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

Alice de Courcy is the former CMO of Cognism. She joined when the company was just below £3M in revenue and left as it was approaching £100M ARR nearly seven years later. Over that time, she built a marketing organization of 28 people operating across European markets and the US. She has since written two books on the CMO role and now works as a fractional CMO.

🎙️ Listen on YouTube, Apple Podcast, Spotify

She joined Cognism as Head of Marketing, not CMO. She earned the title, year after year, by treating each year as if she needed to re-interview for the role. That’s the mindset this entire conversation is built around.

Here’s what I took away.

1) There’s an epidemic of CMOs who won’t execute. CEOs are scared to hire marketing leaders because they expect a positioning document, not a growth engine. Alice’s view is direct: when marketers ask to be “more strategic,” they’re often asking to stop doing the work. Execution never stops, regardless of seniority.

2) Gated content is a math problem. It took 25 demo requests versus 100 gated content leads to close a single deal. The ACV on content leads was also half the size of demo leads. When she modeled that and showed it to the C-suite, the conversation shifted immediately.

3) Builders and managers are not the same hire. Bringing in a senior marketer from Salesforce sounds like a shortcut. But someone who spent 10 years delivering a playbook they didn’t build has never built one. Alice’s position: she wouldn’t make that hire for a build phase. The only exception is someone who has already done a build phase somewhere in between.

4) Internal marketing is the actual job. Always having the numbers. Communicating wins proactively before being asked. Repeating until it lands. That’s what built trust with the CEO and the board, not the strategy deck.

5) The content engine has four layers. Authority content, owning the industry narrative. Product-linked content, connecting topics to the offer. Social proof validation from outside voices. And distribution. Without distribution, the other three don’t exist.

Where to find Alice: https://www.linkedin.com/in/alice-de-courcy-5205516a/

The diary of a first-time CMO: https://amzn.eu/d/0h0vDLCC
The diary of a first-time CMO: Volume 2 : https://amzn.eu/d/04Dn3lUa

Listen now

Enjoy,

— Jordan

Why commission a portrait when a camera captures a face in seconds? What actually happened was the opposite. Fine art markets exploded. The handmade became precious. Mechanical reproduction made crafted work more valuable, not less.

The same dynamic is playing out right now in B2B marketing.

Everyone has the same tools now

When I look across the marketing teams we work with at Bulldozer, there’s a clear pattern: everyone has access to the same AI stack. Claude, ChatGPT, Midjourney…. Same models. Same prompts. Same outputs. Same posts. Same decks.

71% of consumers distrust brands that rely heavily on AI-generated communication (Edelman, 2024). And it’s not because they are anti-AI. It’s because they are pro-craft! They’re rejecting generic. And when your tools are the same as your competitors’ tools, the only differentiator left is what you bring to the prompt.

You and your competitors can spin up a blog post in 45 seconds. You can produce 200 social assets a week. Speed is no longer a differentiator.

What differentiates is how you use those tools. And how you use them is determined by one thing: the quality of the system you built around them.

Craft ≠ artisanal = system.

Let me be precise about what I mean, because this gets misread.

Craft is not about slowing down or going analog. It’s about having a documented, transferable, teachable system for what ‘good’ looks like for your brand specifically.

Your visual identity: not just a logo and a hex code. The specific weight of your font, the exact spacing on your layouts, the way your brand uses negative space, the image style that feels like you and not like a stock library.

Your motion language: how your UI animations feel, whether transitions are snappy or breathe, what energy your brand communicates when things move.

Your tone of voice: the words you never use, the rhythm of your sentences, the level of formality with your audience, the topics you own versus the ones you leave to others.

None of this comes preloaded in ChatGPT.

When you give a well-configured AI agent a detailed brand playbook — colors, voice, examples, explicit do’s and don’ts — the output is sharper, faster, and distinctly yours. When you give the same AI nothing, you get something that looks like everyone else.

When a client comes to us with a documented brand system, we can deploy AI across content, ads, and email sequences and stay on-brand at 10x the volume. A client without one generates volume and loses identity simultaneously.

The bottleneck is (and has always been) judgment.

Three things your brand system must codify

• Visual architecture: not just a logo and a hex code. Your full design system — grid, spacing, hierarchy, image style, layout logic. The more specific, the more useful.

• Voice guidelines: not ‘conversational and expert.’ Specific. What words do you use? What analogies work? What topics do you avoid? Provide examples, not adjectives.

• Agent configuration files: every AI tool you use — whether it’s an LLM for copy, an image model, or an AI analyst — needs a brief. Written. Explicit. Documented. Think of these markdown files as onboarding documents for a creative partner who has never met your brand.

Four moves to make next month

1. Run a brand audit first. Before evaluating new tools, document what you have. Try to have your brand system on 3 pages max.

2. Build your agent brief. A markdown file covering: who you are, who your audience is, what you sell, your tone, examples of good and bad content, words you never say. This is the operating document your team’s entire AI workflow will run on.

3. Set quality gates. AI speed is only valuable if there’s a review step for brand fidelity. Build that gate into your workflow.

4. Treat guidelines as a living product. Update them quarterly. Add new examples, remove outdated ones. Your guidelines are the operating system. Don’t let them go stale.

When everything can be generated, the only thing that matters is whether people recognize it as yours.

What you want is to produce dinstinctive content — at speed.

Taste was never going anywhere.
It just got more expensive to ignore.

Let’s grow 👊

— Jordan

Eli Schwartz is an SEO strategist and the author of Product-Led SEO, one of the most influential books in the field. His last full-time role was at SurveyMonkey, where he led SEO before going independent. He has since spent seven years consulting for companies including Tinder and Mixpanel. He also runs a newsletter followed by nearly +15,000 readers.

🎙️ Listen on YouTube, Apple Podcast, Spotify

Every few years, a new technology appears and the same people declare that SEO is finally dead. TikTok. Voice search. Now AI. Each time, they’re wrong, just early enough to sell you something new.

In this episode, I sat down with Eli Schwartz to figure out what’s actually changed and what hasn’t.

Here’s what I took away.

1) GEO is not a separate investment. It’s just SEO in 2026. Breaking out “AI visibility” from SEO is the same mistake as having a dedicated mobile SEO person alongside your SEO person. Eli’s position: the team you’re already paying for should know how to do this. If they don’t, that’s the problem.

2) AI took over top of funnel. SEO owns everything below. When someone describes symptoms or an open-ended problem, the LLM answers. When they know what they’re looking for, they go to Google. The funnel didn’t disappear, it got redistributed.

3) Citations are just backlinks with a new name. The same manipulation that killed low-quality link building will kill low-quality citation building. What gets cited is what’s already a real brand, not what spammed its way onto a list.

4) Brand before SEO. Always. If you don’t have a brand yet, paid search first to validate search-market fit. Then brand. Then SEO. Spending on SEO before people are even searching for you is wasted money.

5) SEO belongs in product, not marketing. When SEO sits in marketing, it writes tickets. When it sits in product, it ships. Eli’s most successful engagement, at Tinder, worked because he had direct access to engineering.

Where to find Eli : https://www.linkedin.com/in/schwartze/

Product-Led-SEO : https://a.co/d/04cqmiRN

Listen now

Enjoy,

— Jordan

🎙️ Listen on YouTube, Apple Podcast, Spotify

In this extract, Adrian Rosenkranz (CRO at Webflow) shares the data that is redefining the role of the website in B2B marketing:

→ +125% increase in AI bot crawling on Webflow sites. The website is no longer built for humans alone

→ 6x conversion rate from visitors coming through LLMs and why it changes everything about the experience you need to deliver

→ “Own the answer”: the 4-pillar framework (authority, measurement, content, technical setup) to exist in AI-generated answers

→ Is the website dead? What would need to be true for that to happen and why we’re far from it

→ The Wikipedia effect: when information alone isn’t enough, it’s the experience that brings people back

→ Why AI will kill websites optimized only for SEO and unlock creativity for the rest

Where to find Adrian : https://www.linkedin.com/in/adrianrosenkranz/

Listen

Enjoy,

— Jordan

Most of the conversation around it focuses on which jobs AI will replace. But looking at it as a marketer, I had a different question: which channels are structurally out of reach for AI saturation — at least for now? Where is the uncrowded space?

That’s what this edition is about.

In 1983, American Express mailed 25 million letters in a single year. Handwritten salutations. Individual names. Personalized offers. Response rates: consistently above 3%.

Forty years later, that same 3% feels almost mythological. Today’s average B2B cold email gets a 2% open rate on a good day, and a 0.1% reply rate if you’re lucky. LinkedIn InMail? 15-18% open rate when it worked. Now closer to 8%.

B2B CPL across paid channels has inflated 40-70% since 2021 depending on your vertical. LinkedIn CPCs are up 30%+ year-over-year. Google Ads CPCs for competitive SaaS terms regularly clear €20 per click. Email deliverability is under permanent pressure — domain reputation, inbox placement, GDPR friction.

Your 2026 marketing plan probably has more LinkedIn budget, more email sequences, more retargeting layers. So does your competitor’s.

You’re fighting over the same 3-5% of buyers who are actively in-market right now. The other 95%? You’re not reaching them. Or more precisely: you’re reaching them on channels so saturated they don’t see you.

So let’s talk about five channels. All underused. All working.

Direct mail

The DMA’s most recent B2B benchmarks put direct mail response rates at 4.4%. Email averages 0.12%. Direct mail is 36x more likely to get a response.

Physical mail gets opened. When a CMO at a €50M ARR SaaS company receives a physical envelope with their name (not “Marketing Leader at [Company]”!) it gets opened!

There’s no spam filter. No Promotions tab. No unsubscribe button.

At Bulldozer, we’ve run direct mail sequences for three enterprise B2B clients (deals above €50K ACV) over the last year. Same pattern across all three: accounts cold for 60+ days on every digital channel responded within a week of a physical touchpoint. A pattern break in a sea of identical digital touches.

───

📍 The right list: accounts in your CRM for 90+ days with 6+ digital touches and zero response. They’ve seen your emails. The letter is the interrupt.

📍 What to send: one page, signed by your CEO, SDR or AE. Nothing promotional in the first touchpoint. Open a conversation, not a pitch.

📍 One caveat worth naming: direct mail is a precision play, not a volume play. The economics hold at 50-100 accounts. At 5,000, you’re back in the same crowded game.

Gifting and dimensional mail

One step further on the physical spectrum. A book relevant to your prospect’s current challenge. A curated package tied to something specific about their business. Platforms like Sendoso, JeudiMerci (FR), Gyfti (FR) and Reachdesk handle fulfillment and CRM tracking.

Most B2B marketers haven’t touched them. The reason: it feels like too much effort. That’s exactly why it converts. An account that receives a €40 relevant gift from your company doesn’t forget it.

───

📍 Right trigger: a deal gone cold after 4+ weeks of digital touches. The message should reference something specific about their situation — their Q3 results, a recent initiative, a competitor move. Specificity is what makes it land.

Handwritten cards

Genuinely rare in B2B. A VP Sales who sends 10 handwritten notes per week (nothing promotional) generates replies that email sequences don’t. Volume ceiling: 40-50 per month at quality. Scale with Handwrytten or Manuscry (FR) to hold quality at 100+ per month.

Executive dinners and private events

A dinner for 12 CMOs. A private roundtable working through a problem they’re actively navigating. An in-person workshop with real content (not a sales pitch repackaged as education…).

Physical presence with a curated audience is where pipeline builds quietly.

───

📍The math: 12 people in a room, 3 become clients at €80K ACV = €240K pipeline from a €5K dinner. Webinars went digital and crowded. This format hasn’t.

Voicemail drops (and what AI changes)

Ringless voicemail (RVM) delivers a pre-recorded message directly into someone’s voicemail without the phone ringing. No disruption. No cold call awkwardness.

Industry average open rate: 96% 😅

The use case: 20-50 key accounts where you want a decision-maker to hear a human voice. Under 30 seconds. Something like: “I wanted to reach you personally. We’ve been working with three companies in your space and I’ve seen something specific to your situation. Worth 15 minutes?”

At €0.05-0.10 per send, the cost per touchpoint is a fraction of SDR time spent on cold calls that go to voicemail anyway.

Now add voice cloning. ElevenLabs or Bland.ai can clone your CEO’s voice from a 3-minute audio sample. You record once. Your team delivers 500 personalized voicemails with your CEO’s actual voice — each opening with the prospect’s name and one company-specific observation. The CEO’s 20 minutes per week of outbound voice becomes 500 touches without losing the human signal.

The companies running this today are ≈12 months ahead of when it becomes standard practice.

───

📍Legal note: in most EU markets, RVM sits in ambiguous territory under ePrivacy. In France, outbound calls to B2B numbers are generally permitted under legitimate interest. Validate with legal before running at volume. In North America, standard practice.

SMS: build the list now, use it later

98% open rate. Average read time: under 3 minutes.
Email’s best case: 21% open rate, read 6+ hours later.

At Bulldozer: SMS reminder sent 2 hours before a webinar vs email-only control group. Show-up rate increased 28%. Same content, different channel.

SMS has a hard prerequisite in Europe: explicit opt-in. Every conversion touchpoint — demo confirmation, event registration, content download — is an opportunity to collect it. 500 clean permission-based contacts delivers more than 10,000 cold addresses.

───

📍 Use it for: event reminders, deal-stage nudges, re-engagement after email silence.

Five moves to start this month

1. Digital dead-end audit.

Pull your CRM. Find accounts touched 6+ times in the last 90 days with zero engagement. Top 50 by ICP fit. Top 10 get dimensional mail — a relevant book or curated package. The other 40 get a one-page letter from your CEO with one observation specific to their business.

2. Set up a gifting trigger.

Pick the deal stage where you lose the most accounts. Configure: deal goes 4 weeks without activity → €30-50 relevant gift with a handwritten note. Measure meetings rebooked within 30 days against your current no-action baseline.

3. Voice clone test on 20 accounts.

Record your CEO for 3 minutes. Clone the voice with ElevenLabs (free tier gets you started). VP Sales writes 3 scripts — one per core ICP persona. Drop on accounts dark for 45+ days. Measure any re-engagement signal at J+7: email reply, LinkedIn connection, SDR callback.

4. One handwritten note per day.

Your VP Sales, one note per business day to a target account. One observation, nothing promotional. Run it for 6 weeks and track reply rate.

5. Build one SMS opt-in touchpoint.

Add optional SMS opt-in at your highest-intent conversion moment with a clear value statement. Start building the list now. You’ll use it every quarter.

One thing you can’t skip: compliance 🫠

Physical mail to B2B company addresses: permitted under legitimate interest in most EU markets. Validate on your specific scenario.

Gifting: no regulatory issue generally, but enterprise companies have internal policies. Check before sending anything over €50.

Voice cloning + voicemail: same rules as outbound calls in your market. Get legal to sign off before running at volume.

SMS: explicit opt-in required in Europe. No exceptions.

The best growth channel isn't the newest one. It's the uncrowded one.

These channels work because they require effort and judgment. Handwritten notes don’t automate. A curated executive dinner doesn’t scale to 10,000. That friction is the feature. The same automation flooding email and LinkedIn hasn’t reached these formats. When it does, something else will open up.

Let’s grow 👊

— Jordan

Adrian leads revenue at Webflow, overseeing sales, marketing, support and customer success. Formerly at Salesforce and Tableau, he joined Webflow to help brands turn creativity into a competitive edge in a world where AI is changing how people search, discover and convert.

🎙️ Listen on YouTube, Apple Podcast, Spotify

91% of marketers consider their website their most strategic marketing asset.

And yet, the way that website is discovered is undergoing a deep transformation.

In this episode, I spoke with Adrian from Webflow about a topic that affects all of us:
the concrete impact of AI on the web, on SEO, on conversions… and on the real role of the website in this new landscape.

Here’s what I took away.

1) The website is no longer built only for humans

2) Traffic from LLMs converts 6x more

3) “Own the answer” becomes a guiding principle

4) The website is becoming more demanding.

5) The challenge: helping teams adapt fast

Where to find Adrian : https://www.linkedin.com/in/adrianrosenkranz/

Listen

Enjoy,

— Jordan

Prospects challenge us on it at every opportunity. Clients are second-guessing their entire SEO roadmap. LinkedIn is flooded with hot takes. Everyone's got an opinion, a case study, a "game-changing insight."

SEO agencies smell blood in the water. They’re rolling out €50K “AEO strategies” faster than you can say “AI Overview.”

And if you’ve invested in SEO, are currently doing SEO, or plan to invest in it — you’re probably all asking the same question: “Is it still worth it with LLMs taking over? Should we pivot to AEO instead?”.

But here’s what you need to know: SEO and AEO aren’t two different things.

They’re the same discipline with reweighted priorities. And if your foundations are broken (thin content, zero backlinks, unstructured data), no trendy acronym will fix them.

At Bulldozer, I could easily cash in on the hype like everyone else. We’re choosing honesty over hype. Pragmatism over panic sales.

This newsletter gives you:

• Strategic perspective on what AEO actually means

• Proven tactics that move the needles (with Webflow’s use case)

• Quick-win experiments worth testing (10 minutes to implement and minimal downside)

  Thanks for reading Bullish! Subscribe for free to receive new posts and support my work.

Before we dive in

If you're serious about understanding AEO mechanics and want the full technical breakdown, HubSpot is running a masterclass on mastering visibility in the AI era (in French).

📍March 5th, 2026 at 11:30am CET - Sign-up for free

Sign-up for free

And if you’re curious about AI use cases that actually deliver ROI beyond AEO, we’re running a free webinar tomorrow (February 25th at 11 AM CET, in French) covering 4 high-impact plays:

• AEO implementation that works

• Building marketing mini-apps with AI

• Scaling visual creation via AI

• ABX (Account-Based Experience) at scale

Sign-up for free

Now, let’s talk AEO.

Same pillars, different weights

If you rank on Google, you’re already positioned to show up in LLM answers.

A Seer Interactive study (covered by Search Engine Land) found a 0.65 correlation between ranking on Google’s first page and being cited by ChatGPT and other LLMs.

Same pattern on Google’s AI Overviews: 76% of URLs cited in AI Overviews already ranked in the top 10 on traditional Google search.

Translation: LLMs draw from the same pool of high-authority, well-structured content that Google already rewards.

So SEO and AEO aren’t separate disciplines! In fact, the same 3 pillars apply:

• Content – What you publish and how you structure it

• Authority – Who links to you, cites you, talks about you

• Technical – How crawlable and fast your site is

But the difference is in the weight distribution.

In 1998, Google’s big innovation was PageRank. Authority = who links to you. A link from MIT > a link from your cousin’s blog.

That system dominated for 25 years. Every SEO playbook revolved around earning backlinks from “high-authority domains.”

LLMs broke that model.

When ChatGPT trains on the internet, it doesn’t care about your “Domain Authority” score. It learns from conversations where people solve real problems. Reddit threads. Podcast transcripts. GitHub discussions. Discord servers. LinkedIn comment sections.

A mention in a Y Combinator thread about “best CRM for startups” carries more weight than a backlink from Forbes. Because the LLM learned that real buyers ask real questions in that thread — and trust the answers.

So authority matters more in an LLM world.
But it doesn’t come from the same places…

Authority has moved

Webflow’s marketing team ran an experiment last year. They wanted to see if LLM-referred traffic was real or hype.

The results:

• 10% of new signups now come from AI search (8x year-over-year)

• LLM-referred visitors convert 3-6x better than non-brand SEO traffic

• 60% answer coverage across ChatGPT, Perplexity, Claude

How’d they do it? By shifting where they showed up.

🟥 Previously :

• Guest posts on SaaS blogs

• Backlinks from design publications

• Press coverage in TechCrunch

🟩 Now:

• Mentioned in r/webdev threads about no-code tools

• Cited in podcasts comparing website builders

• Referenced in Indie Hackers discussions about landing page tools

Same company. Different conversation venues.

The kicker: LLM-referred traffic converts better because behavior signals intent. Someone who types a 50-word question into ChatGPT has higher commitment than someone who Googles 3 keywords.

What to actually do

Based on Webflow’s data (60% answer coverage across AI platforms, +331 citations from FAQ schema alone):

Do these first

These tactics have been validated by data; they work.

1. Reformat headers as questions — “How does pricing work?” beats “Pricing overview.” LLMs retrieve question-answer pairs.

2. Add FAQ schema to high-traffic pages — Webflow tested this on 6 pages: +331 AI citations, +149K SEO impressions in 90 days.

3. Refresh content every 90 days — Systematic updates drove 40% traffic lift for Webflow’s team.

Worth testing

These tactics have not yet been fully validated by data, but they are so simple to implement that it is worth putting them into practice.

• LLM-specific CTAs — “Ask ChatGPT to compare us to [competitor]” with a ready-made prompt

• Dedicated /for-llms page — Pigment built one with structured company data. Think of it as a “press kit” for AI models.

Skip

It. Does. Not. Work.

• Model-specific formatting (GPT-4 vs Claude differences)

• Separate AEO teams or budgets (👀)

• Rewriting your entire site out of FOMO

Mistakes to avoid

#1. Paying $100/month for something ChatGPT does for free

There’s a growing market of GEO/AEO monitoring tools (you’ve probably seen the ads). They promise dashboards, scores, and tracking of your “AI visibility.”

Here’s how most of them actually work: they ask ChatGPT “What prompts should [brand] rank for?”, then query those prompts and display a score.

That’s it.

You can do this yourself in 5 minutes. Open ChatGPT, Perplexity, or Claude. Ask the question. Read the answer. Free.

Three reasons to be skeptical of these dashboards:

1. The data shifts daily. Ask the same prompt Tuesday and Thursday, get different answers. LLM outputs aren’t stable like Google rankings.

2. Session history biases results. If you’ve mentioned your brand before in a conversation, the model remembers. Your “visibility score” is inflated by your own usage.

3. The scores are fabricated. There’s no equivalent of Google Search Console for LLMs. No official API that says “you were cited X times.” These tools reverse-engineer something that doesn’t have a ground truth.

Save your budget. The manual check I described above (paste your URLs, read what comes back) gives you more honest signal than any dashboard today.

#2. The “AEO agency” trap

Related trend: SEO agencies rebranding overnight as “AEO specialists” with separate — and pricey — engagements.

The reality: AEO is SEO. Same skills, same foundations, adjusted weights. Any serious SEO engagement in 2026 should include AEO by default. If someone tries to sell you AEO as a separate line item on top of your SEO retainer, ask what exactly they’re doing that isn’t already SEO work.

At Bulldozer, all our SEO engagements now include AEO logic natively. No separate package, no acronym tax. The work is the same — done with the right priorities.

#3. Overthink how you track performance

Track three layers:

That’s it.

☑️ Your move this week

Take your top 5 pages (by traffic or revenue). Paste each URL into ChatGPT and Perplexity:

“Explain what [your company] does and who it’s for.”

Note three things:

• Do they cite you at all?

• Is the description accurate?

• What competitors show up instead of you?

That’s your baseline. If you’re not getting cited, your authority problem is bigger than your content problem.

Let’s grow 👊

— Jordan

I’ve been posting on LinkedIn for 6 years now.

5M+ reach per year on average. My profile has been Bulldozer’s #1 acquisition channel in 2023, 2024, 2025 (≈500 leads) and generated 10M€+ in revenue in total.

But here’s what matters more: the content gave me credibility with prospects, peers, and our team. Day-to-day, it helps me close deals faster and land exceptional guests for my podcast.

Less than 1% of LinkedIn users post regularly. I get why.

Because of this:

Today I’ll explain how I use LinkedIn – far from the pseudo-inspirational, moralizing garbage you see in your feed.

A practical guide that will, I hope, get you posting regularly and raising the bar on the platform.

LinkedIn is the best channel for demand gen. Period.

Before we talk tactics, let’s talk strategy.

You already know the dark funnel problem. Gartner says only 5% of your TAM is in-market right now. Everyone’s fighting over that 5% – CACs explode, boards get nervous, you lose.

The 95% sitting out-of-market? They’re researching. Reading. Watching. Building mental models about who solves problems like theirs.

LinkedIn is where B2B buyers build those models.

Your prospects aren’t reading your blog. They’re scrolling LinkedIn during their commute, between meetings, while pretending to pay attention to that all-hands call.

When you post consistently, you’re doing three things:

1. Building top-of-mind presence with the 95% before they enter buying mode

2. Reducing sales cycle length because prospects arrive pre-educated and trusting you

3. Increasing close rates because you’ve already demonstrated expertise

At Bulldozer, we track this. Deals that come through my LinkedIn profile:

• Close 40% faster than cold outbound

• Have 2.3x higher ACV than paid channels

• Convert demo → closed-won at 31% vs 18% company average

Why? Because the “sale” happened over months of reading my posts. The demo is just paperwork.

This is demand generation, not lead generation.

You’re not capturing intent. You’re creating it.

Now let’s talk about how to do this without sounding like an AI thought leader.

#1. Optimize your “landing page”

Beyond the feed, your profile is LinkedIn’s hub. Think of it as a personal landing page. Optimize it the same way you’d optimize a website.

Four sections matter:

1. Header – banner, photo, headline

2. Featured – pin useful links or top posts

3. About – your bio

4. Experience – professional history

Seems obvious. But 30 minutes updating these can improve your credibility (and conversion) significantly.

But the real topic isn’t profile optimization.

It’s content creation. That’s where it gets hard.

#2. The 5 commandments

Imposter syndrome. Lack of ideas. Fear of judgment. Every excuse exists for not posting.

To help, follow these rules:

1. Be honest about your goals

Nothing wrong with wanting likes, followers, or impressing people. But that probably won’t bring you good leads.

I’ve tested a lot on this platform. I’ve noticed “reach” attracts small businesses – makes sense since they outnumber large ones.

Which brings us to commandment #2...

2. Know your audience

You don’t need to pander to create engagement.

You can get high engagement on BOFU topics. The key is finding subjects that make your audience react, even when technical.

Example with this post (which I honestly didn’t understand):

I know, marketing 101. But worth repeating.

3. Give something personal

Probably the hardest, but talking about who you are outside work creates strong hooks with your audience. It gives you ice-breakers, builds connection, breaks the corporate wall.

Example: At the bottom of my bio, I mention I live in the mountains, love street food, practice MMA, and support legalizing psychedelics → Every week I get messages (sometimes hilarious) about one of these topics.

4. Work your hooks

Simple math: unread hook = unread post.

I spend 80% of my writing effort on these few words/sentences.

You need to grab attention without giving everything away, but without overpromising either (the famous “clickbait” hooks). A balancing act that takes many iterations to find the right tipping point.

Thibault Louis talks about “the 75% rule” – I find it spot-on.

Example with this hook from Jason Lemkin:

You get 75% of the information (the topic, his opinion) but you’re missing the 25% that makes you understand it.

5. Ride the waves

It’s common to observe “trends” on the platform. Topics or post formats that perform particularly well for a period.

We’ve had ChatGPT posts, founder stories, infographics, etc.

If it makes sense for you and you nail the timing, I recommend going all-in.

That’s what I did in 2024 with these two posts:

Take note of the performance...

#3. You are a character.

Now you need to post.

Contrary to what you think, you don’t need to be in the top 1% of your field to add value to your peers.

If you want to learn piano tomorrow by watching YouTube videos, you’re much more likely to watch tutorials from someone 2-3 levels above you than a breakdown of a Mozart piece by Lang Lang.

That’s why I recommend rotating between 3 roles:

Leader. You position yourself as a “leader” on your topic and share insights others don’t have – data, contrarian opinions, methods... It’s the role everyone dreams of, but it’s the hardest to maintain (and not always the most interesting).

Discoverer. You’re not the “leader” but want to become one. So you go discover what’s working best and document it. You share your experiments.

Reporter. Like a journalist, you interview experts in your field, extract lessons, and share them with the world. That’s what I do with the Conquête podcast.

The “Discoverer” and “Reporter” roles require some humility (which doesn’t hurt) and people love them. I promise.

#4. Repurpose your content

You’ve posted! Congrats 👏

Now you need to increase cadence. An easy way: repurpose your content. Take your topic and identify every way to talk about it.

Take “SEO backlinks” as an example. Different approaches:

• Actionable. How to find free backlinks?

• Inspirational. Bulldozer: 0 to 118 backlinks in under 90 days.

• Analytical. How Google prevents you from getting backlinks?

• X vs Y. Free vs paid backlinks: performance difference?

• List. 25 free sites to get backlinks

• Tips, myths, stats, hacks, trends, quotes, examples...

You have 1,000 ways to approach 1 topic. Use them.

#5. Random advice (that’s actually critical)

Don’t be fooled – these are simple tips but extremely important:

• 1 post = 1 idea

• Thesis, antithesis, synthesis

• Being understood > sounding smart

• Use humor / memes. Everyone likes to laugh.

• Avoid pandering unless it’s a battle you’re ready to lose

• Clean your feed regularly

• Study the best

Q&A

To finish, I’ll answer the 3 questions I get most about LinkedIn.

Feel free to reply to this email with questions. Happy to answer.

→ Do you get help producing content?

Writing-wise, I write all my posts myself. But Charlotte and Maëlie help with all visual creation (infographics, carousels, etc.).

We also work with Florent for video production.

→ Do you prepare posts in advance?

No. I write my post in the morning when I open my laptop. I could build a “stock” ahead of time, but I like the idea of writing a bit each day.

That said, there are days I know what I’ll talk about. i.e Thursday is reserved for announcing new podcast episodes.

→ Do you use AI to help?

I’ve found my tone, my style, and I believe no one can be “me” but AI helps to write the first draft.

Bonus : how this connects to your pipeline

Let me close with metrics that matter to you as a CMO.

Tracking LinkedIn’s revenue impact:

We treat LinkedIn profile performance like any channel:

• Source attribution in CRM (tag: “LinkedIn Organic”)

• Track: First touch, last touch, multi-touch

• Compare: CAC, sales cycle length, close rate, ACV

What we see consistently:

• CAC: 60-70% lower than paid social

• Sales cycle: 30-40% shorter than cold outbound

• Close rate: 1.5-2x higher than other organic channels

• ACV: 2-3x higher (senior buyers engage)

Why this works:

You’re not interrupting. You’re educating during the research phase. By the time they book a demo, they’ve consumed 10-20 pieces of your content. They trust you. They’ve self-qualified.

This is the dark funnel illuminated.

When a CMO asks “how do I prove content ROI?” I always ask “how do you know me?” and then she smiles. Track the channel like you’d track anything else. The attribution isn’t perfect, but it’s good enough to make budget decisions.

One warning: This takes 6-12 months to compound. If your board wants results in Q2, this isn’t the play. But if you’re building for 2026-2027, start now.

That’s it.

Happy to answer questions.

Let’s grow 👊

— Jordan

Last week, Emmanuelle shared with me this article from the Wall Street Journal. The claim : 43% of marketing leaders expect to cut headcount due to AI in the next year.

CFOs are running spreadsheets on savings. Boards want to see the efficiency gains.

It follows the narrative you see everywhere: AI lets us do the same work with fewer people. And marketers should be afraid.

I’m not.

In February 2025, Air Canada deployed an AI chatbot to handle customer service inquiries. The goal: reduce support costs by automating responses.

The chatbot told a customer they could get a bereavement discount by booking first and applying for a refund later. This was false.

Air Canada’s actual policy required applying before booking.

The customer booked based on the chatbot’s advice. Air Canada refused the refund. The customer sued.

The court ruled against Air Canada. The judge said: “Air Canada argues it cannot be held liable for information provided by one of its agents, servants, or representatives, including a chatbot. This is a remarkable submission.”

Air Canada had to pay. But the real cost was bigger: every major news outlet covered the story. Their brand took a hit. Customer trust dropped.

They optimized for cost savings. They got a PR disaster instead.

What marketing has always been about

Our job as marketers is to create experiences that make customers feel understood. To answer their specific questions. To guide them to the right solution for their exact situation. Period.

The problem was never that we didn’t know what to do. The problem was resource constraints.

Creating a unique landing page for each segment used to mean weeks of copywriting, design and dev work.

Personalizing email sequences at the individual level used to mean manual research on each prospect, custom writing for each email, hours per sequence, etc.

These constraints forced us to be generic. One landing page for everyone. Same email sequence for all prospects. Broad content that tries to speak to everyone (and resonates with no one).

AI didn’t make us more productive at doing the same work. AI removed the constraints that forced us to deliver mediocre experiences.

Before AI: “What can we afford to create?”
With AI: “What experience would actually convert?”

This is a fundamental shift.

The CFO sees AI and thinks: “We can cut 3 content people and save $270K.”
The CMO should see: “We can finally build the personalized journey we’ve been talking about for three years.”

One optimizes for cost. One optimizes for conversion.

Guess which one grows revenue.

The new skillset

But if you’re using AI to create better experiences instead of cutting costs, you need different skills on your team (that’s why I’m bullish on companies like UnlockM for example).

• AI Marketing Leaders These are your modern CMOs. People who understand how AI works, what’s possible today, and what’s coming next. They see the marketing operation as a system of people, AI agents, workflows, and so on.
  

• AI Marketing Strategists These are typically product marketing profiles. People who have deep customer understanding, know how to position products, and have judgment on what messaging will resonate.
  
  They set strategy for each campaign, define the messaging frameworks, and ensure quality stays high. They review AI outputs and make the call on what ships and what gets killed.
  
  Before AI: spent 80% of time producing content and campaigns, 20% strategizing With AI: spend 20% reviewing AI outputs, 80% on strategy and quality control

  
  The shift: their job went from “create the thing” to “design what the thing should be and judge if AI created it correctly.”
  

• AI Marketing Operators These are your "Marketing engineers” (Growth, GTM, etc). People who build workflows, connect systems, and create automation that runs without constant human oversight.

  
  They don’t manually execute campaigns. They build the infrastructure that executes campaigns automatically, personalizes at scale, and adjusts based on performance data.

  
  Skills: Workflow design, tool integration, automation architecture, data modeling, system optimization.
  

• Channel Experts These are deep specialists in specific channels. The person who understands Meta’s algorithm better than Meta’s support team. The LinkedIn expert who knows exactly how B2B campaigns should be structured. The SEO specialist who predicts Google updates before they happen.

  
  They execute at the highest level on their specific channel. They don’t need to understand the whole marketing system. They need to be world-class at their one thing.

How this actually works

At Bulldozer, we’ve spent the last 12 months rebuilding our entire model around this reality. Our clients don’t need full-time people for every function. They need:

• Strategic thinking when making big decisions (fractional leaders and strategists)

• Expert execution when building new systems (fractional operators and channel experts)

• Workflows that run day-to-day automatically (AI + automation)

We come in during build phases. Experts architect the system, create the workflows, optimize the channels. Then it runs. We monitor performance remotely. When clients need to evolve or add something new, experts come back for another build phase.

The result: world-class customer experiences without world-class headcount costs.

But the goal isn’t cost reduction. The goal is creating experiences that convert better because they’re finally personalized at the level customers deserve.

Two paths forward

Look at your marketing operation today. Ask yourself:

Are we using AI to do the same work with fewer people, or are we using AI to finally create the experiences we always knew would work?

Are we optimizing for cost reduction or conversion improvement?

Are we hiring for the old skills (executors who do the work) or the new skills (strategists who design what AI should create, operators who build the systems)?

Path 1: Use AI to cut costs
Fire people. Reduce budgets. Show the CFO savings on a spreadsheet. Watch conversion rates slowly decline as your experiences become more generic and your competitors who chose differently start winning.

Path 2: Use AI to elevate experiences
Keep your budget. Rebuild your team around the new skills. Create the personalized, high-quality customer journeys you always knew would work but couldn’t afford. Watch conversion rates climb as you deliver what customers actually want.

Air Canada chose cost savings over customer experience. They’re now famous for it, in the worst possible way.

Which path are you on?

Let’s grow 👊

— Jordan

Happy 2026 🥳

This year, I’m not wishing you “success” or “growth” or whatever gurus are posting with sparkle emojis.

I’m wishing you clarity. On what actually moves revenue. On what AI can and can’t replace. On where to focus when everyone else is chasing “the new thing”.

Starting with this: stop fighting over the same 5% of buyers.

Best wishes, and thank you for reading.

$600 cost-per-lead. Up from $200 eighteen months ago.

You’ve got better AI. Better optimization. But worse results?

The math doesn’t make sense until you realize: you’re not competing with your direct competitors anymore. You’re competing with every B2B company using the same AI tools to chase the same 5-15% of in-market buyers.

In 1966, before marketing automation, before digital advertising, before AI, a copywriter named Eugene Schwartz published a framework that explains exactly why this happens—and what to do about it.

“Breakthrough Advertising” sold for $125—about $1,200 in today’s money. Used copies now go for $800+ on eBay.

Schwartz identified something that most marketers still get wrong 60 years later: you can’t use the same message for someone who’s never heard of your product and someone who’s ready to buy.

He called it the “5 Stages of Awareness”.

The math problem AI makes worse

If companies change vendors roughly every 5 years, that’s 20% annually—or about 5% in-market at any given moment. Recent Forrester research puts it higher for some categories (15-20%), but the point stands: at least 85% of your potential market isn’t buying today.

AI-powered lead scoring, intent signals, and predictive analytics are incredible at identifying that 5-15%. They’ve made the entire industry better at finding and targeting in-market buyers.

The problem? Everyone else got the same tools. The competition for that 5-15% went from hand-to-hand combat to nuclear warfare.

Gartner’s research backs this up—only 5% of your total addressable market is ready to purchase at any given time.

So what’s everyone else doing? They’re distributed across Schwartz’s awareness stages:

• 40-60% are unaware they even have the problem you solve

• ~20% are problem-aware but don’t know solutions exist

• ~10% are solution-aware and researching options

• ~7% are product-aware and comparing vendors

• ~3-5% are most-aware and ready to buy

When you’re running AI-optimized Google Search ads, using ChatGPT to write pricing page copy, and A/B testing demo request forms with generative variants, you’re fighting over that 3-7% with every other vendor using the exact same AI tools.

That’s why your CPL went from $200 to $600 in 18 months—despite “AI-powered optimization.”

What AI actually changes

AI will transform how you execute awareness-stage marketing:

• Personalized content at scale for different awareness levels

• Real-time message testing across segments

• Intent signal detection as buyers move between stages

• Dynamic creative optimization for cold audiences

AI won’t change why awareness-stage marketing matters:

• Humans still need to understand they have a problem before seeking solutions

• Buying committees still operate at different awareness levels simultaneously

• Trust still takes time to build—you can’t AI your way onto the Day One shortlist

• Dark funnel dynamics (Slack conversations, peer recommendations) still account for 70% of the buyer journey

Where the framework breaks

Schwartz was selling books and newsletters through direct mail. One person made one decision.

In B2B, you’ve got buying committees averaging 5-10 people. Your champion might be “most-aware” while the CFO just learned your product category exists last week. Google and Gartner both confirm what we see with clients: B2B buying isn’t linear—it’s a messy loop through problem identification, solution exploration, validation, and consensus-building.

Plus there’s the dark funnel problem. Up to 70% of the buyer journey happens in Slack channels, private communities, and podcast conversations that your attribution software—no matter how AI-enhanced—will never see.

But here’s what still works: the core principle that message sophistication must match buyer awareness.

We’ve tested this across 100+ clients at Bulldozer. When we map content and channel strategy to awareness stages, we consistently see improved performance at every funnel level—ads, landing pages, nurture sequences, the works. AI made the execution faster. It didn’t change the underlying strategy.

The messaging pyramid

After running these tests, we built what we call the Messaging Pyramid. It shows which proposition type performs best at each awareness level.

Unaware → Lead with the problem
Example: “Did you know you’re located in a flood zone?” (for flood insurance)
Why it works: They don’t know they need a solution until they know they have a problem

Problem-aware → Lead with the solution category
Example: “The coffee alternative that doesn’t crash” (for yerba mate)
Why it works: They’re searching for ways to solve their known problem

Solution-aware → Lead with the product category
Example: “The 5 best ATS platforms for growing teams”
Why it works: They’re comparing options within a category they understand

Product-aware → Lead with benefits
Example: Store shelf teeth whitening shows “Whiter teeth in 7 days”
Why it works: They’re past education—they just want to know what they get

Most-aware → Lead with features
Example: Camera specs for professional photographers
Why it works: Experts want technical details, not marketing fluff

That “sell benefits not features” advice you’ve heard? It’s only true for one stage (product-aware). For most-aware buyers—the experts—talking about benefits instead of specs makes them think you’re treating them like idiots.

AI can help you generate hundreds of headline variants for each stage. It can’t tell you which stage to target or what type of message resonates there. That’s strategy, not execution.

Channel strategy shifts with awareness

Where you find people changes by awareness level:

Unaware/Problem-aware live on LinkedIn, podcasts, industry publications. They’re not searching for solutions yet because they don’t know they need one. Cold targeting on LinkedIn costs $300-500+ per lead and should never aim for direct conversion—use it to build retargeting audiences.

Solution-aware show up on Google, review sites (G2, Capterra), and comparison content. They’re actively researching.

Product/Most-aware are on demo requests, sales calls, pricing pages, and technical forums. They know what they want—they’re just choosing between options.

The companies getting this right allocate roughly 60% to demand creation (building awareness) and 40% to demand capture (converting intent). LinkedIn’s data shows audiences seeing both brand and activation messages are 6x more likely to convert.

The AI application that actually matters

Want to know where AI delivers real value in awareness-stage marketing?

Dynamic awareness-level detection: Tools now analyze prospect behavior across channels to infer awareness stage, then serve appropriate content automatically. A prospect watching your “problem education” video gets different follow-up than someone downloading your comparison guide.

Scaled personalization within stages: AI can generate 50 variants of your problem-aware message, each tailored to different industries, company sizes, or pain points—without changing the core “lead with the problem” strategy.

Predictive stage progression: Machine learning models identify which unaware prospects are most likely to move to problem-aware within 90 days, letting you prioritize effort.

Content gap analysis: AI can audit your existing content against awareness stages and identify gaps—”You have 47 pieces for product-aware buyers and 2 for problem-aware.”

But all of this assumes you’ve mapped your strategy to awareness stages first. AI accelerates the right strategy. It also accelerates the wrong one.

Start bottom-up, then fill gaps

When we diagnose a client’s current strategy, we map everything—content, channels, messaging—against the awareness stages. The pattern is always the same: heavy concentration in solution-aware through most-aware, almost nothing addressing unaware or problem-aware.

That’s actually smart to start. The 5-15% in-market convert faster and prove ROI quickly. But once you’ve saturated that segment, the ceiling appears.

Our recommendation: Start with bottom-of-funnel content (comparison pages, case studies, technical docs), then work upward. You’ll drive revenue faster. But once you hit 70-80% market penetration in the “ready to buy” segment, shift 30-40% of resources to earlier stages.

The data supports this. Companies balancing demand creation with demand capture see 70% higher returns over 12-24 months compared to those doing pure demand capture.

AI doesn’t change this sequence. It just lets you execute it faster once you’ve committed to the strategy.

What this means tomorrow morning

Three things to do this week:

First, audit where your current efforts sit. Pull up your top 10 pieces of content, your main ad campaigns, and your primary channels. Ask: are we addressing unaware? Problem-aware? Or just fighting over the 5-15% already shopping?

Second, identify your gaps. If everything lives in solution-aware through most-aware (like 90% of B2B companies), you’ve found why growth plateaued. No amount of AI optimization will fix a strategy gap.

Third, test one piece of unaware or problem-aware content this month. A LinkedIn video highlighting the problem without pitching your product. A guide addressing the core challenge your category solves. Track it not by immediate conversion, but by retargeting pool growth and influenced pipeline 60-90 days out.

Use AI to create 10 variants of that content across different industries or personas. But don’t use AI to avoid doing the strategic work of identifying which awareness stage to target.

The timeless principle

Marketing technology will keep evolving. In 12 months, we’ll have new AI tools that make today’s capabilities look primitive. The companies using those tools to fight over the same 5-15% will see marginal gains.

The companies using those tools to reach the 85-95% who aren’t shopping today—the unaware and problem-aware majority—will see step-function growth.

The framework is 60 years old. The insight remains timeless: you can’t sell to someone who doesn’t know they have a problem. AI doesn’t change human psychology. It just helps you apply these principles at scale.

The question is whether you’re using AI to build awareness with the 85-95% or using it to fight your competitors for scraps.

Let’s grow 👊

— Jordan

Sources & Further Reading:

- Forrester Research: B2B Buying Trends 2025

- Gartner: The New B2B Buying Journey

- 6sense: The Dark Funnel Report

- LinkedIn B2B Institute: Brand & Demand Research

- Refine Labs: Attribution Gap Study

This is the last edition of Bullish for 2025 🎄🎅

Before we dive in, I wanted to take a moment to thank you for reading, responding, and sharing these newsletters throughout the year. It means a lot.

The next few days are a good time to step back, recharge, and come back sharper in January. I hope you get some proper rest — you’ve earned it.

Wishing you and your loved ones a great holiday season and a strong start to 2026.

—Jordan

In 1965, Secretary of Defense Robert McNamara obsessed over body counts in Vietnam.

His analysts tracked enemy casualties with mathematical precision. Charts went up. Numbers looked good. The data said America was winning.

America was not winning.

McNamara fell into what historians now call the “McNamara Fallacy”: measuring what’s easy to measure, ignoring what actually matters, and assuming that what can’t be measured doesn’t exist.

You might be making the same mistake right now — with AI.

The threat you’re not seeing

Let’s say you’ve adopted AI tools. Lead scoring, content generation, bidding optimization, you’ve got it all. Bonus : your results improved.

But… can you explain why they improved?!

If your CFO asks “What did the AI/algorithm do to decrease CAC?” — do you have an answer beyond “it automatically optimized our audiences”?

73% of marketing teams now use generative AI. That’s a lot.
But only 3% can demonstrate its business value to their boards. That’s… bad.

And then you can’t do the one thing your board needs from you — tell them where to invest and why.

Why your CFO doesn’t trust your numbers

Over 60% of CFOs express skepticism about marketing performance reports.

They’re not wrong.

Research from Refine Labs found a 90% gap between what attribution software reports and what customers actually say drove their decisions. Podcast content was credited with 53% of revenue by customers — and 0% by the software.

Attribution was broken before AI showed up. AI just made it impossible to ignore.

Asking ‘Which touchpoint mattered most?’ is like asking which grain of sand makes a desert.

Consequence, you’ve probably assumed you must sacrifice explainability for performance. That’s a myth.

The opacity isn’t required. It’s a default setting you can change.

What your board actually wants from you

Three questions. That’s it:

1. Is marketing hitting its numbers?

2. What levers should we use to improve?

3. Should we invest more or less?

Dave Kellogg — former CMO, board director, Salesforce veteran — recommends 5-8 slides max. Lead with performance against targets. Speak in pipeline generation, conversion rates, cost per opportunity.

“When we’re talking to the board, we need to be board level,” he says. “Otherwise, they’re going to mistake you for the VP of marketing operations.”

Stop defending your attribution model. Start owning the whole pipeline.

Fun fact: hekeeps a coffee mug that reads "Marketing Attribution is Fake News." He brings it to board meetings. (I want the same one 😂)

Three frameworks to steal

1. The Value Equation Pyramid

Structure your metrics in three tiers:

• Top: Board-level KPIs (revenue growth, market share, margin)

• Middle: Marketing profit multiplier (gross profit ÷ marketing spend)

• Foundation: Channel-specific metrics

Every metric must ladder up. No orphan numbers floating around your deck.

2. Hybrid Attribution

Separate demand capture (what software tracks) from demand creation (what customers tell you).

Add “How did you hear about us?” as a required free-text field on your high-intent forms.

The most accurate attribution? Asking directly.

3. The Four Vs

Replace multi-touch attribution theater with: Volume, Value, conVersion, Velocity.

Give your board journey context instead of false precision about which touchpoint “deserves credit.”

The question you need to answer

Can you explain your results without AI?

If the answer is no, you don’t have a competitive advantage. You have a dependency — and a career risk you can’t see until it’s too late.

CMO tenure among Top 100 Advertisers dropped to 3.1 years. The lowest since 2009. But here’s the flip side: companies with strong CMO-CFO alignment grew EBITDA 12% faster.

The gap between CMOs who thrive and CMOs who churn? Explainability.

McNamara eventually admitted his mistake. In his 1995 memoir: “We were wrong, terribly wrong.”

He had all the data. He had none of the understanding.

You don’t need to wait 30 years.

AI didn’t break your measurement. It exposed that measurement was never as solid as you thought. Use that as an opening — reset expectations with your board about what can and can’t be tracked.

Hope this helps.

Let’s grow 👊

—Jordan

Sources:

• Gartner, CMO Survey

• McKinsey, Marketing & Technology Leaders Study

• IBM, Marketing & Sales Executives Survey, 2025

• IAB, State of Data Report, 2025

• Dave Kellogg, kellblog.com

• Harvard Business Review, David Martens et al., AI Explainability Research

• Funnel.io, Board Pressure on CMOs, 2023-2025