I love about this is this AI tool, this agent you built, sees things that humans were not seeing. The fact that you were surprised of just like, this is a completely different conclusion is such a big deal. This is the whole promise of AI. It's going to do things we aren't even thinking about are capable of. It is. We had a really interesting, one of the things we're doing at Bristol. So, you know, we have any AI clouds.

So people use that to put AI-need features into their customer-facing applications, but they're also using it to build internal applications to, improve productivity or outcomes. And we are talking to a very large airline. And that airline obviously gets tons and tons of support queries. So of course, they would want to go apply AI to, hey, how can we have AI answer these so that our cost to support goes down, sort of the obvious thing. But the more interesting conversation was actually with one of the C-level executives who said, we also actually transcribe every single one of those support calls. And so what I really want to know is why are they calling and how do I make it so that fewer people call the next week? And so again, this is now with AI, you can rapidly go through all of that content and actually be able to much more quickly than having a human, you know, and your CRM sort of pick some status. Why it was that folks were calling the airline this week and what, if anything, you can do. to make it less the case next week. I imagine many people hearing this are like,

I need one of these deal bots and last bots. These are all internal products that you all built. Yes. Is there anything that you've learned about making them this good? Any tips you can share here is that to make a really good bot for sales? Yes. So actually, that's the second half of my answer that I forgot to forgot. That's perfect.

Which is sort of like build versus buy calculus. So I think one of our learnings is that it's not that hard. to build these agents, and they aren't that expensive either. So, you know, I mentioned with the lead agent that was a six-week process with one human, a third of his time. That deal bot, the lost bot version, was like two days. Like, basically, we riffed on it. He had it 40 hours later. You know, now we're continuing to refine it for the other things I mentioned. And what's also interesting about them is they, it's, you know, for better, for worse for

Bursal, but that lead agent, which runs full stack on Bursale, will cost us about $1,000 to run for the entire year. So if you remember, I told you we had 10 people in the SDR function, so I'm paying well over a million dollars

for that, from a salary perspective. I got that down to one, and then behind that, I have a lead agent that cost a thousand bucks. So that's like a, you know, 90 plus percent reduction in total cost there. So, and, you know, there's lots of software for agents out there right now. And I think one of the things we're learning is because this whole space is so nascent. Often your own esoteric context, you know, your content, your workflow is really key to unlocking the power of the agent.

And so I think there's real value in experimenting with your own internal agent development. We may ultimately end up on, you know, better integrated agent platforms in the fullness of time. Or we may find that the CIO increasingly goes from a procurer of software to a builder of software. And you'll have an AI internal platform with a thousand agents running across your org. I'm not really sure yet. but I certainly think there's value in trying it yourself because you may find that it's meaningfully easier than you think and you get returns pretty quickly. So what I'm hearing here is that you're finding that there are not tools out there to plug in play. The alpha is essentially in building your own stuff.

I think that's partially true. And I think because you also have all these tools proliferating right now, you get into the perennial problem where you wind up, with 20 of them to do, you know, the 20 jobs to be done, basically, rather than an integrated platform that's doing all of them. I'm hearing this a lot, actually, when I'm talking to customers right now where their biggest issue in deploying AI is actually just getting through procurement. And it's sort of, because everyone's got an AI mandate, you kind of have a blank check. I recently heard the term of instead of ARR, it's ERR, which is experimental run rate revenue, which is to say, you know, everyone's out there sort of, hey, we're going to give this thing a go for a year

and then TBD on whether or not, you know, we keep it. But, you know, basically you're having to procure 20 different things because most things are getting off the ground. And so, you know, they're solving something relatively narrow and that'll change in the fullness of time. But I do think there's an opportunity to figure out, hey, where do I likely have a more specific work you know, internally. For that, it might be worth building your own agent. And then maybe for the things that are a little bit more generalizable, you go get something off the shelf. Are there any platforms or tools you want to shut out that allow you to build these agents so quickly? I know they sit on Vercel, so shout out of Versel, but just anything that you point people

to you to, like are these SDR, these GTM engineers, they're former salespeople. Are they learning to code? Are they bi-coding these agents? How does that word? Well, so our sales engineers all have CS degrees. So they were engineers in a sales capacity. So they're writing code. And actually, these agents, they're building directly on Versel. So you get the AI gateway that lets you, you know, call different models. You have a sandbox if you're running untrusted code. You've got workflows that let you build the process. You've got fluid compute, which lets you really efficiently use compute when you only need it. So we're just sort of, building it from the ground up here, because again, it's not that hard. Now, you do need to

write code for that. Certainly, there are a lot of vibe coding tools out there that also give you more kind of workflow builders that are somewhere between fully whizzy wig, almost like drag and drop, and a little bit more code forward. So you've got a bunch out there along those lines. But, you know, I do think we've sort of found, like, one of the reasons, actually, the GTM Eng team at Vursell can build these agents so easily is because the Vursell platform is making it that easy to use our, you know, framework to find infrastructure and get that agent onto, into production really, very rapidly. What a neat, unfair advantage you all have to do this stuff. Yes, it is, it is fun to, like, I mean, I do think this company is better than any I've seen at eating its own dog food. And just everyone is constantly, we say, Bersel builds Versel with Bursel.

So you're just always looking for ways to, hey, how can we use our product to go do what we need to do? And as a result, either understand then what a customer would want or what's missing from our product that we could go make better. Along these lines, something that's already come across a lot in the way that you describe this stuff is, I've heard a lot about how you think about go-to-market as a product. A lot of people listening to this, as I've said, are product builders.

So I think this is a really nice way of thinking about. go to market. I'm guessing you already talked about elements of this, but just what's a way to think about go to market as a product? Yeah, I've always, so I had this realization, probably a little over a decade ago in my career. So my first job out of college was working on Gmail in 2004. So Gmail launched on April 1st. I joined on June 1st. And as I'm sure you'll remember as well, Gmail was this incredible innovation, you know, massive JavaScript application. that didn't really exist at the time, and it had this gig of storage. It was a full year before Yahoo Mail caught up and even longer before Hotmail and others did, right?

So that was the level of, like, technical differentiation between, you know, Gmail and the next best. And a decade later, you know, you had cloud computing, enabling folks to do stuff that you never would have been able to do previously. And so I kind of felt like, huh, like software's starting to complete. monetize a little bit. And so, you know, when, when that happens, when technical differentiation kind narrows, what are other things that will differentiate you? And, you know, sort of thinking outside of tech, like, we buy a lot of things because of how we feel about them. And so I started to develop this thesis that actually the experience that you have of being sold to will increasingly actually

differentiate a company and drive buying decisions if products are only different at the margin. And so if you believe that, then you really want to create a customer buying journey that feels like very unique experiences. And so we did a lot of this at Stripe and now we're looking to replicate this here. But an example of one of the things I think we did really nicely at Stripe was, you know, a lot of companies, sales sort of the first call after you're qualified, you know, we've decided you're worth engaging in sales process is discovery, which is basically let me ask you a lot of questions to try to uncover pain, figure out where buying power lies, et cetera. And so that is kind of boring sometimes for a customer. You're basically being

quizzed often on the phone. And so what we started to do at Stripe was that for, session was a whiteboarding session. And we would actually get together and have you, you know, draw your architecture for payments and all the other things that were under the hood to enable you to take money and drive customer outcomes. And through that, we would learn a ton about, you know, what was in your stack, what we were going to have to compete with, displace, where value lied. But the customer also learned a lot themselves because in many cases, they'd never drawn their architecture diagram. So they left that meeting with an asset and a sense of like,

wow, this is a really collaborative person who's like deeply interested in helping me like, you know, develop a mental model for how to think about this. You know, and then we had other things that we would do. So that's sort of how I think about building go to market like a product is basically you need to go through from the first time you become aware of that the company exists to, again, that sort of five-year, heavily retained wall-to-well customer, a set of experiences. And those experiences can feel transactional, flat, boring, or they can feel very human, personalized, and unique. And so, you know, we try to go map those out and figure out how do you,

you know, bring the product to bear, make it really human, and hopefully that creates a customer for life in the end. I love that whiteboarding example. Are there any other examples of what you've done to make it actually work really well in this way? Yeah, another principle we really developed this at Stripe 2, and I brought it to Versal, was just the idea of adding value at any touch point, regardless of whether or not that customer bought, because even if customers don't buy, you often find that if you miss them on that buying cycle,

three or four years later, when they're in another buying cycle, they do come back. You know, I was at Stripe for nine years. And so I saw the number of customers that we lost, and then half a decade later, here they are and they bought. So that was sort of another one. So, you know, examples of this that we're doing at Vursell is we, you can, there's great data on the internet that helps people understand the performance of their website and how fast your website is actually impacts SEO and SEO impacts AEO. and everybody's thinking about AEO right now. And so, you know, one of the things we try to do when we reach out is actually give folks insight immediately into how they're performing on an absolute basis, how they're performing relative to peers.

So ideally, you know, that piques your interest and you want to learn more from us. But even if it doesn't, you still have insights that you may or may not have been aware of that maybe make you contemplate whether or not you've got the optimal setup. Awesome. So what I'm hearing here is when you say, hey, think of it like a product. That's basically a product person thinks about the experience of their product that every step of the journey. Here's the flow. Step 1, 2, 3, 4, 5.

How do we make every step awesome? Keep them going along that journey. And so what you think about is just from the prospect's perspective, how do we make every step of that journey awesome, continue them down that journey? Yeah. Yeah. How do you make it be an experience rather than a transaction? Or it just feel like sales coming at you trying to sell you stuff. Yeah.

Okay. Staying along this track of being staying tactical, I want to go even further there. So what are just some go-to-market tactics that you find really effective these days for people trying to just to be more successful in getting people to pay attention to their stuff, to buy their stuff? I mean, one, I would sort of say, dovetails with where I just ended, but is what are the unique insights that you can bring to bear about your product or, you know, know, how that customer may be in a suboptimal state. So I do think investing in data to tease that out is one thing. I think the other thing, this is straightforward, but often not done enough, is like a lot of good companies invest in docs, you know, good thing to do. And but they stop there. And particularly if you're selling into a slightly larger company doing things.

things like, you know, AWS calls it well-architected guides or blueprints. A lot of customers, particularly larger ones, really want to know the best practice for how exactly to implement your product with their particular setup. A great example of this, this is from Stripe, was, you know, Stripe was excellent at marketplaces. Most, you know, Lyft, Instacart, DoorDash. They were all on Stripe. And so Stripe definitely knew the best way to set up payments for a marketplace because we'd seen them all. And so when you then would go and sell a marketplace and, you know, say, oh, yeah, we've got docs, go check them out. They didn't like that, right? Because they're like, hey, every marketplace runs on Stripe.

I don't want to look at generic docs. I want you to tell me what's the best way to set up payments for a marketplace. And so I think that's another key thing to be doing, particularly as you move past that sort of solo developer, startup founder, as potentially a target audience. And then I don't know this is a tactic per se, but I do think just a good reminder for founders in particular who are still in that maybe founder-led sales moment

is just the value of really good discovery. I often find founders are so excited about talking about their product or, you know, you ask one question and now they've got a hook of like, oh, I can fix that for you. But excellent salespeople typically will talk well under half the time in a conversation because they're out asking questions, probing, often helping a customer arrive at conclusions on their own. And so learning how to, you know, do five lies go deep rather than immediately going into problem solving mode, you know, if they ask a question, you respond. Often, if they ask a question, you should ask a question about the question and then respond, right? So learning to be

great at that, I think differentiates people. So the last tip, I think there's something a lot of, I bet everyone could learn to just listen more and talk less. Yep. On that first piece of advice, this kind of sharing unique insights and how you're suboptimal, is there an example you could share of how you did that, maybe a story of just how you convince someone you're selling striper versus like care or something you're missing here. how this can help you become much better. So with Versal, the sort of is giving an example,

but I'll make it more specific. So, you know, the performance point, you can go and look at Core Web vitals. And so we can actually see the different things within their site that are fast or, you know, load correctly, et cetera. So at that, we then, so anyone can go look that up.

But what we can do is actually then help with benchmarking relative to peers. So that's been a big one that we've gone out and done. The other one that we've spent some good time on is just around helping customers understand MCP servers and when it would make sense to use one. So I think those are all the rage, but often people don't know how to contemplate them

within their own product. So that was another one that we've gone pretty deep on. And then related to the first one is AEO, answer engine optimization is actually, you know, somewhat tangential to Rusal, right? So we drive performance. Performance drives SEO. SEO is an input into AEO. But we have spent a ton of time sharing insights on AEO because we ourselves focus deeply on it

and think we understand it better than many. And so again, as part of just building a trusted relationship, you know, folks may go from those AMAs or that content into, okay, great, you taught me a lot and therefore I want Vursal to help me with performance. But in many cases, they actually now are just like, this is a company that seems insightful. It seems like when I can learn from. And now I'm going to pay a little bit more attention to them. And over the fullness of time, maybe, you know, they see something that triggers them to decide now is the time I want to go investigate that aspect of Versel. Awesome. So what I'm hearing here in many ways, and this resonates at

Jen Able on the podcast recently and it was all about sales skills and how to sell. And one of her tips is you don't want to be focusing on here's the pain and problem we're solving and instead focused on here's how you will be better than your competitors. Here's a big gap and alpha that you can

achieve if you use say versus sell. So here's like you're missing out on the speed and you're going to get screwed in the AEO and all these things. Here's like how you can architect your entire payments art system to be top tier. Does that resonate? Yeah. I was told this stat. It's round number, so I can't imagine it's entirely accurate, but, you know,

basically that customers, 80% of customers buy to avoid pain or reduce risk as opposed to the other one out of five to increase upside, which is a good thing, again, for startup founders to understand. And so, you know, we all love to talk about the art of the possible, you know, everything we're going to enable in the future. It's very exciting. Everyone's visionaries, right? But that's often really a sale that's going to resonate with another founder. And for everybody else, you know, particularly enterprises, you're avoiding the risk of not

making your revenue target next quarter. the risk of having, you know, being outdone by the competition, the risk of having brand damage, et cetera. And so it's really hard, actually, for many startups to make that pivot because it feels off-brand, but it does actually drive more buying behavior, is setting up a little bit of that concern that either I might not be well positioned or, again, through good question asking, I know exactly, where I'm not well positioned and you can help me de-risk that. That is such an important stat you shared. This has come up actually before in this podcast that buying, people are buying in large part

to reduce risks. It basically not hurt themselves in their career, not hurt the company. Like that's a bigger factor in the buying decision than I have this problem. I need to solve. And okay, thank you to solving. And the way April Dunford came in the podcast and talking about this of just like, like it's such a massive career bet. we are going to bring in product X and it's going to become, like Stripe, let's say,

let's not talk about our sell, but let's say Stripe. We're going to adopt Stripe. That's like a huge decision. If it doesn't go well, your career is hurt. Your manager is going to be mad at you. It's going to set your company back. So a lot of the buying decision, as you've said, is I just don't want to screw this up. Right.

Absolutely. Okay. Along the line of tactic, something that I know you're big fan of and help people think about is segmentation. Yes. This is something a lot of founders struggle with. they know, okay, I need to figure out my segmentation strategy and here we're going after. Can you just kind of give us a primer on segmentation, what people should know about why this is important

and then how they might approach this? Yeah. So segmentation is basically how do you carve up the world of companies that exist on the planet to reason about them where they buy differently. So I'll give examples from Strip and Bursle to bring this home. So a very, very typical company segmentation is small, medium, large. That's a rational way to do things. Small, you often have a single decision maker, medium, you know, a small team, and large.

It's complex. It's a committee, et cetera. So the buying process does change across S&B midmarket enterprise. But if you stop there, you are likely missing. Okay, but what are the things within your office? offering that also changed the way something gets sold. So at Stripe, there, there were two ways we further cut the business. Way one was, so think of segmentation as a graph. So X access was size, so small, medium, large, Y-axis was growth potential. And that was important for Stripe because it was

a consumption-based business. So if you were going to grow at 200% year-on-year, you were more valuable to Stripe than if you were going to grow at 8% year on year. And so we wanted to spend more time, spend more money going after the 200% growers than the 8%. So that was one that informed your strategy on who you targeted. And then for Stripe, the other thing that we cut it was business model. So are you a B to B to B, are you B to C? Are you B to B to B, EG, a platform or B2B to C, eG, a marketplace? And why is that relevant? Well, if you're B to B to B, you were going to to need business payments, right? Credit card was useful for a PLG function, or PLG sale, but you were going to need ACH, wires, et cetera, and you probably had a recurring business. So

you were going to want strike billing. You know, if you were B to C, that's consumer. So you're going to want consumer payments. Apple pay is super important. If you were in the like the platform or the marketplace, you were going to buy our connect product. So it helped us basically then craft a more targeted and replicable sales. Ressel's sort of similar deal. So small, medium large, buying complexity. We also do the same thing on growth potential because we are similarly a consumption-based business. But for us, a couple other things on the X-axis, we layer in promote, which is one of the things that is observable is traffic, site traffic on the internet. So Google publishes a crux score, which is basically they

have a bunch of data in Chrome. And so they know that Lenny's site gets, you know, a million X the amount of volume that Gene's site does. And so basically, if you're a small company, but you have super high traffic, that's going to be more complex. Versailles going to make more money. And so we want to promote you. So great example of this would be Open AI. Open AI. I forget these days how many employees it has, let's say it's 3,000. It's probably more than that at this point. But so that's going to put it in the mid-market at most companies. But they're a top 25 traffic site on the internet. So for us, that's going to push them in our enterprise because we need to go, you know, lean in with a much, you know, more in-depth sales process. And then the other thing we layer

on is workload type. So if you are an e-commerce company, that's going to be a very different sale. We're going to have to, you actually use different language. You talk about, product listing pages and product description pages, and you've got an order management system as the back end, super different from a crypto company where you might be running soup to nuts on AWS. And so again, that helps us start to then have a really different buying content for you. Okay, this is awesome. So essentially what you do is you break up this universe going back to your original story at Stripe into help you sort essentially which companies are most likely to buy your product. And what you're coming up with is these attributes that are

correlated with. They are likely to be great potential customers. Do you recommend using this X, Y, access as the approach versus something else. There's like a spreadsheet with like five columns. Like, I don't know. How do you start? There's probably something to be said for the X and Y. Like, I do you think size is going to play into most buying decisions. And then these days there is a fair amount of, you know, consumption happening. So there'll be aspects of this that I think are somewhat universal. But I think basically like when I came to Brussels new product market, product offering for me, it's a new market. I had a lot to learn. But this is one of the first things I did in the first 30 days. And so basically I sat down with the gentleman, Abbey, who leads

data science here and, you know, said, okay, what what drives Reve? So what are the things that you can look at ex-ante about a customer to know this person is likely to pay us $100,000 versus a million? That's probably going to be part of a segmentation framework. And then similarly, okay, what attributes would we look for to cluster where we seem to be winning repeatedly? And that was how we ultimately got at, okay, crux rank is going to be super important because what you pay, Ressel is correlated with your traffic, and then workload type was super important as well. So, you know, and for Roussel, when we did that, it was really interesting because, you know, we saw, wow, like we have a lot of penetration and e-com, not that surprising, actually, given that we, you know, drive highly

performance sites, and e-com having a super-fast performance site really matters. But at the time, if you looked at, as an example, in enterprise, SaaS companies, we didn't have a lot of penetration, even though you would have thought, okay, front-end cloud, very developer-oriented. Of course, software companies would be honest. But in Enterprise, most of those companies built that SaaS offering before Versel existed. And so, you know, migrating 200 or two million lines of code, you know, to Bursal, that's a big lift, right? So it helped us really understand where are we winning, where are we not? You know, and now, as an example, like within SaaS companies and enterprise, we're actually seeing a lot of interest in the AI cloud because those are some of the earlier adopters of, hey, let's add AI-native functionality to our existing SaaS app. And so, again, it helps us figure out what to target where. Okay. So essentially, you're doing kind of this regression analysis on what's working, and then here's the attributes that are most correlated with success. Something I always recommend when founders ask me for how do I figure out my

How do I figure out where to focus? My heuristic is just think of three attributes that narrow them down. So it's like Series A company that's Engelette, that's a marketplace, something like that. That feel like a good like just rule of thumb just to start. I think like beyond three, like, you know, that's getting pretty detailed and reasonably speaking, you're not going to cut like you have five sellers. So you're going to put one seller in five different segments. So I do think three is something you can reason about.

The other thing I'll say on this topic that I think is. really important is a lot of times folks think segmentation is a go-to-market thing. I really think it's a company thing. So when you join Versal, I actually deliver, and every new hire's first week, one of our company values is KYC, or your customer, and I deliver the KYC section and talk through our segmentation framework, how our customer-based maps into those segments, because it's really important as, you know, those new product managers leave the room that when they're building something, they think to themselves, okay, I'm building a new backend product. Who is it targeted at? Is it targeted at an enterprise or a startup? You know, basically, do I have a point of view on

where I'm trying to win and why? And if you're doing that out of the gates, then it's much easier to then go speak the same language with the go-to-market org and figure out, okay, how are we going to take that to market in line with the other motions that we have in play? Okay, this is a great segue. to, there's a couple other things I want to talk about. One is something I've heard from so many people you've worked with is that you're amazing at building a go-to-market org that works really well with product and engineering. So I'll read this quote from your former colleague, Kate Jensen.

She said that your superpower is building a sales org that doesn't feel like a sales org to engineers. So the question she suggested to ask just like, what does it take to do that? What are the ingredients to building a sales org that engineers and product teams really like working with? The litmus test I have always given my sales team is if you are an account executive in my org and I put you in front of 10 engineers at our company,

it should take them 10 minutes to figure out you aren't a product manager. And what I'm trying to get across is you need to have incredible product depth. And the reason for that is twofold. One, it gives you credibility with the product and engineering org. And two, I also believe that the best go-to-market orgs on the company. the planet are equal parts revenue driving and R&D. And the reason I emphasize the latter is if you think about a product management organization, you know, you may have a UXR team, you know, out doing research, product managers certainly should be out talking to customers. Well, if I have a 20-person sales

team, think of the number of customers that we talk to in a week. And so if we can do an excellent job of translating all of that feedback into signal and then feeding that into the roadmap, you know, we can be actually an extension of the product management org. But that takes being really good at discerning signal from noise, understanding when something is an objection that should be overcome versus, you know, a market, an opportunity in the market. So I think, I think those things have helped. I just love this. As a, uh, product manager, maybe former product manager, I don't know what the hell I am these days. I just love the idea of the salesperson, like you not knowing the difference between a product

manager and a salesperson. The most classic challenge is sales orgs, ask for all these features. NPMs are constantly having to push back and think about this this fit into everything. So it feels like that's a big part of this is to understand that deeply. Yeah, you want a sales, you want a sales org that can think like a general manager. So, you know, that's not just trying to get deals done, but is trying to help build a business. And so, again, knows when to say no, knows when an objection handle versus knows, hey, I've actually heard this on the last three calls. And I do think this would be a really big unlock that would make us more competitive, you know, would be something that new that nobody's doing. So, you know, I think that takes looking for a profile that both has sales skills, but also is going to think, with, you know, that product mindset. I love that. Okay, so another quote from Claire Hughes-Johnson, former podcast guest.

Go out. Amazing sales leader. Worked with you at Stripe. She said something along these lines, but a little different. Gene is probably the best go-to-market person in connecting with product and engineering, deeply understanding the product and providing the most valuable input to her counterparts

of any I've ever seen. It sounds like just another ingredient here is just sales feeling like a real partner to product and engineering, actually not just being like, hey, do these things for me, but actually feeling like a partner. You know, ultimately, company strategy is basically product strategy meets go-to-market strategy, right? And so I spend, I guess as a go-to-market leader, I'm constantly trying to figure out, you know, how do I make more money more efficiently?

And you typically do that by having a winning product in the market that is well commercialized. And so that means that I really lean into thinking about product strategy and thinking about pricing strategy. Because if those two things are optimal, you're going to win more often and there'll be less friction in it. And so that's sort of where you've got to put as a revenue leader like a GM hat on and not just think, how do I sell, but actually how do I enable the insights I'm getting from talking to customers constantly to have the company strategy be more effective. Speaking of product, going in a slightly different direction, PLG product-led growth, was, it felt like it was very hot for a while where everyone's like, you got to go PLG.

That's the only way to win now. It's impossible to do sales. There's no, the future is PLG. It feels like that's gone away. In large part, obviously, still companies grow through PLG and work through PLG. But it's just kind of your thoughts on PLG and when does it make sense for a company these days to actually think this is how they will grow for a while? I think a lot, PLG has makes sense for a lot of companies at the outset,

unless you are very explicitly building a product for enterprise. So Sierra, as an example, right? Like they are very clearly going after Global 2000 or, you know, something close to that. So PLG is not going to be overly useful to them because they are trying to win eight-figure deals from day one. But for a lot of products, folks are. targeting a startup audience at the outset, and then they're adding more functionality so that they can ultimately continue to scale up market. So I think PLG is still super relevant. It's a major driver of Rusell's growth. It was a big driver of stripes growth. The thing that folks get wrong

is it does typically have a ceiling. So people are generally not going to, you know, go give you a million dollars via a self-serve flow. So at some point, if you want to sustain growth rates, you're going to have to have your deal sizes get bigger and bigger. And where I think folks get stuck is waiting too long on PLG because it does take a while to build a replicable sales process and a sales process, which often you're getting fed by inbound at the beginning. And then you've got to add outbound. It takes a while actually to turn outbound into. to a predictable engine. So I think where you see companies hit walls is just when they don't add the sales portion

of it soon enough. So essentially every company ends up having to build a sales org. Some start product led and then at sales. Some just start sales and have it from the beginning. Yeah, I would agree. There are, you know, there are probably some good examples of like large vertical SaaS platforms that are S&B, but even they wind up with like a, you know, velocity sales team. So, yeah, I don't know that I can think of like a $100 billion company.

That's PLG only. Yeah, like it just feels like a big, like you're losing, you're leaving money on a table, even if you are growing really fast. I know the last yearn was a long time PLG company, but eventually succumbed. I don't know if that's the way to put it. Okay. You mentioned pricing. I know you have strong opinions on pricing and pricing strategy.

What's just like a couple of tips you might share with someone thinking about how to price their product. Yeah. So I, uh, it's kind of a theme, but I think the first thing is like, you got to think about pricing like a product. Um, so it's another one where, um, it actually really matters how you choose to price a product. Um, do you really understand where customers are going to drive value? Do you really understand where you incur costs? And are you doing a smart job of, aligning those things. You know, you've got lots of examples of companies grossly underpricing because you're sort of afraid to charge for the value that you actually provide. I think there are a lot of examples where people default to including a freemium strategy without that actually

being a strategy. Like a good example at Strip, we launched Strip billing years ago. It had a premium strategy because that's what you do. And then we sort of looked at it and we're like, you know, actually integrating straight billing takes a little bit of work. So if you do that, you're probably going to stay. And so we killed that, killed that, killed the free trial to zero downside. So, you know, that's, that's another one. At Roussel, we've been going through that transition where, you know, we're a consumption based business model ultimately. But for at the outset, that we basically kind of bundled that into what looked like a SaaS-like price. And, you know, as we've added a lot more functionality, that that wasn't working anymore.

And so we did an unbundling. And right now, actually, we did a pretty substantial pricing change in August, where we have an enterprise at a pro skew. And if you looked at the enterprise skew, it's called enterprise for a reason. It's meant to be sold to an enterprise. and actually about half of the folks on the enterprise skew were startups, which suggests that there's stuff in the enterprise queue that a startup really wants. So we kicked a lot of that stuff out of the enterprise skew and made it so you could buy it, self-serve online.

And what do you know, people are? So, you know, so now that's like really driven a lot of growth in our PLG funnel, which is awesome for startups because it's super efficient. They can just buy things. They want that. It's awesome for us because you don't have to have a human, intermediate that. So getting all of these knobs really tuned is a key to both a great customer experience and optimal revenue outcomes.

Maybe just one more question before we get to our very exciting lightning rounds. Give you a combo question. I hear you have a hot take on kind of sales com, how to comp sales people that's different from other people. And also who to hire when you're hiring folks in sales. Can you just talk about your takes there? I struggle with sales comp because, you know, it's all about pay for performance, which I'm obviously a fan of. But it is, it makes your organization less flexible because you basically have to decide 12 months in advance.

These are things I value. And particularly in this moment, that could be different. as a great example of this, when, you know, we wrote the sales plans for this year at Versel, the AI cloud did not exist. We were selling our front end cloud and we were selling V0 and, you know, introduced the AI cloud halfway through the year. Now, we had all sorts of good ways to still incentivize that. But, you know, I think you want to be able to be innovative and pivot. And, you know, when you have a well-designed. design sales plan or a very structured sales plan, that can be challenging. So that's,

that's a little bit of, of my hot take is just, I'm trying to figure out how do you have the upside of sales of, you know, motivates people. It's a quantitative function, which is great, but also the flexibility to change your mind because I think a lot of companies right now are having a hard time doing annual planning. So, so that's one. On profiles, I have always valued what I just sort of a diversified portfolio. So I strongly believe that sales is a skill. And so you want salespeople with actual sales experience in your organization. But I think there's value in pairing them with more non-traditional backgrounds,

in particular, consulting or banking background. those folks are really good at, you know, more quantitative and analytical aspects of sales. So getting into that consultative, you know, part, which I think we talked about at the outset. And so I find that when you mix these together, the sort of, you know, consultant banker profile realizes, oh, wait a minute, sales is a skill and I didn't really have it. And so they go learn from, you know, your account executives with that background. And then your AEs learn more about, okay, how do I think about a P&L? How can I talk to a CFO? You know, how do I present a TCO analysis more effectively?

And so it just creates a much richer learning environment where people are bouncing ideas off each other. That is awesome. I love that strategy. Okay. Final question. Just, does there anything else you wanted to share? Anything else you want to leave listeners with before we get to our very exciting lightning room? Oh, man.

I feel like we've been very thorough. I think so, too. Yeah, I'm going to, you stumped me on that one. Okay, that's the goal. With that, Gene, we reached our very exciting lightning round. I'm going to make it very quick because I know you got to run. I'm going to ask you just two questions. Okay. One is, I'm going to skip to your life motto. Do you have a favorite life motto that you often come back to find useful in work or in life?

I do. I actually found that I'm known for saying a handful of things that I didn't necessarily realize it, but when you leave an organization, people tend to, you know, tell you what stuck with them. But there is one that I think I'm known for saying, growing up my mom always said to me, when the going gets tough, the tough get going. And I, you know, in sales, you're always going when you're not on pace. And so that's one that I feel like I pull on, um, not infrequently because, uh, you know, there's, in my view, there's another, another version of this. My mom also what it always says was where there's a will, there's a way. So, you know, I think you can always choose to find a path forward even when that's not super clear. I love these. Okay, last question.

I read that you were a very competitive diver in college early on. I'm just curious if there's something you learned from that experience that brought with you that helps you be as successful as you've become. Well, I mean, first of all, I should say, I was generally coming in like third place out of three on my team. So I managed, managed to do it in college, but that was the extent of that career. So I do think so diving is a precision sport and it is a repetitive sport. And it is also a sport where when you land flat on your back and literally as you are swimming to the side of the pool, like welts are forbing on it, you always 100% of the time will be forced to immediately get back on the diving board and do that exact same dive again. And so I think that has a lot of stuff

that's transferable to work and to sales. So, you know, for me, I just have an obsession with excellence. And within sales, sales is about replicability. How do you drive predictable outcomes? You know, how excellent are you at your ability to forecast? And so I think I bring that to bear within sales a lot. And then similarly, like, you get a lot of nose and sales. And so, you know, another phrase that a sales guru said to me once or in a training was, yes,es are great, nose are great, maybe it's will kill you. And so how do you get really comfortable that no is a great thing? And that just gave you data. And now you can go do something with it.

This is a really inspiring and empowering way to end the conversation. Gene, thank you so much for being here. Thanks so much for having me, Lenny. It was a lot of fun. Bye, everyone. Thank you so much for listening. If you found this valuable, you can subscribe to the show on Apple Podcasts, Spotify, or your favorite podcast app. Also, please consider giving us a rating or leaving a review, as that really helps other listeners find the podcast. You can find all past episodes or learn more about the show at Lenny'spodcast.com.

See you in the next episode.