Jonathan Chemouny leads Sales Development for Europe at ElevenLabs. Before that, he spent years in classic SaaS, including at Intercom. At ElevenLabs, he has no official job title, like everyone else on the team. He is responsible for pipeline sourcing across the entire European continent, with a team that prospects face-to-face with large accounts, organizes workshops, and pitches at conferences.

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Most sales teams dream of having 95% of their pipeline come from inbound. Jonathan Chemouny says that when he arrived at ElevenLabs in that situation, he understood it was a problem to solve.

In this episode, we talked about what AI is actually changing in outbound, why the SDR of the future will be out in the field, and how ElevenLabs uses its own products to prospect.

Here’s what I took away:

1) 95% inbound is fragile.
When Jonathan joined ElevenLabs just over a year ago, inbound represented almost all of the pipeline. The problem: the moment there’s a slowdown, you’re immediately impacted. You can’t build a predictable business on inbound alone. Today, in Europe, outbound accounts for 45% of pipeline volume. Last month, it was 50-50. At the same time, total pipeline volume tripled, if not quadrupled.

2) The SDR of the future gets out from behind the screen.
Jonathan hires more experienced profiles than what was traditionally expected of a SDR. He wants people who can pitch to large accounts, visit prospects on-site, organize workshops, and present ElevenLabs at conferences. He gives concrete examples: one member of his team presented to Kering. Another presented to several airlines at a trade show. When a candidate tells him that what matters to them is booking meetings, that’s a red flag.

3) The KPIs that matter: opportunities, ACV, and percentage of outbound.
Number of emails sent, number of calls made: Jonathan no longer tracks those. What he cares about is the number of opportunities created, the size of the deals sourced, and the share of outbound in the mix. Outbound is where you control your destiny and the quality of what you go after.

4) AI makes SDRs significantly more productive. It doesn’t replace them.
The promises from AI SDR companies claiming that pipeline will arrive on its own, automatically, with no effort required: Jonathan says it doesn’t work, and that everyone who has tried it has seen that. What AI actually does is amplify SDR productivity. He uses the electric bike analogy: you go much further with the same effort.

5) At ElevenLabs, the demo is the prospecting message.
Jonathan sends voice notes to prospects generated with a clone of his voice via ElevenLabs. The message: “I wanted to invite you to a demo, but the demo is right now, because what you’re listening to was recorded with ElevenLabs and a clone of my voice.” He also builds mini-sites on Lovable for each target prospect, with ElevenLabs integrated directly. And he developed a voice agent that prospects can test themselves: a 5 to 6 minute conversation that ends with a booked meeting.

6) Their AI SDR handles inbound, in real time, in every language.
When ElevenLabs launched a new model a year ago, the team was too small to absorb the inbound volume. They built an AI SDR capable of qualifying leads in real time, in any language, 24 hours a day, and routing them to the right account executive. Qualification accuracy is at 100%. Before, a lead who reached out on a Friday evening had a meeting scheduled for the following Wednesday. Today, it’s Monday morning.

7) No job titles at ElevenLabs. Everyone is GTM.
Nobody has an official title. Everyone on the sales team is “GTM.” It removes hierarchy, frees up initiative, and gives the same weight to a junior SDR and the equivalent of a VP. And the unexpected bonus: when prospecting, prospects confuse GTM with General Manager. Open and reply rates are significantly higher.

Find Jonathan on LinkedIn : https://www.linkedin.com/in/chemouny/

Listen now

Enjoy,

— Jordan

For a sense of scale, HR budget growth is set to fall from 2.4% in 2026 to 0.7% in 2027 🌀

That stings.

So the cut you are bracing for in your 2027 planning is not coming. Your CFO is not sharpening a knife with your name on it.

BUT (obviously), you are still going to get squeezed.

The Gartner survey, run with 401 CMOs between January and March, shows that inside that budget, 15.3% is now going to AI.

Tech. Paid for by marketing.

See the sleight of hand?

We are seeing the same thing land in our own inbound. Since 1 May 2026, across more than 200 sales conversations, 26.3% of inbound requests at Bulldozer are about deploying AI inside the marketing organization. These are internal transformation projects, paid for with marketing money.

(The data starts in May because before that, nobody was asking us for this.)

Who pays for the 15.3%

A Gartner study (again) published in June 2026 asks the blunt question: where did the money for your marketing AI program come from?

The answer: 86% funded it by cutting inside their own budget. Production, headcount, agency fees, media. Only 14% got new money from the company.

75% have no reinvestment plan. When the savings show up, they go back to the central budget.

Marketing pays for the experiment. The company keeps the ROI.

The obvious objection is that all of this is rational. If AI genuinely makes marketing cheaper, funding it from the marketing budget and handing back the savings is exactly what a well-run company should do. In The CMO Survey, CMOs report a 14.6% reduction in their own overhead thanks to AI, up from 7.2% in late 2023. On those numbers, finance is right and this newsletter is wrong.

Except somebody measured the actual return on those programs: between 0 and 10% on average 🥲

Only 7% have fully autonomous agents running in production. And 44% are funding the next AI wave with the savings from the last one.

So you are trading a known line for a return that lands at half its promise, and that you do not even get to keep 🥲🥲

Meanwhile belts are tightening, and it is starting to show.

Flat budget. Rising revenue. A sixth of the envelope reallocated. Technically that is not a cut, which is exactly why it clears without a fight.

Measuring better, deciding less

Marketing measurement has never been this good, thanks to AI. Unfortunately, it has never bought so little.

1️⃣ Measurement made a spectacular jump.

Marketing teams using AI to measure performance and produce reporting went from 24.8% in late 2023 to 46.3% in early 2026. Google open-sourced Meridian in January 2025. Meta open-sourced Robyn before that. Just over half of US teams now run incrementality tests. The tooling problem that has occupied the last ten (twenty?!) years is largely solved.

2️⃣ It bought you almost nothing.

In July 2026, the World Federation of Advertisers surveyed 71 CMOs responsible for around $40 billion in annual ad spend. 80% already run marketing mix models and brand lift studies. 15% say that evidence actually determines how their budget gets set.

The bottleneck is authority, and it sits one floor above the analytics team. Ten years of “marketers must prove their value” advice was aimed at a problem the tooling had already solved.

3️⃣ Marketing will never become an asset.

That is the structural reason, and almost nobody in marketing talks about it!!!

Accounting standards require advertising to be expensed as soon as it is incurred, and explicitly forbid putting an internally built brand or customer list on the balance sheet. The same standards require part of internal software development to be capitalized. Cut a euro of marketing and operating profit improves immediately and in full. Cut a euro of engineering and you hit a capitalized line, so the effect is partial and delayed.

Your CFO is not being unfair. They are applying a framework in which everything you build is a cost, and everything engineering builds is property. Even when you start building things yourself, with AI!

So marketing leaders are funding their own death, enthusiastically, and calling it modernization.

Seven decisions before the budget round

1. Ask for a separate AI line, now. Not after the budget round. Once AI lives inside the marketing envelope, every future euro of AI comes out of media, people or agencies. A separate line makes the trade visible to the people making it.

2. Price what you are about to cut. Before killing a production line or an agency retainer to fund a pilot, calculate the pipeline that line produced last year. Bring both numbers to the same meeting.

3. Break the budget into blocks. Patrick Shea, who runs the marketing budget at BlueVoyant, splits his by category, then by region, then by sub-category. When finance understands that each block behaves differently, it stops challenging line items one by one.

4. Replace cost per lead with cost per opportunity. Total spend divided by all new opportunities created, including the pipeline sales generate without you. Shea’s view: if marketing is doing its job, sales close their own deals more easily too (and I completely agree). Owning the whole number ends the attribution argument before it starts.

5. Show the calculation before anyone asks for it. Set a target off the baseline. If cost per opportunity is 5,000 euros, commit to 2,500 euros, and run the same math on every significant spend before you launch it.

6. See finance every week, not every quarter. Shea keeps a plain spreadsheet with his finance counterpart. Every week: pending approval, approved, remaining, person by person. Quarterly, you defend three months of decisions at once.

7. Pick a workflow, not a task! The marketing leaders getting real results took the workflow that cost the most repetitive time, built the smallest version that could work, and proved it before scaling. Nobody who bought five tools and announced a program is in that group.

That is where we have seen the biggest results with our clients, for what it’s worth.

One budget, two options

Your 2027 budget will not be cut. It will be reallocated, from the inside, by you.

So there are two honest positions, and no third one.

Either you accept that marketing funds the company’s AI program and hands back the savings, and you say it out loud in the planning meeting, so that at least everyone knows what was traded.

Or you walk into the budget round asking for a separate line, with the cost of what you would otherwise cut already calculated.

The window is open right now, for a few weeks, while the 2027 numbers are still being written. After that, the 15.3% becomes the baseline, and next year starts from there.

Let’s grow 👊

Jordan

Sources

• Gartner, 2026 CMO Spend Survey, 401 CMOs, fielded January to March 2026

• The Future Works Institute, 36 CMOs and budget holders, June 2026, via Marketing Week

• Bain & Company, Automation and AI Pathfinder Survey, 951 companies, June 2026

• The CMO Survey, Duke Fuqua, 308 leaders, fielded January 2026

• World Federation of Advertisers and Ebiquity, 71 leaders, July 2026

• Exit Five, survey of 540 B2B marketers, June 2026

• Accounting standards and annual filings reviewed since January 2025

• Bulldozer, qualified inbound requests since 1 May 2026, more than 200 respondents

George Bonaci is VP of Growth at Ramp, one of the fastest-growing B2B companies in the US. He studied biochemistry and spent years in analytical chemistry and synthetic biology research before moving into growth marketing. At Ramp, his team is responsible for pipeline and SQLs, covering performance marketing, SEO, lifecycle, and events. Growth reports into the CTO, a structural choice that says a lot about how Ramp thinks about distribution.

🎙️ Listen on YouTube, Apple Podcast, Spotify

Most growth teams run experiments. Very few run them the way George Bonaci does at Ramp: with a scientific mindset, a tolerance for nine consecutive failures, and the willingness to test incentives directly on LLMs.

In this episode, we went deep on how Ramp structures growth, why time to results is the most underrated variable in any experiment, and what the most overlooked shift in marketing looks like right now.

Here’s what I took away:

1) Growth reports to the CTO. Because everyone at Ramp is a builder.
Most companies have growth inside marketing. At Ramp, it sits inside a technical organization. The reason is philosophical: everyone is expected to build, regardless of function. The practical benefit is direct access to engineering resources at a moment when distribution has become more valuable than product development speed. George joined because of that philosophy. It’s also why he hires scientists, engineers, and finance people onto growth teams.

2) The ICE framework has two missing variables: time to results and confidence.
Impact, confidence, ease. George adds time to results because at Ramp’s velocity, a four-month experiment is a bad experiment. He needs to know something in two weeks. Confidence matters because the people who have run a lot of experiments develop an intuition for what will and won’t work that they can’t always put into words. That gut feel is worth something. Hiring people who are right a lot, even when they can’t explain why, is part of the job.

3) When something works, go 10x. Find the asymptote fast.
Most teams that find a working channel do 50% more. George’s approach is to saturate as quickly as possible. A working channel is a temporary competitive advantage and should be maximized immediately. Wasting a little money finding the ceiling in a week is better than testing your way there over quarters. The ROI of speed is consistently underestimated.

4) Ramp tested incentives on LLMs. It worked.
The hypothesis: if you can offer incentives to humans to take a meeting or try a product, why not offer incentives to machines? Ramp ran the experiment, published the results, and found that Claude picks it up clearly, ChatGPT does not, and everything in between has its own nuance. It helped AEO visibility, employer brand, and even organic SEO as a side effect. George’s point is that marketing to machines is already happening and almost nobody is acting on it yet.

5) Ramp’s data moat is the content moat.
Ramp processes a significant share of US business spend. That data, aggregated and anonymized, is something no competitor can replicate. The content strategy is built around it. For companies without a proprietary data set, George sees three options in order of difficulty: find something unique in your customer base, run original research, or go structure publicly available government data that exists but is impossible for an LLM to crawl and digest. Most companies have more interesting data than they think. They just haven’t looked.

6) George pivoted his SEO team almost entirely to AEO.
Some run-rate SEO work continues, but the direction is clear. AEO and SEO overlap enough that the transition is not a full restart, but the bets are going on AEO. Google’s AI mode and AI overviews have validated that direction. George tells his team he doesn’t care about missing numbers this week or this month if it sets them up for a year from now.

7) The role of VP of Growth may not exist in five years.
George brought this up with his boss. His boss did not disagree. The prediction: very flat organizations where everyone is a builder and an agentic operator. Professional managers coordinating large teams through layers of hierarchy will be replaced by people who can build systems, manage fleets of agents, and provide value as individual contributors. For anyone preparing for a growth role in 2030, his advice is simple: go build something.

Find George on LinkedIn : https://www.linkedin.com/in/georgebonaci/

Listen now

Enjoy,

— Jordan

Trevor Pyle is Head of Marketing at Profound, a Series C company building the visibility and analytics infrastructure for the answer engine era. He joined when the company was around 25 people. It is now around 280, with offices in New York, Buenos Aires, London, and San Francisco. He now sits at the intersection of AEO, brand, and agentic marketing, and hosts Profound's flagship event, Zero Click.

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Most marketing teams are still debating whether AEO will replace SEO. Trevor Pyle thinks that’s the wrong conversation. The real question is what kind of marketer you need to become when answer engines are reshaping discovery, attention is harder to earn than ever, and a new role is quietly emerging at the center of it all: the marketing engineer.

In this episode, we went deep on how Profound thinks about brand, attention, attribution, and what the marketing team of the future actually looks like.

Here’s what I took away:

1) AEO expanded the SEO toolkit. It didn’t kill it.
The old SEO problem was about visibility and ranking. AEO is about influence, guidance, and shaping answers across a corpus that is far larger than anything Google pulled from. The result is that SEOs now have latitude to work across PR, comms, content operations, and technical teams rather than just their own domain.

2) The evergreen pillar page is over.
LLMs pull from content less than 13 weeks old. The page that ranked number one for two years and never needed updating is no longer a viable strategy. Agents cycle content in and out based on recency, relevance, and freshness across the entire web.

3) Attention is the new moat. Quality and velocity together.
Trevor credits this framing to George Warnocki, VP of Growth at Ramp. The argument: traditional performance channels are overcrowded, CPCs keep rising, and AI is flooding the market with new competitors daily. The companies that stay top of mind are treating their marketing team like a production house, creating content that is genuinely beautiful, aesthetically considered, and connected to the value prop. Quality alone is not enough. Pace and launch velocity are equally important.

4) “Aura” is a real marketing metric. It just isn’t easy to quantify.
Trevor’s CEO tracks aura as one of marketing’s most important outputs. The way Trevor measures it: performance lift across direct response channels during brand campaigns, event waitlist demand, and employer brand data. When candidates consistently say they applied because they saw a video on LinkedIn or spotted an out-of-home ad in the Union Square subway, the aura work is landing. It starts with performance data and ends with how confident the internal team feels about what they are building.

5) Zero Click went from 300 to 1,000 attendees in one year, with thousands on the waitlist.
The format is community-first, not pipeline-first. Profound doesn’t tie the event directly to a pipeline number. The goal is to put the right people in a room, create an experience they talk about, and let the content live on social afterward. Every piece of food is tasted in advance. Every pixel is checked before doors open. The book Trevor puts on every new marketing team member’s desk is Unreasonable Hospitality by the owner of Eleven Madison Park.

6) The marketing team has three layers: PMM at the core, marketing engineers, and in-person experience.
Product marketing sits at the foundation and acts across the full funnel. Marketing engineers systematize and optimize the workflows of every other team. The third group owns in-person hospitality and event experience, bridging into ABM and digital. As product becomes easier to build, brand becomes harder to ignore and more necessary to invest in. The marketing engineer is the role that connects those two realities.

7) Attribution is a mixed model problem
Profound uses data science to build telemetry across every deal from start to finish, mapping all touchpoints before creating models around the mix. The philosophical alignment with sales leadership is simple: revenue is what matters, regardless of where it comes from. The question Trevor and his head of sales ask together is what they feel is working, not which team gets credit.

Find Trevor on LinkedIn : https://www.linkedin.com/in/trevor-pyle/

Listen now

Enjoy,

— Jordan

Sandrine Puichaffret is an international expansion consultant specializing in the North American market. She spent 15 years at Disney with dual English and American reporting lines, then led communications for Quick across the Middle East and Africa before going independent. She now helps French and European companies enter the US and Canadian markets, focusing on the cultural, legal, and structural mistakes that derail most attempts before they gain traction.

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Most French companies think going to the US is a bigger version of going to Belgium. It isn’t. The rules are different, the culture is different, and the mental model that made you successful in France will actively work against you on the other side of the Atlantic.

In this episode, Sandrine Puichaffret broke down what French companies consistently get wrong and what the ones who succeed do differently.

Here’s what I took away:

1) You’re not going to “the US.” You’re going to one city, one state, one ultra-specific segment
A hundred leads from a trade show does not open a market. The companies that make it pick a niche so narrow it feels almost absurd, identify which segment they’ve closed fastest in France, check whether their existing French clients have US subsidiaries, and start there. Only once that works do they expand to adjacent segments or geographies.

2) Register your trademark before you do anything else
Any American company that sees your brand can register it in your place. If they do, you lose the ability to operate under your own name on the US market. This costs roughly $30,000 to protect yourself upfront. Skipping it is a category error, not a cost-saving decision.

3) American contracts are not French contracts translated into English
The US has a strong litigation culture. Your contract needs to anticipate every possible dispute scenario, because the client's lawyer will. At-will employment works both ways: trust is extended immediately upon agreement, but the relationship can be terminated at any moment. One company Sandrine followed fired someone at-will and faced a claim of $500,000 to $600,000 in compensation. Understanding the legal framework before entering the market is mandatory.

4) The American pitch is 30 seconds then emotional storytelling
French pitches typically open with company history, technology, and product features, with pricing buried in the last slides. American buyers want to know in the first 30 seconds: why you over the competition, how much they will gain, and how fast. After that, they want a story that makes them believe it. Logical argumentation without emotional resonance does not close deals in the US.

5) Small talk is the operating system
Americans do business through community. The Protestant culture of mutual aid runs deep: they make introductions freely, they refer without expecting immediate return, and they build relationships across every surface of their lives. Without being part of the community, you do not get the introductions. Mastering small talk is how the market works.

6) American companies treat marketing as a compounding asset
They spend more on marketing because brand strength and customer conviction are what make a business valuable over time. Sandrine spent years having to explain to French executives what marketing actually does. CMOs heading to the US need to make this shift themselves before they can compete.

7) International expansion requires full executive commitment
When the board is aligned, the product team is ready to adapt, the legal and pricing structures have been rethought, and the leadership is prepared to sustain the effort through the slow phases, things can work. When any of those are missing, the result is a few deals that flatter the ego and a budget that disappears. Going international is a company transformation, and it needs to be treated as one.

8) Don't open in New York
It costs too much, and it may not be where your clients are. Atlanta is a major hub. Americans take planes like buses. One week in New York, one week in San Francisco, structured around where your actual accounts are located, is a more effective and significantly cheaper strategy than anchoring in the most expensive city in the country.

Find Sandrine on LinkedIn : https://www.linkedin.com/in/sandrinepuichaffret/

Listen now

Enjoy,

— Jordan

The real bottleneck is everything around the model: your context, your workflows, who can access what, what gets updated and what rots in a corner. An org problem, not a model problem.

Kieran Flanagan, put it plainly on Momentum: “model capabilities are moving really really rapidly, but integration into how companies work is moving really really slowly.”

So here’s my conviction. The next work surface for modern marketing teams won’t be a new tab in a SaaS tool, and it won’t be another dashboard. It’ll be Claude (or Codex, or an equivalent). Engineering already made that move. Marketing is next.

The market has noticed. Notion, Clay, Profound, DOJO: they all sell you a “system” now. Four different words for the same idea, the AI marketing OS.

(Be honest: when your competitor, your CRM, and your note-taking app all call themselves an OS, the word stops meaning much.)

Before playing critic, we built our own OS.

To do it, we mapped every marketing workflow we run at Bulldozer. Webinar production, the Momentum podcast, ICP (Ideal Customer Profile) and jobs-to-be-done pulled from sales calls, SEO/GEO, LinkedIn ads management, the performance dashboard... Thirty-two in total.

We didn’t rebuild all thirty-two. We ranked them by frequency × revenue impact, kept the top three, and rebuilt those properly: documented, AI in the loop, one measurable output each.

Eight weeks later, monthly pipeline went from €1.7M to €2.4M. That’s +41%, with no new hire.

I’m not sharing this to show off or wave a trophy (I already posted the number in May). I’m sharing it for what came next, and for what it cost to get there.

What the landing pages leave out: building an OS is mostly writing down what you never wrote down.

Most of the companies we see getting started are rebuilding the internal wiki they never took the time to create. The ICP that lived in one person’s head. The positioning that changed twice and got documented zero times. The data infrastructure nobody had time to lay down.

That work is worth doing. It’s how you onboard people faster, and how agents finally get real material to run on. BUT be honest about what it is: documentation. It doesn’t make your team smarter. Writing a “skill” for your job doesn’t deepen the expertise behind the job.

That’s where most AI OS projects stall. The wiki isn’t refreshed. It ages in place. Six months in, half of it is wrong and the agents are running on stale context. In short, you used AI to build... a filing cabinet.

Context has two layers:

• Everyone has the first: who your ICP is, what you sell, how you position.

• The second is the hard one: domain expertise. Not “here’s our brand voice,” but “in this situation you make this call, in that one you make the opposite.”

That second layer is exactly why companies hired agencies for thirty years. A good agency has seen your situation fifty times, across fifty clients. It knows which lever to pull and when, before you’ve finished describing the problem. That judgment doesn’t come out of a “skill” you cloned off GitHub last week.

The gap between a wiki and an OS is that judgment layer, kept alive. That’s exactly the direction we’re taking at Bulldozer (and rolling out with clients over the past few weeks): running the change with a team until AI is genuinely productive everywhere, instead of shipping a template and leaving.

If you want to build one, here’s the order I’d recommend.

1. Split context into two layers. Personal knowledge (your notes, your methods) lives in .md files in Obsidian, private. Company knowledge (ICP, objections, positioning, proof) lives in Notion, shared with the team. One partner reads both. Two tools, because two jobs.

2. Add a hierarchy of context files. One master file for who you are and how you work, then one per folder explaining what’s inside and how to read it. When an agent works in a folder, it reads the local file first. Right context, never too much, never expired.

3. Turn your recurring prompts into named “skills.” A skill is like a recipe. When I say I’m making a “tiramisu,” that naturally includes eggs, sugar, mascarpone... in a precise order with precise quantities. With skills, you stop rewriting the same instructions and build a library of recipes instead. Our “Monday” routine chains several skills and turns two hours of content production into twenty minutes.

4. Schedule routines to keep context alive. A monthly task that rereads the month’s sales calls, pulls new objections verbatim, and proposes updates to Notion and Obsidian before anything rots. A six-month-old context is a dead context.

5. Build feedback loops. An agent that scans what you shipped, pulls the performance data, and updates the system based on what worked. Without it, you’re guessing.

6. Dictate your context, don’t type it. Tools like WisprFlow let you talk into any field. Two minutes of voice after a prospect call carry more nuance than the three lines you’d type between meetings. Roughly 3x more context in the same time.

And build it inside a cloud or agent environment, Claude Code or Codex, not a chat window. That’s where the work surface is moving.

Every “AI OS” on the market sells the same promise: plug it in, the system runs itself.

None of them ship the two things that matter: your domain expertise, and someone to keep it alive.

You can keep buying this kind of tool and hope it’s plug and play (spoiler: it isn’t). Or you can start building the connection layer yourself, one context file at a time, and own the judgment inside it.

The tool was never the question. The context was. We can help you work on it. Reach out if you’d like to talk :)

Let’s grow 👊

— Jordan

Selma Chauvin est CRO d'Agorapulse, solution de gestion des réseaux sociaux. Elle a rejoint l'entreprise côté marketing avant d'être promue à la direction du revenu il y a 18 mois. Dans son scope : le marketing, les RevOps, les équipes commerciales et le partner management. Elle est l'une des rares CRO à avoir grandi du côté marketing plutôt que sales.

🎙️ Listen on YouTube, Apple Podcast, Spotify

Quand une boîte cherche un CRO, elle embauche presque toujours quelqu’un qui vient des sales. Selma Chauvin, ancienne CMO apporte un nouveau de point de vue.

Dans cet épisode, elle explique comment elle a restructuré toute l’organisation Revenue d’Agorapulse et généré 40 % de croissance en partant d’un principe simple : le vrai problème était en amont.

Voici ce que j’en retiens :

1) Un CRO issu des sales sur un problème de pipe met juste plus de pression sur le court terme.
Si tu mets un sales aux commandes du revenu quand le pipe est vide, il va chercher des leads mais il ne va pas construire le système qui les génère. Il va pousser sur les résultats immédiats, ignorer la qualité, et reproduire en boucle le même débat avec le marketing. La seule façon de régler un problème de pipe, c’est d’avoir quelqu’un qui maîtrise la construction du funnel de bout en bout.

2) Les BDR avaient foiré deux fois, pour la mauvaise raison.
Avant l’arrivée de Selma, Agorapulse avait tenté deux fois de créer une équipe BDR. Les deux fois, ça avait planté. L’attente était irréaliste : on leur donnait une liste de comptes et on leur demandait de faire de la prospection à froid depuis zéro. Ça fait dix ans que ça ne marche plus. Selma a reconstruit l’équipe autour d’un principe différent : les BDR traitent d’abord les leads marketing entrants, et apprennent progressivement à faire de la prospection intelligente.

3) L’organisation n’est plus structurée par canaux, mais par étapes du funnel.
Fini le modèle classique avec un head of SEO, un head of paid, un head of content qui ne se parlent pas. Selma a créé trois pôles opérationnels responsables chacun d’une étape : brand et content pour générer de l’audience, demand gen et PMM pour la conversion en MQL, BDR pour transformer les MQL en rendez-vous. Chaque équipe a un objectif de volume à livrer à l’équipe suivante, et des taux de conversion à respecter. Si les taux se dégradent, c’est leur problème.

4) Chaque membre de l’équipe doit générer 20 à 30 % de son résultat lui-même.
Les objectifs sont calibrés de façon à ce que la chaîne inbound soit suffisante pour tenir mais que chacun ait besoin de générer sa propre partie pour atteindre son quota. Résultat : quand un commercial vient se plaindre du volume de leads, la première question est : où en est ta propre prospection ? Ce mécanisme a mis fin à la quasi-totalité des débats sur le pipe.

5) La localisation coûtait cher et rapportait peu. Agorapulse est passée à tout en anglais.
Le constat était brutal : une équipe dédiée à l’Allemagne (contenu, field, BDR senior, un AE) pour 2 000 € de MRR par mois. L’équipe ne se payait pas. Selma a choisi d’assumer d’être moins bien positionnée sur les marchés locaux plutôt que de dilapider du budget sur des opérations non rentables. Désormais, tout part en anglais. Ce qui fonctionne est localisé. Le reste touche des leads qui coûtent dix fois moins cher.

6) La démo automatisée arrive pour les deals transactionnels.
Mettre un commercial sur un deal à 200 € par mois est une erreur économique et humaine. Le commercial s’énerve, part, et le coût d’acquisition explose. Agorapulse déploie un outil de démo IA qui se promène dans la plateforme, clique, guide le prospect sans vidéo ni screenshot à maintenir. Les points de friction déclenchent une reprise en main humaine. L’objectif n’est pas de supprimer le contact : c’est de le réserver aux moments où il a de la valeur.

Où retrouver Selma : https://www.linkedin.com/in/selma-chauvin

Listen now

Enjoy,

— Jordan

For fifteen years, getting customers meant one thing: going after the people already looking. Someone types “payroll software” into Google, lands on your ad or your page, you capture the lead. Done.

Comfortable, measurable, reassuring for the board. All good.

The catch is that everyone learned to do that, on the same keywords, the same audiences, the same buyers, and the pie isn’t growing.

And AI just took another slice. People now ask ChatGPT and get their answer without clicking. The active demand (the one you were going to convert) is shrinking as you read this.

And by the way, you stopped clicking on Google links a few months ago too.

You rank higher, and you lose customers

One number, from a Bulldozer client (a B2B software company).

In two years, their site climbed from page 2 to the top of page 1 on Google. On paper, a win.

Except at the same position, their click-through rate went from 18% to 3%. Let me repeat that: the same rank now brings six times fewer visitors than it did two years ago.

The site didn’t drop in Google’s rankings. The click disappeared, swallowed by the AI answers at the top of the page, and by ChatGPT.

The whole promise of 2010s SEO, collapsing.

Same fight on paid, where leads cost more and more. From our calls: a cost per lead that went from €20 to €58 on Meta, €74 on Google. A retail client whose qualified leads dropped fourfold (quoting the CMO: “the audience is exhausted”). A founder doing the math: his lead costs €30 organically, €150 in paid to replace it.

Multiply your budget by five to get back what free used to give you. That’s the real bill for depending on capture.

How to dig your own grave

The same movie plays out, in this order.

First, the bidding war. Everyone piles onto the same keywords, increasingly onto their own brand name. One client tripled to quadrupled their “brand” budget just to stay visible when someone types their name (and to keep showing nice paid CPLs). They pay to defend ground they owned for free a few months ago.

Then, the doubling down. When a channel stops delivering, the reflex is to widen the audience. One client took monthly spend from €3,000 to €6,500. The result: “the more we spend, the more we reach off-target people.” You pay more and more for prospects who care less and less.

Finally, the panic. This is the one that traps you. A marketing lead told us about her backstage: her CEO writing every day, “we put in more budget, why aren’t there more leads?” “Total panic, all hands on deck, we have to fill an air pocket.” Under pressure, you cut what pays slowly (content, brand) to fund what shows up immediately (paid). You dig the hole you’re trying to fill.

That’s the spiral: cost rises, the board panics, you pour more into the most expensive channel, cost rises again. You never get out the top.

The way out: go back to creating demand

Let me start with what “creating demand” means to me, because it gets interpreted every which way.

It’s making people want you before they go looking, so you stop depending only on those already searching. Concretely:

First, change what you watch. Track brand search instead: the number of people typing your name into Google, ChatGPT, wherever. That’s the real thermometer of the demand you create. As long as you only steer paid cost-per-lead, you’re steering capture, not creation.

Then, reallocate. Take a slice of the budget that just rebuys the same audiences and put it into content that says something about you, to people who don’t know you yet. Not generic content AI recopies and summarizes in two lines. A point of view, your own numbers, a stance you own.

What should guide the effort: being there before the search. Your buyers spend their days on LinkedIn, YouTube, podcasts, communities, long before they open Google. When HubSpot lost 15 to 40 million blog visits in a year, their answer wasn’t to pour more into search. They went and occupied those spaces.

(Worth a listen: my episodes with Kieran Flanagan, SVP Marketing at HubSpot, and Kipp Bodnar, its CMO, who walk through the decisions behind it.)

Put faces forward. The founder and the experts speaking in their own name build more attachment than any banner. It’s slow, it demands a point of view, and it’s one of the few channels that doesn’t get repriced at auction.

Hold the line. Demand creation pays in months, not days. That’s exactly why people abandon it at the first air pocket. And exactly why it stays an edge: almost nobody has the patience to keep it up.

Bid more, or create more

You have two options.

Keep paying ever more for an ever-smaller slice of people already searching.

Or go back to creating demand, so people come looking for you.

The first is comfortable. It gives you a number Monday morning. It’s also the one that locks you in the spiral.

A founder hesitated to start: “what if we’re not even good at capture yet?” My answer: in a lot of markets, there simply isn’t enough demand left to capture.

We get into the detail in a free webinar.

Let’s grow 👊

— Jordan

Kipp Bodnar is CMO of HubSpot, a role he has held for over a decade. He co-authored the inbound marketing methodology that shaped a generation of B2B marketers, and has since co-written a new book with Kieran Flanagan called Loop, a framework for marketing in the post-AI world.

🎙️ Listen on YouTube, Apple Podcast, Spotify

Kipp Bodnar helped build the inbound marketing playbook. Then he watched AI dismantle it in real time, from inside one of the companies most publicly exposed to that disruption.

In this episode, we covered what HubSpot did when the traffic collapsed, how to think about marketing in a world where the funnel no longer holds, and what separates good CMOs from great ones when everything is changing at once.

Here’s what I took away.

1) The old playbook is a liability, not a foundation.
Most marketing teams are paying break-even return on ad spend just to keep volume going. The game is not optimized. It is stuck. Kipp's position: if you walk into your board in 2026 with the same plan you had last year, you are showing them exactly how you intend to fail.

2) Loop replaces the funnel. Four steps: Express, Tailor, Amplify, Evolve.
Express: start with your own differentiated point of view, not what you think buyers want to hear. Tailor: personalize that message to the individual, not the segment. Amplify: distribute across the channels where your buyers actually spend time, including creators and answer engines. Evolve: learn fast and rebuild the next cycle on top of what actually worked.

3) Differentiation has moved from tactics back to story.
For 20 years, you could win on better meta ads or cheaper CPCs. AI is closing that arbitrage permanently. The test Kipp inherited from HubSpot co-founder Brian Halligan: could a yoga studio say this? If any company could put their logo on your message, you're just adding to the noise.

4) SEO is not dead. But it is now a Venn diagram with AEO.
HubSpot’s shift was not to abandon search, but to layer answer engine optimization on top of it. The mechanics are different: where Google rewarded authority and backlinks, AI answer engines build consensus. LinkedIn, YouTube, and Reddit are the three platforms cited most frequently in AI-generated answers right now. If you are not present across those three, you are invisible to the models your buyers are using for discovery.

5) Creator ROI is not a cost-per-lead calculation.
Most companies measuring creator partnerships on cost per lead are undervaluing them by design. The equation Kipp uses factors in lead cost, estimated AI visibility impact, and brand lift. The reason: a creator with a stranglehold level of trust over an audience delivers something that a Google ad cannot. Pricing it the same way is a category error. When you cannot find on-screen talent internally, there are three paths: acquire a creator's channel, pay an internal expert at market rate to create, or develop a partner who already has partial credibility in your space

6) The best CMOs run their team like Sequoia
Kipp's framework for what separates good from great: good CMOs optimize paid campaigns, run email, do product marketing. Great CMOs find two or three asymmetric bets unique to their company and go all in. Table stakes still get done. But the real leverage comes from the bets with outsized potential return, not the low-variance plays that everyone else is also making. The other thing great CMOs understand: the CEO is always the CMO.

Where to find Kipp: https://www.linkedin.com/in/kippbodnar/

Listen now

Enjoy,

— Jordan

I was the fourth AI talk of the day. The first three were about humanity. Mine was about your org chart.

I’ll start with the part where the cutters are right. A senior content strategist costs 90 grand a year. An enterprise Claude seat costs $200 a month. Four CMOs in ten are cutting labor costs this year, and on a 12-month view they win: the margin goes up, the board claps.

But a business doesn’t expire in 12 months. Your 2030 seniors are your 2026 juniors, and there is no other supply.

Cut the bottom of the ladder today, and in five years you bid for senior talent against every rival who made the same cut.

This already shows in payroll data. Stanford read 50 million American records: since ChatGPT launched, workers aged 22 to 25 in the most AI-exposed jobs are down 13%, while older workers in the same jobs are up 9%.

A leaner team is the wrong target. You want the one that comes out of the shock stronger, because it kept hiring and training juniors while everyone else cut. Nassim Taleb has a word for it: anti-fragile.

It doesn’t happen by accident. You build it, and here’s how.

Stop organizing marketing by channel

I run a marketing agency and most of you run marketing, so I’ll build this where I know it best. The shape holds in legal, finance, sales.

Open your org chart. Content. PMM. Design. Paid. SEO. Ops. Every box is a channel or a craft. That structure was built when production was scarce: you needed a team per format because making things was slow and expensive.

Production is now cheap. Claude writes the brief, the ten variations, and the report on the variations. So the anti-fragile org I recommend, the one we run at Bulldozer, rebuilds around three teams.

1️⃣ Engine.

Execution and AI. Distribution at scale, agents, the workflows that turn one idea into two hundred assets. Paid, ops, martech and most of production collapse here, and it runs like an engineering team: skills, QA loops, shipping cadence. Its junior is the GTM Engineer.

2️⃣ Creativity.

The idea, the angle, the brand, the point of view no model reaches on its own. When everyone ships at volume, the only edge left is the thing worth shipping. Taste lives here.

3️⃣ Relationship.

Events, dinners, communities, partners, the customers who buy because they trust you. The more the market drowns in AI output, the more a handshake is worth. No agent earns trust.

One thing sits across all three, never inside one: quality. I don’t believe in a “quality team” any more than a “good-decisions team”. Every box needs someone who owns the bar.

Run the test I give every leader: pull four sentences from your last reporting, slip in one written by AI, read them to your team. Half pick wrong. That trained eye is now a job inside each of the three.

The boxes re-sort. Paid and ops go to Engine; brand, content and positioning go to Creativity; field, community and customer marketing go to Relationship. Nobody keeps a team because the channel existed in 2019.

Take my own team. I’m a control freak. I re-edit everyone’s work, especially in marketing, because the quality bar is the one thing I won’t hand off. For years that meant me, personally, behind every draft my content manager Charlotte shipped, the exact same way I sat behind everyone’s.

Today Claude takes that same nitpicking with zero ego in the room, as many rounds as I want. And Charlotte moved to Relationship: events, dinners, customer rooms, the work no agent will ever touch.

The bar didn’t move into a box. It stayed on me, where it belongs.

Growth makes the firing question disappear

“Claude replaced my marketing team.” “Claude replaced my SDRs.” You’ve read the posts. Here’s the part nobody posts: Anthropic, the company that makes Claude, is one of the most aggressive hirers on the market right now, with 447 open roles while everyone else freezes.

The reason is growth. Revenue went from $1B to $30B in 15 months, and Dario Amodei said it on stage: they planned for 10x a year and saw 80. When you grow 80x, you stop asking who to fire and start asking how fast you can hire.

The firing debate is a symptom of flat growth, not of AI. Cut costs with no topline plan, and AI just buys you a cheaper way to shrink.

The market is already staffing the anti-fragile org. “GTM Engineer” went from 63 postings worldwide in 2024 to 3,342 today, median junior comp $110,000, no CS degree required. The same role is surfacing everywhere: AI Legal Analyst at Latham, Quant ML Engineer at Goldman, AI Research Engineer at McKinsey. Same generation, different jobs.

The juniors being hired are not the juniors being fired.

And juniors capture the AI gain while seniors barely move. Stanford measured AI across 5,172 support agents: novices gained 35%, top experts close to zero. The junior inside your Engine produces like a 2022 mid-level after three months.

Replace, re-sort, raise, grow

The playbook. Four moves, in order.

1. Replace the work you supervise at 100%. Every brief you re-edit, every report you proof. Hand it to Claude with skills that capture your standards: your banned words, your examples of great. The content system I broke down two weeks ago is this move, executed. Fire no one yet.

2. Re-sort the humans into the three teams. Engine, Creativity, Relationship. Map every freed-up person to one. Default is pivot. The exit conversation, if there is one, comes last.

3. Raise the bar. Ten times the output means ten times the rejections. Standards become the bottleneck, so reward rigor in onboarding the way you used to reward volume.

4. Set growth, then hire AI-native juniors. 20% year over year as the floor. Then open the GTM Engineer req and staff the bench you’ll need in 2030.

Be the leader who’s hiring

Two playbooks share step one: replace the supervised work.

The fragile one then fires the humans, books the cost win, and cracks in year seven.

The anti-fragile one re-sorts the team, keeps planting juniors, and grows.

By 2030 the first is bidding for seniors in a market it helped empty. Korn Ferry already priced that auction: $11,164 a year of premium per skilled worker.

The people being hired are not the people being fired. Be the leader who’s hiring.

(Your CFO already ran the cost tab. Send them this one.)

Let’s grow 👊

— Jordan

Dave Kellogg is Executive in Residence at Balderton Capital, the London-based VC that closed $1.3 billion in new funds in 2024. Before Balderton, he spent nearly a decade as CEO of Host Analytics and before that held senior marketing roles at BusinessObjects and Salesforce, among others. He writes at kellblog.com, one of the most analytically rigorous marketing and strategy blogs in B2B, and has been doing so for over 15 years.

🎙️ Listen on YouTube, Apple Podcast, Spotify

Dave Kellogg started his career watching a technically superior product lose to Oracle, year after year. That experience imprinted one thing permanently: the best product doesn’t win. Everything he has built since then follows from that.

In this episode, we covered the full arc: how marketing’s job has changed over 30 years, why traditional SaaS companies are becoming zombies, and why the CMO role is being structurally dismantled at companies that have simply run out of patience.

Here’s what I took away.

1) Marketing’s job has changed five times in 30 years. The next version is “go get customers who drive outcomes.” From boxes of leads at trade shows, to qualified leads, to opportunities, to weighted pipeline, to deals that renew and expand. Each shift added one more layer of accountability. The next one, driven by outcome-based pricing, is go get customers who actually consume and generate results. The CMOs who don’t see that coming are still optimizing for metrics that stopped mattering two transitions ago.

2) Marketing exists to make sales easier. That’s it. Dave heard this from a VP at Oracle early in his career and it has guided everything since. His view: the CMOs who resist it are the ones who secretly want sales to be their pawns, a model that works in consumer goods and nowhere else in B2B. Embrace it and you become a full-funnel operator with a medical bag full of tools. Resist it and you spend your career fighting a losing political battle.

3) The three fastest ways to get fired as a CMO. First: refusing to be accountable for a pipeline target. Second: dismissing attribution as too imperfect to bother with. Dave’s position is clear — attribution is imperfect, but the CEO, CFO, and board will not stop asking. Your job is to provide it and manage the misinterpretation. Third: treating win rate as a sales problem. If only 5% of stage-two opportunities are closing, that is a marketing problem too.

4) Traditional SaaS companies are becoming zombies. There is no middle ground anymore. You either have a genuine AI product with an AI growth rate to match, or you are a profitable financial investment. The companies stuck between those two things, moderate growth, no compelling AI story, break-even economics, are the zombies. And the old trick of selling a future AI story in slides no longer works. The growth rate is the proof now.

5) Trust is gained in drops and lost in buckets. Dave’s response to AI-generated content flooding every channel: don’t be in the slop business. His AI slop detector is simple: his brain stops when he is reading something. The em-dashes give it away. The solution is not a content framework. It is a hard filter on everything you publish and the discipline to never over-claim. One credibility failure can erase a year of trust-building.

6) The CMO role is being dismantled. Function by function. Dave is seeing it happen more frequently at companies that have burned through multiple CMOs. Product marketing moves under the product team. Demand generation moves under the CRO. Brand and communications go directly to the CEO. What is left is sometimes called a CMO, but the job has been hollowed out. Dave is not recommending this. He is describing it. His diagnosis: CEOs cannot find the full-funnel athlete the role requires, and after the third or fourth failure, they stop trying.

Where to find Dave: https://www.linkedin.com/in/kelloggdave/

Listen now

Enjoy,

— Jordan

You’re reading the machine

This newsletter was produced by the system it describes.

So was every LinkedIn post, every tweet, every SEO brief we shipped this week.

One person. One weekly routine. We call it /week.

It opens on a Monday, and by lunch a full week of content exists: personal and company LinkedIn posts (one a day), this newsletter, SEO pages, the visuals, the tweets. And when there’s a podcast episode, a webinar, or a physical event in the pipeline, the full sequence that comes with it: emails, reminders, announcements.

The part the hype skips: when I produce with AI, the ideas, the angles, the direction stay mine. Claude doesn’t replace my thinking. It challenges it, it organizes it, it keeps the quality bar consistent across every format.

The output sounds like me for a reason. I wrote more than 1,000 LinkedIn posts and hundreds of newsletters by hand before any of this existed. The model didn’t hand me a voice. It learned mine. People still recognize my tone, because it is my tone.

The input is human. The final read is human.

The machine makes the work faster, not the thinking.

Now the architecture, because that’s the whole point.

We didn’t write one giant prompt. We split the content operation into dozens of small skills and slash commands, each with one precise job:

• one writes the newsletter

• one turns a transcript into posts

• one drafts the signature post for a new client

• one handles SEO briefs

32 skills, 29 commands, at last count.

On top of them sits one meta command. When we run /week, Claude doesn’t wait for instructions. It runs the interview:

• Is there a newsletter this week?

• A podcast episode? If yes, the transcript?

• A new client to announce? A webinar to push?

It asks, we answer, it routes each job to the right skill. The system is a conversation, not a button. That part took the longest to get right, and it’s the part nobody shows you.

One input feeds many formats. A podcast becomes a newsletter, the newsletter becomes LinkedIn posts, a post becomes tweets.

Text is the easy part. Most of it gets drafted, checked against our brand and anti-slop rules, and queued.

Visuals plug in at specific points only: the newsletter, the LinkedIn infographics, the webinar thumbnails. Everywhere else the post is text, and we don’t force an image where it doesn’t belong.

Yes, a human still reads everything before it ships. And this human is me ✌️

The shift for a CMO: the question stopped being “how many marketers do I have.” It became “what system do they run.”

Two times the output, one fifth of the time

Before the system, we produced about half of what we produce today, and a single week of it took two and a half days.

Now that same week takes half a day, and we ship twice the volume.

Stack the two together and you land on a tenfold jump in throughput: twice the output, in a fifth of the time 😵‍💫

The honest caveat: that 10x is on production, not on judgment. The system writes faster. It doesn’t decide what’s worth saying. That still sits with a human.

What actually breaks when you wire AI into content

Three things we learned the hard way.

1️⃣ Sequencing is the real bottleneck.

Writing was never the hard part. The model drafts fine.

One piece feeds several formats, and the order is everything. The newsletter sets the week’s thesis. The podcast deepens it. LinkedIn carries it. X sharpens it.

Get the order wrong and the whole week turns into rework. We iterated for months before the cascade settled. Now that order is baked into the meta command: the newsletter first, then the podcast, then the rest.

2️⃣ Visuals are still the weak link.

Watch a Gemini or OpenAI demo and you’d bet images are the first thing to fall into place. In production it’s the opposite.

Today our visual layer is a mix: some made with Nano Banana (Gemini), some built directly in responsive HTML, which lets us play with formats. Outside templated assets like webinar thumbnails, it’s still shaky.

The demos won’t tell you that. We will.

3️⃣ The human role moves up, not out.

When production becomes a system, the person on content stops drafting and starts directing: owning the cascade, supervising tone, choosing the angle.

Our content manager just moved into field marketing. The work that compresses into a system went to the system. The work that doesn’t compress, judgment and taste and relationships, went to the human.

How to build your own content machine

1. Treat content as a system, not a pile of tasks. One input, every format.

2. Split the operation into sub-tasks. Each one becomes a named command you can run, fix, and improve on its own. We covered the method for splitting work into sub-tasks in our previous edition, so we won’t repeat it here.

3. Put a sparring partner on top. A meta command that interviews you and routes the work beats a folder of clever prompts.

4. Design the cascade before you grow volume. The edge is in the sequence, not the tool.

5. The stack: Claude Code, 32 skills, 29 slash commands, a memory file, and about a dozen MCP connections (HubSpot, Notion, Buffer, Webflow, Ahrefs, GA4, Search Console, Slack, Gmail, Canva).

6. Guard quality. Every output passes an anti-slop filter before it ships. We keep the list of what counts as slop public, here: Wikipedia: Signs of AI writing. Speed is worthless if the copy reads like a robot.

7. Keep a human on visuals. Templatize what works, hand-build the rest. Don’t pretend it’s solved.

Build the system or staff the backlog

You can keep hiring to produce more content. Or you can build the system, ship ten times the throughput, and move your best people to the work a system can’t do.

One of those choices compounds. The other just grows the payroll.

Let’s grow 👊

— Jordan

Bruno Estrella is Head of Marketing at Clay. Before joining in late 2023, he spent five years at Webflow leading the growth team responsible for self-serve acquisition. At Clay, he oversees everything from community programs and executive content to billboard campaigns and international expansion. He joined when the company was 18 people. It is now 400.

🎙️ Listen on YouTube, Apple Podcast, Spotify

Clay didn’t grow because it outspent the competition. It grew because it built a machine where the community does the marketing, the executives become the content, and frequency does the rest.

In this episode, Bruno Estrella broke down exactly how that machine works and what it actually takes to build it.

Here’s what I took away.

1) Creators want brand association, no money. Clay’s community flywheel doesn’t run on sponsored posts. When Bruno’s team approached creators and agency owners about posting about Clay, they discovered that what people actually wanted was to be publicly associated with a product that felt innovative and credible. That’s what Clay built programs around: experts, partners, educators. Not cheques.

2) One persona, one executive, one writer. Clay’s internal content system is entirely mapped to audience segments. One executive per persona, each paired with a dedicated writer they meet with weekly. Bruno for marketing leaders. Varun, the CEO, for founders. Their head of GTM engineering for ops profiles. The writer captures their thinking. The executive owns the voice. Everything funnels into LinkedIn.

3) Right channel, right people, high frequency. All three are required. Bruno’s observation: you can have the right channel and the right voices, but if the frequency drops, people forget you within three months. The obsession at Clay isn’t reach. It’s consistency. Frequency is what creates the feeling that Clay is everywhere.

4) Qualified engagements, not vanity metrics. Clay measures its LinkedIn presence through a custom metric: qualified engagements. They track every post mentioning Clay, then cross-reference the people engaging with those posts against their named account lists. The question isn’t how many likes. It’s whether the right people are in the audience.

5) Clay invented the GTM Engineer role. Then watched the market adopt it. Bruno and his team noticed that the best go-to-market operators at companies like Ramp and Rippling were neither traditional growth marketers nor classic ops people. They were something in between: highly technical, able to build revenue systems, fluent in APIs and automation. Clay named that profile, created content around it, and waited to see if people would self-identify. They did. Companies started opening roles for it. That’s how you know a category is real.

6) Early stage: brute force execution. Later stage: conviction over attribution. When Bruno joined Clay from Webflow, he tried to import the bi-weekly sprint planning and weekly metrics reviews he’d used at a much larger company. It didn’t fit. At 18 people moving fast, what mattered was shipping, not measuring. Now at 400 people, Clay has the cadence. But Bruno’s obsession remains the same: don’t let the metrics culture kill the speed. Some of the most important bets, like international expansion through community, can’t be modeled cleanly. You have to have conviction.

7) Seven figures on billboards. The goal was legitimacy. When Bruno joined Clay, the product was being associated with the grey-area growth hacking tools of a previous era. Billboards weren’t a reach play. They were a credibility signal. When people in your ICP see you on a billboard in San Francisco during Dreamforce, they stop wondering if you’re a legitimate company. Clay concentrated spend in a tight geographic window, mapped named accounts to specific billboard locations, and tracked results through discovery calls. Recruiting also improved significantly as a side effect.

Where to find Bruno: https://www.linkedin.com/in/brunoestrella/

Work with Clay: https://www.clay.com/

Listen now

Enjoy,

— Jordan

Engineering measures lines of code shipped. Sales measures pipeline per rep. Customer support counts tickets resolved. Customer success watches account usage. Each one can correlate “% of team using AI” with a real output number, and decide whether to push further or pull back.

Marketing has… LinkedIn posts?!

This is not a small problem. Very soon, every CMO will have a quarterly board slide titled “AI productivity,” and every CFO will expect a precise number. Except today, that number doesn’t exist! And the consequences on your budget are going to hurt…

Kieran was honest: he hasn’t cracked it at HubSpot either.
So I spent two weeks cogitating, mapping what we do at Bulldozer, and rebuilding the metric from first principles. Here is where I landed.

Two companies. Same story.

Klarna, Q1 2023. About 7,000 employees. ~$1B annual revenue. Revenue per employee: about $140,000.

Klarna, 2026. About 3,000 employees. ~$3B annual revenue. Revenue per employee: about $1M.

RPE multiplied by seven in three years 😵‍💫
Average salary moved from $126K to $203K over the same window.
Same Klarna. More output. Fewer, better paid people.

Shopify ran the same play differently. Headcount peaked at 11,600 in 2022, dropped to 8,100 by end of 2024. Annualized revenue: $10.7B. Revenue per employee: $1.32M.

Tobi Lütke’s April 2025 memo to his team made it explicit: prove to me that AI can’t do the job you want to hire for, before asking for more headcount!

We’re watching the same mechanic at our scale at Bulldozer. The marketing team has not moved for two months. Same five people:

• Charlotte on content

• Paul on growth

• Sylvain (SDR intern)

• Enguerrand and me

In that window, monthly generated pipeline went from €1.7M to €2.4M. Up 41% in 8 weeks. No new hire.

The point is not “fire your team.” The point is that Revenue per Employee (RPE: annual revenue ÷ total headcount, your CFO’s favourite ratio) is not an abstract horizon. It’s a number you can move within a quarter. Provided you have two other metrics underneath that predict it.

Three patterns we see across 50 marketing teams

1️⃣ Adoption theatre.

A Slack channel called #ai-workflows and self-reported “hours saved this week.” The numbers look great. They map to nothing measurable downstream. Pipeline didn’t move. Brand search didn’t move.
Spoiler: the CFO doesn’t care, because a vanity metric isn’t actionable.

Be honest — you have this Slack channel too.

2️⃣ Adoption rate is not rebuild rate.

Adopting Claude is free and instant. Three clicks. Anyone can do it.
Rebuilding a workflow is something else: you map the inputs, you define the expected output, you decide where the AI sits in the loop, you test it on 5 to 10 real cases, you measure quality drift, you iterate prompts for 4 to 12 weeks until it ships.

Adoption is a checkbox. Rebuild is engineering work applied to marketing.

The two metrics often correlate negatively in month one, because real rebuild is slower than fake adoption.

3️⃣ Outcome metric measured too early.

“Revenue per Employee” drops in the first 6 months of an AI program. Always.
You’re paying for tooling, paying for prompt iteration time, your team is shipping more outputs but the quality is worse before it gets better.
CMOs who report RPE to the board at month 3 panic and cut the program.

CMOs who report “rebuild rate” at month 1 to 3, then Pipeline per Employee at month 4 to 6, then Revenue per Employee at month 7 to 9, get to ship the full program.

Sequence matters more than the metrics themselves.

The lesson is mechanical: these three metrics are not independent. They are chained in time. Mis-sequence them and you self-sabotage.

Where to plug each metric (and when)

📆 Month 1-2: map your marketing workflows. Make the list exhaustive. At Bulldozer ours runs:

• webinar (announce, reminders, replay)

• Momentum podcast production (transcript to derived content)

• ICP and JTBD generation from sales call transcripts

• SEO and GEO (audit and production)

The total count is the denominator of your “rebuild rate.” Tool: Notion or a flat doc, it doesn’t matter. What matters is that nothing is missing.

📆 Month 2-3: rebuild your top three.

Not all at once. Pick three workflows ranked by frequency × value. Rebuild means documented, AI-in-the-loop, measurable output quality.
Track weekly: workflows rebuilt ÷ workflows mapped. That is your rebuild rate.

A 5-person marketing team should expect to rebuild 3 to 5 workflows in the first week.

📆 Month 3+: track pipeline per marketing FTE per month.

The formula is simple: new pipeline generated this month ÷ your number of marketing FTEs.

At Bulldozer right now: €2.4M ÷ 5 = €480K per FTE per month. Up from €340K eight weeks ago.

If your “rebuild rate” is climbing but pipeline per FTE is flat, you’re rebuilding the wrong workflows. Tool: HubSpot for pipeline tracking, Sheets or Looker for the FTE math.

📆 Month 6+: RPE goes to the board.

This is the metric your CFO and CEO are already watching. You don’t need to argue for it. You have been feeding it for six months without saying so.
If “rebuild rate,” pipeline per FTE, and the other GTM functions all moved in parallel, RPE moves in 2 to 3 quarters.

The rule is non-negotiable: never measure a layer without the one before it.

• RPE without pipeline per FTE is a macro number you can’t steer.

• Pipeline per FTE without “rebuild rate” is an output without a lever.

• “Rebuild rate” alone is just process work.

BUT the three together form a coherent system.

Two choices, no middle ground

You walk out of your next board meeting with these three metrics on your AI productivity slide. Or your marketing team stays the only GTM function invisible in the report.

(Your CFO already has a metric for AI productivity. Yours is the one missing.)

The rebuild starts this week, or it starts next quarter.

Let’s grow 👊
— Jordan

April Dunford is a positioning consultant and the author of Obviously Awesome, one of the most widely read books on product positioning in B2B. She spent years as a VP Marketing and CMO at a series of enterprise technology companies before going independent. She has since worked with over 200 B2B companies on positioning, from early-stage startups to companies approaching $100M in revenue. In 2026, she released a fully updated second edition of Obviously Awesome, seven years after the original.

🎙️ Listen on YouTube, Apple Podcast, Spotify

Most companies treat positioning as a one-time exercise. They do it at Series A, put it on the homepage, and move on. April Dunford has spent the last two decades cleaning up what happens next.

In this episode, we went deep on when to reposition, why deals go dark, and what most marketing teams get fundamentally wrong about competitive threats.

Here’s what I took away.

1) Your first wave of customers exists to invalidate your thesis, not confirm it. Every product launches with assumptions about who the buyer is, what the competition looks like, and what value the product delivers. Those assumptions are almost never right. April’s advice: keep positioning loose at the start so the market can pull you toward where you actually win. Tighten it only once the first wave has shown you where you were wrong.

2) Marketing teams change positioning because they’re bored, not because they should. April’s observation from running marketing teams: the team gets tired of the homepage at exactly the moment it starts working. Customers aren’t staring at it every day. The last thing you want is to change something that’s gaining traction just because the people closest to it have seen it too many times.

3) Don’t position against ghosts. In early 2025, April ran a positioning workshop with a company in the ITSM space. Their head of product insisted they needed to position against vibe coding tools. April’s question to the head of sales: has any customer ever mentioned it? Not once. Her position: if it’s not showing up in your deals, you don’t have to position against it. Following up a year later, it still wasn’t showing up. Monitor it. Don’t react to a threat your customers haven’t discovered yet.

4) 40 to 60% of B2B deals end in no decision. That’s a positioning problem. The data comes from a study of 2 million sales calls. In the majority of no-decision cases, the cause was indecision, not a preference for a competitor. Either all the solutions looked the same, or the risk felt too high. Both are positioning failures. The fix is sharper differentiation, clearer competitive framing, and a deliberate strategy to de-risk the purchase.

5) “Too expensive” usually means “too risky.” When customers say the price is too high, they often mean the commitment feels too large. April’s approach: break the deal into phases rather than drop the price. Let the customer prove the relationship before they have to bet everything on it.

6) Investor positioning and customer positioning are not the same document. Investors are evaluating a 10-year bet. Customers are solving a problem they have today. When a founding team comes out of a fundraising sprint and starts leaking investor language into the sales pitch, it confuses buyers who don’t share that time horizon and don’t care about the same risks.

7) Category creation is mostly magical thinking. Almost every company that has IPO’d in recent years competes in an existing category. April’s view: inventing a category name doesn’t mean you win it. The job of a market category is to orient the customer at a macro level. Existing categories do that work for free. New ones require you to explain the category before you can even begin the sales conversation.

Where to find April: https://www.linkedin.com/in/aprildunford/

Obviously Awesome: How to Nail Product Positioning so Customers Get it, Buy it, Love it (2026) : https://a.co/d/0eMZX1K9

Listen now

Enjoy,

— Jordan

Across 10+ B2B clients between 2024 and 2026, on 11x, Topo and Artisan, with 10,000+ voice calls placed:

Zero. Pipeline. Closed.

That’s not “below benchmark.” That’s not “needs more time.” That’s the bill.

The Information reported in February 2025 that 11x customers were churning inside 90 days of six-figure contracts. Topo quietly stopped calling itself an “AI SDR” mid-2025. The receipts came in. They’re expensive.

The 30x discount that lied

The triangle is short. On a Bulldozer-grade cold email setup — premium ICP, tight list, hand-written copy — we book meetings at roughly 2-3% of emails sent. Add a human SDR on follow-up to the same list and the meeting rate doubles to ~5%. Run the same 10,000 contacts through an AI voice SDR and you book… well, we did, repeatedly.

Zero pipeline.

Not 0.4%. Not 0.04%. Zero.

Math nerds will appreciate that “infinite worse” is mathematically correct when the denominator is zero.

So the apparent ROI was only ever the apparent half. Artisan’s entry pricing sits around $1,500/mo. 11x mid-market deals run $36K-$120K ARR. The Bridge Group benchmark for a fully loaded human SDR is $120K-$150K in the US, €75K-€90K in France. On the spreadsheet, AI looked 30x cheaper.

What the spreadsheet didn’t carry was everything else. Domain warming damage that bled into the email channel for weeks. Brand carryover damage on tier-1 accounts where a target’s CMO heard our pitch from a robot and remembered. The roughly 400 meetings we would have booked the human way on the same volume of contacts. Never booked.

Vendors quote calls placed. They quote meetings booked. They don’t quote meetings held times stage-2 conversion times closed-won attribution. That column is missing from every AI SDR dashboard for a reason.

Why every voice deployment broke

Three patterns. Same three, every time.

Pattern 1 — Reply quality. Prospects spot the voice in the first five seconds. They don’t say “stop calling.” They quietly add the brand to a mental blacklist and move on. The damage is invisible until the next quarter when the same accounts won’t take a real call from a real rep. As John Short framed it: outbound is humans having a conversation. Removing the human doesn’t compress the conversation, it eliminates it.

Pattern 2 — The deliverability tax, voice edition. Carriers spam-flag the number. The number burns. The next number burns faster. Carrier reputation systems are now sophisticated enough to fingerprint AI voice patterns across providers, which means burning numbers at one vendor follows you to the next. And the spillover lands on the email channel too — a deliverability score earned back over six weeks of careful warming gets torched in two weeks of AI calling.

Pattern 3 — Buyer-side AI is real. Seller-side AI doesn’t fit yet. Dave Gerhardt put this cleanly on Momentum: “Until my AI agent is doing research for me and buying on my behalf and is exclusively talking to your AI agent, humans are still involved in the buying process.” Today, the buying journey still routes through a human. Replacing the seller in that conversation means showing up to a meeting that never happens.

Some of the AI demos are good. So is a chess prodigy at eight. You still don’t put him in the CFO seat.

Where AI still earns a budget

The voice failed. The wider stack didn’t. Six moves we kept after 18 months.

1. Hand AI the research. Keep the message human. Clay or equivalent for enrichment, signal aggregation, list construction. Humans write the copy. Tools that pulled their weight across our deployments: Clay, lemlist, LinkedIn Sales Navigator.

2. AI on prep, drafts, and variants — never on first contact. GPT and Claude generate three versions of a sequence before lunch. A senior SDR picks the one that sounds like them, edits, ships. Five tested variants per hour beats one perfect one per day.

3. Three months of heavy human supervision. Minimum. Treat the AI tool like onboarding a promising intern. Roughly 80% of outputs need editing in month one, 50% in month two, 30% in month three. If you cannot afford the editor’s hours, you cannot afford the AI.

If “three months of human babysitting” wasn’t in the demo, that’s because it wasn’t in the deck.

4. Voice stays human until further notice. No exceptions on tier-1 or tier-2 accounts. Maybe — maybe — AI on tier-4 long-tail follow-up after six months of fine-tuning on your specific call recordings. Probably not, this year.

5. Track the metric that closes deals. Not calls placed. Not meetings booked. Meetings held × stage-2 conversion × closed-won attribution. AI dashboards omit the part of the funnel where the meeting doesn’t happen, and that’s the part of the funnel where the truth lives.

6. Read the vendor pivots. When Topo stopped calling itself an “AI SDR” and started calling itself an “AI research agent” mid-2025, that wasn’t a marketing tweak. That was the market correcting the autonomous-outbound narrative in real time. Listen.

Two choices for 2026

Option one: keep the seat. Pay $1,500-$10,000/month for the AI voice SDR. Watch the domain reputation collapse across the next 18 months. Read another LinkedIn post about how AI is replacing your team.

Option two: pay the humans. Use AI on the work that doesn’t need a voice — research, drafts, variants. Three months of supervision, then maybe a fourth. Wait for the tools to actually mature.

One option saves you €90K this year. The other saves you three years of pipeline.

Pick. (And if you pick the first one, please don’t put me on your call list 🙃)

Let’s grow 👊

— Jordan

Kieran Flanagan is SVP Marketing at HubSpot, where he has spent the last decade leading growth across the go-to-market organization. Before HubSpot, he was VP Growth at Salesforce. He started his career as a software engineer, a background that shapes directly how he thinks about AI, systems, and what marketing teams will look like in three years. He also co-hosts Marketing Against the Grain on YouTube with Kip Bodnar, and writes on Substack at kieranflanagan.io.

🎙️ Listen on YouTube, Apple Podcast, Spotify

Most marketing leaders are obsessing over AI adoption. Who’s using it, how often, which tools. But that’s the wrong conversation.

In this episode, Kieran Flanagan opened his screen and walked me through the content system he built himself in Claude Code. A SVP Marketing, live in his terminal, showing his actual workflow. Beyond that, we went deep on what AI is doing to marketing organizations structurally, operationally, and strategically. It was one of the most concrete conversations I’ve had on this topic.

Here’s what I took away.

1) The most dangerous employee in your org has high AI agency and low domain expertise. They ship fast. They ship a lot. And almost none of it is good. Kieran's view: we're in the messy middle, where AI has handed everyone superpowers, but without real craft and taste behind the output, it just generates more noise. The best people in any organization right now are the ones who combine genuine domain expertise with high AI agency.

2) AI is flattening the marketing org, but not for the reasons most people think. Marketing is structurally unlike any other go-to-market function. Sales, support, CS, and engineering all have relatively uniform roles. Marketing has always been a collection of niche specialists who largely can't do each other's jobs. AI changes that. The org gets flatter, middle management thins out and the individual contributors who remain are generalists who can run the full loop on their own, no handoffs required.

3) Measuring AI adoption in marketing is a genuinely unsolved problem. In engineering, you correlate AI usage with shipped code. In sales, it's pipeline per rep. In marketing, there's no clean equivalent. Kieran's answer : show and tells. A dedicated Slack channel where the team shares what they're building. And leaders who are AI native enough themselves to know when something is actually working and when it isn't.

4) Data quality is where AI projects live or die. The personalization workflow Kieran built at HubSpot to book meetings took three to six months to get right. Because the data wasn't clean enough. His minimum viable definition: whatever raises your AI output above the average in your specific industry. No more, no less.

5) Most B2B brands will eventually have an AI mascot. If your executives won't show their faces publicly and you don't have a creator in-house, Kieran's prediction is that synthetic AI avatars become the face of B2B brands on personality-led channels. It's already happening in B2C. A blind study he cited found that people, when they don't know it's AI, actually prefer AI-generated content because it outperforms the average human output.

Where to find Kieran: https://www.linkedin.com/in/kieranjflanagan/

His YouTube channel : https://www.youtube.com/@MATGpod

His Substack : kieranflanagan.io

Listen now

Enjoy,

— Jordan

Monday morning, you open your CRM and more than half the pipeline sits under “direct” or “organic”. Deals you can’t trace, can’t explain, can’t defend at the next QBR.

This is the scene we see with a lot of CMO we sit down with, and the sequence that follows rarely varies. The CMO freezes, can’t justify the spend, and ends up retreating to “brand” as the catch-all explanation. The CFO doesn’t buy it, budget comes under pressure, and a quarter later someone else is writing the marketing plan (feels familiar?!).

When I asked Dave Gerhardt on the podcast if he works on attribution, his answer was “Fuck no.” Fair enough — Dave runs a media company, sells to his own audience, and owns his P&L. If you report to a board, though, “Fuck no” is not a line you can take to the CFO on a Wednesday 😅

Truth is, the dark funnel stopped being a measurement problem a while back; it’s now a behavior problem. Most of the 70% is now measurable — if you know where to look. The 72% number we’ll open with is where that starts.

The 72% Gap

Two CMOs, same market, same budget.

1️⃣ The first one opens her CRM on Monday, sees paid search at the top of the list with 28% of trackable pipeline, and doubles the paid budget for Q2. The numbers say so — case closed.

2️⃣ The second one does something much smaller. She adds a single field to every form: “How did you hear about us?” Three months later, she compares the two data sets line by line. Her CRM still says paid search, but her buyers are telling a different story — “your CEO’s LinkedIn post,” “the podcast episode with [expert],” “a peer mentioned you in a Slack group.”

At Bulldozer, 72% of our inbound leads report discovering us through LinkedIn or a podcast, and our CRM calls that exact same traffic “direct” or “organic.” The gap between what the system records and what the buyer actually remembers is massive, and we’re not an outlier — Refine Labs pegs it at 62% of closed-won revenue misattributed, HockeyStack sees 55-65% of pipeline stuck in the dark, and Dreamdata found 75% non-attribution on deals above $100K. For enterprise cycles, three-quarters of the pipeline shows up as a ghost.

Then the math gets worse. Alice de Courcy, who took Cognism from €3M to near €100M ARR, ran the comparison side by side and found 25 demo requests close one deal, while 100 content leads close one deal — at half the average contract value. The pipeline your CRM sees isn’t just the wrong pipeline; it’s also the cheaper one. You’re paying to measure the deals that are worth the least.

The Three Dark Zones

The dark funnel isn’t one thing — it’s three zones, and each one needs a different play.

Zone 1 — Measurable (20%).

Clicks, form fills, demo requests. Classic attribution works fine here, and it’s genuinely useful for tactical paid decisions, campaign tests, and creative selection. What it’s not useful for is justifying your total budget — you’d be explaining one-fifth of reality and calling it the full story.

Zone 2 — Observable (30%).

This is the zone most teams are blind in by choice, because it produces signals rather than leads — anonymous visits on the pricing page, scroll depth on comparison content, repeat LinkedIn engagement from the same account, downloads with no conversion attached…

This zone is the core of what we do at Bulldozer in ABX: we capture signals through Clay for real-time enrichment, score accounts and lead-level intent, and trigger outreach when the time is right. What’s hard is that you stop hunting leads to convert and start identifying accounts that entered an active research phase six to nine months before they’ll ever speak to sales.

By the time they reach out, you’re already on the shortlist, and 95% of the time the vendor on the shortlist on day one is the vendor that wins.

Zone 3 — Dark Social (50%).

Slack conversations, peer DMs, mentions in private communities, recommendations over coffee at a conference — historically impossible to track. This is where the narrative used to stop with a shrug: “accept that you won’t see it.”

That stopped being true in 2026. Zone 3 picked up a new dominant contributor this year — AI search — and it’s the first piece of the dark social you can actually measure.

73% of B2B buyers now use LLMs during their purchase research, but only 11% of domains appear in both ChatGPT and Perplexity (Averi.ai, 2026) — each model has its own citation graph.

More striking: only 10% of AI citations overlap with page-one Google results, which means 90% of AI visibility is completely untouched by classic SEO. Content older than 30 days gets 3.2x fewer citations, and AI-referred visitors convert 3-5x better than traditional organic (Source : Seer Interactive) — with Claude-referred traffic closing at 16.8% and ChatGPT-referred at 15.9%.

One counter-intuitive note worth holding in your head: being cited is not the same as being chosen. 40% of buyers remember a brand as “one of several options,” and only 26% remember one as the primary recommendation. LLM visibility is necessary, not sufficient — brand still does the closing.

The 90-Day Visibility Sprint

Three people, no new budget, twelve weeks. Here’s the sequence we run with clients.

Strategic moves (1-3).

1. Report by zone, not by channel. Build a three-column dashboard: Influence (what’s growing future demand), Demand (what’s converting now), Revenue (what’s closing). Kieran Flanagan, has shared the cleanest version I’ve seen — Grow Influence puts LLM Share of Voice as a top-line metric alongside brand search and brand impressions, Grow Demand tracks leads and meetings, and Grow Revenue tracks deal velocity, close rate, and touches to close. The CFO reads column two, the board reads column one, and you cover both without lying about attribution.

2. Reallocate time (not just budget). 20% on measurable (paid, tactical), 30% on observable (ABX, intent capture, account plays), 50% on dark (brand, content that circulates, AI visibility). Most teams invert this ratio and then wonder why they plateau.

3. Make Share of Voice in LLMs a board metric. Not a marketing KPI — a revenue metric tracked monthly and correlated with inbound demos at a 2-3 month lag. What we’ve seen with our clients: SoV up 20% tends to produce inbound demos up 25-35% one quarter later. So that’s a pipeline predictor :)

Operational moves (4-6).

1. Self-reported attribution, this week. Add “How did you hear about us?” on every form, export quarterly, and compare to CRM line by line. The gap you find is your “observable dark funnel”.

2. AI visibility audit. Pick 20-30 queries your ICP actually types, test them across ChatGPT, Claude, Perplexity, and Gemini, and score each: 3 points for first mention, 2 for top three, 1 for mentioned, 0 for absent. Tools our clients use: Profound, Otterly.ai, Getmint.

3. AI-ready content playbook. Refresh anything older than 30 days, front-load answers (44% of citations come from the first 30% of the page), add original stats (+40% visibility lift), and distribute where LLMs actually pull from — LinkedIn captures 62% of B2B software citations, then Reddit and G2.

Advanced moves (7-8).

1. Correlation tracking. SoV LLM → pipeline, 2-3 month lag, monthly cadence. You now have a leading indicator the CFO will sign off on.

2. New board dashboard. Retire “which channel generated this deal.” Replace with “Is our total influence growing?” — five lines: SoV LLM, brand search volume, inbound demos, sales cycle length, win rate vs. top three competitors.

Your Call

Two CMOs. Same market, same budget, same team.

Twelve months from now, both of them stand in front of their board. The first explains 30% of pipeline with attribution data and hand-waves the other 70%. The second presents a framework that covers 100% — measured where it can be, flagged where it can’t, blurry but accounted for.

Only one of them still has a budget a year later.

Which one are you?

Let’s grow 👊

— Jordan

About Dave

Dave Gerhardt is the founder of Exit Five, a membership community for B2B marketing professionals. He previously served as CMO at Drift and Privy, where he built his reputation writing about marketing on LinkedIn and running a podcast on the side. He started what became Exit Five as a $10/month Patreon while still a CMO and reached $10,000 in monthly recurring revenue before ever going full time on it.

🎙️ Listen on YouTube, Apple Podcast, Spotify

Every company wants a community. Very few are willing to do what it actually takes to build one.

In this episode, I sat down with Dave Gerhardt to understand what separates Exit Five from the graveyard of dead Slack groups and what most B2B marketers get fundamentally wrong about storytelling, content, and building in public.

Here’s what I took away.

1) Most communities die because they're not the product. The companies building dead Slack groups aren't really building communities. They're building nurture sequences with a community skin. Dave's position: when a vendor owns the community, there's always an ulterior motive. Exit Five works because the membership is the business. There is no SaaS tool waiting at the end of the journey.

2) Charging money is a forcing function, not a cash grab. The two complaints Dave received from his Arizona CMO event came from the two people who had free tickets. Pricing signals commitment. It also filters out the people who would dilute the quality of the room.

3) The founder has to do the unscalable work first. Dave has written roughly 8,000 posts inside Exit Five and left 40,000 comments. He personally policed poorly written posts, tagged members with relevant expertise when a question went unanswered, and publicly praised the content worth celebrating.

4) Storytelling isn't a framework you can download. You can't prompt your way to taste. Dave's view: the marketers who are genuinely good at brand and storytelling have wide interests outside of marketing (music, food, design) and they're curious enough to steal from places nobody else is looking.

5) Using AI well is still slow work. Dave uses Claude to write his newsletter. It takes him two hours. He feeds it the transcript, past writing for reference, context on the guest, and asks it to come back with questions before it writes a single word. The output is better but it is not faster. AI slop is what happens when the subject matter expert leaves the room.

Where to find Dave: https://www.linkedin.com/in/davegerhardt/

Join Exit Five: https://exitfive.com/

Listen now

Enjoy,

— Jordan

Though 68% of enterprise ABM programs still fail to generate expected results, and only 26% of teams call their program “truly successful” (N.Rich, 2025).

The common explanation: bad execution, wrong tools, not enough budget. After running 40+ ABM and ABX programs at Bulldozer, we see something different. The failure is almost never in the execution layer. It is in the targeting layer. Specifically: fuzzy ICP.

Today, Much of what companies call ABM is actually sales executions using marketing channels. And the reason it happens is that teams skip the ICP work and jump straight to the tech.

A Tale of Two Account Lists

We ran two ABM programs in the same 6-month window. Same agency. Same playbook. The results had nothing in common.

Company A sells food-waste software. Their CMO walked into our first call and said something most marketing leaders cannot: “There are exactly 450 integrated retailers in the world who could buy this. Here are their names.” Four buyer personas. No debate. No territory math. Just a list that fit on two pages.

Three weeks in, qualified leads started coming. By month six: 25+ MQLs across three markets, cost per lead at €186 in their best region, and a LinkedIn CTR that peaked at 6%. Their stack was Lemlist, HubSpot, and LinkedIn Ads. Nothing fancy.

Company B is a $100M+ SaaS. They came to us with 20,000 accounts loaded in Clay. Six segments, built by RevOps to carve equal sales territories. The VP Growth told us in our second meeting: “Those six segments? I don’t believe in them.” 🥲
The scoring model was probabilistic, multi-dimensional, inherited from their self-serve funnel. We flagged it early: “You are applying self-serve logic to ABM.”

Three months later, leads were piling up. Sales was processing half of them, two days late.

Same agency. Same methods. Company A spent a fraction of the budget. Had 1/40th the accounts. Used simpler tools. The difference: Company A knew exactly who to talk to. Company B was still figuring it out.

External data backs this up: companies with a documented ICP see 68% higher account win rates (TOPO/Gartner).

The Three Traps That Kill ABM Before It Starts

Three patterns repeat across the 40+ programs we have run.

🩸 The Territory Trap.

Most ABM target lists come from RevOps. They are built for equal sales coverage, not for conversion probability. One client had an ICP defined as “250 to 5,000 employees.” Our growth lead looked at it and said: “That looks like an addressable market, not an ICP.” They had a €3,500 monthly budget for 3,500 accounts. One euro per account per month. Looks like a mailing list to me…

🩸 The Stack Delusion.

Teams buy Clay, 6sense, and Demandbase before they can answer “who are our 50 best-fit accounts?”. The Snowflake ABM team wrote about this: “Companies buy into the promise that ABM can be done via technology as an all-in-one, without realizing they have to power that tech with their own strategies.”
Vendors sell ABM like it is a button you press. ABM is a strategy, not software.

🩸 The Activation Gap.

Targeting can be right and the program still fails when sales cannot process the output. 56% of opportunities handed to sales never close (Forrester, 2024). A former Head of Marketing at a $1B+ analytics company told us about rolling ABM out to 60 salespeople: “Maybe 6 or 7 were actually motivated. If you do it at too large a scale, it is the best way to waste money.” They later cut from 2,000 accounts to 500. That is when it started working.

I like Allan Dib’s analogy: a 100-watt lightbulb lights a room; a 100-watt laser cuts steel. Same energy, different focus. The companies spending less on ABM with a tight ICP outperform those spending more with a wide net.

Five Moves to Fix the Foundation

1. Run the ICP regression before buying any tool.

Pull your closed-won deals from the last 24 months. Segment by ACV, sales cycle length, expansion rate, win rate. Kellogg (soon on Momentum 👀) calls this the shift from “ICP as aspiration” to “ICP as regression.” At Bulldozer, we analyzed 1,495 deals and found a 0.84 correlation between initial budget above €25K and high customer lifetime value. Your ICP is in your deal data. Not in your CRM segments.

2. Apply the Geoffrey Moore test.

“Big enough to matter, small enough to lead, good fit with your crown jewels.”
If your target account list has more than 500 accounts, run the math: monthly ABM budget divided by number of accounts. Below €50 per account? You are spreading too thin. Company A had 450. The D-Day logic: concentrate force on one beachhead. Do not spread across the coast.

3. Score Activation Readiness, not just Account Fit.

Most scoring stops at firmographics and intent. We use four layers:
+ Fit (firmographic + technographic, static)
+ Context (hiring signals, funding rounds, stack changes, semi-dynamic)
+ Intent (pricing page visits, content engagement, ad clicks, dynamic)
+ Activation Readiness (can sales contact within 1 hour? is there a feedback loop? operational).

That fourth layer is what everyone skips. From our data: even if context is strong and there is no engagement, the account is not ready. Marketing needs to keep working. And the inverse: a perfect-fit account where sales takes 2 days to respond is a burned lead.

4. Report pipeline by ICP tier in every QBR.

Kellogg flags the “false focus” trap: most ARR sits outside your top ICP tiers and nobody notices. Make ICP tier a required CRM field. Review quarterly. If more than 40% of pipeline comes from outside Tier 1-2, your ABM program is running on the wrong list.

5. Run the €1 test.

Monthly ABM budget divided by number of target accounts. If the result is under €50, you do not have enough budget for that list size. Shrink the list or increase the budget. No middle ground.

450 Accounts or 20,000?

You can keep optimizing the stack. Better scoring, more intent signals, fancier dashboards. Or you can answer one question first: do you actually know who your best customers are?

Company A knew. 450 accounts. Leads in 3 weeks.
Company B did not. 20,000 accounts. Three months of expensive silence.

The most expensive ABM program is the one targeting the wrong accounts.

Let’s grow 👊

— Jordan