Sam Dunning is a B2B SaaS marketer who helps software companies attract sales-ready prospects through SEO and AI search. His advice focuses on buyer-focused comparison pages, useful product evidence, and measuring how marketing contributes to demos and revenue.
Useful for B2B SaaS SEO and AI search advice that turns buyer comparison pages into sales-ready demos.
Best for
B2B SaaS brands or agencies seeking organic demos through AI search and buyer-intent pages.
Not for
Enterprise launch needing one sponsored post, gated whitepaper, no website changes, no ongoing content.
Key takeaways
Target buyer-intent searches like competitor alternatives and best category software, not broad informational queries.
Keep pricing, proof, and specifications readable as text, not locked in images, widgets, or video.
Last touch can obscure how search, content, and recommendations built trust before outbound reply.
Check first: Confirm audience demographics and whether cited client results are independently substantiated.
Main ideas
Target searches that signal a buying decision · Give buyers evidence, not more generic AI-written pages · Make commercial pages useful to AI search systems
Sam Dunning is a B2B SaaS marketer known for advice on turning SEO and AI search into sales-ready demos.
His posts focus on pages for buyers comparing products, choosing alternatives or evaluating fit—not generic traffic.
He also writes about making a product’s evidence clear on the page and tracking how marketing influences a sale, even when another channel gets the final credit.
Dunning makes search strategy concrete by linking buyer questions to page choices and sales evidence.
He recommends starting with specific, high-intent searches, then building or improving comparison, alternatives and use-case pages with pricing, product proof, customer examples and real sales FAQs.
In a post about RB2B, he argues that this information should be readable on the page and kept current, rather than hidden in images, widgets or video.
He also points to a measurement problem: a buyer may research through search, content and recommendations before replying to an outbound email, so the last interaction can obscure what helped build trust.
Read his work for practical examples of this commercial, evidence-led approach; the client results he cites are claims in his posts, not independently substantiated in the supplied material.
Sam Dunning’s recurring point is that B2B SEO should help buyers choose, not just attract visitors.
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He argues for specific pages with useful product evidence, then measuring how those pages influence sales—even when the final recorded touch is outbound. His advice applies that same buyer-focused approach to AI search.
Target searches that signal a buying decision
Dunning favors narrower searches from people comparing vendors over broad, early-stage queries.
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His reasoning is that a small number of people with a clear need may matter more than a large audience reading general advice.
He contrasts “what is a CRM” with searches such as “best proposal software for fintech teams” and “LinkedIn ads agency for B2B.” He reports that pages for the latter agency searches brought one client three inbound demos worth $30,000 or more in annual contract value within 12 days.
Give buyers evidence, not more generic AI-written pages
AI can help with research and first drafts, Dunning says, but it cannot supply a company’s customer knowledge or make interchangeable content trustworthy.
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Pages should show how a product works, explain the limits of its proof, answer sales questions, and say who may not be a good fit.
His checklist includes real product screenshots, questions drawn from sales and customer-success conversations, setup requirements, and customer examples or original data. He names VerifyTreatment and Proposify as clients for whom Breaking B2B builds pages this way.
This quality test complements his focus on producing pages for particular buying searches: the right topic is not enough if the page offers no distinct evidence.
Dunning argues that AI assistants do not need a separate content strategy; they need clear, current answers to buyer questions.
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Product details and evidence must be readable on the page, and comparison or pricing information needs updating as products and competitors change.
In a post about RB2B, he describes building alternatives, comparisons, use-case, pricing, and review pages. He says the pages explain what the product does, who it suits, where another tool may fit better, and include pricing, features, and customer feedback.
Do not mistake the last touch for the whole buying journey
Dunning warns that a sales email can get recorded as the source of a deal even when search, comparisons, video, or social content helped make the buyer ready to respond.
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He recommends recording both how a buyer first found the business and what influenced the decision.
His proposed discovery-call questions cover tracked source, first discovery, and buying influence. He also suggests asking signup and demo leads how they heard about the company to capture research that happened inside AI tools without a site visit.
Search changes, but buyers still compare expensive software
Dunning pushes back on repeated claims that SEO is finished.
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His argument is not that search stays the same: buyers now move among search engines, AI tools, review sites, video, and podcasts. The continuing opportunity, in his view, is to help them compare vendors, check pricing, and solve costly problems.
He lists earlier predictions about Google Ads, social media, voice search, and zero-click results before discussing AI assistants and agents. He describes LinkedIn, YouTube, the podcast, Google, and AI search as parts of one content engine.
It answers "what does Sam Dunning actually do day to day?" and lays out the sequence a newcomer would otherwise have to piece together from a dozen posts.
A seven-step walkthrough for turning SEO into sales conversations.
The steps: pick buyer-intent keywords and AI prompts instead of early-stage searches like "what is a CRM"; combine a four-column matrix (how people describe the offer, best-fit industries, competitors, customer pain points) into specific searches such as "best proposal software for fintech teams" or "Warmly alternatives"; build the page types AI pulls from, especially listicle-style "best of" pages with comparison tables, quotes, pros and cons and sales-derived FAQs; fix existing money pages that slipped or never get cited before writing new ones; earn brand mentions, not just backlinks, by getting into roundups, podcasts and unlinked name mentions; make sure AI crawlers and search engines can actually reach the site and that pages load fast; and track "dark AI search" by adding a "how did you hear about us" field to signup and demo forms and asking on sales calls.
Sam Dunning says this is based on running AI SEO for 51 SaaS clients at Breaking B2B and cites Wynter research that over 60% of B2B buyers use LLMs and Google together when evaluating software.
For brands planning a campaign
A sponsor with real competitors to compare against could use this as the brief for a sponsored comparison or alternatives page, or as the spine of a workshop for a marketing audience. It is also a checklist for assessing whether the sponsor's own money pages are good enough for an AI answer to cite.
This answers the practical question behind the method: does any of it actually get a brand named inside AI assistants?
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It is the closest thing in the supplied material to a case study.
Sam Dunning describes how RB2B, a visitor-identification product that shows which people and companies are browsing a site, came to be recommended by Meta's Muse and ChatGPT's Dot.
The account says there was no separate assistant-specific strategy; the work was building close-to-purchase pages — competitor alternatives, comparisons, use-case pages, pricing and review guides, and high-intent listicles — and keeping the decision-making information readable on the page rather than locked in images, JavaScript widgets or video.
Each page covers what the product is and who it is for, the buyer question in plain English, where it fits and where another tool might suit better, plus pricing, features and customer feedback.
Pages are refreshed as competitors change pricing and products add features.
Sam Dunning calls the current moment the biggest opportunity in 25 years of SEO and warns that businesses running SEO the old way are effectively getting their competitors cited instead of themselves.
For brands planning a campaign
A brand chasing AI-search visibility can lift the page types and the "agent-readable" requirement directly. A campaign could pair a creator explainer with an audit and rewrite of the sponsor's own commercial pages, using the RB2B example as the promised outcome.
Read this to understand how Sam Dunning responds to the recurring claim that search marketing is finished, and to see the business history in their own words.
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It is the best single piece for context on why the advice looks the way it does.
A timeline of predictions that SEO was over: Google Ads, social and mobile (2010–2015); voice search, featured snippets and zero-click (2015–2020); TikTok search, AI Overviews, ChatGPT and Claude (2020–2024); and AI agents (2026 onward).
Sam Dunning argues the constant is that buyers still research expensive software before talking to sales.
The 2020–2024 entry says buyers now move between Google, ChatGPT, review sites, competitors, YouTube and podcasts before a sales call, and describes an engine where LinkedIn, YouTube, the podcast, Google and AI search feed each other.
The post says that engine took the business from cold calling in a small room to what it describes as 2–3 inbound $60k+ demos daily and $300k+ in monthly recurring revenue.
For brands planning a campaign
Useful for a brand deciding whether to keep funding organic and AI-search work when outbound appears to be producing the pipeline. It also shows the channel mix as integrated rather than one-off — a sponsoring brand would be buying into a system that spans posts, video, podcast and pages.
This answers "why does SEO get cut even when it seems to be working?"
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It explains the measurement problem in plain narrative form and is the clearest example of how Sam Dunning frames the business case, not just the tactics.
A scenario in which a buyer finds a SaaS product in Google AI Overviews, reads comparison pages, checks competitors, watches YouTube and follows the founder on LinkedIn over several weeks, then replies to an SDR email.
The CRM records the deal as outbound-sourced, and at the next marketing meeting someone asks why money is still going into SEO.
Sam Dunning recommends capturing three things on every opportunity: tracked source (which channel and campaign brought them in), first discovery (where they originally heard about the business) and buying influence (what content, recommendations or interactions convinced them to talk).
The advice is to ask these on discovery calls, record the answers in the CRM and compare them with tracking data, especially in B2B where buyers research for weeks.
The warning: cut SEO because outbound is getting the credit, then wonder why outbound gets harder.
For brands planning a campaign
Strong fit for sponsors selling attribution, CRM or sales-intelligence software, or for a piece on marketing measurement. A campaign angle is a sponsored breakdown of how a specific company should re-instrument its discovery calls and CRM fields.
It answers "if volume AI content is easy and worthless, what is the actual standard?" in a short, usable checklist.
The argument starts with what volume AI content looks like: headings, FAQs, other people's statistics, a confident conclusion and a "book a demo" button — crawlable but forgettable, because the buyer still asks "why should I trust you?".
The fix is to give people enough evidence to decide: show the product or workflow in action with real screenshots, explain how the proof was collected and when it applies, pull recurring objections and FAQs from sales and customer-success calls, state where the product is a poor fit or what setup is required, and add customer examples or original data competitors cannot copy.
Two named client examples are given, VerifyTreatment and Proposify.
The recommendation is to use AI for research, outlines and first drafts, then add internal material from Slack and meetings — opinions, customer research and product positioning.
For brands planning a campaign
A sponsor shipping AI-assisted content can run their pages against this checklist. A campaign could be a teardown where Sam Dunning applies the criteria to the sponsor's existing pages on camera or in writing.
This answers "how fast does this work, and who is it for?" with named examples and timescales instead of claims.
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It is the piece to read before setting expectations on a campaign.
Two examples.
George runs LinkedIn and Meta ads for B2B clients and was not showing up when people searched for an agency to run those campaigns; pages targeting "LinkedIn ads agency for B2B" and "B2B Meta ads agency", some keywords estimated at around 10 searches a month, produced three quality inbound demos worth $30k+ ACV each within 12 days of going live, with two already opportunities.
Samu runs a B2B/SaaS YouTube agency working toward $3M ARR and was already getting 40 demos a month through LinkedIn and YouTube; long-tail pages such as "best YouTube agencies for B2B SaaS", some estimated at 20 searches a month, brought 7 x $60k+ inbound demos a few weeks in.
The post argues a 10-search-a-month keyword with clear buying intent beats 10,000 people reading a generic marketing article, and that competing with HubSpot or Salesforce on head terms is the wrong starting point for a smaller company.
For brands planning a campaign
Use this when a sponsor needs evidence that organic and AI-search work can produce revenue in weeks rather than a year, and to set realistic scope: low-volume, high-intent searches first. It also gives the shape of the deliverable — a set of comparison and alternatives pages.
Read this to see the formats and how they connect.
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It answers "what does a content day actually produce?" and shows the multi-channel habit behind the advice.
A plan for one paid day: short-form "ask the public" videos built around questions like "what is AEO", "how do you get recommended on Claude" and "what is the point of LinkedIn"; a recording with Tom Hunt breaking down a $10M ARR AI SEO strategy and his LinkedIn approach for the Breaking B2B YouTube channel and podcast; at least one long-form solo breakdown of a named SaaS company's AI SEO; and a wildcard guerrilla marketing play. The stated purpose is to feed what Sam Dunning calls a media flywheel, with the return framed simply: landing one client more than repays the spend.
For brands planning a campaign
Shows the format range a sponsor would be buying into — short-form video, long-form video, podcast and in-person content — and the habit of tying content days to client acquisition. Useful for planning a multi-format campaign rather than a single post.
Money keywords: low-volume searches with buying intent3
Differentiation against AI-generated content3
Attribution and the case for not cutting SEO3
The media flywheel: LinkedIn, YouTube, podcast and live content3
Format: numbered playbooks with client proof3
The recurring "SEO is dead" argument2
Money keywords: low-volume searches with buying intent
Sam Dunning's core argument is that B2B SaaS organic growth comes from searches made by people close to buying, not broad informational traffic.
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Sam Dunning points to phrases such as "[competitor] alternatives", "best [category] software" and niche terms like "LinkedIn ads agency for B2B", some with roughly 10 to 20 estimated monthly searches, and reports short paths from those pages to demos: three inbound demos worth $30k+ ACV each in 12 days for a LinkedIn ads agency client called George, and seven $60k+ inbound demos within weeks for a YouTube agency client called Samu. The posts contrast this with chasing "what is a CRM" queries and head terms dominated by HubSpot and Salesforce.
For brands planning a campaign
A brand selling a considered B2B purchase can judge this approach by whether its own category has comparison and alternative searches a small set of pages could win. It suits products with a defined competitor set and a long sales cycle, where a handful of qualified demos matter more than traffic volume.
Later posts centre on getting a brand cited and recommended by AI assistants.
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Sam Dunning describes Meta's Muse and ChatGPT's Dot recommending RB2B to users and argues the same pages that win Google AI Overviews, Perplexity and ChatGPT win those recommendations: competitor alternatives, comparisons, use-case pages, pricing and review guides, and buying-intent listicles.
The decision-making detail has to be readable on the page, not locked in images, JavaScript widgets or video, and commercial pages have to be refreshed as competitors change pricing and products add features.
In a seven-rule playbook Sam Dunning adds checking that robots.txt and crawler settings are not blocking AI tools, and that pages are indexed on Google and Bing.
For brands planning a campaign
Brands in software categories where buyers now ask an assistant "what's the best X for us?" can assess Sam Dunning on whether his pages make their product legible to those systems. A campaign fits products whose pricing, comparisons and proof can be published openly and kept current, rather than products that depend on gated content.
Sam Dunning repeatedly argues that publishing large volumes of AI-written posts is easy and largely pointless on its own.
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The test applied is whether a page gives a buyer enough to decide: real screenshots of the product or workflow in action, an explanation of how proof was collected and when it applies, FAQs pulled from sales and customer conversations, honest statements of where the product is a poor fit or needs setup, and customer examples or data competitors cannot copy. VerifyTreatment and Proposify are named as clients built this way, with AI used for research, outlines and first drafts before internal context is added.
The framing used across several posts is a three-part pattern: name the common belief, explain why it is wrong, then give the action and a client proof point. One post adds a four-column targeting matrix covering ways buyers describe the offer, best-fit industries, top competitors and real customer pain points, drawn from sales and support calls, then combined into long-tail phrases such as "best proposal software for fintech teams".
There is a tension in the corpus rather than a settled position: one playbook suggests asking Claude which pain points come up most, while another post insists AI does not know your customers, product or what sales hears on calls.
For brands planning a campaign
A brand whose product needs explaining can weigh whether the insistence on product evidence and fit language matches its position. It is a poor fit for brands unwilling to publish limitations or sales-call detail, and a good fit for products with a demo, workflow or dataset worth showing.
Sam Dunning argues that last-touch attribution understates marketing.
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The scenario described: a buyer sees the brand in AI overviews, reads comparison pages, checks competitors, watches YouTube and follows the founder on LinkedIn, then replies to a sales email that CRM records as outbound-sourced, prompting the question of why SEO is being funded at all.
The suggested fix is capturing tracked source, first discovery and buying influence on discovery calls and comparing those answers with tracking data.
A related post addresses "dark AI search", research done entirely inside ChatGPT or Claude that never reaches the site, by adding a source question to signup and demo forms and asking on every sales call.
For brands planning a campaign
Brands that need to justify organic spend internally can evaluate Sam Dunning on whether the contribution to pipeline becomes visible, not just whether leads arrive. A campaign could pair content with measurement work, since the posts treat attribution as part of the deliverable.
Sam Dunning returns often to predictions that SEO is finished, listing Google Ads, social and mobile, then voice search, featured snippets and zero-click, then TikTok search and AI Overviews, and now AI agents.
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The position taken is that each wave changed research behaviour without removing the moment a buyer compares vendors, checks pricing or solves an expensive problem, and that the generic-traffic version of SEO was already failing. The argument is tied to personal results: a cold-calling start in a box room, $300k+ monthly recurring revenue, and roughly two to three inbound $60k+ demos a day.
For brands planning a campaign
This framing gives a brand a ready counter-narrative for sceptical stakeholders and shows how Sam Dunning positions against commentary declaring search obsolete. Brands wanting a combative, revenue-proof angle on search marketing fit; brands wanting neutral analyst commentary do not.
The media flywheel: LinkedIn, YouTube, podcast and live content
Sam Dunning describes an engine where LinkedIn, YouTube, the podcast, Google and AI search feed each other.
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A post planning one day in London for $750 lists short-form "ask the public" videos on questions like what AEO is and how to get recommended on Claude, a live breakdown of a $10M ARR AI SEO strategy recorded with Tom Hunt for the Breaking B2B YouTube and podcast, at least one long-form breakdown, and a guerrilla marketing play, all intended to feed future content and land a client. A separate post promotes a free conference session and notes there will be no slides.
For brands planning a campaign
A brand can see how a single production day is turned into multiple formats and reused across channels. Partnership or sponsorship built around filming days, podcast episodes or event sessions matches how Sam Dunning already works; a one-off static placement does not.
Most posts are structured as numbered rules ("7 money steps to drive organic demos in 90 days"), myth lists using a why-it's-wrong, what-to-do, proof pattern, or short client case studies.
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They close with a pointer to a downloadable "90 days to revenue" AI SEO playbook and video, and typically name clients with specific figures such as monthly demo counts or inbound demo value.
For brands planning a campaign
A brand can reuse the numbered framework in co-marketing and should expect concrete proof points plus a lead-magnet call to action. Sponsoring the playbook or a rules-style post fits the format; abstract thought-leadership placements do not.
Sam Dunning's posts describe Breaking B2B as the business he runs, with "our clients" and a reference to himself as a bootstrap founder, and they name client work for RB2B, VerifyTreatment and Proposify.
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The same posts describe two further client engagements — a LinkedIn/Meta ads agency and a B2B/SaaS YouTube agency — whose businesses are not named, so they are not listed as brands. No sponsored, affiliate or advertising arrangement is disclosed in the reviewed posts; the brand names that appear only as examples or keyword illustrations (Warmly, HubSpot, Salesforce, Cloudflare, Wynter) are not relationships.
Breaking B2B
Owned business
Sam Dunning's posts describe Breaking B2B as the business he runs: "We run this system for 50+ fast growth SaaS orgs at Breaking B2B," and "based on data running AI SEO for 51 SaaS clients at Breaking B2B."
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One post cites going "from cold calling in a box room to $300k+ MRR" and another says the agency now sees "2-3 inbound $60k+ demos daily"; a London content day is framed as an investment meant to "return 67X for Breaking B2B," and the writer describes chatting with another founder "as fellow bootstrap founders." The posts direct readers to breakingb2b.com/playbook.
A post details work done for RB2B — getting Meta's Muse and ChatGPT's Dot to recommend the visitor-identification product to users — and closes with "grab my 90 days to revenue AI SEO playbook & video we run for RB2B."
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The work described is money pages, comparison and use-case content, and readable product proof.
Named as a client and used as the proof point for a results claim: "our client, VerifyTreatment booked 5 to 32 demos / mo from AI SEO, after having zero inbounds the year before."
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Another post lists VerifyTreatment among the SaaS companies the writer builds pages for.
Named alongside VerifyTreatment as a SaaS company the writer builds commercial pages for: "When we build pages for SaaS orgs like VerifyTreatment and Proposify, we're trying to answer the questions buyers ask when they're comparing options."
A post promoting a session at the B2B Full Circle show, where the writer says they will share their AI SEO approach without slides and answer questions live, alongside other listed speakers.
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This is a speaking appearance at an event, not an advertising or sponsorship arrangement.
Sam Dunning writes about getting B2B software buyers to a demo through organic and AI search rather than through traffic volume.
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A reader can start with the numbered playbooks that set out a fixed sequence of steps, then follow the money-keyword idea further by comparing the searches he names ("best proposal software for fintech teams", "LinkedIn ads agency for B2B") with the page types he builds for them.
The posts on Meta's Muse, ChatGPT's Dot and RB2B read as a worked example of how commercial pages get cited by assistants, and the "SEO has died about 15 times" post gives the longer view on why he thinks comparison searches survive each change in search behaviour.
The attribution posts are a separate thread worth reading together: they explain why he treats a sales-sourced deal as the end of a research process rather than its cause.
Nearly everything is a LinkedIn post, so the practical path is to read the rule-based posts first, then the client case studies, then the posts on tracking and AI assistants.
Both publish practical SEO diagnosis drawn from client work rather than theory, and both have moved into AI search: Moragli talks about analysing 10,000+ prompts and what ecommerce brands should do next in AI search, while Dunning writes about getting cited by assistants such as Meta's Muse and ChatGPT's Dot.
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A reader who follows one for their search advice will find the other useful for the same reason: both test search changes against commercial outcomes.
Moragli's subject is large ecommerce sites and technical clean-up, such as index bloat from filters, parameters and internal search pages, and she teaches an audit-led approach to pruning and prioritising URLs.
Dunning stays on the commercial-page side for B2B SaaS, where the work is comparison, alternative, pricing and review pages aimed at a handful of qualified demos.
Readers with a store-like catalogue should start with Moragli; readers selling considered software should start with Dunning.
Berg writes the closest parallel to Dunning's argument: focus on solution-aware and bottom-of-funnel content instead of problem-aware or TOFU content, expect AI assistants to strip referral data, and use self-reported attribution to show what AI search actually contributed.
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Both give numbered checklists with a starting point and a stop-doing instruction, and both write for B2B SaaS teams trying to prove AI search produces demos.
Berg's posts read as a marketing leader's playbook and point to a newsletter, with the attribution case built on one before-and-after number (13 deals to 702 after self-reported attribution was added).
Dunning writes from running an agency and pairs the same advice with a wider content operation: LinkedIn, YouTube, a podcast and filmed sessions that feed each other.
Berg is the tighter read on attribution and content prioritisation; Dunning shows how the same advice is produced and distributed across channels.
Gallegos works the same basic problem from a different market: choose keywords and write content that reaches a narrowly defined commercial audience in that audience's own language, then turn the resulting traffic into qualified inquiries.
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His posts on keyword selection and on running Facebook ads for very small, highly niched professional-services audiences share Dunning's interest in relevance and inquiry quality over reach.
Gallegos's material is about commercial real estate and other local professional services, and his two posts date from 2018, before the AI-assistant questions that dominate Dunning's recent writing.
There is no SaaS product, no AI search visibility and no attribution method in his posts.
Treat him as background on niche-audience marketing rather than as an alternative for someone weighing AI search for a software company.
The supplied evidence gives a scale figure but not an audience composition.
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Sam Dunning's LinkedIn account had 44,272 followers as of 2026-10-08.
That establishes reach, not who those followers are: no demographic breakdown (geography, age, gender, employer, job title, or seniority) and no audience survey are supplied.
The nine supplied posts are aimed at B2B SaaS marketers and founders working on organic demo generation, AI search visibility, and revenue attribution, so the topics align with an organic-demo brief, but the evidence does not show what share of followers hold those roles.
For a placement decision, treat the follower count as a first check on scale, not as proof of buyer relevance.
Before booking, ask for recent LinkedIn audience demographics or an audience survey covering the B2B SaaS job functions, seniority, and company size the campaign needs; that is the missing evidence that would let a buyer compare this audience with the brief.
The reviewed material does not document a sponsorship or creator-partnership program for Sam Dunning.
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The posts show a marketing agency operator (Breaking B2B) publishing LinkedIn, YouTube, and podcast content, and appearing at the B2B Full Circle event, but nothing in them sets out partner formats, pricing, audience specifications for sponsors, disclosure practices, or an inquiry route for paid collaborations.
The recurring call to action in nearly every post points to a free playbook download (breakingb2b.com/playbook), not to a sponsorship or media-kit page, and that address should not be treated as a partnership contact.
Because no official sponsor policy, rate card, or disclosed sponsored placement appears in the evidence, no partnership formats are listed here and no inquiry link is set.
For a buyer, this is an open research gap rather than a finding that Sam Dunning declines paid partnerships: the practical next step is to ask directly about sponsored placements, event or podcast integration, and disclosure terms before building a budget line.
Any plan that assumes a defined sponsorship product would be resting on assumption, not on this evidence.
Brief A: a B2B SaaS marketing agency wants more organic demos through AI search and is deciding whether a campaign with this creator will help.
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Fit: strong, on the evidence reviewed.
The published method is the brief's problem.
Across the reviewed posts Sam Dunning sets out a repeatable sequence for exactly this goal — target buyer-intent searches such as "[competitor] alternatives" and "best [category] software" (some estimated at 10–20 searches a month) instead of broad informational queries; build the comparison, alternatives, use-case, pricing and review pages that both Google AI Overviews and assistants such as Meta's Muse and ChatGPT's Dot pull from; keep pricing, proof and specifications readable as text rather than locked in images, widgets or video; and refresh commercial pages as competitors change.
The stated results are concrete: three inbound demos worth $30k+ ACV each within 12 days for a LinkedIn ads agency client, and seven $60k+ inbound demos within weeks for a YouTube agency client.
The content formats fit the audience a B2B marketing agency wants to reach: numbered playbooks, myth-busting posts built on a why-it-is-wrong / what-to-do / proof pattern, and short client case studies, closing on a free playbook download rather than a pitch.
The topics recorded in the accepted sections and the posts themselves point at B2B SaaS marketers and founders working on organic demo generation and AI search visibility, which is the brief's target reader.
Two things a buyer should settle before treating this as a straight fit.
First, Sam Dunning runs a competing service — the posts claim 51 SaaS clients at Breaking B2B — so a paid placement could read as a competitor promoting itself to the agency's own prospect pool; co-marketing, a joint breakdown, or a podcast or YouTube appearance is a more natural frame than a stand-alone endorsement.
Second, no sponsorship product, rate card or enquiry route is documented in the reviewed material, and no audience demographics are supplied, so budget and targeting cannot be confirmed from evidence alone.
When to choose a specialist
Brief B: a B2B SaaS company preparing an enterprise product launch wants one sponsored LinkedIn post plus a gated whitepaper download, aimed at IT buyers, with no changes to its own website and no ongoing content programme.
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Fit: poor.
The mechanism this creator sells depends on things that brief rules out.
The method is an operating system for a company's own commercial pages — writing and then repeatedly refreshing comparison, alternatives and pricing pages, and making decision-making detail readable to crawlers and assistants.
The reviewed posts argue directly against the brief's shape: that publishing volume content with borrowed statistics and a "book a demo" button leaves the buyer asking "why should I trust you?", that claims should be checked by a human, that AI-generated first drafts only work once internal sales and product detail is added, and that gated or image-bound information is the reason assistants cite competitors instead.
A single post driving to a gated download delivers none of the pages, comparisons or refresh cycle the evidence presents as the source of results.
There are also practical gaps for this brief type.
No sponsored-placement format, rate card, disclosure practice or partnership enquiry route appears in the reviewed material, so a one-off placement cannot be scoped or priced from evidence.
No audience demographics are supplied, so whether the audience skews to the IT buyers this brief needs is unverified.
The performance figures quoted are for client programmes of this creator's own service, not for sponsored placements, and the YouTube, podcast and downloadable playbook referenced in the posts were not available for review, so their reach and results remain claims rather than measured evidence.
None of that rules out a campaign; it means a brief built around a single gated asset would be buying an audience and a personality, not the method being promoted.
A worked campaign example
Worked angle for Brief A — "Money page teardown, filmed".
Brief: a B2B SaaS marketing agency wants to bring in more organic demos through AI search, and wants a campaign that shows its own prospects the shift is real rather than asserting it.
Campaign angle: build one campaign around a live teardown of a real commercial page, using the seven-step sequence from the "7 money steps to drive organic demos in 90 days" post as the running order.
Deliverables, shaped to the formats the reviewed posts already use: one long-form video or podcast episode in which Sam Dunning works through a page the agency chooses — pick the buyer-intent phrase, show the four-column targeting matrix (how buyers describe the offer, best-fit industries, competitors, real customer pain points), then rebuild the page on screen with a comparison table, pros and cons, sales-derived FAQs and an honest note on where the product is not the right fit; one numbered-rule LinkedIn post summarising the teardown and linking to the recording; and one short follow-up post using a before-and-after screenshot, which is the kind of evidence the reviewed posts say buyers and assistants need on the page itself.
Sequence: agree the page and the target phrase first, record the teardown second, publish the post and recording together third, then a 60–90 day refresh checkpoint on the rebuilt page.
Measurement, taking the attribution post as the model: add tracked source, first discovery and buying influence fields to the agency's own signup and demo forms, ask the same questions on sales calls, and compare answers with tracking data rather than last-touch.
Expect a lag — the reviewed posts describe buyers researching over weeks across AI answers, comparison pages, video and LinkedIn before replying to an email, so judge the campaign on demo quality and sourcing answers over a quarter, not on week-one traffic.
Before briefing, confirm four things the evidence does not settle: whether the agency is comfortable appearing alongside a competitor in the same category, the audience demographics behind the 44,272 LinkedIn followers recorded on 2026-10-08, what paid-placement or sponsorship terms and disclosure would apply, and whether any third-party verification exists for the client demo figures quoted in the posts.
Best alternative when the campaign decision is the same — a B2B SaaS agency trying to generate organic demos through AI search — but the agency's harder problem is proving the channel internally.
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Justin Berg's reviewed posts are written for exactly that reader: a VP marketing being asked to show a CEO that AI search is worth funding, and a marketing team whose AI pipeline lands in the CRM as direct traffic.
One post is a five-step stop/start checklist (stop problem-aware and top-of-funnel content, ship solution-aware listicles and competitor comparisons, skip llms.txt and visibility-dashboard tinkering, add self-reported attribution), the other shows the attribution argument with concrete numbers: 13 deals visible in the CRM rising to 702 after self-reported attribution was added for one vertical SaaS client.
If the brief includes a measurement or budget-defence requirement, or the buyer wants the 90-day programme stated as a short checklist rather than a longer playbook, this is the stronger pick.
The tradeoff is depth on the page-building method.
Justin Berg states which content types to prioritise but does not set out the mechanics Sam Dunning's posts cover — how to build the buyer-intent keyword matrix from sales and support calls, which page elements assistants pull from, or how to keep comparison and pricing pages refreshed.
Two posts also overlap in substance with Sam Dunning's, so a buyer choosing between them gets a similar thesis with less of the operating detail.
Like Sam Dunning, the reviewed material shows no sponsorship format, rate card, audience demographics or enquiry route, and the newsletter referenced in the posts was not available for review, so its reach is a claim rather than measured evidence.
Better when the site is a large ecommerce catalogue, not a B2B SaaS product, or when the discovery problem is technical scale rather than demo generation.
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One reviewed post describes ecommerce sites with 300,000+ indexable pages where filters, parameters, internal search results and duplicate category combinations drain crawl budget and compete with the pages that make money, arguing that growth can start with removing 250,000 pages.
The other covers prompt research across more than 10,000 prompts to work out what ecommerce brands should do to win in AI search.
An agency with an ecommerce client, or one whose first problem is index bloat and duplicate URLs, gets a more relevant method here than from a B2B demo-generation playbook.
The buyer task changes with the fit.
The reviewed posts are ecommerce-specific and weighted to technical and index hygiene; they do not cover the B2B buyer-intent keyword sequence, competitor-alternative page building, or the demo-generation results Sam Dunning's posts are built around.
A B2B SaaS agency would be buying a different discipline — useful if it runs ecommerce clients, a mismatch if the brief stays on high-ticket software demos.
No follower figures, audience demographics, sponsorship terms or enquiry route appear in the reviewed material, and the prompt-research results are reported as a conference talk rather than published data.
Consider only if the campaign's audience is commercial real estate or another highly niche professional service and the channel is paid social feeding a local lead funnel.
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One reviewed post asks how to choose keywords and create content that pulls Google traffic to a commercial real estate website and raises conversions; the other covers running effective Facebook ads when the addressable audience is too small to use standard targeting. It is a closer fit for a niche local-service brief with a paid-social element than for a software demo-generation brief.
For the campaign decision used across this section, this is a different task.
The reviewed posts date from September 2018, before AI search and assistant recommendations were part of the picture, and they contain no B2B SaaS content, no demo-generation method and no comparison or alternatives page guidance.
The emphasis is Facebook advertising and local conversion rather than organic and AI search, and the posts read as short promotional prompts toward a free analysis rather than a detailed method.
A buyer whose brief is B2B SaaS demos through AI search would be trading the mechanism for a related-sounding name.
This profile draws on nine LinkedIn posts by Sam Dunning, read in full text.
They cover buyer-intent keyword targeting, getting commercial pages cited by AI assistants, content differentiation, attribution, the recurring "SEO is dead" argument, and multi-channel production across LinkedIn, YouTube, a podcast, and live sessions.
The posts carry no publication dates in the material reviewed here, so their themes are grouped rather than placed on a timeline.
The summary and topics draw on the accepted Overview, Ideas and priorities, Brands and businesses, Topics and similar creators, and Campaign fit sections, plus the account record listing 44,272 LinkedIn followers observed on 8 October 2026.
Client results, the follower count, and the YouTube videos, podcast episodes, and downloadable playbook referenced in the posts are reported as claims made in the posts, not independently verified.
No video, podcast audio, long-form article, client report, rate card, or audience demographics were available for review.
Recent engagement
Across their last 8 LinkedIn posts, the median post got 34 reactions and 10 comments. Checked 2026-11-02.
