Joshua Stout shares practical advice and experiments on LinkedIn Ads and B2B demand generation, including campaign targeting, measurement, and how paid and organic marketing can support longer sales cycles.
Joshua Stout is most useful for diagnosing LinkedIn Ads and B2B demand generation problems with practical tests.
Best for
B2B SaaS or mid-market software teams with LinkedIn Ads clicks but few workable sales leads.
Not for
Consumer, retail, or short-window paid social campaigns on Instagram or TikTok judged by purchases.
Key takeaways
A one-month personalization test saw CPM and click-through rate double while cost per click stayed level.
Stout says it is too early to credit personalization for the result.
Failure patterns include Audience Expansion widening a tight target and CRM recording paid touch as Direct.
Check first: Confirm audience demographics, seniority, job function, and engagement data before contacting them.
Main ideas
B2B demand generation is slow, and paid and organic work together · Sell to the buying committee, not to one lead · Test one variable at a time, and do not call the result after a month
Joshua Stout writes about LinkedIn Ads and B2B demand generation, with a practical focus on campaign targeting, measurement, and the long path from early engagement to a sale. Their posts help readers understand why campaigns underperform—and why a click or short-term lead count may not tell the whole story.
Stout pairs concrete campaign problems with specific reasoning.
In two Halloween-themed posts, humor makes issues such as audience expansion, targeting drift, weak lead quality, and premature campaign shutdown easy to recognize.
A separate personalization test tracks the numbers over a month: CPM and click-through rate initially doubled while cost per click stayed level, then cost per click fell slightly below the control.
They describe the setup, consider CRM attribution, and say it is too early to credit personalization for the result.
In a post about a Factors.ai report, they use reported findings on buying committees and long sales cycles to argue that paid and organic engagement can precede a deal by months.
Together, these examples offer readers a vocabulary for diagnosing LinkedIn Ads and a grounded example of how to interpret early results without overstating what they prove.
Joshua Stout writes about LinkedIn Ads and B2B demand generation from the position that most campaign problems are process problems you can name and check.
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Three priorities run through the posts: demand generation works on a long timeline and across a whole buying committee rather than a single lead; changes should be tested one variable at a time and read against CRM data instead of early headline metrics; and the fastest way to help a practitioner is to name the failure patterns they will actually meet — audience expansion, attribution that credits marketing with nothing, campaigns left running or killed too soon, and every audience sent to the same demo page.
B2B demand generation is slow, and paid and organic work together
Stout argues that companies see ads and content for months before they ever appear as a lead or deal, so measuring LinkedIn Ads on short-window lead counts misreads what the channel does.
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In this view, demand generation means building awareness, nurturing prospects, staying in front of the buying committee, and building recall and trust over time, with organic and paid supporting each other rather than competing for credit.
The Factors.ai study Stout summarises found that meaningful LinkedIn engagement starts an average of 124 days before a deal exists in the CRM, and that accounts engaging with both paid and organic LinkedIn show a win rate 10.8 points above baseline. They say this matches what they had been seeing, with the report backing it up at a much larger scale.
Stout's second recurring point is that a deal is shaped by several people, so a campaign judged on single leads undercounts its effect.
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Engaging more people before a deal is created is associated with better outcomes, and the combination of roles matters more than headcount alone.
The same study found that engaging six or more people before a deal is created is associated with a 17.1 percentage point lift in win rate, and that engaging the end user, the influencer and the technical evaluator together is associated with a 22 point lift. They highlight these as the findings that stood out.
Test one variable at a time, and do not call the result after a month
Stout treats careful testing as the way to settle LinkedIn Ads questions: duplicate the campaign, change one thing, keep targeting and setup the same, then read results beyond clicks — including which campaign the CRM credits with the lead.
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The same post tracks LinkedIn's own personalization rollout as it moved from ad-copy intros into Lead Gen Forms and, reportedly, Message Ads subject lines. Stout does not declare a winner on early data: the test looked like a loss on some metrics and a gain on others at once.
In a one-month personalization test, the personalization campaign's cost per thousand impressions and click-through rate were both double the control, leaving cost per click exactly the same for a few weeks.
Over the following week, the cost per thousand impressions began to drop and cost per click fell slightly below the control.
Both campaigns attributed an opportunity in the CRM, but the personalization campaign also attributed a lead, while reach, frequency and audience penetration were about the same.
Stout interpreted the attribution path but said it was still too early to say whether personalization was responsible.
The early numbers moved in an unexpected direction before settling.
Stout described the doubled cost per thousand impressions alongside doubled click-through as 'just weird,' then reported the cost per thousand impressions falling and cost per click dipping below the control.
They kept the test running rather than drawing a conclusion.
The personalization feature itself is also described as expanding — from ad-copy intros using company name, first name, industry and job title, to Lead Gen Forms, to a reported move into Message Ads subject lines.
Stout credits AJ Wilcox's podcast, where Wilcox in turn credited Anthony Blatner.
Stout's most distinctive habit is teaching through comic catalogues of what goes wrong in LinkedIn Ads, in roughly the order a practitioner meets it: audience expansion wrecking a carefully built target, campaigns left running long after anyone remembers why, attribution that records a demo as coming from nowhere, every audience funnelled to the same demo page regardless of temperature, and campaigns judged within days of launch.
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The humour is the delivery method; the substance is a checklist readers can run against a real account.
The haunted house post walks room by room: a campaign with audience expansion turned on, a $32 cost per click, a CRM listing the source as 'Direct,' a client pausing a campaign six days after launch because it had no leads, a plan to send all traffic to the demo page, and a lead report where 14 leads include 13 free email addresses — ending in a room containing only a laptop showing 0 leads against $1,738 in spend and a whisper that LinkedIn Ads don't work. The companion post names seven patterns as horror villains, including the Audience Expander turning 45,000 qualified prospects into 1.8 million, the Zombie Campaign still spending $200 a day after 14 months on the same ads, and the ICP Shapeshifter turning directors at manufacturing companies of 500 to 5,000 employees into 27 industries and 84 job titles.
It answers "what actually goes wrong in LinkedIn Ads campaigns?" in one short read and gives newcomers the vocabulary — expansion, attribution, demo-page routing, lead quality — that Joshua Stout's other posts assume.
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An October post that walks the reader through a haunted house where each room is a familiar LinkedIn Ads problem.
The rooms are specific: a campaign with Audience Expansion turned on, a $32 cost per click, a CRM that lists the source as "Direct," a client who paused a campaign six days after launch because it had no leads, a plan to send all traffic to the demo page, and a lead report where 14 leads include 13 free email addresses.
The last room holds nothing but a laptop with Campaign Manager open showing 0 leads against $1,738 in spend, and a voice behind the reader saying "LinkedIn Ads don't work."
The humor is the delivery method; the content is a list of failure patterns in the order a practitioner tends to meet them.
For brands planning a campaign
Useful as an opening diagnostic when a brand says its LinkedIn Ads are not working.
Each room poses a concrete question about the account: Is Audience Expansion on?
Is the CRM crediting paid touchpoints?
Was the campaign given enough time?
Does every audience get the same demo-page destination?
Are leads reaching an address the sales team can use?
It offers the clearest short statement of the problems Joshua Stout writes about, covering campaign lifespan, network expansion, and targeting drift — topics the haunted house post doesn't.
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Readers looking for the recurring ideas in one place should start with this post and the haunted house post.
A second October post casts seven recurring mistakes as horror villains.
The Audience Expander turns a carefully built audience of 45,000 qualified prospects into 1.8 million.
The Zombie Campaign has run for 14 months with the same ads, copy, and audiences at $200 a day, and nobody remembers who launched it.
The Attribution Ghost haunts a prospect who sees an ad, returns three weeks later, and books a demo, only for the CRM to record the source as "Direct."
The Budget Vampire sells cheaper clicks through Audience Network and delivers impressions nobody can place.
The Demo Page Slasher sends cold prospects, warm prospects, and someone who watched three seconds of video to the same demo page.
The Six-Day Executioner asks on Friday why a Monday campaign has no leads.
The ICP Shapeshifter lets a tight target of directors at manufacturing companies with 500–5,000 employees expand to 27 industries and 84 job titles.
For brands planning a campaign
Use it as a checklist for evaluating an agency, an in-house team, or a proposed plan. The named patterns translate into questions about account structure, retargeting rules, and how quickly a program will be judged — issues a brand should settle before approving budget.
It answers "what does careful LinkedIn Ads testing look like?" with an actual design and early numbers, including the awkward first month.
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Newcomers can see how Joshua Stout isolates a variable and reads attribution rather than reporting only the best-looking metric.
A progress report on a controlled test of personalization in LinkedIn Ads.
The setup is clear: one campaign without personalization as the control, and a duplicate with personalization added to the intros of Single Image bottom-of-funnel ads.
Everything else stayed the same, including a direct call to action to book a demo and an audience of prospects who had already engaged with the brand.
After one month, the personalization campaign showed double the control's cost per thousand impressions and double its click-through rate, leaving the cost per click exactly the same.
That pattern held for a few weeks.
Later, the cost per thousand impressions began to fall and the cost per click dropped slightly below the control.
Both campaigns attributed an opportunity in the CRM, but the personalization campaign also attributed a lead.
Reach, frequency, and audience penetration were about the same.
Stout interprets the attribution path but says it is too early to tell whether personalization is responsible; the test will continue.
For brands planning a campaign
A practical model for testing a LinkedIn Ads change: duplicate the campaign, vary one thing, keep the targeting and setup the same, and look beyond clicks to see which campaign the CRM credits with the lead. The post also cautions against stopping or scaling based on a single early metric: the headline numbers moved in an unexpected direction before settling.
It answers "why do LinkedIn Ads look slow, and who is in the buying group?" with numbers from a large study.
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It also shows how Joshua Stout interprets evidence that challenges short-term campaign judgments.
A post summarizing a third-party report on LinkedIn Ads and adding the author's own analysis.
The report covered more than 850 B2B companies, over $150 million in LinkedIn ad spend, and 50,000-plus closed deals worth over $5 billion.
It highlights four findings: meaningful LinkedIn engagement starts an average of 124 days before a deal exists in the CRM; engaging six or more people before a deal is created is associated with a 17.1 percentage point lift in win rate; engaging the end user, the influencer, and the technical evaluator is associated with a 22 point lift; and accounts that engage with both paid and organic LinkedIn show a 10.8 point higher win rate than baseline.
Stout connects these findings to a point they have made before: companies see ads and content for months before becoming a lead or a deal, so demand generation takes time and paid and organic work together.
For brands planning a campaign
Use it to set internal expectations: measure success over a long horizon, plan for several people on the buying committee rather than one lead, and fund organic content alongside paid. The figures also give finance or sales teams a starting point for conversations when they judge LinkedIn Ads on short-window lead counts.
Joshua Stout tests whether adding personalization—such as First Name, Industry, or Job Title—to LinkedIn ad copy increases engagement and conversion rates, even when initial CPC is higher.
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For brands planning a campaign
Brands should personalize ads to increase click-through rates and lead quality; personalization appears to drive deeper engagement and leads that convert into opportunities.
Research shows that B2B win rates increase by up to 22 percentage points when strategies target multiple decision-makers—the end user, influencer, and technical evaluator—instead of a single point of contact.
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For brands planning a campaign
Campaigns should reach the entire buying committee, not focus on individual leads, to maximize deal velocity.
Joshua Stout advises B2B marketers to avoid common pitfalls: overly broad Audience Expand features, campaigns that run too long without results (Zombie Campaigns), and sending all traffic to a generic demo page.
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For brands planning a campaign
Brands need clear measurement goals and well-defined Ideal Customer Profiles (ICP) to avoid wasting ad spend on irrelevant audiences or stale campaigns.
The posts don’t document any sponsorship, client, employment or ownership relationship.
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Joshua Stout writes about running LinkedIn Ads campaigns and shares third-party research.
They discuss and recommend a report from Factors.ai without stating any commercial tie.
Other practitioners and platforms appear only in passing.
Ask about any ongoing relationships before booking.
Factors.ai
Mention
Stout discusses a Factors.ai report on LinkedIn Ads and B2B buying behavior, summarizes its figures and recommends it to people running LinkedIn Ads or working in B2B marketing.
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The post doesn’t state whether Stout was paid, partnered with Factors.ai or worked for the company; ask about any commercial relationship before booking.
Joshua Stout's material is a running set of field notes from someone who manages LinkedIn Ads campaigns.
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Start with the two Halloween-themed posts, which offer a checklist of account failure patterns: audience expansion widening a carefully built target, CRM records showing "Direct" when ads did the work, campaigns paused six days after launch, 14 leads that turn out to be 13 free email addresses, and every audience sent to the same demo page regardless of how warm it is.
The personalization test report shows the method behind their opinions: duplicate the campaign, change one thing, compare CRM outcomes as well as clicks, and refuse to call a winner after a month.
A third post develops the second theme: B2B demand generation runs on a long timeline and across a buying committee, with figures relayed from a Factors.ai study.
Follow this work for LinkedIn Ads tactics, testing discipline, and arguments about how demand generation should be measured.
Both write for people running B2B LinkedIn Ads, and both argue the platform is usually blamed for problems that are really about friction or measurement.
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Tom Casano's post on 80% of LinkedIn users being on mobile argues that sending mobile users to a website costs a premium click and converts badly, with Lead Gen Forms as the fix.
A companion post argues that last-touch attribution structurally cannot capture a nine-month, multi-stakeholder deal, and that influenced pipeline is the metric that matters.
Joshua Stout covers the same territory, arguing about influenced versus last-click attribution and friction.
Tom Casano writes prescriptive, number-led posts aimed at fixing an account now, using conversion-rate ranges and cost-per-lead comparisons.
They also cover LinkedIn Lead Gen Forms and attribution tooling as standalone topics.
Joshua Stout narrates their own running experiments and turns mistakes into satirical catalogues.
The study they summarise focuses on demand-generation timelines and buying-committee coverage, rather than campaign mechanics.
Both treat hidden measurement failure as the reason paid campaigns appear to underperform.
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Victoria Chapman's post describes conversion tracking breaking silently for six months while dashboards still look fine and CRM records fall back to "direct" or "unknown"—the same complaint Joshua Stout makes about CRM attribution crediting marketing with nothing.
A second post attacks agency reports full of impressions and click-through rates, arguing for cost per qualified lead and lead-to-opportunity rates instead.
That is close to the argument that a channel should be judged on pipeline rather than headline metrics.
Victoria Chapman writes from inside a UK lead generation agency, addressing clients choosing or leaving an agency and covering Google Ads and Meta alongside LinkedIn. Joshua Stout writes as a practitioner testing LinkedIn Ads campaigns, and their posts stay within that channel.
The overlap is in reasoning style more than subject: both take a tactic that looks like a straightforward saving and show that the visible cost is not the real cost.
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Both also ask what a change lets you do that you cannot do today. Isaac Hyman's email platform post runs the arithmetic of saving $50,000 on software while losing $500,000 in revenue, much like Joshua Stout holds cost per click fixed while checking which campaign the CRM actually credits.
Isaac Hyman works in ecommerce email and lifecycle marketing, covering ESP migrations, segmentation, and Klaviyo design—not LinkedIn Ads or B2B demand generation.
If the campaign is aimed at LinkedIn Ads and B2B demand-generation practitioners, Joshua Stout's LinkedIn presence is relevant by subject matter: the posts discuss LinkedIn Ads testing, personalization, audience expansion, attribution, and demand-generation timelines.
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The only measured audience figure is 12,014 LinkedIn followers on 11 Oct 2026.
That figure shows a sizable LinkedIn following but does not establish audience geography, seniority, company size, job function, or engagement rate, so it cannot by itself confirm fit with a specific buyer's ideal customer profile.
Treat the posts as an indication of topic appeal to B2B marketers, not as measured audience composition.
A practical next step is to request recent LinkedIn follower demographics and, if seniority or job function matters to a campaign aimed at LinkedIn Ads and B2B demand-generation practitioners, audience survey results or first-party campaign data broken out by segment.
Four LinkedIn posts by Joshua Stout describe no sponsorship, paid partnership, or brand-collaboration format, and provide no official partnership inquiry route.
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They write as a practitioner sharing LinkedIn Ads testing and demand generation analysis: a one-month personalization test with campaign setup details, a summary of the Factors.ai LinkedIn Ads report, and two Halloween-themed posts cataloguing common B2B advertising failures.
The Factors.ai report and an AJ Wilcox podcast episode are cited as sources they read and reacted to, not as commercial arrangements.
Ask Stout directly by LinkedIn message what paid formats are available and what disclosure and approval practices apply.
A campaign aimed at a B2B SaaS or mid-market software company whose LinkedIn Ads program produces clicks but few leads the sales team can work—and whose team is debating whether to keep funding it.
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Joshua Stout fits this brief because of specific work, not follower count.
Their posts show a practitioner running controlled LinkedIn Ads tests on their own accounts and reporting the awkward middle, not just the win.
In the personalization test, they set up one campaign as a control, duplicated it with personalization added to the intros of Single Image bottom-of-funnel ads, kept everything else the same, and then read cost per thousand impressions, click-through rate, cost per click, reach, frequency, audience penetration, and which campaign the CRM credited with a lead and an opportunity.
That design discipline helps a brand determine whether a change worked or the month was simply different.
The second reason is diagnostic vocabulary.
The haunted house and horror-villain posts give buyers a concrete list of failure patterns: Audience Expansion widening a tight target; a CRM recording a paid touch as "Direct"; a campaign judged after six days; every audience routed to the same demo page; lead lists full of free email addresses; campaigns running 14 months untouched at $200 a day; and an ideal customer profile drifting from directors at manufacturing companies with 500 to 5,000 employees to 27 industries and 84 job titles.
A brand can check its own account against that list before spending more.
The third reason is expectation setting.
The Factors.ai summary gives a brand numbers to take to finance or sales teams that judge paid social on short-window lead counts: meaningful engagement starting an average of 124 days before a deal exists in the CRM; a 17.1 percentage point win-rate lift associated with engaging six or more people before a deal is created; a 22 point lift associated with reaching the end user, the influencer, and the technical evaluator; and a 10.8 point higher win rate for accounts engaging with both paid and organic LinkedIn.
Whether the brand accepts those figures, they give the conversation a concrete shape.
The posts do not specify sponsorship or brand-collaboration formats, rates, availability, or an inquiry route.
Buyers should ask directly about available formats and terms before treating this as a packaged sponsored placement; consulting, advisory, or co-created content may be a better starting point.
When to choose a specialist
A campaign aimed at a consumer packaged-goods or direct-to-consumer brand seeking a creator-led paid social push on Instagram or TikTok, judged on purchases or sign-ups within three to four weeks.
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The posts do not support this brief.
They address B2B marketers on LinkedIn, covering LinkedIn Ads mechanics, CRM attribution, buying committees, and demand-generation timelines; they show no consumer, retail, or entertainment work, nor short-window conversion campaigns.
They also mention no Instagram, TikTok, YouTube, podcast, newsletter, or video channel, and provide no transcript or video content to assess.
The only audience measurement published is a LinkedIn follower count observed on 11 Oct 2026; there is no audience composition, geography, seniority, purchase behavior, or engagement data.
A consumer brand should ask for evidence about relevant channels and audience fit before proceeding.
The timeline is another mismatch.
The strongest content-led finding is that meaningful engagement can start around 124 days before a deal exists in the CRM.
That supports patience in a B2B cycle but is a poor fit for a four-week consumer performance test, which would end before the pattern described has a chance to appear.
The posts do not specify sponsorship formats, a rate card, media kit, or disclosure and approval practices.
A brand relying on a standard sponsored post or integrated paid placement would be making an assumption the posts do not support.
A closer, but still unconfirmed, fit is a B2B company seeking a guaranteed, packaged ad placement with published pricing: the topic fit is strong, but the commercial fit needs to be confirmed directly.
A worked campaign example
Angle
Run the creator’s one-variable personalization test, then add a buying-committee expansion so results are judged on pipeline, not clicks.
Brief
A mid-market B2B SaaS company, roughly 90 days into the campaign, with LinkedIn Ads already running and a retargeting audience of engaged prospects. The team believes personalization is worth testing, wants more leads the sales team accepts, and needs evidence for a sceptical sales director who calls the channel slow.
Deliverables
Account diagnostic. Check the account against the named failure patterns: whether Audience Expansion is on; whether the CRM credits paid touches or defaults to "Direct"; how long the team gives a campaign before pausing; whether every audience lands on the same demo page; and what share of recent leads used a free email address. Deliver a short, ranked list of causes and the check used for each.
Test design. Duplicate the existing bottom-of-funnel Single Image campaign. Keep targeting, bids, formats, and the call to book a demo identical. Add personalization to the intros in one copy only, varying the company name, job title, or industry. Define in advance which numbers to read and when: cost per thousand impressions, click-through rate, cost per click, reach, frequency, audience penetration, and, separately, which campaign the CRM credits with a lead and an opportunity.
Reading discipline. Report the first month even if the headline pattern looks odd, as it did in the creator’s own test: cost per thousand impressions and click-through rate both doubled, leaving cost per click unchanged, before shifting in the following weeks. State plainly whether the results support a conclusion or require more time, and record the apparent attribution path, not just the end count.
Buying-committee layer. Add a second audience layer that reaches the end user, the influencer, and the technical evaluator separately, rather than targeting one lead contact. Brief the team that the reviewed study associates engaging six or more people before a deal is created with a 17.1 percentage point lift in win rate, and reaching those three roles with a 22 point lift.
Internal expectations brief. Prepare a one-page note for the sales director separating what the channel does in the first weeks from what the reviewed study says happens over months. Include the 124-day average gap between meaningful engagement and a deal entering the CRM, and the 10.8 point higher win rate the study associates with accounts engaging on both paid and organic LinkedIn.
Why this fits
Every element draws on the creator’s posts rather than a generic description: the test structure comes from the personalization post, the diagnostic questions from the haunted house and horror-villain posts, and the committee and timeline arguments from the Factors.ai summary. The angle does not depend on a documented sponsorship format, rate card, or audience demographics, and it gives the brand a decision to make at the end of 90 days, even if commercial terms still need to be agreed directly.
Choose Victoria Chapman over Joshua Stout when the need is multi-channel, not LinkedIn-only.
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The two posts are written for companies running Google Ads, LinkedIn Ads, and Meta at once, and describe measurement failures rather than ad-setup failures.
The June 3 post covers conversion tracking that broke in December and went unnoticed until May, leaving six months of budget decisions based on partial data: campaigns were cut because their conversions stopped logging, while others got credit simply because everything else was broken.
The June 4 post describes agency reports full of impressions, clicks, and conversion rates while pipeline and revenue stay flat.
It names the alternatives it wants reported: cost per qualified lead, lead-to-opportunity conversion rate, and customer acquisition cost against lifetime value.
A B2B SaaS company whose internal argument is “we think the channel works but the CRM can't prove it” will get more from this framing than from a LinkedIn campaign experiment.
Chapman is also the better choice when the buyer wants an agency relationship rather than an advisor or co-created content: both posts close with a direct agency offer aimed at companies spending around £2,000 a month or more.
The coverage is broad but shallow for a campaign aimed specifically at LinkedIn.
Neither post shows a controlled test on LinkedIn itself, so a brand wanting to know whether a specific campaign change moved results gets a tracking audit and reporting framework instead of a test design.
The advice applies broadly to paid search and paid social: verify the tags, check attribution, and report on revenue-connected metrics.
It does not tell a team which LinkedIn objective, format, or audience setting to change.
Chapman is a stronger fit when the buyer needs measurement discipline across several channels, and a weaker fit when they need the LinkedIn account diagnosed and tested.
Choose Tom Casano over Joshua Stout when the account is producing clicks but leads stop at the form.
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The June 3 post makes one argument with a single lever: most LinkedIn ad accounts underperform because campaigns send mobile users to a website, while the platform's own lead forms remove that friction.
It walks through the cost of the current setup, from seeing an ad while scrolling to typing details with a thumb, then offers an alternative with conversion ranges: gated content at 10 to 30 percent, bottom-of-funnel demos at 1 to 5 percent, and a typical website landing page around 1 percent.
The May 27 post addresses the other half of the challenge: overcoming executive skepticism about LinkedIn.
It contrasts last-touch attribution, built for banner ads, with a nine-month deal involving seven stakeholders, and points to influenced pipeline and revenue, LinkedIn's revenue attribution report, and third-party tools that match closed-won deals back to ad exposure even when nobody clicked.
A brand whose sales director dismisses the channel because the CRM shows “Direct” gets a ready-made argument.
Casano is also the better pick when the buyer has one narrow tactic to fix rather than a programme to design.
The recommendations are confident and use round numbers, but the posts do not show the underlying test, sample, or control.
Joshua Stout reports the awkward middle of an experiment, including a month when cost per thousand impressions and click-through rate both doubled, leaving cost per click unchanged.
Casano's posts present conclusions without explaining how they were reached, so a cautious buyer should verify the claims against their own account before acting.
The scope is also tighter: both posts stay within LinkedIn Ads mechanics and attribution reporting; neither covers buying committees, demand-generation timelines, or the multi-month gap between engagement and a deal that appears in Joshua Stout's posts.
Choose Casano for a decisive tactical change, and Joshua Stout when the buyer needs to see how a conclusion was reached.
Choose Isaac Hyman only if the need is not customer acquisition at all.
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The October 2 post argues against switching email service providers to lower platform fees because migration affects segmentation, integrations, automations, reporting, and personalization—and a $50,000 annual software saving can cost $500,000 in incremental revenue.
The December 26 post argues that low click rates are a strategy problem, not a design problem.
It describes a campaign using dynamic content to show different creative to different segments, producing $180,000 from half the list.
For a B2B SaaS company, the useful scenario is narrower than the others: ads are working and leads arrive, but revenue still does not move because value leaks from the follow-up email, nurture sequence, or landing page after the click.
Hyman is the right alternative when the buyer wants to fix the destination rather than the traffic.
The buyer task is different, not just the method.
Both posts come from an ecommerce retention and email-marketing context, use revenue-per-send and click-rate metrics that a B2B sales cycle does not share, and offer no guidance on LinkedIn Ads, buying committees, or pipeline attribution.
A B2B brand adopting the framing would have to translate segmentation and dynamic content into its own list structure and buying-group logic; the posts offer no B2B example to follow.
Hyman's strongest fit is as a second specialist brought in after the acquisition channel is sorted, not as a replacement for the LinkedIn work Joshua Stout describes.
This page covers four LinkedIn posts by Joshua Stout.
Two are October-themed posts that catalogue common LinkedIn Ads failures, one reports the first month of a personalization test with a control campaign, and one summarises a Factors.ai report on LinkedIn buying behaviour.
The posts do not state publication dates, so the content is described by theme rather than by date.
The account's follower count was observed on 11 October 2026.
The Factors.ai report itself is not included here, so its figures are presented as Joshua Stout's summary of that report and credited to it, not as Stout's own measurements; the same applies to podcast remarks Stout attributes to other practitioners by name.
The posts cover LinkedIn only.
They do not document sponsorship or partnership formats, audience demographics or engagement data, or an earlier body of work such as a book, show, or newsletter, so no claim is made about those.
