Best of LinkedIn: Go-to-Market CW 46/ 47

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, forty-six and forty-seven.

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: Welcome back to the deep dive.

00:00:20: For the last couple of weeks, we've been tracking a A pretty critical shift in B to B go-to-market conversations, especially across LinkedIn.

00:00:27: It feels like we're finally moving past these isolated tactical campaigns and, you know, those vague strategy documents.

00:00:34: The entire focus now seems to be squarely on building repeatable, measurable, and highly engineered revenue operating systems.

00:00:43: That's it.

00:00:44: That's the core insight here.

00:00:45: I mean, our mission today is really to distill what the top practitioners are saying about this new era of go-to-market engineering.

00:00:50: A whole new role.

00:00:51: It is.

00:00:52: And it's solidifying as this critical connective tissue.

00:00:55: You know, linking the raw data, all the specialized tools, and the actual revenue outcomes.

00:00:59: So companies are realizing they need architecture, not just activity.

00:01:03: Exactly.

00:01:03: They need architecture to drive predict- growth.

00:01:07: The goal is to move from reactive chaos to proactive, well, architecture.

00:01:12: Okay, so let's unpack this.

00:01:14: We should probably start with the first major theme we saw, which is how leaders are sharpening their focus, really demanding precision over just raw volume.

00:01:24: Yeah, the foundational shift is moving GTM from a static plan to a continuous kind of multi-stage journey.

00:01:31: And Nir, for example, she really emphasized that high-performing teams don't just react to the market.

00:01:36: They get ahead of it.

00:01:37: They

00:01:37: get ahead of it.

00:01:38: They actively engineer change by building rigorous sequencing into their strategy.

00:01:42: I think her line was, if you don't engineer the change, you accept the chaos.

00:01:47: And that engineering requires absolute clarity on what GTM is even supposed to be.

00:01:51: Right.

00:01:51: I saw Ivalice Arroyo made a great point that a lot of people are still confusing advertising with true GTM strategy.

00:01:57: That is a huge one.

00:01:58: Advertising just asks, how do we get attention?

00:02:00: It's promotion.

00:02:01: But GTM is this complex system that asks, how do we turn the right attention into predictable revenue?

00:02:06: It's a crucial distinction, isn't it?

00:02:08: Because GTM dictates your targeting, your positioning, the channel mix, the whole rev-up structure you need to actually follow through.

00:02:16: Right.

00:02:16: You can't promote your way out of a faulty system.

00:02:18: You just can't.

00:02:19: And Heidi Atendorf, she highlighted three immediate areas where this GTM engineering has to focus.

00:02:26: First, a truly precise, ideal customer profile.

00:02:30: Second, modern, outcome-driven storytelling.

00:02:33: And third, making sure execution measures real impact, not just vanity metrics.

00:02:38: I think the biggest constraint everyone is finally, finally getting is the economic one.

00:02:43: Two hours are summarized perfectly.

00:02:44: He said, you don't choose your GTM motion, your deal size already dead.

00:02:48: The constraint model.

00:02:49: Exactly.

00:02:50: If your ACV is low, let's say under eight thousand dollars, you have to prioritize self serve and high automation.

00:02:55: No other choice.

00:02:56: But

00:02:56: if you're chasing high ACV named accounts, you need those expensive high touch field sales teams.

00:03:02: and trying to run a field sales team on a low ACV product, well, you're just stalling your business.

00:03:07: And that constraint model, it leads you naturally to the power of exclusion.

00:03:12: Everyone wants volume, but the most successful strategies are defined by who they choose not to target.

00:03:18: Right.

00:03:19: Finn Thormeyer shared an updated take on that legendary Chet Holmes, one sixty seven account playbook, but for, you know, the modern B to B world, he argues.

00:03:28: success now means focusing intensely on just fifty to two hundred dream accounts.

00:03:33: Wow, that seems like a tiny number for a major push.

00:03:36: Yeah.

00:03:36: I mean, how do they justify that kind of focus?

00:03:39: Because the strategy demands quality over quantity, and it's over a prolonged period.

00:03:44: The goal is to be the first one to engage, delivering genuine, continuous, valuable insights to that small group for like, twelve to twenty four months.

00:03:52: So

00:03:53: you're not trying to get them to buy today?

00:03:54: Not at all.

00:03:55: You're positioning yourself as the inevitable partner for when their pain point finally hits a crisis level.

00:04:00: Quality relationships built on insights, they just consistently win.

00:04:03: And that level of focus even changes how you execute geographically, right?

00:04:08: I saw Irene, who's David, to share some fascinating examples from Europe.

00:04:11: She was saying generic global scaling just isn't working as well as tailored motions.

00:04:15: Oh, absolutely.

00:04:16: I mean, think about the rhythm of trust.

00:04:19: In the DACH region, for a GTM motion to succeed, it has to focus on reliability, deep structure, proven methodology.

00:04:28: But if you try to replicate that same heavily structured approach in Benelux, you fail.

00:04:33: That market demands faster, more practical, immediate conversations.

00:04:37: The system has to adapt.

00:04:39: Which is a key job for GTM engineering.

00:04:41: Exactly.

00:04:41: So all

00:04:42: of that strategic precision, the ICP, the ACV, alignment, regional customization, it only works if you have the infrastructure to back it up, which brings us perfectly to our second theme, systems, data, and revops architecture.

00:04:55: Right.

00:04:55: The goal here is to replace guesswork with a repeatable, accountable machine.

00:04:59: Kerala's goddess put it perfectly.

00:05:01: The revenue architecture has to function like an operating system that just gets rid of random tactics.

00:05:06: In favor of what, though?

00:05:07: In favor of growth motions that can be consistently measured, iterated on, and, you know, relied upon.

00:05:13: We hear

00:05:13: so much about data, but what does that modern data architecture actually look like?

00:05:17: Sumit Nan had a great metaphor.

00:05:19: He said traditional dashboards are just rear view mirrors.

00:05:21: I love that.

00:05:22: They only tell you where you've been and they usually show you a pipeline problem way too late to fix it.

00:05:27: The new standard is a predictive GTM command center.

00:05:30: Summit described building a self-updating system that predicts next month's pipeline with ninety-two percent accuracy.

00:05:36: Okay, that's impressive.

00:05:37: But more importantly, it auto-triggers actions like it'll re-sequence contacts or update scoring the moment a deal starts to stall.

00:05:45: And this requires live data loops with tools like clay for enrichment, smartly for execution, and HubSpot as the core.

00:05:52: That sounds incredibly sophisticated.

00:05:54: And this is probably where we need to draw a sharp line between the old RevOps idea and this new GTM engineering concept.

00:06:01: Raphael Tarank clarified this relationship and I think it's essential for anyone listening.

00:06:05: It absolutely is.

00:06:06: So think of RevOps as the essential foundation.

00:06:08: It's the stability layer and make sure you have clean data, trustworthy reporting, CRM reliability.

00:06:13: RevOps is like the CFO of your tool stack.

00:06:15: Okay, the CFO, I get that.

00:06:17: GTM engineering, on the other hand, is the innovation layer.

00:06:20: It's responsible for driving growth acceleration, experimenting with new signals, building out AI workflows.

00:06:26: You need that solid rev-op space before you can bolt on the high-speed GTM engine.

00:06:30: I see.

00:06:31: So if you force rev-ops to do all the innovation, everything grinds to a halt.

00:06:35: And if GTM engineering tries to build workflows on dirty data, Everything breaks.

00:06:40: Yeah.

00:06:40: It's a symbiotic split of duties.

00:06:42: Precisely.

00:06:43: And Sarah Paris-Maschichi tied this architectural shift back to the human element.

00:06:48: She emphasized that all these systems are useless without organizational change.

00:06:52: The real differentiator is moving from vague alignment to shared accountability.

00:06:58: Ah,

00:06:58: shared accountability.

00:06:59: Sounds great on a slide, but in practice, when quotas are on the line, doesn't everyone just go back to pointing fingers?

00:07:04: How do you actually enforce that?

00:07:06: You have to engineer the reporting structure itself to ensure mutual dependency.

00:07:10: The system has to report on joint success metrics, not isolated departmental KPIs.

00:07:16: If marketing's bonus depends only on pipeline generated and sales only on closed deals, they will always find a way to operate in silos.

00:07:24: The GTM engineering function becomes this neutral party that builds the systems that force everyone to rely on a single clean source of truth.

00:07:33: So it makes joint accountability the path of least resistance.

00:07:35: Exactly.

00:07:36: The system becomes the enforcer.

00:07:38: That's

00:07:38: a powerful mechanism.

00:07:40: Speaking of powerful mechanisms, let's move to theme three.

00:07:43: AI and automation, the accelerator.

00:07:46: We're seeing AI become a huge force multiplier here.

00:07:49: Amon Boozied highlighted how tools like Gemini.io aren't just enhancing efficiency, they're structurally lowering the cost of execution.

00:07:57: This is a deep economic change.

00:07:58: Amon specifically noted that some outbound automation efforts are now running on half the traditional token budget.

00:08:04: And for anyone listening, tokens are basically the cost units for large language models.

00:08:08: So you can run twice the volume of prospecting or personalization for the same price.

00:08:12: Exactly.

00:08:13: It fundamentally changes the math for scaled outreach.

00:08:16: It's efficiency at scale.

00:08:18: But that speed has consequences.

00:08:20: Jeremy Grundion said that AI doesn't change the fundamental laws of GTM.

00:08:25: It just accelerates them.

00:08:26: Yeah.

00:08:27: If you're unique and constantly testing, you win faster.

00:08:30: But if your motion is generic, AI just ensures you saturate the market and fail faster.

00:08:35: And that speed is creating chaos.

00:08:37: That's a serious concern.

00:08:39: Deepinder Singh Dingra raised a critical warning.

00:08:43: Uncoordinated autonomous AI agents for prospecting, for sequencing, for content, they'll inevitably create more chaos than uncoordinated humans ever did.

00:08:53: Because they'll just flood your funnel with noise at machine speed.

00:08:56: So orchestration isn't optional anymore.

00:08:58: It's the only way to harness this speed, but it is the hype matching the reality.

00:09:01: Jonis X. Surveied hundreds of Revov's leaders, and he confirmed that while the enthusiasm is high, most GTM AI tech is feeling to meet expectations right

00:09:10: now.

00:09:11: That expectation gap is real.

00:09:12: The reality is that AI SDRs are largely underperforming.

00:09:15: They lack that nuanced human ability to qualify complex signals and build early trust.

00:09:20: So what is AI good for right now?

00:09:22: The primary

00:09:22: achievable benefit of GTMAI today is automating the nitty-gritty repetitive workflows for the human team.

00:09:29: It's making the human SDR or marketer more effective, not necessarily replacing them, at least not yet.

00:09:35: That focus on human effectiveness leads perfectly to this shift in competitive advantage.

00:09:40: With AI making features commoditize making PLG faster to deploy, the differentiation moves away from the product specs.

00:09:48: Wes Bush and Akash Gupta stress that, taste is the new moat.

00:09:51: I love that phrase, taste is the new moat.

00:09:54: It means obsessive attention to detail.

00:09:57: eliminating every rough edge in the customer experience, only showing people what they need when they need it, building processes that feel native, not transactional.

00:10:06: So if everyone can build a tool quickly with AI, the quality of the experience is what keeps the customer.

00:10:10: That's

00:10:11: the moat.

00:10:11: Which brings us to the people who build and maintain this experience.

00:10:14: Our final theme, GTM engineering and talent the architects of revenue.

00:10:18: This is now a distinct career path with serious earning potential.

00:10:22: Noemi J detailed that top GTM engineers can earn up to two hundred and fifty thousand dollars.

00:10:27: Yeah, and that salary comes from a very specific skill set.

00:10:31: the ability to build the entire revenue engine.

00:10:34: We're not talking about someone who just knows how to manage one tool.

00:10:37: This requires true mastery of systems, advanced data fluency, deep API integration knowledge, and the ability to solve business problems with code and logic.

00:10:48: It sounds like a high leverage role, but also highly specialized.

00:10:52: Stephen Bayes had a strong prediction here.

00:10:54: He suggested a shakeout is imminent, where seventy percent of people with the GTM engineer title might be

00:11:01: wiped out.

00:11:01: I believe it.

00:11:02: This shakeout will separate the true revenue architects from those who are just, you know, title driven and focus on simple list building.

00:11:08: This role demands system thinking, not just task completion.

00:11:12: So for our listeners looking to get into this field, or maybe just validate themselves, what should they focus on?

00:11:17: Natasha Odiemi's guide for beginners was really helpful here.

00:11:20: Her advice is to avoid the mistake of trying to master every single tool or copying complex workflows right away.

00:11:26: GTM engineering starts with defining the problem and the input signals.

00:11:30: And the Then you select the tool.

00:11:31: Skills first.

00:11:32: Skills first.

00:11:33: Master APIs and web hooks, prompt engineering web scraping, and get really good with a flexible tool like clay.

00:11:40: That system's first approach defines the toolkit.

00:11:43: Christmas Banta gave a great summary of the modern GTM engineering stack.

00:11:47: Yeah, it's becoming a standardized set of functions now.

00:11:50: Clay is sort of the brain box for data enrichment and campaign logic.

00:11:54: Data sources are coming from Apollo or full and rich.

00:11:57: Okay.

00:11:58: Outbound execution is usually smart lid or instantly dot AI.

00:12:02: And then you have an eight N or Zapier acting as the essential automation glue connecting all these different systems.

00:12:08: These are highly technical roles, but scaling them requires different leadership.

00:12:13: Peter Wharton focused on the critical, non-technical traits needed in leaders, especially in competitive markets like Europe.

00:12:19: And that list is really instructive.

00:12:21: It's adaptability, data fluency, being able to switch from metrics to storytelling and cross-market empathy, but also crucially emotional resilience.

00:12:30: When you're moving at the speed of AI, leaders have to stabilize their teams when things inevitably shift.

00:12:35: So wrapping up this deep dive, the overwhelming message is pretty clear.

00:12:39: We are done with random tactics and vague alignment.

00:12:43: GTM is moving from strategic plans to engineered, measurable operating systems.

00:12:49: This demands radical precision in your ICPs and signals, and it requires absolute discipline in how you implement automation.

00:12:56: The future of revenue really belongs to the architects and operators who can bring this rigor to their GTM stack.

00:13:02: And if you take away just one fundamental truth to guide your efforts, let it be the reminder from Jamie Walsh.

00:13:07: He said, never forget that the signal is greater than the system.

00:13:11: every single time.

00:13:12: That's a great final thought.

00:13:13: It is.

00:13:14: Don't spend months building these immaculate systems if you haven't identified the actual predictive buyer behaviors that signal intent.

00:13:22: Find those signals first, then build the engineered system that honors them.

00:13:26: If you enjoyed this episode, new episodes drop every two weeks.

00:13:29: Also, check out our other editions on account-based marketing, field marketing, channel marketing, MarTech, social selling, and AI in BDB marketing.

00:13:37: Thank you for joining us for this deep dive into GTM engineering.

00:13:40: If this conversation resonated with you, make sure you subscribe so you don't miss our next analysis.