Best of LinkedIn: Go-to-Market CW 50 - 01

00:00:00: This deep dive is provided by Thomas Allgaier and Frennis, based on the most relevant LinkedIn posts about go-to-market in calendar weeks, fifty through oh one.

00:00:09: Frennis is a B to B market research company, helping enterprises gain the customer competitive and positioning insights needed to drive GTM success.

00:00:19: Welcome back to the deep dive.

00:00:21: You know, if you thought the B to B go-to-market world was taking a breath over the holiday slowdown, you definitely missed the memo.

00:00:28: Not at all.

00:00:29: We spent the turn of the year digging through the most critical LinkedIn discourse, and the message is just, well, it's resounding.

00:00:35: GTM is shifting from a collection of tactics to a professional, engineered operating system.

00:00:41: That is the definitive trend, isn't it?

00:00:42: The whole era of fragmented campaigns and one-off efforts is being replaced by this focus on durable... integrated revenue systems.

00:00:51: It's all about predictability.

00:00:53: It is.

00:00:53: It's not just about being efficient anymore.

00:00:55: It's about establishing that predictability.

00:00:57: And that's exactly what we're here to unpack for you.

00:00:59: We've synthesized the top go-to-market trends we saw and we've structured this around four key clusters of insight.

00:01:05: Right.

00:01:06: From the architects building the system to the AI that fuels it and of course the foundational strategy that dictates whether any of it succeeds.

00:01:13: Our mission as always is to give you the most important takeaways in the fastest possible time.

00:01:19: So let's start with the central figure here, the person driving this whole transformation, the GTM engineer.

00:01:26: This is more than just a new title, right?

00:01:28: Oh, absolutely.

00:01:28: This is no longer just a fancy name for an ops person.

00:01:32: It's emerging as a distinct high leverage discipline that's really focused on designing the entire revenue system end to end.

00:01:40: Okay.

00:01:40: So if they're the architect of revenue, what's in their blueprint?

00:01:43: I mean, what specialized skills does this role actually demand?

00:01:46: It demands a truly unique blend.

00:01:48: Alessandro Lombardo detailed this really well.

00:01:50: You need someone who is at their core, a builder.

00:01:53: A builder.

00:01:53: Yeah.

00:01:54: This role, it's this Right at the intersection of sales, marketing, and product growth, the non-negotiables are workflow mastery.

00:02:02: So deep skill in tools like NE&N, Zapier, API knowledge.

00:02:07: So they're stitching everything together.

00:02:09: Exactly.

00:02:09: They are basically writing the integration layer for the business.

00:02:12: That sounds

00:02:13: like a revops person with a massive dose of technical literacy.

00:02:17: They aren't just managing the CRM.

00:02:19: No, they're bending it to their will.

00:02:21: It's obsessive CRM mastery paired with AI tooling knowledge.

00:02:25: But the core mindset is product-centric.

00:02:27: They're shipping leverage, a systemic improvement, not just administrative features.

00:02:32: Which is probably why Felix Frank called it one of the hardest jobs in revenue.

00:02:35: It

00:02:35: is.

00:02:35: It takes elites, strategic... technical, and even account management skills, all tied directly to measurable results.

00:02:43: That high pressure, higher reward profile?

00:02:45: I mean, retention has got to be brutal if you don't manage it correctly.

00:02:49: It is.

00:02:50: Noemi J raised a huge flag here.

00:02:52: She warned that if you hire a great GTM engineer and then give them a vague scope and no clear priorities, you're going to lose them

00:02:59: fast.

00:02:59: You have to give them a mandate.

00:03:00: Absolutely.

00:03:01: You've hired an architect to give them a runway.

00:03:04: Lawrence Nize made the point that GTM engineering should be treated like product development.

00:03:08: They own the roadmap, run sprints, and issue release notes for process changes.

00:03:13: That transparency seems key.

00:03:15: It's everything.

00:03:16: And we're already seeing this professional evolution play out.

00:03:19: Benjamin Reed and Vivek Sudarsan both track the career path, and it's moving rapidly toward revenue engineer and senior REVOPS leadership.

00:03:28: So systems ownership is now a senior mandate.

00:03:31: Right.

00:03:31: But we have to address the critique here, though.

00:03:33: Olivier Tytgat had a pretty sharp take.

00:03:36: Okay.

00:03:36: He suggested that many people claiming the GTM engineer title today are often just specialists learning in public.

00:03:43: You know, mastering a specific tool like clay for list building, but maybe lacking the skills for deep architectural maintenance.

00:03:50: That's a difficult tightrope, isn't it?

00:03:52: How do you, as a leader, distinguish between an operator running a play and an engineer who can design the whole stadium?

00:04:00: I think it comes back to whether they're shipping systemic improvements or just fulfilling reactive tasks.

00:04:05: A true engineer can articulate the coherence of the whole system.

00:04:09: Right,

00:04:09: not just one part of it.

00:04:10: And that idea of a coherent system, it brings us directly to our second major theme, GTMAI.

00:04:16: Specifically, the shift to agentic and data-led acceleration.

00:04:20: The expectations for AI have definitely matured.

00:04:24: It's not about fun pilots anymore.

00:04:25: No, not at all.

00:04:26: It's about generating results in quarters, not years.

00:04:30: The time for experimentation is over.

00:04:32: The time for implementation is now.

00:04:34: And that pressure for time to impact is fundamentally changing data strategy.

00:04:39: Joseph Santos noted this.

00:04:41: Teams are leaning heavily on external referential data to accelerate their AI initiatives immediately.

00:04:47: So pre-resolved identities, verified contacts, that sort of thing.

00:04:51: Exactly, to bypass months of internal data normalization and, you know, CRM cleanup.

00:04:57: But that raises a big question for me.

00:04:59: If you're building your AI on someone else's data foundation, how do you prevent baking in their biases?

00:05:05: It feels like you're trading speed for ultimate control.

00:05:08: It's a calculated risk, for sure.

00:05:10: You're trading months of internal work for immediate actionability, but the key is integrating that data strategically.

00:05:17: And Danny Chapenko outlined the new system architecture for this.

00:05:20: He sees four distinct motions emerging in the tech

00:05:22: stack.

00:05:23: Four.

00:05:23: Okay, what are they?

00:05:24: So first, you have your AI native CRMs, then you have specialized tools for GTM engineering like clay, then individual co-pilots.

00:05:32: And finally, the really ambitious GTM platforms that aim to connect everything.

00:05:37: And within that architecture, the actual labor is being done by something new.

00:05:42: Sumita N argued that high-performing GTM teams, the ones hitting over a million in revenue per employee, they run on agents.

00:05:50: not headcount.

00:05:51: The shift to agentic AI.

00:05:53: Exactly.

00:05:53: And this is the key differentiator.

00:05:55: Agentic AI means the AI can handle complex logic and sequencing on its own.

00:06:00: It's not just generating copy.

00:06:02: No, it's figuring out who to research, what to enrich, and when to execute a multi-step sequence.

00:06:08: all without a human pushing a button for every single task.

00:06:11: Jonathan MK gave a perfect example of this with Apollo's AI assistant.

00:06:14: It can orchestrate these really complicated GTM motions, qualifying accounts, building personalized sequences all through simple conversational prompts.

00:06:22: So it's a tool designed to remove work, not just add another step to the process.

00:06:26: That's the goal.

00:06:28: And Scott Wischinski and Evelyn Jansen articulated the philosophy behind this so well, they argued the problem isn't a lack of effort.

00:06:35: It's a flawed architecture.

00:06:36: Precisely.

00:06:38: An AI native GTM system moves past just optimizing single tasks.

00:06:43: It's about building orchestrated workflows where AI is the fuel and signals flow freely across acquisition and expansion.

00:06:51: And that design shift means your returns become exponential, not linear.

00:06:54: Right.

00:06:55: A smarter system just keeps accelerating its own returns.

00:06:57: But all this scale has to be balanced.

00:07:00: Area eleven emphasized that the volume at all costs era is dead.

00:07:05: The new model relies on agentic AI for scale, but it demands that humans focus on the relationships.

00:07:11: The trust economy.

00:07:12: Yes,

00:07:12: the trust economy, where your personalized social presence and your email reputation become the real human differentiator.

00:07:18: You can automate the execution, but you can't automate authenticity.

00:07:22: Which brings us sharply to our third theme, the non-negotiable foundation of this whole system, strategy, ICP, and the return to clarity.

00:07:30: This is where most failure originates.

00:07:32: I mean, Mike Davis put it so succinctly.

00:07:34: He said, a product without a go-to-market strategy is just an expensive experiment.

00:07:40: That's a powerful.

00:07:41: It is.

00:07:42: Products don't usually fail because they're technically flawed.

00:07:45: They fail because the company never nailed the strategy for who, why, and how to sell it.

00:07:50: And Valentina Rue Martinez observed that most GTM problems are actually clarity problems, not sales problems.

00:07:56: Right.

00:07:57: Reactive or unfocused setups lead to this random, expensive growth that doesn't have any real leverage.

00:08:02: So what are the big mistakes?

00:08:04: What are these fatal flaws?

00:08:06: TK Kater detailed three big ones.

00:08:08: First, diluted messaging.

00:08:10: You know, marketing language so generic it could apply to any competitor.

00:08:13: Second, zero positioning.

00:08:15: This is where you lose deals, not to arrival, but to the status quo.

00:08:19: The customer just keeps using their spreadsheet.

00:08:21: I'm the third.

00:08:22: The massive ICP, the classic attempt to boil the ocean by trying to target everyone.

00:08:27: So how do we get crystal clear?

00:08:29: Hank and Goomer gave a pretty blunt diagnosis on this.

00:08:32: I think I know the quote you mean.

00:08:33: He said, if your GTM plan lacks a clear ICP, segments and lead scores, you do not have a GTM plan.

00:08:41: You have a slide deck.

00:08:42: You have vibes.

00:08:43: That phrase just sticks.

00:08:44: because it's so true.

00:08:46: His blueprint demands specifics, defining your ICP not just by broad industry, but by sub-industry, by tech stack patterns, by critical trigger events.

00:08:55: And your segmentation has to clearly tier accounts.

00:08:58: Tier one, two, and three.

00:08:59: So you can allocate resources where they'll have the most impact.

00:09:02: Right.

00:09:03: And we've also seen people like Jamie Walsh suggest moving beyond static personas entirely.

00:09:08: To jobs to be done.

00:09:09: Exactly.

00:09:10: Anchor your messaging in the JTBD framework.

00:09:12: It forces you to focus on the specific job.

00:09:15: the buyer is under pressure to finish, which makes your outreach inherently more relevant.

00:09:20: All of this clarity, though, it requires real leadership courage.

00:09:24: Sophie Bonanasi made a great point that the hardest GTM decisions aren't made when the business is struggling.

00:09:29: It's when things are working.

00:09:30: Exactly.

00:09:30: When you have to disrupt current success to design for the future.

00:09:34: And Christopher Campbell added that organizations repeat GTM mistakes because they reset activity every January, new quotas, new campaigns.

00:09:42: But they rarely reset their understanding of why they truly won or lost the year before.

00:09:47: You can't engineer success if you don't audit failure.

00:09:50: That's a great takeaway.

00:09:51: Which brings us to our final cluster of insights, the engineered revenue system.

00:09:56: How we put this all into practice?

00:09:57: with data, alignment, and trust.

00:10:00: So how do we operationalize all this clarity?

00:10:02: Well, we start by recognizing the philosophical shift around data.

00:10:06: Alan C. advocated moving from being data-driven, which just means you have dashboards.

00:10:10: Right, everyone has dashboards.

00:10:11: To being

00:10:11: data-led, which means you proactively extract concrete insight and then take intentional, measurable action based on

00:10:19: it.

00:10:19: And that transformation needs a systematic way to score accounts.

00:10:23: Bill Stathopoulos had a really practical framework for this.

00:10:27: The twenty-twenty-six GTM prioritization matrix.

00:10:30: It's a simple two-by-two grid.

00:10:31: It uses account footscore.

00:10:33: How well they match your ICP and engagement score.

00:10:35: They're active behavior, like visiting the pricing page.

00:10:38: Right.

00:10:39: And it uses that to clearly tier accounts into buckets.

00:10:43: Target, nurture, opportunistic, and deprioritize.

00:10:46: That

00:10:47: scoring mechanism becomes the alignment engine.

00:10:49: But at the same time, we're seeing this renewed emphasis on high-touch channels.

00:10:54: Renee Marek calls events a crucial trust accelerator.

00:10:57: It makes sense.

00:10:58: It collapses the funnel by shifting these passive digital lurkers into engaged prospects through real human interaction.

00:11:05: It's the ultimate counterpoint to all the automation we've been talking about.

00:11:08: Chris Jenkins is even calling for event engineering.

00:11:11: treating in-person intent with the same rigor as digital data.

00:11:15: Exactly.

00:11:16: If you're going to invest in events, you have to track the ROI like you would in any other channel.

00:11:20: But

00:11:20: you know, sometimes the most effective system is built on simplicity.

00:11:24: There was a fascinating case study from Natasha Odiemi.

00:11:27: No, the

00:11:27: LinkedIn outreach one.

00:11:28: Yes.

00:11:29: A GTM agency got a sixty-five percent reply rate on cold LinkedIn outreach just by using simple human messaging.

00:11:36: They even included deliberate minor errors to signal it wasn't some polished automated campaign.

00:11:42: It's proof that the ultimate leverage is still human connection.

00:11:44: It is, but none of this works without system control.

00:11:48: Stefan Mays argued GTM systems often fail because local fixes marketing fixes volume, sales fixes outbound, don't interact correctly.

00:11:57: So local optimizations actually degrade the total system performance?

00:12:02: They can, yeah.

00:12:03: It's not about capability, it's about governance.

00:12:06: Tim Hillison noted that tech just amplifies your existing structure.

00:12:09: If your system is coherent, output improves.

00:12:12: If it's confused, confusion just accelerates.

00:12:14: So governance controlling how the GCM system is allowed to behave from end to end?

00:12:19: That's the real path to consistency.

00:12:21: It

00:12:21: is.

00:12:21: So if we circle back to our big theme, what we've seen is this consensus that go-to-market must be treated as an engineered intentional system.

00:12:29: Catherine Chwagamahinja, Phil Charlin, they all affirm that predictable, scalable growth only follows when this architectural predictability is established first.

00:12:36: The

00:12:37: focus is squarely on architecture and systems that outlast tactical fixes.

00:12:41: But there is a final really powerful thought for you to consider as you design your own GTM engine.

00:12:45: This is

00:12:46: where we go beyond predictability and into emergence.

00:12:49: Exactly.

00:12:50: Jonathan Moss made this fascinating argument that the math governing growth systems doesn't really follow the tidy bell curves we assume.

00:12:58: Instead, they often follow power laws and criticality.

00:13:02: What that means is extreme asymmetry is the default.

00:13:05: One customer.

00:13:06: one piece of content or one sales rep carries a disproportionate amount of the results.

00:13:10: The eighty twenty rule on steroids.

00:13:12: Precisely.

00:13:13: Yeah.

00:13:13: So if a tiny fraction of your efforts drives the vast majority of your success then designing your GTM system purely for consistency or for the average result is well it's counterproductive.

00:13:24: So instead.

00:13:25: Instead you should be architecting the conditions for those high leverage spikes those rare emergent events to happen more often.

00:13:31: That means building a system designed not just for consistency, but for emergence.

00:13:35: A

00:13:35: very provocative thought to end on.

00:13:37: If you enjoyed this deep dive, new additions drop every two weeks.

00:13:41: Also check out our other additions on account-based marketing, field marketing, channel marketing, MarTech, social selling and AI in B to B marketing.

00:13:48: Thank you for diving deep with us and be sure to subscribe for more essential insights.