Best of LinkedIn: Go-to-Market CW 28/ 29

00:00:00: Provided by Thomas Allgeier and Frennis, based on the most relevant LinkedIn posts about go-to market in calendar weeks twenty eight and twenty nine.

00:00:07: Frenness is a B to B Market Research Partner helping ICT and tech providers identify niche channel partners by compressing.

00:00:23: What if I told you that booking three times as many sales meetings could actually, destroy your revenue pipeline?

00:00:30: It sounds completely counter-intuitive.

00:00:32: Why would any sales or marketing leader complain about tripling their calendar bookings?

00:00:36: right exactly but As you're going to hear today the data actually proves at the traditional volume based sales funnel is officially broken and Just a set expectations early for you listening If you're looking for basic definitions are standard marketing fluff.

00:00:49: this deep dive is definitely not for you.

00:00:52: We are going straight into the deep end for strategic B-to-B professionals.

00:00:56: Yeah, no basics today!

00:01:10: Right, because this isn't just about you know grabbing a new tool to add to your tech stack.

00:01:14: It's fundamental shift in execution which means before we even touch the futuristic strategies We have to look at people pulling strings.

00:01:22: Yeah

00:01:23: they're builders

00:01:23: Exactly!

00:01:24: We are seeing this sudden massive shift.

00:01:26: who actually runs B-to-B marketing engine today?

00:01:30: The traditional marketing ops and revops roles.

00:01:33: They are evolving into something much more technical.

00:01:35: it is fascinating structural shifts.

00:01:37: honestly Misty Sharma highlighted this in a recent analysis.

00:01:40: She pointed out that the title GTM engineer was essentially just made up a couple of years ago, like it didn't exist in corporate directories.

00:01:46: Wow

00:01:47: really?

00:01:47: Just a couple years Yeah.

00:01:48: But now based on looking at almost ten thousand companies hiring for that specific role has exploded with twenty three times growth since early twenty-twenty.

00:01:57: Three half the company's on the Forbes AI fifty list are aggressively hiring for right Now.

00:02:02: okay I have to pause and push back lightly here because you know we've seen corporate rebranding trends before.

00:02:08: Isn't this just a rebranded RevOps person?

00:02:10: It feels like calling a janitor a sanitation engineer.

00:02:15: Are the daily tasks actually any

00:02:17: different?".

00:02:18: Well, that's exactly what skepticism leaders have at first... but underlying work is fundamentally different!

00:02:24: Wayne Johnson actually provided some great clarity on where this role sits.

00:02:28: He frames the GTM engineer as operating directly in the white space between software engineering and revenue operations.

00:02:34: Oh, interesting!

00:02:35: So it's more technical?

00:02:36: Very A traditional Revov person might spend their day inside a CRM building reports or standardizing data fields but a GTM Engineer is writing scripts utilizing APIs to actively connect disparate systems.

00:02:49: They're tying data providers marketing automation platforms, and analytics engines into this single organism.

00:02:56: So if the traditional role is checking the pressure of the pipes?

00:02:58: The GTM engineer is actually like building and routing the plumbing itself.

00:03:02: Precisely!

00:03:03: And Lorenzo Amade takes it a step further pointing out that these two functions are essentially colliding in to one super valuable hybrid rule.

00:03:11: Because you need both skill sets, right?

00:03:13: Right?

00:03:14: Revop specialists understand the business logic of a CRM at a microscopic level but The GT-M engineer brings in advanced automations and asynchronous workflows to execute that logic At a scale humans physically cannot match.

00:03:28: So if you can master both the logic And the infrastructure your golden

00:03:33: exactly.

00:03:34: You close a skill gap That the market is desperately paying A premium for.

00:03:38: Mischty actually noted a freelancer who went from making one thousand dollars per month on Upwork to twenty-thousand of months simply by mastering this specific engineering discipline.

00:03:47: Twenty grand a month, that's wild!

00:03:49: But it makes complete sense when you look at what happens when its done poorly.

00:03:52: Um...Sumajit Nandi offered a really grounded perspective on this.

00:03:55: He admitted early in his career.

00:03:57: he thought being great GTM engineer just meant knowing how string together twenty different software tools.

00:04:02: Oh like a Frankenstein tech stack

00:04:04: Exactly.

00:04:04: You buy one tool to scrape data, another to enrich the emails and send out messages.

00:04:10: but true go-to market engineering isn't about stacking point solutions like digital lego bricks.

00:04:15: it's about systemic continuity

00:04:17: Connecting entire buyer journey into one measurable

00:04:20: flow.

00:04:21: Yes From moment an initial signal is captured say someone visits a pricing page identifying account finding contact deploying message booking meeting.

00:04:32: If that underlying infrastructure is rushed, or if your ICP is cast way too wide the campaign structurally fails long before first email is even drafted.

00:04:41: And naturally leads to next massive shift.

00:04:44: so the GTM engineer builds piping but pipes are entirely useless without water and right now every company trying pump through these newly built systems.

00:04:54: artificial intelligence.

00:04:56: But there's a massive disconnect between what executives think AI is doing and what it's actually doing in the trenches.

00:05:02: We definitely need a reality check there, Jake Dunlap shared but only thirty-three percent actually use it to fully automate a workflow task.

00:05:18: Which tells us that the vast majority of teams are just using AI as a shinier interface next their existing manual work.

00:05:25: That's the perfect way to describe.

00:05:27: if you're listening and looking at your own daily routine, You probably realizing asking chatbot to write cold email in side tab isn't true automation?

00:05:37: No, because you're still doing the heavy lifting.

00:05:38: Exactly!

00:05:39: You are still the one copying the prompt pasting output formatting it and hitting send.

00:05:43: It's the exact same manual playbook from twenty fifteen just with a new browser window open.

00:05:48: that is not digital transformation.

00:05:50: Right its' just faster horse.

00:05:52: If we want to see what true embedded agentic AI looks like Cody Scheider provided phenomenal real world case study.

00:06:00: He deployed an autonomous Facebook ads agent for startup And this wasn't an assistant offering advice on ad copy.

00:06:06: So what did it actually do?

00:06:07: This agent autonomously researches the pain points of The Target Demographic.

00:06:12: It generates five net new visual ads every single day, it publishes them to the platform Manages the AdSet parameters and ruthlessly turns off the losing ads without human intervention.

00:06:24: Wait I want make sure i'm hearing the mechanics of that correctly...it is allocating the actual financial budget On its own..It

00:06:29: Is!

00:06:30: It forces the Ads To Compete reallocates the budget to the winners, and then learns from those parameters to generate the next day's creative.

00:06:38: The financial outcome was massive—it halved their cost per lead from a hundred dollars down to fifty... Oh

00:06:43: wow!

00:06:43: ...and it now continuously drives over one-hundred qualified leads a week.

00:06:47: That is what happens when AI moves from chat interference into an autonomous workflow….

00:06:52: …that level of automation almost intimidating.

00:06:55: By the way, to you listening if your finding this breakdown useful as you evaluate your own GTM stack make sure hit subscribe so catch our future additions.

00:07:04: But looking at Cody's example it begs an existential question.

00:07:07: If AI agent is doing all of execution managing daily budget analyzing metrics writing copy do we actually need humans in loop anymore?

00:07:17: Emphatically yes but nature changes entirely.

00:07:21: Bill Stathbos introduces a framework for set run ship.

00:07:25: He argues that AI cannot govern the entire motion end-to-end

00:07:28: because if you just point an agent at your target accounts and walk away It's a disaster exactly.

00:07:33: The result is generic slop zero replies in an empty pipeline.

00:07:38: Humans must firmly own the ends of the process.

00:07:41: You set this strategy defining who the ideal customer?

00:07:43: Is determining why they need to buy right now?

00:07:45: yeah, and crafting the core offer.

00:07:47: Okay So that's the set

00:07:48: part.

00:07:48: Right then the AI runs the messy middle researching the accounts, enriching the data and drafting variations of a copy at massive scale.

00:07:58: Finally... The human ships.

00:08:00: You serve as final editorial checkpoint correcting hallucinations and approving what actually goes out the door.

00:08:06: you own ends machine runs middle.

00:08:09: that framework perfectly solves major pitfall Abhishek Ratna warned about.

00:08:15: He made a profound point about the danger of automating execution on top of a generic large language model.

00:08:22: What was his take?

00:08:23: he said that if you do that without human oversight, You aren't scaling your judgment...you are scaling the absence of judgement.

00:08:29: Oh!

00:08:30: That phrasing gets right to the heart of this issue.

00:08:31: Yeah..he pointed out that your internal strategy documents capture final conclusions but they don't capture underlying reasoning and human experience that produce them.

00:08:41: So if you hand those to an autonomous agent, it applies your positioning mechanically without actually understanding.

00:08:46: It

00:08:46: just blindly follows rules

00:08:47: right?

00:08:48: If you don't build a human judgment layer into that ship phase You're just broadcasting your lack of taste a thousand times a day.

00:08:54: and when we connect this AI driven middle layer To the broader picture it really forces us to reconsider The entire architecture of the sales funnel.

00:09:04: because if AI handles the heavy lifting And humans focus entirely on strategy on the ends What should that strategy physically look like?

00:09:11: Well, the traditional volume-based spray and pray funnel is irreparably broken.

00:09:16: Completely shattered!

00:09:17: The modern BDBE buyer has evolved which means our legacy frameworks have to retire.

00:09:22: This actually brings us back to that counterintuitive hook for the beginning of Our Deep Dive.

00:09:26: Ah...the split test.

00:09:27: Yes

00:09:28: Jonathan MK ran a fascinating test Contrasting the old way versus new way.

00:09:33: He tested his team's approach against heavily funded competitor.

00:09:37: The competitor used the traditional playbook.

00:09:39: They gated all their valuable content behind forms, forced prospects to submit emails and immediately had sales reps aggressively pushing those leads into meetings.

00:09:47: A

00:09:48: classic high-friction capture strategy.

00:09:50: exactly the playbook we've been taught for a decade.

00:09:53: It's...exactly But Jonathan's team ungated everything.

00:09:57: they let buyers self progress consume the content anonymously And only reach out human contact when fully ready.

00:10:05: And the contrast in the data is just a perfect indictment of The Old Funnel.

00:10:10: Because on paper, the competitor destroyed them in initial stages right?

00:10:14: The competitor booked nearly three times as many sales meetings.

00:10:18: But when you look at the actual pipeline revenue created from those meetings Right.

00:10:22: Eighty-three

00:10:23: percent of total pipeline value landed on Jonathan's side because his buyers were allowed to self educate.

00:10:30: every single meeting he took was worth thirteen thousand dollars in Pipeline.

00:10:35: The competitors' meetings, despite the massive volume were only worth nine hundred and fifty-four dollars each.

00:10:41: The competitor won the vanity metric of calendar density but they completely lost their revenue game.

00:10:47: It makes perfect psychological sense though it's like forcing a restaurant customer to talk to a waiter And give their phone number.

00:10:53: just be allowed see them menu.

00:10:54: They just

00:10:55: leave

00:10:55: Exactly!

00:10:56: The customer gets annoyed... ...and the waiter wastes time pitching someone who might not even like food.

00:11:02: If you just let them read the menu themselves, they'll already know that want a steak.

00:11:05: by time we walk over

00:11:09: to Eighty-two percent of a customer's total lifetime revenue actually arrives after the initial deal is closed.

00:11:34: That number should fundamentally change.

00:11:36: how budgets are allocated, eighty two percent right?

00:11:39: It comes through renewals upselling cross selling seed expansions.

00:11:42: yet legacy companies obsess almost entirely over that initial eighteen percent acquisition.

00:11:48: So Yuri notes that the industry is aggressively moving toward a bow tie

00:11:51: model about.

00:11:52: I like expanding back out

00:11:54: Yes imagine to traditional funnel on the left for acquisition, narrowing down to the point of sale in the middle.

00:12:00: But then it expands back out on the right side.

00:12:03: that right site is an equally rigorous post-sale engineering engine focused on onboarding adoption and expansion.

00:12:12: one side wins the customer but The other side actually grows the company's valuation.

00:12:16: And when we were operating On that left side of the bowtie trying To win the initial deal We have to drastically alter how we pitch value especially as buying committees get tighter.

00:12:28: Seringram Vajra shared a great story about a CEO struggling to sell their GTM technology.

00:12:33: What was the roadblock?

00:12:34: Well by the time the seller finally got on a call, The CFO was demanding a rigid return-on investment calculation and the deal would instantly die.

00:12:42: Because they were assuming there's only one monolithic definition of

00:12:45: ROI?

00:12:45: Exactly!

00:12:46: Sangram breaks it down into five distinct types of ROI And pitching the wrong one is literally Deal Poison.

00:12:51: First There's attributable ROI like directly linking software to pipeline generated.

00:12:56: CFOs love it, but it's notoriously the hardest to actually prove.

00:13:00: Second is efficiency ROI doing the exact same output But with fewer human resources or lower costs.

00:13:07: third is necessity ROI.

00:13:09: You buy it simply because not having it introduces catastrophic risk like compliant software.

00:13:15: I mean you don't ask for a revenue multiple on your fire insurance right?

00:13:18: Spot-on.

00:13:19: fourth Is transformational ROI which fundamentally changes the business model and needs a CEO level sponsor.

00:13:26: And finally, there's Defensive ROI which is buying a tool simply to protect existing market share from a disruptor.

00:13:33: So sellers are just defaulting the first

00:13:34: one?

00:13:35: Yes!

00:13:35: Most B-to-B Sellers blindly pitch attributable revenue every single buyer.

00:13:41: But if you pitch attributable ROI to a buyer who was actually looking for efficiency ROI You're speaking in wrong love language and CFO will kill purchase order.

00:13:50: Synthesizing all of this, we've examined the rise in GTM engineers who build systems.

00:13:54: We have explored the agentic AI tools that run autonomously within middle those systems and detailed modernized frameworks like self-progression and post sale bow tie model.

00:14:03: The natural question is what happens to unit economics.

00:14:06: when a company nails three elements at scale?

00:14:10: That's where case studies come into.

00:14:12: numbers are just staggering.

00:14:14: Let us look at a company called Sierra which Ivan Landobosso broke down.

00:14:18: Sierra scaled to two hundred million dollars in ARR and hit a fifteen point eight billion dollar valuation.

00:14:24: In a fraction of the time it normally takes enterprise companies.

00:14:28: I mean, The first assumption people make is that a company growing that fast simply raised a massive war chest And outspent everyone on ads.

00:14:35: did they just buy their way to growth?

00:14:37: No not at all.

00:14:39: Sierra scale wasn't born from ad spend.

00:14:41: It was a masterclass and aligning there go-to market mechanics with the buyers.

00:14:45: financial reality.

00:14:46: They sell AI for customer service, but the genius was their pricing model.

00:14:51: What did they do differently?

00:14:52: The abandoned traditional software licensing entirely and used outcome-based pricing... ...they only got paid when their AIs successfully resolved a customer service ticket.

00:15:00: Wait

00:15:01: really if the AI failed And the case had to be escalated into human agent Sierra earned nothing

00:15:07: correct zero.

00:15:09: Let's look at the mechanics of why that works so well.

00:15:12: A human-handled customer service call costs a company roughly ten dollars.

00:15:17: Sierra's per resolution cost was between ten cents and one dollar.

00:15:20: That is massive difference

00:15:22: Right?

00:15:23: And because they only charge for resolved cases, They fundamentally changed the financial classification of their product.

00:15:29: They shifted their software from a massive capital expenditure, a capex that requires eighteen months Of security audits CFO approvals & risk assessments into variable operating expense or OPEX.

00:15:41: Because if the software doesn't work, The client pays zero dollars

00:15:44: exactly.

00:15:44: that completely eliminates the buyer's financial risk which allowed Sierra to bypass the traditional enterprise sales cycle entirely deploying in major retailers.

00:15:53: and just five weeks instead of eighteen months.

00:15:55: That is the ultimate example of matching your system's architecture to the buyer's reality.

00:16:00: I want to share one more wild example this modern architecture from Othmane Khadri, he detailed the results a B-to-B website that was entirely built and managed by AI agents.

00:16:09: Entirely

00:16:09: build by AI?

00:16:11: Yep!

00:16:12: In just sixty days... This site autonomously booked over hundred highly qualified sales calls closed two hundred thousand dollars in contracts And billed another five hundred thousand dollar in active pipeline.

00:16:23: Wow But how does an AI agent mechanically generate that kind of qualified inbound volume without human intervention?

00:16:32: It comes down to continuous automated SEO optimization.

00:16:36: They utilize proprietary GTM agents that read the Google Search Console data every single morning

00:16:43: every morning.

00:16:43: Every morning!

00:16:44: The agent analyzes the exact search queries people are using to find this site, identifies the gaps in intent and autonomously rewrites website's landing pages...to pull-in higher-intent traffic.

00:16:55: That

00:16:55: is incredibly dynamic

00:16:57: And it doesn't stop there.

00:16:58: Once the traffic hits the site secondary agents enrich visitors IP address score them against ideal customer profile.

00:17:04: if they're a match push straight into personalized outbound sequence.

00:17:08: that perfectly crystallizes concluding thought from Tim Hillison.

00:17:12: He noted a reality that every marketer really has to face.

00:17:15: AI is making the mechanical execution of go-to-market incredibly cheap, right?

00:17:19: The execution part

00:17:21: exactly.

00:17:21: you can automate lead routing personalize the thousand emails and map CRM data fields for fractions of ascent.

00:17:29: But because execution is commoditized human judgment systems design an overarching strategy are exponentially more valuable than ever before

00:17:38: Because the tool can't give you this strategy

00:17:40: Exactly.

00:17:41: The technical wizardry only matters if the business model translates it into undeniable customer value.

00:17:47: That human judgment is the ONLY thing separating companies compounding their pipeline from those who are just playing with shiny new

00:17:55: tools.".

00:17:55: We have covered a massive amount of ground in this deep dive, From the emergence of GTM engineering to mechanics of agentic tools and outcome-based pricing... But I want you leave the listener with final thought as you look at your own strategies this week!

00:18:08: Something really think about?

00:18:10: We reviewed a post from Gary W that highlighted a profound shift on the horizon.

00:18:15: AI is rapidly becoming the primary intermediary in the B-to-B customer relationship.

00:18:20: Buyers are increasingly using personal, AI agents and language models to conduct their vendor research summarize white papers And build their shortlist long before human sales rep Is ever involved

00:18:30: which changes everything about how we present ourselves online

00:18:33: completely.

00:18:33: so ask yourself?

00:18:35: Is your company's brand Your product data and you're underlying proof points actually machine legible?

00:18:40: Or are you still spending all your resources optimizing a traditional landing page just for human eyes?

00:18:46: Because if the algorithm scraping the web cannot parse your value proposition, The Human Buyer will never even know you exist.

00:18:53: If you enjoyed this episode new episodes drop every two weeks.

00:18:56: Also check out our other editions on field marketing channel and partner marketing AI and B to be Martek ABM in social selling.

00:19:03: Thank You so much for joining us On This Deep Dive.

00:19:05: don't forget to hit subscribe And we'll see you next time.