S2E6: "Your List Is the Strategy" | Benjamin Douablin

All right. Welcome back to the GTM Engineer Podcast Season 2. From hype to operating system, because now that we know that GTM engineering is here to stay. We want to know where it's heading. And today we're getting into the infrastructure layer that makes GTM engineering actually work: data. Benjamin Douablon is the CEO and co-founder of Fullenrich, the first waterfall enrichment platform built specifically for the GTM engineering era. And if you're running clay tables or any outbound motion at scale, you probably hit the wall where one data provider isn't enough. And that's where Fullenrich comes in, aggregating 20+ vendors and queries them sequentially until you get the email or mobile number that you actually need. Ben's background is pure sales tech. He came up through Sales Ramp and Jellyfish before starting Fullenrich, so he's not building tools from the outside. He's built them from the trenches, knowing exactly where the gaps are. And today we'll dive deeper into what's GTM engineering has been like for Ben and the team at Fullenrich. We'll chat outbound and cold calling, hiring best practices for GTM engineering, and we'll also get Ben's take on where the data layer is headed. I'll definitely want to know more about that. So Ben, we're super pumped to have you on the show.

Thank you, Matteo. Thanks Jared also for the invite.

Great to have you, Ben.

Cool. And thanks for the intro. Can also continue by introducing myself for your audience. So Ben, co-founder and CEO at Fullenrich. We started this company 2 years and a half ago after building other tech companies. And we ended up thinking that even if there is like a ton of data vendors, you can't rely only on one data provider if you want to have like a global expansion targeting multiple verticals. So we ended up building an internal tool in our previous venture that was aggregating multiple data vendors and verifying the data. And that's what made us move to this company, Full Reach, that is now like a pure data player aggregating 20+ data vendors, making sure the data is accurate. We have like a simple business model that is purely usage-based because we wanted to align the user interest with ours. So, and we, we want to create like a product that can support the sales team, the growth team, the marketing team, also the HR team, basically all the teams that need to rely on global and accurate data and also compliant data. So we build a product that is also integrated with all the, the tools and the ecosystem the user use because we want to put this data in the end of the end user, whether they are directly in our platform, but also in other tools like the N8N, the Zapier, the Clay, of course, the CRM and all the automation tools, the bond tools. So we have like a huge execution on how to integrate our embedded API and also MCP server. We'll talk about it later, probably with the, the new GTM engineering stack with all the AI agents. But yeah, glad to be on your show, guys.

Love it. Thanks so much for the intro, man. Yeah, I mean, like, we've been also working together in the past. I know, like, how the prowess of your team and the vision, you know, like, and I love how we actually, like, delivered and kept delivering on the vision just in the last 6 to 9 months we last worked together. And yeah, like in the last year or so, almost everything has changed again in the market and in GTM engineering. So we now know that clearly this new discipline is here to stay and it's grown, it's expanding. How has it, has you seen it changing in the last year or so? What's new?

Yeah, so the, the GTM engineering is like a sort of new movement in the market as we had like the forward engineer launched by Palantir. And I think it's for the good because it's, it's not a new job, I would say, because we had like the sales automation guys, we had like the CRM manager, the revops owning like different parts of what can define the GTM engineering role, but it makes it more precise on a specific ecosystem of tools, process, purpose. And it's growing fast, man. I was like looking at how many job offers are live on this new job title. It's almost everywhere now. I think we can thank Square for that because they launched this movement. So they did it great. And, but the role is very different from like a very early stage company to like a well-established company. So as every new trend, there is good stuff and there is some stuff that needs to be refined and, and, and precise. But, uh, it's, uh, I think it's great for the ecosystem because also with all the AI movement, we tend to constantly optimize the productivity of the sales team and the people that are in front of the customer. We don't want them to spend time with bad leads. So the GTM engineering for me is here to help, like talking to the right person at the right moment. So getting the right signals, intent, targeting, and doing an extra mile to avoid a sales team, SDR team talking to just people that are not a good fit.

Awesome.

And you're primarily servicing the needs of the RevOps team, go-to-market engineering, and so forth, right? But you also mentioned other departments, HR, and so on and so forth. So who's your interface in those scenarios?

Yeah, we basically serve all the teams that need to do cold outreach. Outreach can be for people that look at investors, candidates, new customers. We started with like the sales team, GTM team, because it's an abuse use case. You need to find new customers every day. So, but over time, we also figured out that the recruiter talent acquisition teams, they didn't do so. And for Henrichs, our goal is not to create a platform that can fit all the use cases and personal use cases. So that's also why we create this data, because it's the last piece you need to execute perfectly a workflow. If you have the best product, but at the end, the data is not accurate, you're just stopped in your process. So we integrate with the HR tools and also most of the talent recruiters, they know how to use the sales tool. It's just the HR tech is not that great in terms of product. So they know to use all the sales engagement platforms. So that way we serve them, but they are probably looking for like mobile phone numbers, personal emails, because if they want to reach out to you to offer you a job, they don't want to reach out to you on the Work Email. So just different type of data, different integrations. But yeah, we don't believe that there is one sales engagement platform or marketing tech that can fit all the type of maturity. So we need to be integrated with all those platforms because as our goal is to produce the best global verified and compliant data. We won't make it by just having people in our platform because the needs are not the same when you are like a small team with 5 salespeople that can be happy with like a very simple UX/UI than when you have like 500 sales reps in your team hosting the data in your Snowflake instead of the CRM because the system of record is not the CRM anymore for the like fast-growing tech companies. They use data warehouse. Our mission is to be also integrated in all those systems and platforms.

Yeah, that makes sense. Are there certain areas that you're seeing more of an improvement and other areas where you're seeing that maybe this go-to-market engineering movement is sort of being oversold or people are thinking it can do more than it can?

Yeah, I think the— we saw a lot of ex-outbound agencies that were doing like pure cold email, spray and pray, that just rebrand them by being GTM engineers. So also we need to be careful to this and not mixing up just to have the hype and being able to leverage a job title that get pay you way more than just a lead gen agency. So But at the same time, the mission is the same. We need to, you need to bring good leads to your customer as a lead gen agency. The GTM engineering is just, you probably leverage a bit more tools and yeah, that's so I see a lot of people that rebrand themselves as a GTM engineer. They probably need to also like a strong growth mindset, being able to leverage different tools, coding sometimes some stuff. So having a sort of consultant mindset that knows how to assess the current state and improve it by technology, by process, by also knowing that now you can do AI search, you can do better scoring, you can get this intent. So it's also having the maturity and the knowledge of the market. And every month it's changing. Every month the AI models are evolving. So you need to stay at the top and constantly try. So it's not for everyone. It's for people also that like to explore, like to test, fail fast. But the limit I see is like having only the zero to one people type of people in the GTM engineering. It's a risk because at some point you need to industrialize. The, the process and build a real go-to-market architecture. And, oh yeah, depending on the maturity, you can have like different type of GTM engineer.

When you see this working really well, does the GTM engineer or each GTM engineer team, do they really enable the rest of the sales org to sort of 10x their output? Or is that the wrong framing?

Yeah, it's a great question because if you hire your sales team like 5 years ago, you probably, I don't know, but you probably don't have the best type of brain, I would say, to fit the scorecard of what can be a good team engineer now. I saw a lot of companies just giving the mission of like SDR that was like very ops in the mindset, salespeople that really love to talk to other people that are more like people-focused and not tool or process-focused probably won't fit, but a good GTM engineer can't work on his own. You need to talk to the sales team, get the best practices on how to target, what's the best ICP for us. But yeah, probably like a sales team who is very analytical mindset and ops mindset could make the move, but no, not sure for the rest.

So, you know, you mentioned how things are moving so fast and last month is hype about one thing, this month is hype about another thing. Well, right now we're in this, uh, Claude Code hype peak phase. Is work moving to, to Claude Code or what Claude Code can output? Is it moving there from other tools? Are you seeing that at Fullenrich? And is it enabling new capabilities for companies?

Yeah, we only scratched the surface for now. We say the people that are curious and that embrace the AI era will probably use it in their day-to-day. I don't know yet how it will be industrialized and how good it will be because the, I see that in a way that is super siloed and I don't know how it will scale with like a team usage, just not clear in my mind yet for that. So I can easily imagine like super small lean team crushing it through code or whatever, but bigger team, I don't know, it's as soon as it's like crushing cross people. Um, I don't know yet how it will be managed, but yeah, for sure it's a big shift. We just released our MCP server so people can now search for leads directly into Clockcode, enrich it, push it to their HubSpot MCP or whatever. So there's plenty of different use cases and over time we'll have way more MCP servers on the shelf. So time will tell.

And is that, let me ask you honestly, is that cool? Like a toy that's neat to play with, or is it actually, does it actually make salespeople more efficient? Is that their preferred mode? Chatting, you know, chatting with an LLM about prospects and then having that information go back into a CRM or to a sequencer? Yeah.

So I see a lot of sales that have ADHD or stuff like this that don't really that can't really focus long and love to talk. So having the option to just chat with an AI and ask for what they want, because they know how to express it. Sometimes they just don't know how to write it down. They don't know how to use properly the tool. So it will for sure unlock new usage. But a chat also is like a blank page. So if you don't know what to ask, what to look for, Nice. It seems cool at the beginning, but after like doing this research, you're like, okay, what's next? And so we'll see if it replaced the great product. For sure, it will replace the bad one, but also having a UI that is very well done with nice UX and exactly what you need. Also guide the the value of a great product is also to guide the user on the best practices. And you can discuss it, but Salesforce, 20 years ago, when they forced people to follow like 5 steps, demo, negotiation, closing, whatever, also it forced people to follow the right step and be better at what they do. So yeah, the AI is not for everyone. It's for like very creative people also that know what they look for. So we see, I do believe that it's great to have options and to find also what works for you. The question is, how does it scale?

Switching topics a little bit, some listeners might be thinking, that data, data enrichment, there's a lot of providers out there, it's a commodity. I mean, there's hundreds of providers of signals and enrichment. So why should someone invest their time in choosing these providers carefully? What is the benefit of that?

Yeah, I agree, like average or bad data is a commodity. You still today, I don't know anyone that is really happy with their data providers, even if they multiply them, nobody really cares on the data provider side about the data quality. For some reason, it's also counterproductive for their initial business model. So it creates a lot of frustration. Nobody likes to benchmark a data provider. You don't like to spend time like uploading a CSV checking if the delivery is okay, checking if the phone call can connect. So it's a commodity, but it's necessary. And I think the state of the art is not there yet. So that's also why we want to create this new standard in terms of coverage, data quality, and you don't want to be stopped in your go-to-market or expansion just because you don't have the right data. Because work with a lot of customers that were in the US, want to expand in Europe, don't have the right data provider. They're like, okay, we just hire like 10 reps, but they can't reach out their prospect because we don't have the right data providers. Oh, and at the end, it's just also having the right data in the right place. So it's hard to build a new integration with a new provider, building the integration with your CRM, deduplicating the data. For me, it doesn't make sense to multiply them, to connect them with the system. And of course, I'm preaching for my company, but that's also why we build this orchestration layer on top of the data enrichment market, because we do believe that we need those data providers that need to be focused on specific market, specific vertical, just to be great at gathering the data in the right way, making sure the data is up to date, compliant, and everything.

So say more about that orchestration layer. What, what do I do in there? What, what are the different use cases for leveraging that?

Yeah, let's say you want to reach out like the healthcare industry.

Which industry?

Healthcare. Healthcare. Okay.

Mm-hmm.

Construction or retails. You probably won't choose the same data provider for that. You, if you sell different type of services, you can have like multiple verticals. So you will, it will require you to multiply the data vendors because if you just rely on one generalist, you would just get 30% ish enrichment rate. So you won't cover properly your addressable market. And also, if you want to expand in LATAM, the US data provider probably won't have the right data. So there's the region, the industries, the company size also, because the Fortune 500 probably like specific data providers for that. The generalists won't.

And so these are the first filters that I put in. When I'm gonna go connect Fullenrich to my CRM, I configure these, these levers, and then you help me find the— just help understand for, help explain for the audience how that, how that works.

So the, our waterfall and aggregation system is like fully managed for the customers cuz they, Nobody really knows who's great at this kind of data, but we monitor it like in real time. So we choose the data provider for them depending like their targeting. We know, okay, you need this type of provider. So we make, we have an engine for that, that will pick and choose the right provider in specific order, depending what they need.

Hmm. Okay.

And we have like a more bespoke and custom— customize Waterfall for like the enterprise grade that want to bring their own API keys and stuff like this. But, uh, right.

So, so in some other tools we, we might be familiar with, with, with Waterfalls, it, it's kind of a random selection of data sources. It's not really specific to my industry, my segment, my geo.

Yeah.

But you've solved that. Is that, that's what you're saying?

Yeah.

Nice. Okay. You, I know you've been, you're very vocal about cold calling as a growth lever. How would you stand up a cold calling motion if you were just to start from scratch today?

Yeah, I, so we found our first 40 customers at Fully Enriched through cold calls. I was handling it. At some point we changed the motion because we wanted to scale differently and using more like marketing motion. But for me, when you start to cold call, the most important thing is like to spend time on your list. Your list is the strategy. If you don't know who you want to talk to, just don't even think about it. Don't start because you will get rejected. It would be hard. So I used to spend a long time just preparing the list, not because I like it, just I wanted to make sure that Every call, I was like, I really need to talk to them. They need my solution. I was convinced that it's a blessing for them to have the opportunity to talk to me. You need to convince yourself, uh, at this level to pick up the phone. And of course, the basic, but, uh, if you know who you will talk to, uh, you would be better. You I'm not like an hardcore cold caller that push, push. Happy if I leave the call with just information and not a meeting booked. So it's also a way to gather information depending on your stage. If you do the 0-to-1, for me, it's one of the best ways to fine-tune your product-market fit and see if you bring value. To the market with your product. If you scale it and sell like ACV up to more than $10,000 to $15,000, probably worth it to do cold calls. Under that, I'm not sure that the motion worth it. So you need also to adapt the motion depending what you sell, what's the goal. But if you don't spend time on the list and making sure you really need to talk to them, you will quit early if you are not like a pure cold caller guy. So that's my thing. And yeah, being— the tone of voice is important. You don't want to sound salesy. You don't want to push too hard. You ask permission to— so I can share like a ton of tips and best practices to make it work. But Yeah, and don't push too much pressure on your shoulder if you do it. It's okay, it's fine to be rejected, it's part of the game. So you need to acknowledge this and having the right tools also, the phone call, the CRM, but the simpler the better. And yeah, don't overthink it. I would say, because I see a ton of people that spend weeks crafting the script, and when they make the first phone call, they forget everything. So just pick up the phone, learn, and it will be fine.

So you're saying research your prospect really well, make sure everybody really needs your solution, that it'd be a blessing for them to speak with you, what you have to offer. but then don't stress too much on the script. Pick up the phone, call them, learn, iterate.

Yeah, and there is a thing that is you need to have a reason to call them. So having a because makes them have their attention. And if you have a because, and can be just reaching out because, so you have the GTM Engineering School, that's it. It's enough. And I have their attention. And you can say why you are calling them.

So just having the structure of the copy, I'm reaching out because blah, blah, blah, but what comes after because, you don't need to overthink that either. It's more like the psychology of having that in the intro is what works. Is that what you're saying?

Yeah, and what I like to do also is like to highlight the potential issues or problem, your solution or service can solve, but you don't want to touch their ego because if I'm telling you, oh, you don't do your job well, so you need my solution, you'll be like, oh, I'm doing a good job, so I won't listen to you. But if I tell you that, you know what, other people in your industry, in your region, face this kind of problem and this kind of problem and this kind of problem. Does it resonate with you too? Oh yes. Which one? Oh, number 2. Oh, tell me, tell me what, why? And you deep dive on the problem and you don't even need to pitch your solution. You're like, my solution can help you fix this. Do you want to set up a meeting tomorrow, 3 PM? You just enlighten the issue and the problem you solve. You are not pitching. And if there is no issue, you don't need to talk to them. Don't spend time booking a meeting with someone that don't have the issue you want to solve.

That's great. Very simple, but very great, uh, very great advice. What's next for the enrichment industry? What, what's, what are the biggest bets that you're taking at Full Enrich?

We want to be even more global. We have like 3,000+ customers, um, US, Europe, APAC, MENA, LATAM region, uh, strong presence in the US, strong presence in Europe. So want also to expand globally and being the one-stop shop in terms of data. So we partner even more with new data providers, sometimes very niche, but very accurate, uh, opening new regions in terms of data coverage and Yeah, also helping the data enrichment market to elevate the bar and the standard. We do believe also that if tomorrow it's a fully agentic era, we want to be the data infrastructure for that. So AI agents will fail if the data is not good. So Fully Nourished is well positioned to support this AI ecosystem. And yeah, that's necessary pain that is still unsolved. So we work a lot on the verification layer. It's a lot of work because it's country per country. If you want to verify the phone number, it's— there is a lot of work to just having the right partnership, the right technology, the right engine. Also, we will probably help our customer to decide which prospect is the right one at the right moment. So having a sort of decision layer, as we are also connected to the CRM, internal, external data, being able to help them to target the right ICP, helping them also understanding what is a great ICP for you. So, yeah. And yeah, staying focused on great quality, great coverage, compliancy for the data, I think we'll be fine.

I love where you guys are going. I mean, there's so much, but yeah, being focused on quality of data, that it will always, I think, be an evergreen solution that is going to be appreciated by a lot of like GTM teams, especially as again, like the market gets flooded with noise and more AI slop, right? And thinking about AI, you know, like it's probably on everyone's mouth that it's coming for our jobs, you know, like especially starting with knowledge work, you know, like there's obviously this urgency in the market about upskilling or reskilling yourself. And it's all, it's, I think like it's been harder than ever to stay relevant. You mentioned even before, you know, like GTM engineers need to be, need to not just be process experts and tool experts. They also need to have that growth mindset that, you know, like keep them always current, always at the bleeding edge, always researching for what's next. How, you know, like have you been, when you hire, at Full Enrich for these type of positions? You know, like what separates the good from the great from, in your experience?

Yeah, it's when you hire someone that already did the work on his or her side, having sort of small side project or just not all the companies offer the opportunity to really leverage AI in your day-to-day job for security reasons, for many reasons, but you want to have the people that are naturally curious about it, that embrace it, that try. And in the startup ecosystem, you need to fail fast, but execute fast. And it's just mandatory. You can't have someone that is the old-fashioned way of thinking that think that AI won't really change the day-to-day. You can produce way more with building your agents. The productivity just skyrockets. But at the same time as the productivity skyrockets in the startup world, not like a finite roadmap that is, oh, if we do these 3 things, it's fine. It's like constant grind and there is always more. So you increase your productivity, but at the same time, you, you, so you produce more, but it's never-ending. So you need to also people that have the capacity to handle this work. I think one of the limit of the AI in general, even if you can produce more, so you can say, oh, maybe one person can do the job of 10. Oh, for me, the only limit. Is like the context switching, because, well, in general, not like the humans are not really great at switching from 5 to 10 different contexts. And you want to have people so accountable on specific metrics, KPIs, topics. And what if you own now like 10 projects? You can excel on 5 of them and fail on 5. You still excel on 5. So, but if the 10 are important for the company, it's not a good situation. So I think you, you need to have like experienced people that can own the topic and produce probably way more. I'm just afraid for the junior people because the trade-off is like Do I want to have like senior people that need to, that know how to use AI? Do I want to hire like a junior and giving like all the long work, the long hours? You don't need them anymore, at least from what I see. Just, it's hard to assess how competent people are on using AI, but if they can prove it also by, I don't know, I launched this, I launched this, I did it on my own. And it's very important for the senior people to embrace it because it's also coming from the top. And at Full Reach, we are thinking about like doing an AI week also. So having everyone like, how can we use better AI in our day-to-day. So it comes also from the leadership, from the management to make people move. Not everybody is like an entrepreneur. So it's also your responsibility as a leader or manager in the company to help them embrace it, even if you always prefer that when it comes naturally, but you, we don't have the time for that. So we also incentivize people to do it. And also we're in a startup, so for us it's easy. We only have people with this mindset, otherwise it won't work.

Yeah, fair enough. I mean, like, definitely, yeah, as I pick up from your answer, like, what separates the good from the great, you know, like resilience, you know, being able to lead others into being aware and actually adopt AI, not be just dismissive about it or being scared about it. Know that it's like a tool for you to harness and for you to amplify, again, not just your output, but also the team and company's wider outcome and success. And yeah, I really like, I mean, I got, I really empathize with what you mentioned also about the junior, more junior recruits. I think like it's a very delicate, you know, moment for like the future generations because I think it goes back also to what you mentioned before, you know, like the bad products are going to be easily replaced, but the best products are probably going to survive. And I find that this polarization is probably also happening in the talent market, you know, like the one that are more dismissive about AI, uh, or that decide that, uh, for just a matter of principles, you know, I'm not going to use it, uh, because it doesn't work for my job or whatever reason, you know, like I think they're just missing the point, uh, and they're probably going to be the first, uh, to be impacted by the actual AI taking over our jobs. Whereas, you know, like the polarization on the other hand, on the other end of the spectrum, it was going to be even, you know, like stronger, you know, like the who is hungry, who has agency, who has vision, you know, who has the creativity and clarity to actually know what to, what good looks like and what not good looks like, you know, like that will be the one that will make the most advantage out of it, you know, like, and I'm wondering if, you know, like it's going to be a matter of the richer that is going to get, are going to get even more rich, you know, and the poor are going to get poorer. I hope that's not going to be the case because, you know, like I hope that this technology is going to like get some, like, uh, some, uh, level the playing field a little bit, but it's a wild time for sure.

Yeah.

And having people that know how great looks like is very important because the also danger of AI is just a very well articulated MBA type of intern that know to write down very articulated way. But when you go into details, sometimes, I don't want to say most of the time, but you need to be picky with—

Of course.

The AI to get like great results because you have the haha moment every time you try something like, wow, amazing. But when you look at more precisely, like, not that good. So I would be curious also to see the real productivity gain of the time we spend just playing with the tools versus just if we do the work on our own with like a great result from the beginning because we know how to do it. So I don't know, I don't have any stats on that, but I would be curious to see what's the real productivity gain for like more like company-wide because of course for the AI coding for it's pretty easy to measure, but company-wide, it's, yeah, just very curious about it. Yeah.

And, you know, talking about company, you know, structure or team structure, I'd love to, you know, get your thoughts about how you guys organize your GTM org specifically. I mean, like, I know you guys have, you know, your early stage and you're probably like below what, 100 people?

Still?

Oh yeah, we are 25.

25 people. So, okay. So how you've been, and you know, because you obviously, you are also, you sell a GTM product, right? So like you definitely have like a GTM org with sales reps and stuff. Like how do you structure your GTM organization and, you know, in the pre and post GTM engineering era? What did you take in mind to kind of like, uh, with, with this new structure?

Yeah, I never start with the tools or with the skills. So the GTM engineering, I don't want to even think about, oh, GTM engineering is something great. So let's see how it can apply to our organization. I always start from my audience and users and structure it that way. And I'll give you some examples. We have a product like Growth Motion and product like sales. So we do marketing stuff, get signups on the product, and depending how the user use the product and how we score them, we trigger a sales action. So the sales team will decide to call them because they want to sell more like enterprise type of deal or expand with other teams, but it starts with the product. And we have like 3 different audiences. We have the go-to-market teams with RevOps, sales, G-team engineers, but they use our platform for their own needs. They use it to find their next customers. We have the agencies, lead generation agencies, talent recruiter agencies, uh, they use it for the account of their own customers. So it's a different approach. They, they're interested in having revenue share. They're interested in like multiplying the, the workspaces in their own workspace. So, um, also different product needs, different, um, acquisition motion. And we have another motion that is more like partnership-led. Where other technologies and AI agents buy our API and embed our API or MCP server directly in their product. So it's more like, it's not the same person that we talk to, CTO, CTO, CEO, head of partnership, depending the size of the data partner, the tech partners. And we call them product builders, agencies, and GTM team. And that's the base. And after that, we'll create like sort of squad or pod to serve those audiences because it's not the same need. And I can go, I can narrow down it on the GTM team. We have the SMB, the middle market, the enterprise, not the same needs. So not the same acquisition motion, but just taking this example, we'll have a squad with the right GTM motion that fits this audience. Maybe the outbound cold call doesn't make any sense to target SMBs at scale. Maybe it's more like a marketing play. The opposite, talking to a bunch of product builders, we want to make sure we talk to them. So we have an outbound motion for that, or ABM. And it starts with the audience, but it's a complete company organization. We have like the product team, the tech team that is also focused on the specific needs for these audiences. And of course the go-to-market and we'll hire the people to fit what works the best for those audiences. And as we grow, because we are only 25 now, so we do, we have multi-hats, but as we scale, we have, we have like more specialized people for each audiences and to also treat them properly on their own life cycle. So we start from the audience, find what works the best and hire the— define the scorecard because we don't want to start with, oh, we have this dude in the team, seems great, what can we do? It's more like, okay, what's the best way to get in front of this type of customer and find the best one. The GTM engineering is probably a good resource for us for the more enterprise or the partnership, the product builders, because we need to get signals every time there is like a new AI startup that is created. We need to reach out fast to the, to the, to the founders, but maybe also want to talk to their investor to get an intro. So we find different way, but it's always specific to who we want to talk to. And yeah, that's how it works internally. Awesome.

I love like how the, yeah, it's pure like GTM vision again, like you work back from the customer and you serve their needs. Very, very cool. And typically, like when you also like advise other people in the space, I mean, like I know you also work with some, uh, freelancers or in some other people you decide to bring in-house. When do you think organizations should lean more towards, you know, getting, working with GTM engineering agencies versus getting them in-house? What are the kind of like tenets that you think through when making these decisions?

Yeah, it's an excellent question because we experience many different things. I think the, the core, so your main acquisition motion needs to be like internal people, because you need to have them completely committed and in the details and have the context about all that different stuff. When you want to launch a new motion that is not what exactly you know how to do, I would suggest to get like a $20K to $50K budget and take experts because you want to speed up the launch of this motion. And that sounds hard, but also getting the expertise from the expert. And if the motion is working, maybe you will think about like, oh, should we internalize it? And doesn't mean you will stop to work with the expert. It will help you to hire, to identify what exactly you need, because most of the time you don't really know what you need. So also working with consultants, experts can help you to do the zero to one, also to fine-tune what you need. Sometimes you start with someone and after a few days you're like, okay, this is not what we need. I'm sorry, but it's part of the game. The hourly rate is probably way higher when you are a consultant. It's part of the game. You're like sometimes short-term contract. So it's fine. You don't want to do that with your employee. You don't want to hire someone and 2 weeks after, like, ah, sorry, it's not what we need. It's not fair. So having this setup of doing sort of zero to one with agencies and our consultant freelancer, and then if it makes sense. Internalized, but otherwise, yeah. And when I give you another example, we work with an ad agency. It's not, I don't know much in ads, but if I need to hire someone, I won't be able to evaluate someone. I don't, I won't be able to, to get the best talent. So I'm happy to work with the best agencies and Same if you want to expand specific regions, specific industry, the time you catch up, even if you are smart, even if you grind and learn a lot, reading as a startup, you don't have the time. So your only advantage compared to the big guys is how fast you can move. And so that's where the consultant, the freelancer, the agencies can help also bringing an expertise to your company. And you can decide later if you want to internalize it.

Yeah, very smart.

Very true. Yeah, it's very true. I know we're, uh, we're sort of coming to the close of this episode, and I want, but I wanted to ask, if you were advising Ben, if you were advising a VP of marketing or revenue today, what is the one thing that they should stop doing immediately because of because of go-to-market engineering and how it's changing with AI?

It really, yeah, for sure. It depends on what they are doing on the day-to-day and how old-fashioned they can be. But I think the time of AI is you try and demonstrate it could work before trying to build internal deck to sell the project to the rest of the team, at least thinking of this for VPs, because if you have a VP, you probably have a team, probably have other execs. So I would say, you know what, before like trying to do politics and trying to onboard other people on your project, just do it and get, get someone to help you execute it, uh, having like a Avenger type of team to do same sort of small 0 to 1 to prove it, then you will pitch it. So having this mentality of entrepreneur also is very important because everything is moving fast. If you spend 3 months just trying to convince your colleague, it's too late and the learning won't be there. If you have an idea, of course you need to assess the opportunity, the risk, the impact, the resources, But I would hire someone that can help me because as a VP, you can't execute everything by yourself. So you probably need to hire someone that can help you do this kind of zero-to-one using AI before trying to do like a 25-slide.

That's great advice. Great advice. Don't need to pitch so much. The bar is lowered to execute. So do it. Find your successes and then scale them up. So great. So today, you know, Ben gave us a masterclass in what modern go-to-market engineering or go-to-market really in general looks like. You start with your audience, you build your list with real intention, and you accept that your list is the strategy basically. I think today Ben, he drew a sharp line between spray and pray tactics versus true GTM engineering where you're going to be combining data, process, and the tools, of course, to make sure that reps are talking to the right people at the right time, as we all always want to do. And then I think maybe most importantly, what stood out for me is that it pushed us all to lean into AI with a real growth mindset, using it to amplify our curiosity, the experimentation like you just talked about, and then lean into that craft rather than trying to kind of ignore it all and hope it goes away. So I really enjoyed this conversation today. Where, Ben, can listeners connect with you, or, you know, are there certain calls to action for offers or anything coming up?

Any—

you're going to be anywhere, or people can meet you in person?

Yeah, so I'm based in San Francisco, as you can hear, originally from France, but moved to SF. So in person in SF, Otherwise, online on LinkedIn or my email is benjamin@fullandrich.com. So choose what's the best for you. Always happy to meet other GTM folks participating to events. So yeah, feel free to reach out.

Fantastic.

Thanks so much for jumping on, Ben. This was great. We appreciate you.

Thank you very much, guys. Uh, it was a pleasure and, uh, cool.

Okay, Ben, just, um, wait till it— make sure like you, when, after I hit stop, just make sure it uploads completely. You're at 99%, so it should, but just before you close out the, the window, just make sure it's uploaded. Sounds good.

Sounds good.

Thanks. This is fantastic.

Yeah. Thanks for very much, guys. Thanks so much. Talk to you soon.

Yeah.