Best of LinkedIn: Go-to-Market CW 02/ 03

00:00:00: This episode is provided by Thomas Allgaier and Frennis, based on the most relevant LinkedIn posts about go-to-market in calendar weeks O-two and O-three.

00:00:08: Frennis is a B-to-B market research company, helping enterprises gain the customer, competitive, and positioning insights needed to drive GTM success.

00:00:17: And today, we are doing a deep dive into the state of go to market as we get into the swing of twenty twenty six.

00:00:24: Yeah, it's been a really interesting start to the year.

00:00:26: We've gone through, I mean, a huge stack of sources from the last couple of weeks on LinkedIn.

00:00:30: And the big picture feels like the profession is having a bit of a. I don't know, an identity crisis maybe?

00:00:38: Or an evolution?

00:00:39: I wouldn't call it a crisis, but definitely a metamorphosis.

00:00:42: The overarching theme is that go-to-market is really moving away from that old campaign mindset.

00:00:48: Right.

00:00:48: The old, let's come up with a clever idea, launch it, and hope it works model.

00:00:53: Exactly.

00:00:53: And it's moving towards systems, engineering even.

00:00:56: The sources are all suggesting that strategy, tooling, and talent are all merging together.

00:01:00: Your modern marketing department is starting to look a lot less like a creative agency.

00:01:04: And more like Yickel.

00:01:06: More like a software engineering team.

00:01:07: Okay,

00:01:08: that is a massive shift.

00:01:09: Yeah.

00:01:10: So let's dig into that because marketing is engineering sound.

00:01:13: I mean, it sounds like a nice catchphrase, but what does it actually look like in practice?

00:01:17: Yeah, which I think brings us to our first theme.

00:01:19: the rise of GTM engineering.

00:01:21: Right.

00:01:22: And we have to start with just the sheer numbers.

00:01:24: Chris Hill flagged this in one of the posts we analyzed.

00:01:27: What did he find?

00:01:28: He looked at the data and found over fifteen thousand people.

00:01:32: Fifteen thousand.

00:01:33: Yeah, fifteen thousand people now list GTM engineer as their actual job title on LinkedIn.

00:01:40: Okay, so that's not a fringe experiment anymore.

00:01:42: But is this just, I mean, is this just ops people giving themselves a new title to get a raise?

00:01:48: Or is the role fundamentally different?

00:01:50: It

00:01:50: seems to be, yeah.

00:01:51: Hill defines it as a technical operator.

00:01:53: This isn't just someone who's good with spreadsheets.

00:01:55: This is someone using AI, using APIs, and advanced automation to build the actual infrastructure for research outreach targeting.

00:02:05: So it's like

00:02:05: if sales and marketing hire their own dedicated engineer.

00:02:08: That's the analogy he used.

00:02:10: They're not writing the copy.

00:02:11: They're building the pipes that deliver the copy.

00:02:13: But it's messy.

00:02:15: Dylan Garrity makes a really important point about the mindset here.

00:02:19: He says GTM engineering needs immense patience because these systems break constantly.

00:02:24: Because

00:02:24: you're stitching together all these third-party APIs and data flows.

00:02:28: Exactly.

00:02:29: You're connecting your CRM to an enrichment tool, to an AI agent, to an email sequencer.

00:02:34: If one little thing changes in an API, the whole change snaps.

00:02:39: So you have to be comfortable with failure and iteration.

00:02:42: It's a living system.

00:02:43: It's a living system, not a one-off campaign.

00:02:45: I think there's a risk here, though.

00:02:47: You know, when people hear automation and engineering and sales, their minds immediately go to spam.

00:02:53: Great.

00:02:54: Now we can send ten thousand bad emails a day instead of just a hundred.

00:02:58: That is the trap.

00:02:59: But Matthew Joseph pushes back hard on that.

00:03:01: He says, if you think GTM engineering is just about cold email at scale, you are missing the entire point.

00:03:08: He actually broke it down into fourteen.

00:03:09: Fourteen

00:03:10: archetypes.

00:03:10: For a role that, I mean, barely existed a couple years ago.

00:03:13: It just shows the depth.

00:03:15: Of course, you have your outbound systems glitter, but he also lists roles like pricing engineer or even a compliance GTM engineer.

00:03:24: It covers a whole revenue life cycle.

00:03:26: Right.

00:03:27: So acquisition, operations, retention, the whole thing.

00:03:30: It's about engineering the business logic, not just the spam cannon.

00:03:34: That lines up with what Alex Tailandier has seen.

00:03:37: He talked about a structural split in modern teams.

00:03:40: Layer one, he calls them the system builders.

00:03:42: Those are the engineers.

00:03:44: And then layer two, the relationship builders.

00:03:46: Which is such a necessary distinction.

00:03:48: I mean, you can automate research.

00:03:50: You can automate signal tracking, even the first touch point.

00:03:53: But you can't automate the trust you need to close a complex deal.

00:03:57: Right.

00:03:58: The system builders design machine and the relationship builders run the revenue through it.

00:04:02: So here's the million dollar question for a hiring manager listening to this.

00:04:05: Who do you hire for layer one?

00:04:08: Do you grab a software engineer and try to teach them what an MQL is?

00:04:12: Or do you take a marketer and teach them some Python?

00:04:14: Max Pidvone has a really strong opinion on this.

00:04:17: He does.

00:04:17: He argues you should always bet on the marketer who learns to code.

00:04:20: Really?

00:04:21: I think the pure software engineer would build a better system.

00:04:24: They'd write cleaner code, for sure.

00:04:26: But Pidvani's point is that context is the real bottleneck.

00:04:30: It's much, much harder to teach a developer the nuances of pipeline economics, you know, why a customer churns, what messaging actually works.

00:04:39: Then it is to teach a marketer how to call an API endpoint.

00:04:42: Exactly.

00:04:42: You can learn a tool like Clay or write a basic script in a few weeks.

00:04:46: Business intuition takes years.

00:04:48: Okay, so we have... the who, these GTM engineers, let's talk about the what, the tools.

00:04:54: Because if this is engineering, the tech stack is everything.

00:04:57: This gets us to our second big theme, AI infrastructure.

00:05:00: And the speed here is, frankly, it's a little frightening.

00:05:03: Ryan Staley had a warning that really stood out.

00:05:06: He said the window to use AI as a competitive advantage is closing.

00:05:10: fast.

00:05:11: How fast are we talking?

00:05:12: He predicts by twenty twenty seven, systematic AI adoption will just be table stakes.

00:05:18: If you're not doing it, you're not just behind, you are structurally disadvantaged.

00:05:21: It's like bringing a knife to a gunfight.

00:05:24: Okay, so let's look at the actual tools driving this.

00:05:27: We saw a lot of chatter about Clay.

00:05:29: Jasper Rucci's basically gave a masterclass on it.

00:05:32: Clay is just popping up everywhere.

00:05:33: Think of it as a a no-co GTM workbench.

00:05:37: So for anyone who hasn't used it, is it like a database?

00:05:40: It's more than that.

00:05:41: It's a builder.

00:05:42: You can pull data in, sure, but then you can run actions on it.

00:05:46: You can do things like use web scraping as a qualification signal, like go to this company's career page and see if they're hiring salespeople.

00:05:54: And then use AI to write a line based on that specific job opening.

00:05:59: Right.

00:06:00: Then use waterfall enrichment to find the right person's email.

00:06:03: It lets a non-coder build some seriously complex data software.

00:06:06: So that's the system builder tool.

00:06:08: What about the internal side?

00:06:09: Because adoption is always the place AI initiatives go to die.

00:06:12: That's where Patrick Spychalski brought up a tool called Dust.

00:06:16: He calls it an MCP tool, a Model Context Protocol.

00:06:19: Okay, hold on.

00:06:19: Model Context Protocol.

00:06:21: Can you translate that into English for us?

00:06:22: Uh, yeah.

00:06:24: Put simply, it's a way for AI assistants to access data from all your different systems, your Google Drive, your Slack, your Notions Safely and with Context.

00:06:34: Spychalski loves Dust because it integrates right into Slack.

00:06:38: Ah, so you're not logging into some separate AI portal.

00:06:40: You just asked the question in the channel where you're already working.

00:06:44: Precisely.

00:06:44: It breaks down the silo.

00:06:46: Adoption happens because it lives right inside your existing workflow.

00:06:50: And speaking of speed, Sophie Bonasisi said something about cloud co-work.

00:06:53: that was kind of mind blowing.

00:06:55: It was.

00:06:56: She said software creation time is compressing like crazy.

00:06:59: Her team shipped a fully functional internal tool in ten days using AI coding assistance.

00:07:04: Ten days.

00:07:05: Ten days.

00:07:06: And her big takeaway is that the constraint is no longer writing the code.

00:07:09: The constraint is what then?

00:07:10: Distribution.

00:07:12: If anyone can build an app in a week, the market gets flooded.

00:07:15: The winner isn't who builds it first, it's who can sell it best.

00:07:18: Which creates a huge problem.

00:07:20: If building is easy and buying tools is easy, you're going to end up with a mess of tech debt.

00:07:26: a graveyard of unused software subscriptions.

00:07:28: And Kevin Payne has a solution for that.

00:07:31: A really strict five-question framework you should use before you buy anything.

00:07:35: Okay, let's hear

00:07:35: it.

00:07:35: Question one, does it have API capabilities?

00:07:38: If it can't talk to your other tools, it's an island.

00:07:41: Don't buy islands.

00:07:42: Question two, does it support MCP?

00:07:45: Is it ready for AI agents?

00:07:47: Question three, two-way data sync.

00:07:50: Let's

00:07:50: double click on that one.

00:07:51: Why is two-way sync so critical?

00:07:54: Because if a tool updates a contact, let's say it finds a new phone number, but it doesn't push that information back to your CRM.

00:08:01: Your CRM becomes obsolete.

00:08:02: You have two sources of truth.

00:08:04: You create conflicting realities in your data.

00:08:06: It has to push and pull.

00:08:07: The last two are R&D investment.

00:08:09: Are they actually innovating?

00:08:11: And then security and SSO compliance.

00:08:13: Pain is blunt.

00:08:14: If it scores low, just don't buy it.

00:08:16: It will cause more problems than it solves.

00:08:17: I appreciate that discipline.

00:08:19: It's so easy to get distracted by shining new tools.

00:08:22: Okay, so we have the engineers.

00:08:23: the tools.

00:08:24: But if you automate a bad process, you just get bad results faster.

00:08:28: We have to talk about strategy and readiness.

00:08:30: And this is where we need to step back from the tech for a minute.

00:08:33: Bill Hobb shared a rule that I think every leader needs to hear.

00:08:36: He calls it the one page rule.

00:08:39: It sounds simple.

00:08:40: but it's incredibly hard to actually do.

00:08:42: It's

00:08:43: so hard.

00:08:44: Habib says, if you can't explain your GTM plan on a single page, your team cannot execute it.

00:08:49: You need your business objectives, your key results, and maybe three to five key initiatives.

00:08:54: That's it.

00:08:54: If you have twenty priorities, you have zero.

00:08:56: Sangram Vajray made a similar point.

00:08:58: He talks about needing to identify your valley of death.

00:09:02: Right.

00:09:03: You have to know where you are truly stuck.

00:09:06: Is your problem awareness?

00:09:08: Conversion?

00:09:09: or is it retention?

00:09:10: You can't fix all three at once.

00:09:12: He suggests answering eight simple GTMOS questions on a single slide to force that focus.

00:09:17: But why do strategies fail, even when they look good on a slide?

00:09:20: Dowry Rester argues, it's usually not because the product is bad.

00:09:24: No, he points to blind spots, areas you just aren't measuring.

00:09:27: And Philippe Rutten's ad that what looks like a marketing performance problem is often actually a GTM readiness problem.

00:09:33: Give me an example of a readiness problem.

00:09:35: Misaligned C-suite expectations.

00:09:38: or a lack of clear ROI.

00:09:40: If your CEO expects a flood of leads in week one, but your strategy is built around long-term community building, the marketing team is going to get fired in month three.

00:09:49: The strategy wasn't wrong.

00:09:51: The organization just wasn't ready for it.

00:09:52: Exactly.

00:09:53: The foundation was cracked.

00:09:55: So, assuming we have the strategy and we're ready, Now we have to actually execute.

00:10:00: Let's talk about distribution and channels.

00:10:02: This is where the rubber meets the road and submit and warns against what he calls the hero channel mentality.

00:10:08: Right.

00:10:08: relying on just one way to get customers.

00:10:11: It's so dangerous.

00:10:13: And algorithm changes, costs go up, and your business is gone overnight.

00:10:17: Summit proposes a seven motion system.

00:10:19: Things like direct sales partners.

00:10:21: Right.

00:10:21: Direct sales, strategic partners, cloud partners.

00:10:23: But he also had a really unusual one in there, agentic governments.

00:10:26: I saw that using government AI deployment as a distribution channel.

00:10:30: It sounds a bit wild, but it just shows how creative you have to be.

00:10:34: It's about embedding your product into these massive ecosystems.

00:10:37: Speaking of creative, Michelle Lieben shared a case study from a company called Valley.

00:10:42: This one blew my mind.

00:10:43: One person booking over a hundred and fifty meetings a month.

00:10:47: A hundred and fifty.

00:10:47: I mean, that used to be the quarter for a whole team of SDRs.

00:10:50: So

00:10:50: how are they doing it?

00:10:51: Is it just high volume spam?

00:10:53: No, and that's the genius of it.

00:10:54: It's a content machine.

00:10:56: They recycle video into text and shorts, so they're everywhere.

00:11:00: And then it's all signal-based outbound.

00:11:02: They track who views profiles, who engages with content, and the outreach is automated but hyper-relevant.

00:11:08: So it's, hey, I saw you like my post about X, not Dear Sir or Madam.

00:11:12: Exactly.

00:11:13: But not every channel works for everyone.

00:11:15: Kyle Poyar released some data showing a real split between startups and scale-ups.

00:11:19: This is critical for anyone listening.

00:11:22: If you're an early-stage startup, Poyar's data says... Stick to LinkedIn.

00:11:25: The founder brand still works.

00:11:27: It's effective.

00:11:27: But once you hit that scale-up phase, let's say over ten million in ARR, that channel gets saturated.

00:11:33: You can't just post more.

00:11:35: He found that scale-ups are actually pivoting back to the real world.

00:11:38: Intimate in-person events and big conferences.

00:11:41: That is fascinating.

00:11:43: The more digital we get, the more valuable a real handshake becomes.

00:11:47: It's the cycle of trust.

00:11:48: When your inbox is just flooded with AI generated emails, the only thing you trust is the person standing in front of you.

00:11:54: And sometimes you shouldn't even be the one selling.

00:11:57: Justine Stevens-Clarke had a great point about indirect distribution.

00:12:01: Yeah, she realized that for some markets, a partner, like a European agency, is just a better path to market.

00:12:08: Why try to build a sales team in Germany from scratch when you can partner with someone who already has all the relationships?

00:12:15: That regional piece is so tricky.

00:12:17: Matthew Borthwick had a big warning about just copy pasting a playbook from one country to another.

00:12:23: An absolute disaster waiting to happen.

00:12:25: He used the UK to Germany example.

00:12:27: You have this killer sales playbook in London.

00:12:29: You take it to Berlin and it just falls flat.

00:12:31: Why?

00:12:32: What's the core difference?

00:12:33: In Germany, Borthwick says decision cycles are forty to sixty percent longer.

00:12:37: Trust is earned.

00:12:38: through, you know, technical precision and relationships, not a flashy high-pressure demo.

00:12:42: You push too hard, you lose the deal.

00:12:44: And Amon Fizal Khan noted the same thing for the GCC region, Saudi Arabia and the UAE.

00:12:49: Right.

00:12:50: He called it hyperlocalization.

00:12:52: And he mentioned dark social.

00:12:54: Dark

00:12:55: social.

00:12:56: The conversations you can't track.

00:12:57: They're happening in WhatsApp groups, private communities.

00:13:00: You can't attribute that sale to a Google ad.

00:13:02: You have to actually be in the community to earn the trust.

00:13:06: So we have the tech, the strategy, the complex distribution.

00:13:10: But at the end of the day, people are still running the show.

00:13:13: Let's talk about the human element.

00:13:14: And we have

00:13:14: to start with the unsung heroes, as Mike Tumulti calls them, the sales engineers.

00:13:19: Yes,

00:13:19: I love this.

00:13:20: The SCs are always the ones who save the deal after the account executive over promises something.

00:13:26: Tumulti actually has the data.

00:13:28: He said involving SEs in the sales process increased their win rates from twenty percent to thirty-five percent and the deal size jumped fifty-five

00:13:35: percent.

00:13:36: Wow!

00:13:37: Why such a big jump?

00:13:38: Because buyers are skeptical now.

00:13:40: The sales rep sells the dream.

00:13:41: The sales engineer explains the reality.

00:13:44: They bridge that gap between the promise and the product.

00:13:47: That technical validation is gold.

00:13:49: Okay, speaking of the human element, there's this big fear hanging over all of this.

00:13:53: You know, if AI can do my research and my outreach, am I going to lose my job?

00:13:59: But Brendan Short brought up an economic concept that suggests the opposite, Jevons Paradox.

00:14:06: This

00:14:06: is one of my favorite concepts.

00:14:07: It's so counterintuitive.

00:14:09: Jevons Paradox basically states that as technology makes something more efficient, the total consumption of that thing actually increases.

00:14:16: Okay.

00:14:17: Break that down with an example.

00:14:18: Spreadsheets.

00:14:19: Before Excel, financial analysts did math on paper.

00:14:23: When Excel came along, the fear was, oh, we won't need analysts anymore.

00:14:26: That's not what happened.

00:14:27: Not at all.

00:14:28: The number of analysts exploded.

00:14:30: Because financial analysis became cheaper, companies could suddenly ask a thousand questions about their data instead of just one.

00:14:36: The demand for analysis went up because the cost of doing it went down.

00:14:40: So a short prediction is the same thing will happen with GTM.

00:14:43: Exactly.

00:14:44: AI makes the grunt work of GTM cheaper, so companies won't fire their teams.

00:14:48: They'll say, great, now we can run ten times as many experiments.

00:14:51: We can target five new verticals we were ignoring before.

00:14:55: The workload increases because the ambition scales.

00:14:58: That's a hopeful take.

00:14:59: But there is a huge barrier to that future.

00:15:02: Management.

00:15:03: Sean Graham raises a massive red flag here.

00:15:05: He warns that middle management can kill GTM strategy simply through a lack of context.

00:15:11: How does that happen?

00:15:12: He gives this example of a builder, one of our GTM engineers, getting a task that just says, scrape one thousand doctors.

00:15:20: If that task has trickled down through three layers of management, all the context is lost.

00:15:24: So the engineer builds the scraper, task complete.

00:15:28: But maybe scraping doctors was the wrong idea in the first place.

00:15:30: Exactly.

00:15:31: If that engineer had been in the strategy meeting, they might have said, wait, looking at this data, we shouldn't target doctors.

00:15:37: We need to target hospital administrators.

00:15:39: They hold the budget.

00:15:40: The builder needs a seat at the strategy table.

00:15:43: Yes.

00:15:44: If you treat your GTM engineers like factory workers who just tighten screws, you will build the wrong thing very, very efficiently.

00:15:52: They have to understand the why, not just the what.

00:15:56: So let's try to pull this all together.

00:15:57: It's a fundamental shift.

00:15:59: GTM is becoming an engineering discipline.

00:16:01: We have thousands of new engineers, a powerful suite of tools like clay and dust.

00:16:06: But those tools are dangerous without a simple strategy.

00:16:09: Remember the one page rule.

00:16:11: And distribution is getting more complex.

00:16:13: It requires a mix of high tech automation and high touch human connection.

00:16:17: And finally, the humans are not going away.

00:16:19: Whether it's the sales engineer closing the deal or the GTM engineer designing the system, the human element is still the absolute linchpin.

00:16:26: Absolutely.

00:16:27: The campaign is dead.

00:16:28: Long live the system.

00:16:30: I think that's the perfect place to leave it for this deep dive.

00:16:32: One final thought for everyone listening, though.

00:16:34: We talked about Jevon's paradox, how efficiency creates more work.

00:16:38: So I want you to ask yourself, if your GTM team suddenly became ten times more efficient tomorrow, if all that runt work vanished, What's the new problem you would go solve?

00:16:50: Not just doing the old stuff faster, but what new territory would you finally have the bandwidth to explore?

00:16:54: That is a great question to chew on.

00:16:57: If you enjoyed this episode, new episodes drop every two weeks.

00:17:00: Also, check out our other editions on account-based marketing, field marketing, channel marketing, martech, social selling, and AI in B to B marketing.

00:17:07: Thanks for listening and don't forget to subscribe.

00:17:09: See you next time.