Ferdinand Goetzen shares perspectives on B2B marketing, content strategy, and practical AI adoption. Their posts discuss making content distinctive, testing AI workflows in real marketing work, and gathering input from B2B marketing leaders.
Ferdinand Goetzen is most useful for B2B content strategy and practical AI adoption perspectives.
Best for
A B2B martech brand wanting content on practical AI workflow adoption for in-house teams.
Not for
A high-volume sponsored post package with rates, reach guarantees, and demographic targeting.
Key takeaways
Content marketing no longer works by default, so distinctiveness is the test.
AI workflows fail in real use because they are built without marketing knowledge.
Test AI in a safe environment before implementing it where it counts.
Check first: Confirm commercial terms and recent sponsored results on comparable AI workflow topics.
Main ideas
Content marketing no longer works by default, so distinctiveness is the test · AI workflows fail in real use because they are built without marketing knowledge · Test AI in a safe environment before implementing it where it counts
Ferdinand Goetzen writes about B2B content strategy and practical AI adoption.
Their posts argue that content must offer something distinctive to buyers, and that AI workflows need to be tested in real marketing work rather than judged by polished demos.
They also share research projects, including a survey for B2B marketing leaders.
Follow their work for a practitioner’s view of what makes marketing useful and what it takes to make new tools work in practice.
Goetzen connects marketing ideas to the conditions teams face.
In one post, they explain why publishing on a schedule no longer guarantees results, then offer a concrete test: would a buyer notice if the article appeared on a competitor’s site with the logo changed?
In another, they contrast time-pressed in-house teams with workflow builders who may lack day-to-day marketing experience, and argue for testing AI in a safe setting before wider use.
Their survey post favors a targeted group of marketing leaders over a much larger but less relevant sample.
The posts also make the commercial case for agencies as a way to test tools and bring working approaches to clients.
Ferdinand Goetzen publishes on B2B content strategy and how marketing teams adopt AI.
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Three arguments run through these posts.
The default content playbook — keyword research, a full calendar, publishing on schedule — has stopped returning results, so the test is whether a piece would interest a buyer who had never heard of the company.
Most AI marketing workflows fail in real use because their builders understand the tooling but not how a B2B marketing function runs, while the in-house teams expected to adopt them have no time to explore.
Goetzen's proposed fix is to test a workflow in a safe environment before implementing it where it counts.
A separate priority is original research recruited person to person, where sample quality matters more than sample size — the same preference for distinctiveness over volume that runs through the content argument.
Content marketing no longer works by default, so distinctiveness is the test
Goetzen's position is that content marketing still works, but no longer automatically.
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For years, a team could do keyword research, fill the calendar, publish on schedule and get something back for the effort.
That floor is gone: AI answers now sit between the article and the click, and every channel is crowded with lookalike pieces.
What survives is content a buyer would want even if they had never heard of the company.
The quality bar is a single test, not a metric: if an article could sit on a competitor's blog with the logo swapped and nobody would notice, it has added to the noise rather than risen above it.
The swap-the-logo test, applied to the routine publishing habits it is meant to replace.
Goetzen points readers to a longer piece with the data and five company examples behind the argument.
The team's head of content, Adrian, assembled that piece, so its research and company examples are Adrian's work, not Goetzen's.
AI workflows fail in real use because they are built without marketing knowledge
Goetzen argues that almost every AI workflow that looks incredible in a demo falls apart in real-life use because technology experts build them without understanding how a B2B marketing function runs day to day.
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The automations look fine on the surface but miss something fundamental about how the work happens, so the output lacks quality when it enters a business.
In-house marketing teams fail from the opposite direction.
They know AI matters, but keeping up with every new tool and capability is a full-time job, and they already have one.
So they guess, pick and choose, and squeeze implementation into whatever time is left.
The result: under-tested workflows on one side and rushed adoption on the other.
The demo-versus-use split: a slick automation produces impressive output in a controlled setting, then work a marketing team cannot use when real campaign targets, deadlines and handoffs come into play. Goetzen describes a pattern across what they have seen, rather than naming specific tools, workflows or a single case.
Test AI in a safe environment before implementing it where it counts
The prescription: try a workflow somewhere safe first, then implement it in the real operation and judge it by whether it survives real business contexts, not by how the demo looked.
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Goetzen argues that closing the gap between builders and marketing teams requires people who stay on top of the technology and test it in real business contexts. An agency, they say, is well placed to do this: it can dedicate time to staying current, test on its own operations before working with clients, then bring what works to clients across different industries — a harder task for a team that is already maxed out.
Test workflows in the service provider's own operations before bringing them to client work. Readers weighing the advice should ask for client results or before-and-after details: the posts provide neither, and the recommendation comes from someone publishing inside a marketing team that also runs its own research.
Original research built by recruiting the right respondents, not the largest sample
Goetzen treats the State of AI in B2B Marketing survey as a flagship content asset, not a lead-capture formality.
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It takes five to seven minutes; the link is deliberately not public and is sent person to person because the aim is marketing leaders, not a random sample.
Respondents get the report before it goes out publicly, and the stated preference is for the right 200 people over any 2,000.
The survey gathers data for content: the previous edition drew answers from 110 marketing leaders, and Goetzen calls the resulting report the most valuable piece of content published that year.
Readers comment "survey" or message Goetzen for the link, and are asked to tag people who should be included.
The survey and report are the content; the distribution method is the quality filter.
The post does not explain how the self-selected leader sample is accounted for in the findings, so buyers should ask about that before relying on the results.
Answers a newcomer's first question about Goetzen: what do they think is wrong with B2B content right now, and what should replace it?
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The post states their quality bar in a few lines, giving readers a useful frame for everything else they publish.
Goetzen argues that content marketing still works but no longer works by default.
The old routine—keyword research, a full calendar and publishing on schedule—used to return something for the effort.
That floor is gone: AI answers now sit between an article and the click, and every channel is crowded with lookalike pieces.
Their test is blunt: if your article could sit on a competitor's blog with the logo swapped and nobody would notice, you have added noise rather than risen above it.
They point readers to a longer piece assembled by their head of content, Adrian, which carries the data and five company examples behind the argument.
For brands planning a campaign
Positions Goetzen as a partner for differentiation and editorial standards rather than publishing volume. A martech partnership could provide the proprietary data or customer examples their central test depends on: the argument only lands when a brand can show a piece a buyer would want even without knowing the company.
Answers where Goetzen stands on AI in marketing, the subject most likely to bring someone to their name.
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This is their clearest statement of the problem they claim to solve, making it the right starting point for a campaign to support AI adoption.
Goetzen opens by saying almost every AI process they have seen that looks incredible in a demo falls apart in a real-life situation.
They attribute that to processes built by people who do not understand marketing.
They split the problem into two groups failing from opposite sides.
In-house marketing teams want to use AI but cannot pause campaigns to explore every new tool, so they guess and squeeze implementation into leftover time.
The builders produce slick automations but are technology experts without day-to-day knowledge of how a B2B marketing function runs, so their output looks fine on the surface while missing something fundamental about how the work happens.
Goetzen's fix is to have people who stay current with the technology and test it in real business contexts: try it in a safe environment, then implement it where it counts.
They say an agency is well placed for this because it can dedicate time to staying current, test on its own operations first, and then bring what works to clients across industries.
For brands planning a campaign
This is Goetzen's most direct case for practical adoption of AI processes, and it names the gap a technology partner would be buying into.
A partnership could be structured around a tested pilot: pick one process, run it in a safe environment, and publish what held up and what did not.
That way, the collaboration produces concrete findings rather than another demo.
Answers what original research Goetzen produces and how they reach the audience.
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It shows a recurring survey franchise and a deliberate distribution choice, which matters to anyone weighing a research-led partnership.
Goetzen invites people who run B2B marketing at companies of any size to take part in the State of AI in B2B Marketing survey, which takes five to seven minutes.
They say 110 marketing leaders answered the previous year and call the resulting report the most valuable piece of content they published that year.
The link is deliberately not public: they want marketing leaders rather than a random sample, so they send it person to person and ask people to comment "survey" or message them, and to tag anyone who should be included.
Participants receive the report before it goes out publicly.
Their stated preference is for the right 200 people over any 2,000.
For brands planning a campaign
Shows a research-led route into a curated audience of senior B2B marketers rather than a broad follower count. A sponsor could co-fund or help distribute the next edition, reaching respondents and readers who self-identify as marketing leaders, with the added draw of early access to the findings.
Content that earns attention after the default playbook stopped working
Ferdinand Goetzen argues that content marketing still works, but no longer by default.
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AI answers now sit between an article and the click, and every channel is full of similar pieces.
Routine keyword-and-calendar publishing no longer delivers what it once did.
Content that survives is what a buyer would want even if they had never heard of the company.
The test: could an article sit on a competitor’s blog with the logo swapped and nobody notice?
Proposes a swap-the-logo test for distinctiveness and points readers to a longer piece with data and five company examples.
For brands planning a campaign
For a B2B martech company planning a practical AI workflow adoption partnership, differentiated, buyer-useful content may matter more than publishing volume. A joint project could apply the swap-the-logo test to AI workflow content: show where generic AI-generated posts blend in and what makes a workflow explainer worth reading on its own.
The gap between impressive AI demos and real marketing work
Ferdinand Goetzen says many AI workflows look strong in demos but fall apart in real use.
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Technology experts build them without understanding how a B2B marketing function runs day to day.
In-house teams face the opposite problem: they know AI matters, but keeping up with every new tool is a full-time job, and they already have one.
The result is under-tested workflows on one side and rushed implementation on the other.
Identifies two groups with opposing constraints: in-house teams with no time to explore and technical builders with limited marketing-operations context.
For brands planning a campaign
A practical AI workflow adoption partnership could position the martech company as a bridge between tool builders and marketing teams. Rather than another automation demo, the content could show what changes when a workflow is tested against real campaign constraints, handoffs, and output quality checks.
Test AI in a safe environment before operational deployment
Ferdinand Goetzen recommends trying an AI workflow in a safe environment before implementing it where it counts.
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Agencies can dedicate time to staying current, test on their own operations, and bring what works to clients across industries. The measure is whether the workflow survives real business contexts, not whether its demo looks slick.
Recommends testing in a safe environment before moving to production.
Ferdinand Goetzen frames agencies as one source of testing capacity, which brands may need to weigh against their internal capacity.
For brands planning a campaign
For a martech company selling AI workflow adoption, this supports a pilot-first campaign: document a sandbox test, decide what moves into production, and give the client an account of what changed. The partnership could produce a joint test-and-learn resource rather than a product announcement.
Original research as lead content, distributed person to person
Ferdinand Goetzen promotes the State of AI in B2B Marketing survey as a report based on marketing leaders’ answers.
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The survey takes 5–7 minutes, is sent person to person rather than through a public link, gives participants early access to the report, and aims for the right 200 respondents rather than any 2,000. Last year, 110 marketing leaders answered.
Recruits respondents directly, gates the survey link to preserve sample quality, and offers participants the report before public release.
Ask to review the methodology, question design, and how the report accounts for self-selected marketing-leader respondents.
For brands planning a campaign
A content partnership on AI workflow adoption could include a survey or benchmark, with controlled recruitment and value for participants. Ferdinand Goetzen treats original research as a primary content asset and distributes it through direct outreach, a fit for campaigns seeking qualified marketing-leader input rather than broad anonymous traffic.
Ferdinand Goetzen's three LinkedIn posts cover B2B content strategy, AI workflow adoption, and a recurring State of AI in B2B Marketing survey, but name no brand relationship or company they own, work for, promote, or represent.
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One refers to an unnamed employer's content team and to agencies in general terms. Ask for disclosure of any relevant relationships with named businesses.
Ferdinand Goetzen writes on LinkedIn about B2B content strategy and how marketing teams take on AI.
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A two-part argument runs through the posts: content marketing still works but has stopped working by default, so each piece should pass this test — would a buyer who had never heard of the company still want it?
And most AI workflows that look impressive in a demo fall apart in real use because they are built by people who understand the tooling but not how a marketing function actually runs.
Goetzen’s proposed fix is to test a workflow in a safe environment before putting it to work where it counts.
A survey post shows original research assembled by recruiting marketing leaders person to person instead of through an open link.
The style is short, first-person argument built around a single test, threshold or tradeoff rather than a list of tools.
For adjacent reading, the creators below work in different functions but share the same diagnostic, quality-over-volume approach.
Mansoor Ali takes the quality-over-volume argument Goetzen makes about content one step further down the funnel: publishing dozens of articles does not by itself produce visibility, and a report showing 30 backlinks, DR 70+ sites and 100% delivery still says little about whether a placement strengthened the brand.
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Both writers replace a volume metric with a harder question — does the work stand out, and is the placement relevant? Both end with a diagnostic question rather than announcing a result.
Ali focuses on SEO placement: guest posting, link building and outreach for client accounts, with relevance measured by whether a SaaS, fintech or ecommerce brand appears where its buyers already are. Goetzen works at the content and workflow layer, asking what makes a piece worth reading and how an AI workflow holds up in real campaign use.
Jake Huber writes like Goetzen: take a decision a buyer is about to make, list the costs the budget conversation leaves out, and name the threshold at which the decision flips.
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Huber’s split between a sourcing problem and a process problem mirrors Goetzen’s distinction between technology experts who cannot see how a marketing function runs and in-house teams with no time to explore.
Diagnose which problem you have before buying the wrong fix.
Both also write as sellers who argue against the obvious purchase.
Huber focuses on recruiting for seed and Series A startups: recruiter economics, ramp time, sourcing versus process, and when a full-time hire can be kept busy.
Their posts close with a direct pitch for their service.
Goetzen writes about B2B content and AI workflow adoption without using the posts to sell a service.
Raymond C. teaches through a specific operational failure rather than general advice: images hosted on a departing vendor’s server quietly blanking every live email flow.
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They follow with a numbered corrective procedure backed by real numbers — rebuild the baseline from repeat openers, hold a 40% blended threshold, and keep sampling across the next 6 to 10 campaigns. Goetzen’s account of the gap between AI workflows in demos and real use, and their safe-environment-first remedy, follow the same pattern: a concrete failure, a diagnosis and a bounded procedure.
Raymond C. works on ecommerce lifecycle email — deliverability, list reactivation, Klaviyo and Shopify migrations, and sender reputation — for brands selling to consumers. Goetzen focuses on B2B content strategy and marketing teams adopting AI.
For a B2B martech company weighing a content partnership on practical AI marketing workflow adoption, Ferdinand Goetzen’s posts speak directly to B2B marketing leaders and teams applying AI in real marketing work.
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They had 11,596 LinkedIn followers as of 8 October 2026. Before treating audience relevance as established, ask for recent sponsored results on comparable AI workflow topics and audience survey or LinkedIn demographic data covering role, seniority, employer, industry, geography, age and gender.
LinkedIn followers
11,596
LinkedIn profile audience size; no composition breakdown supplied · 2026-10-08
Ferdinand Goetzen's posts do not set out sponsorship terms.
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Across three LinkedIn posts, they write as the person commissioning and publishing content rather than as a creator advertising inventory: they point readers to an article written by the head of content, invite senior B2B marketing leaders into a survey the team runs, and argue that AI marketing workflows need testing inside real businesses—a case for working with an agency.
The posts publish no media kit, rate card, sponsorship policy, disclosure statement, or public inquiry route.
Ask directly for partnership terms and how to make an inquiry.
A B2B martech company wants a content partnership on practical AI marketing workflow adoption for in-house B2B marketing teams who know AI matters but cannot pause campaigns to learn it.
Decision
Strong fit on subject, framing, and audience. Proceed to terms, with two open checks noted below.
Open checks
The posts set no sponsorship terms: no rate card, media kit, disclosure policy, or public inquiry route. Treat commercial terms as unconfirmed and negotiate them directly.
Audience relevance is asserted, not measured. The only figure available is 11,596 LinkedIn followers recorded on 8 October 2026, with no breakdown by seniority, function, industry, or geography. Ask for recent sponsored results on comparable workflow topics before committing budget.
Reasons
The buyer problem for a partnership on practical AI marketing workflow adoption is the one Goetzen argues from. In a post on AI workflows, they say almost every workflow that looks incredible in a demo falls apart in real use, because it was built by people who understand the tooling but not how a B2B marketing function runs day to day. They describe in-house teams as knowing AI matters but lacking time to explore, so they guess and squeeze implementation into leftover hours. A practical adoption partnership addresses that gap instead of restating it.
Goetzen already sequences adoption the way a pilot-first campaign would. Their stated method is to try a workflow in a safe environment first, then implement it where it counts. They frame agencies as useful precisely because they can spend time staying current and test on their own operations before bringing what works to clients. A campaign can document a sandbox test and its results, rather than pair a product announcement with a demo.
Goetzen sets a usable editorial quality bar. In a content-marketing post, they ask whether an article could sit on a competitor's blog with the logo swapped and nobody would notice. Applied to a partnership, that standard steers the sponsor away from generic AI-generated posts and gives the collaboration a reason to exist beyond publishing volume.
Goetzen conducts original research with the same audience a partnership on practical AI marketing workflow adoption would target. In a survey post, they ask people who run B2B marketing to take a five-to-seven-minute survey, report 110 marketing leaders in the previous edition, keep the link non-public to avoid a random sample, offer participants the report before release, and say they would rather have the right 200 respondents than any 2,000. That offers a route to first-party findings and senior-marketing-leader contact, not just impressions.
Goetzen has a route to longer assets. A content-marketing post points readers to a longer piece assembled by their head of content, giving a collaboration an obvious home beyond a single social post.
When to choose a specialist
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Brief
A B2B SaaS company wants a high-volume sponsored post package: a fixed number of promotional LinkedIn posts per month, published rates, reach or engagement guarantees, and demographic data proving the audience matches its target account list.
Decision
Poor fit as specified. The posts cannot support rate cards, guarantees, or demographic targeting, and the format runs against the argument Goetzen publishes under their own name.
Reasons
The posts describe no commercial inventory: no media kit, rate card, sponsorship policy, disclosure statement, or partnership inquiry link. Ask directly what paid formats are available and on which channels.
The posts read as Goetzen's own editorial publishing, not sponsored placements: they point readers to an article written by their head of content and personally recruit survey respondents with a non-public link. Neither is a sponsored slot.
The only audience measurement available is 11,596 LinkedIn followers recorded on 8 October 2026, with no composition data. A brief promising seniority, industry, or account-level targeting cannot be evidenced from what was reviewed.
The posts publish no engagement or past sponsored-performance figures, so reach or engagement guarantees would have no basis.
Follower-volume-driven campaigns sit awkwardly with Goetzen's stated bar. They have written that content which could sit on a competitor's blog with the logo swapped adds noise, and prefer a curated 200 survey respondents to any 2,000—a preference for fit over volume.
A campaign that requires disclosed sponsorship formats cannot be scoped from this evidence. Ask directly what paid formats exist and on which channels rather than assuming a standard package.
Brief
A vendor wants step-by-step technical tutorials: tool-by-tool setup walkthroughs and implementation instructions for building AI workflows.
Decision
The posts do not support this, though they do not rule it out.
Reasons
The three posts argue that demos fail in real marketing work and that testing should precede deployment. They do not teach a specific tool or show a build sequence.
The posts show no instructional format. Before offering a sponsor reproducible tutorials, ask whether Goetzen can provide that format.
A worked campaign example
Angle
Take Goetzen's method literally: make the campaign a documented sandbox test, not another slick demo. The sponsor and creator agree on one workflow for one real, defined marketing task; run it in a safe environment first on the sponsor's own tooling; record where it breaks; then publish what survives and move it into production with the sponsor's product in the path.
Brief
A B2B martech company's need, restated as a worked angle: co-publish a practical AI workflow adoption series for in-house B2B marketing teams.
Sequence
Start with a working session. Pick the single workflow and real task it will be tested against. The posts do not describe Goetzen's internal workflows, so choose the specific workflow with them rather than promising one for the campaign.
Sandbox run. Practitioners at the sponsor use the workflow on a live but lower-stakes campaign, recording output quality and failure points. Goetzen's stated sequence is to test in a safe environment first, then implement where it counts; the campaign should visibly follow that order.
Publish the result: a longer joint write-up, the kind of asset the content-marketing post already routes readers to, plus one post from Goetzen stating the problem in their own framing, with the test results attached.
Apply the swap-the-logo test before publishing and say in the piece that it was applied. This uses Goetzen's own published standard as the editorial hook and gives the write-up a reason to stand on its own.
Research hook, if agreed: add a question to the next edition of the State of AI in B2B Marketing survey on where AI workflow adoption stalls, and give the sponsor early sight of that section. Goetzen shares the report with participants before public release; this proposal would need to be negotiated, not treated as an existing offer.
Handoff asset: a short decision guide for in-house teams on what to test in a sandbox and what has to be true before a workflow goes into production, with a next step into the sponsor's product.
What to verify before contracting
Confirm which entity would contract and publish, who signs, and whether sponsor content runs through Goetzen's personal channels or an organizational one.
Confirm the price, disclosure wording, approval rights, and whether Goetzen will put their name on sponsor-branded content.
Ask for audience composition and results from any past sponsorships on comparable topics; the only available figure is a follower count with no breakdown.
Confirm whether a video, webinar, or event format is available before including one in the brief.
Do not include reach or engagement guarantees in the brief. Nothing reviewed supports them; the campaign's value rests on documented usefulness, not volume.
Why it works
It turns a claim Goetzen already makes publicly into a test with a visible result—the difference between a demo and a clear result.
It targets in-house teams with no exploration time by doing the exploration and publishing the outcome, matching Goetzen's description of who is stuck and why.
The published write-up would still help a buyer who had never heard of the sponsor, meeting Goetzen's stated standard for content that survives after routine publishing stopped returning results.
Choose this route when the campaign's real objective is search visibility and authority building inside one vertical, rather than helping a marketing team put AI into its working week.
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In two posts, Mansoor Ali draws a line between publishing and being seen: content and visibility are two different things; a brand can publish dozens of articles and still struggle in search.
They argue that visibility comes from being present on the right platforms in front of the right audience, and that quality placements beat producing more content.
A second post sharpens the point: 30 backlinks, DR 70+ sites, 20 referring domains and 100% link delivery still say little about whether placements strengthened the brand, because a SaaS company should be visible around SaaS and a fintech company around finance.
If the campaign is really about category authority through placements and the workflow-adoption framing is incidental, this is the more direct match on subject.
The stated decision is a content partnership on practical AI marketing workflow adoption for in-house B2B marketing teams who lack time to explore.
These posts focus instead on where content gets published and whether placements build context and authority—not on AI tooling, workflow testing, demos failing in real use or adoption sequencing.
Switching to this route means buying search-authority work; the martech company would still need someone else for the adoption story.
Before proceeding, ask whether partner placements are offered and request sponsorship formats, rates and audience composition.
Choose this route when the bottleneck the buyer is trying to fix is hiring capacity, not content.
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In two posts, Jake Huber runs the arithmetic on a first full-time recruiter for Seed and Series A teams: benefits, taxes, an ATS, a LinkedIn Recruiter seat and sourcing tools sit on top of base salary; ramp takes four to six weeks; and the hiring plan the recruiter was hired against can change inside a quarter.
They argue the move is clearly right only above roughly 15 hires a year across repeatable roles.
A second post separates two failure modes a founder usually confuses: nobody good entering the funnel is a sourcing problem, while good candidates dying inside the process is a process problem.
Buying sourcing when the problem is process makes the leak more expensive.
If a sponsor's actual goal is to reach founders and early-stage operators deciding how to staff up, this framing speaks to that decision directly.
This creator has no overlap with a content partnership on practical AI marketing workflow adoption for in-house B2B marketing teams.
The posts never discuss B2B content, AI marketing workflows, in-house marketing teams or sponsorship; the call to action is a direct message for hiring help.
The audience is Seed and Series A founders and hiring managers, not in-house B2B marketing teams under pressure to hit campaign targets.
Before proceeding, ask for sponsorship formats, rates and an audience breakdown.
Choose this route when the channel is owned email for an ecommerce or direct-to-consumer brand, and the work is diagnosing and repairing live lifecycle flows.
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The posts offer unusually concrete methods.
In one, a brand that switched email vendors discovered a month later that every image in every automation had vanished because the old vendor had hosted them.
Raymond C. found the problem in week one of onboarding and traced it to flows running unattended: nobody opens their own welcome flow, so broken automation stays broken.
The fix is specific: host images inside the brand's own account, and audit flows weekly by opening them on a phone as a subscriber.
A second post sets out a reactivation procedure after a year on a different sending platform: build a baseline from everyone who opened at least twice in the last three months, split the rest into a second bucket, run a 25% sample, keep the blend near a 40% open line, hold the sample size rather than raising it to bring people back faster, then rebuild the engagement baseline across 30, 60, 90 and 120 days.
For a retention-focused sponsor, this is a tested operating sequence rather than a point of view.
The buyer, channel and deliverable all differ from a content partnership on practical AI marketing workflow adoption for in-house B2B marketing teams.
The audience is ecommerce and DTC retention teams working in Klaviyo or Shopify, not in-house B2B marketing teams; the subject is email deliverability, list hygiene and flow QA, not AI workflow adoption.
The closest shared idea is testing before committing at scale, but these posts describe post-hoc repair of automations already running to paying customers—not a sandbox run on lower-stakes work before a broader rollout—so the campaign angle would not transfer.
Before proceeding, ask for sponsorship formats, rates and audience data.
Ferdinand Goetzen has three LinkedIn posts here: one on why content marketing no longer works by default and what makes content distinctive, one on AI workflows failing outside demos and how to test them before real use, and one recruiting B2B marketing leaders into a State of AI in B2B Marketing survey.
The posts have no publication dates, so they are presented as a set rather than a sequence.
LinkedIn showed about 11,600 followers on 8 October 2026; this figure does not indicate the creator's ranking or audience quality.
There are only three posts here.
They do not document an employer, sponsorship terms, rates, a media kit, past sponsored results, or audience demographics, and they do not cover Ferdinand Goetzen's wider career or signature work.
Posts by Mansoor Ali, Jake Huber, and Raymond C. offer partial comparisons; the texts are not verified as complete posts, and their account identities were not independently confirmed.
