Crevan O'Malley is most useful for written LinkedIn thought leadership on HubSpot, CRM, customer data, and AI in go-to-market.
Best for
A B2B SaaS CRM or AI-in-go-to-market brand comfortable with HubSpot-oriented LinkedIn thought leadership.
Not for
Not for competing CRM vendors or independent B2B software brands seeking neutral comparisons and measurable enterprise demo requests.
Key takeaways
Poor context makes AI worse than no context at all.
AI belongs inside the CRM and inside the tools teams already use.
Judge AI by outcomes, not by adoption.
Check first: Confirm paid partnership status and request recent results plus audience demographics.
Main ideas
Poor context makes AI worse than no context at all · AI belongs inside the CRM and inside the tools teams already use · Judge AI by outcomes, not by adoption
Audience
7,161 LinkedIn followers · 4 LinkedIn posts between 14 and 25 September 2026 Recent posting activity
Crevan O’Malley writes about HubSpot, CRM and B2B growth.
In LinkedIn posts, they connect AI and product news to practical questions about demand, sales and customer service.
The posts cover HubSpot’s UNBOUND event, a CRM comparison with Salesforce, and a DMEXCO conference recap.
They make a case for business-specific, current customer data as a foundation for useful AI.
O’Malley turns product and event updates into arguments grounded in recognizable business problems.
In one post, stale or generic data leads AI to recommend discontinued products or contact buyers who have left, making the case for better context concrete.
In another, they acknowledge Salesforce’s announcement with Anthropic, then shift the discussion from choosing an AI model to whether customer data can move between tools.
Their work goes beyond product advocacy: the DMEXCO recap gives space to customer conversations, colleagues and the event itself before making a broader claim about AI.
Follow their work for a HubSpot-oriented view of how CRM, customer data and AI intersect.
The reviewed examples are LinkedIn posts and mostly discuss HubSpot’s products.
Across four LinkedIn posts, Crevan O'Malley returns to one argument: AI is only as useful as the business information behind it.
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They push back on treating AI adoption as the goal, picking a single model as the decision, and treating AI as a replacement for the CRM. Their posts point to HubSpot product releases and conference floors, focusing on data quality, data movement and measuring results rather than features.
Poor context makes AI worse than no context at all
O'Malley calls 'context' the buzzword of the moment, then turns the argument around: most context is generic, and poor-quality context is worse than none because AI acts on whatever it is given.
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The remedy is 'Growth Context', a single, current picture of a business, its customers and its team, plus a Smart CRM that automatically captures calls, emails and meetings so the data does not go stale. The argument is about the data layer, not the model.
The failure modes are written for a sales audience: AI recommending discontinued products, analysing pipeline against the wrong numbers, and drafting outreach for buyers who left six months ago. O'Malley argues that this drags a team backwards rather than optimising it.
AI belongs inside the CRM and inside the tools teams already use
Rather than replacing the CRM, O'Malley argues that AI should work in, on and through it.
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The more interesting question, they say, is which CRM is ready for that today.
The value here is not in choosing a winning model but in whether customer data travels with the model the team already uses.
They also concede that Salesforce's announcement with Anthropic is good news for the whole SaaS and CRM category, keeping the piece a category argument rather than an attack.
A picture of a buyer's reality: marketers in Claude, sales reps in ChatGPT, ops in Copilot, Gemini elsewhere. HubSpot is described as having live connectors across all four major AI platforms, with Claude and ChatGPT fully two-way and in production rather than pilot.
At UNBOUND in Boston, O'Malley contrasts two figures attributed to Yamini Rangan: 90% of companies use AI, but only 6% see transformational results.
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They argue that the 6% are not chasing technology but staying focused on demand, deals and customers.
The useful question, they say, is whether AI moves an established business goal, not whether a company has adopted the latest tool.
The post presents the product figures as HubSpot's own; buyers should ask for independent validation.
Three UNBOUND announcements, each tied to a number: Breeze Assistant, with customers generating 2.2x more leads; a self-updating Smart CRM that syncs calls, emails and meetings; and Marketing Studio, with early customers running 81% more campaigns.
The DMEXCO recap frames AI as a platform-level transformation, with O'Malley saying companies must adapt or risk being disrupted on someone else's terms.
That urgency sits alongside their call to stop chasing technology.
Both arguments point to the same conclusion, but differ on how much of the change is driven by the technology itself.
Adaptation is a team and field problem, not a purchase
In the DMEXCO recap, O'Malley describes cross-functional, customer-facing work, with DACH go-to-market teams across marketing, sales, services, channel and business development showing up for customers and partners.
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They credit colleagues by name and use the week's conversations to show where AI actually sits in the business, putting people and field observation ahead of the product narrative.
A bulleted recap of O'Malley's first DMEXCO in Cologne: two days at a busy stand, conversations with partners and customers, a tech-stage panel on how data impacts growth featuring colleague Julia Pilkes, a customer dinner, and the atmosphere in hall 6. Jens Leucke and the wider DACH crew are name-checked, with Larissa Pilat singled out for leading the HubSpot DMEXCO team.
A quick look at how O'Malley turns a product release into an argument, not a feature list.
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Start here to hear their case for HubSpot in their own words.
The post opens with two figures attributed to HubSpot's Yamini Rangan at UNBOUND in Boston: 90% of companies use AI, but only 6% see transformational results.
That gap drives the argument: the 6% are not chasing technology but staying focused on demand, deals and customers.
Three announcements follow, each tied to one adoption number: Breeze Assistant, a single tool for getting work done across HubSpot, whose customers generate 2.2x more leads; a self-updating Smart CRM that automatically captures calls, emails and meetings; and Marketing Studio, whose early customers run 81% more campaigns.
The post closes with the recurring hashtag #SuccessLeavesClues and a direct invitation to look at the release.
For brands planning a campaign
A workable structure for launch or demand-generation content without a customer story: lead with a market-level gap, attach exactly one number to each product claim, and end with a single action.
It answers a sceptical buyer's first question — why does data quality change what AI produces? — and shows the argument template O'Malley uses elsewhere.
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O'Malley opens by naming “context” as the buzzword of the moment, then turns the idea around: most context is generic, and poor-quality context is worse than none.
The failure modes are concrete and aimed at sales teams — AI recommending discontinued products, analysing pipeline against the wrong numbers, and drafting outreach for buyers who left six months ago.
HubSpot's answer is “Growth Context”: intelligence about a business, its customers and its team, kept current in one place and supported by a self-updating Smart CRM.
Three metrics follow: companies on HubSpot create nearly 3.6x more MQLs, win 3.2x more deals and close over 2x more tickets.
The piece ends with Context Home, launched at UNBOUND, and the claim that the quality of context determines the quality of outcomes, alongside a team photo caption for the EMEA Upmarket group.
For brands planning a campaign
Useful for briefs that need a problem-first angle rather than a product tour: define the buzzword, name the specific operational failure, then present the product as the fix, backed by proof numbers.
The clearest example of O'Malley writing from an event floor and crediting colleagues — useful when assessing them for a field-marketing or event-led brief.
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A bulleted recap of a first DMEXCO in Cologne: two days at a busy stand, conversations with partners and customers, a tech-stage panel on how data affects growth featuring colleague Julia Pilkes, a customer dinner, and the atmosphere in hall 6.
O'Malley names Jens Leucke and the wider DACH crew across marketing, sales, services, channel and business development, and singles out Larissa Pilat for leading the HubSpot DMEXCO team.
The post makes one broad claim: AI is no longer a feature conversation but a platform-level transformation, and companies must either plan to adapt or get disrupted on someone else's terms.
It connects the week's conversations to HubSpot's Agentic Customer Platform and Growth Context.
For brands planning a campaign
A repeatable event-recap format: details about people and moments build credibility, while one category-level claim does the persuasive work at the end.
The strongest example here of O'Malley responding to a competitor's news and framing a category argument without turning it into an attack.
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O'Malley sets UNBOUND in Boston and Dreamforce in San Francisco against the backdrop of a decades-old rivalry between the two cities, then concedes that Salesforce's announcement with Anthropic is good news for the whole SaaS and CRM category, not a threat.
The argument: AI won't replace the CRM but will work in, on and through it; the more interesting question is which CRM is ready today.
They sketch a buyer's reality — marketers in Claude, sales reps in ChatGPT, ops in Copilot, Gemini elsewhere — and argue that value lies not in choosing a model but in whether customer data travels with it.
HubSpot has live connectors across all four major AI platforms, with Claude and ChatGPT fully two-way and in production rather than pilot.
The post closes with the head-to-head sports record across MLB, NFL, NBA and NHL and a friendly aside to Dreamforce attendees.
For brands planning a campaign
A pattern for competitive or category commentary: acknowledge a rival's good news, reframe the debate around a question you can answer, land one proof point, and keep the tone human so the positioning doesn't sound defensive.
O’Malley argues that AI output depends on the quality and freshness of the customer and business information it can use.
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They contrast useful, specific context with generic or outdated data, which can lead to recommendations based on the wrong products, deal stages or contacts. In a separate post, they describe HubSpot’s “Growth Context” as a way to bring business, customer and team information together.
Explains a problem and its consequences, then connects the remedy to a named product capability.
For brands planning a campaign
A CRM or AI brand could use this angle for educational content about data quality and AI readiness.
Make it concrete with examples of stale records and what teams should check before relying on AI.
O’Malley contrasts widespread AI use with a much smaller share of companies reporting transformational results.
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They argue that the useful question is whether AI helps with established goals such as generating demand, winning deals and serving customers—not whether a company is using the latest technology.
Contrasts two figures, then makes a business-priority argument and explains a product release.
For brands planning a campaign
A demand-generation brief could frame AI adoption around a specific business outcome, then show how a team would measure progress. Check the original source and context before using the post’s statistics.
AI should work through the CRM and the tools teams already use
In a post comparing conferences in Boston and San Francisco, O’Malley argues that AI should work with a company’s CRM rather than replace it.
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They focus on whether customer data can move between the CRM and the AI tools different teams use, and highlight HubSpot connectors as the example.
Uses an industry comparison and a light city rivalry to introduce a practical question about the GTM stack.
This post gives more space to competitive positioning and product capability than those focused on AI outcomes and data quality.
For brands planning a campaign
A CRM or integration brand could build a thought-leadership angle around data portability and how sales, marketing and operations teams use different AI tools. Explain the workflow and its limits rather than treating connector availability alone as proof of business value.
Show how cross-functional teams support customer growth
At DMEXCO, O’Malley describes conversations with customers and partners, a panel on data and growth, and the work of HubSpot’s DACH go-to-market teams across marketing, sales, services, channel and business development.
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They connect those observations to the challenge of adapting to AI in the SaaS and CRM sector.
Combines event details, named colleagues and team activity with a broader industry takeaway.
For brands planning a campaign
A B2B company could use this event-recap approach to show how several functions contribute to a customer or market goal. It suits a brand brief with real event observations and team examples; a generic conference summary would lose the specific cross-functional point.
O'Malley writes from inside HubSpot: they describe product releases as things “we shipped”, report from the HubSpot stand at industry events, and name HubSpot colleagues as part of the team.
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HubSpot is the one brand relationship the posts establish clearly.
Salesforce, Anthropic, OpenAI, Microsoft and Google appear as market context or competitive comparisons, not as partners or sponsors.
Ask whether there are any sponsorship, affiliate or client arrangements.
HubSpot
Employment
O'Malley posts as a HubSpot insider.
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The DMEXCO recap describes the busy HubSpot stand, calls the DACH colleagues “our wonderful DACH crew” and the wider group “our full GTM machine”, and says the week made them “proud to be part of this team”.
In the UNBOUND coverage, they write “we shipped what I believe is our most significant product release in years”, refer to “our Smart CRM”, and introduce Breeze Assistant, Smart CRM and Marketing Studio as the company's products.
They position HubSpot against rival CRM vendors and invite readers to use HubSpot.
O'Malley commented on Salesforce's announcement with Anthropic, calling it “great news” for the whole SaaS and CRM category and arguing that AI works in, on and through a CRM rather than replacing it.
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They used the announcement to frame a Boston-versus-San Francisco comparison between HubSpot's UNBOUND and Salesforce's Dreamforce, and to argue that HubSpot's connectors are already in production. Ask whether this competitive commentary involves a commercial relationship.
Crevan O'Malley's posts connect HubSpot's AI releases with how sales and marketing teams use them.
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Start with the argument that stale or generic business context makes AI worse than none, then the post that judges AI by demand and deal outcomes rather than adoption, followed by the piece on customer data travelling between the CRM and the AI tools each team already uses.
The DMEXCO recap offers a field-side counterpart, showing the cross-functional go-to-market work behind the product story.
For readers choosing who else to follow, Yamini Rangan supplies the product-announcement framing, Gabriel Marguglio the agency view on AEO and CRM, and Jasper Meurs the conference learning and adoption-data angle.
O'Malley's own post quotes Rangan's UNBOUND keynote figures and makes the case that AI should be judged by results rather than adoption.
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Both explain HubSpot's AI releases to a sales and marketing audience from the same event.
Rangan writes from the announcement itself, setting out Marketing Studio, the self-updating CRM and Breeze Assistant in first-person product detail and drawing on a sales career, while O'Malley interprets the releases afterwards, using the conference floor, customer conversations and competitor framing as evidence.
Marguglio makes a closely related case: AI without context is just automation, and AEO work plus a clean HubSpot CRM decide whether a brand shows up in AI answers.
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That mirrors O'Malley's argument about data travelling between the CRM and the AI tools teams use.
Marguglio writes as an agency owner, leading with client reviews, service capability and agency history and treating AEO as a commercial offer, whereas O'Malley writes company-side commentary and event recaps without a service pitch.
Meurs draws on the same conference material and outside research, including UNBOUND sessions and KPMG adoption data, and turns it into practical questions about whether a team has built the context AI needs.
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That echoes O'Malley's point that poor context is worse than none.
Meurs organises the learning, hosting a virtual UNBOUND planning event with other speakers, polling attendees on their goals and aggregating third-party studies, while O'Malley reports from their company's stand, keynote and field conversations.
Crevan O'Malley’s LinkedIn following offers scale for a CRM or B2B SaaS campaign, but the four posts shown don’t identify who follows them.
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They show 7,161 LinkedIn followers as of 8 October 2026.
O'Malley writes from inside HubSpot’s go-to-market motion about AI in CRM, Growth Context, agentic platforms and industry events (DMEXCO in Cologne, UNBOUND in Boston).
For a CRM company planning a thought leadership or demand generation campaign, that suggests the content resonates with people following CRM and AI-in-GTM conversations, but doesn’t establish their job titles, seniority or buying authority.
Before committing budget, request recent results for comparable topics, depth and formats, and ask whether O'Malley has a follower demographics report or audience survey covering geography, seniority, industry and company size.
If the campaign targets a specific buyer group, that information—not follower count—should guide the decision.
LinkedIn followers
7,161
LinkedIn profile (@crevanomalley), audience record · 2026-10-08
Recent posting activity
4 LinkedIn posts between 14 and 25 September 2026
Posts on DMEXCO, UNBOUND, Growth Context and AI in CRM · 2026-09-14
No partnership or sponsorship offer is documented in the material reviewed for Crevan O'Malley.
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The available posts offer event and product commentary tied to HubSpot, in an employee or team voice rather than as a paid creator arrangement.
They promote HubSpot's Agentic Customer Platform, Growth Context, Smart CRM, Breeze Assistant and Marketing Studio, and refer to "our customers," "our GTM machine," and "our most significant product release in years."
Naming a company, product or event hashtag does not establish payment; the posts disclose no sponsorship terms, rates, deliverables or media kit.
Brand planners should treat the profile as having no verified commercial formats at the time of review, not assume O'Malley never accepts sponsorship work.
For a partnership inquiry, contact them through the LinkedIn profile linked on this page; no official partnership or sponsorship inquiry page was found.
Good fit: a B2B SaaS CRM or AI-in-go-to-market company seeking written LinkedIn thought leadership or demand generation for sales, marketing and RevOps readers, provided the brand is comfortable with a HubSpot-perspective creator.
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The strongest evidence comes from the content itself.
Crevan O'Malley writes problem-first explainers: a post on generic context names concrete AI failures, including recommending discontinued products, analysing pipeline against the wrong numbers and drafting outreach to buyers who left six months ago, then argues that current, business-specific context is the fix.
The UNBOUND recap turns a product release into an argument, opening with a 90% versus 6% AI gap and attaching an adoption figure to each product claim.
The Boston versus San Francisco post shows how they handle category competition: concede a rival's good news, reframe the question around data portability and land one proof point.
The DMEXCO recap demonstrates field-marketing credibility through specific people, teams and moments.
These are useful structures for a CRM or AI vendor that needs educational posts rather than feature lists.
The audience evidence supports a broad category audience: 7,161 LinkedIn followers as of 8 October 2026, with content for people already following CRM and AI-in-GTM conversations.
Follower count alone does not establish job title, seniority, geography, industry or buying authority.
The fit is strong for awareness and thought leadership, conditional for pipeline generation, and weak if the brief requires a guaranteed enterprise buyer audience.
The posts read as HubSpot employee or team advocacy, promoting HubSpot's Agentic Customer Platform, Growth Context, Smart CRM, Breeze Assistant and Marketing Studio.
A HubSpot-adjacent brand, or a company comfortable with that perspective, can use the fit.
A competing CRM vendor seeking a neutral analyst voice should not.
When to choose a specialist
Poor fit: a competing CRM vendor or independent B2B software brand seeking a neutral head-to-head comparison against HubSpot and measurable enterprise demo requests.
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Crevan O'Malley's posts do not support neutrality.
Each is tied to HubSpot: UNBOUND product announcements, Growth Context, Smart CRM, Breeze Assistant, Marketing Studio, HubSpot connectors and the DMEXCO HubSpot team.
The writing consistently promotes HubSpot's position, for example, framing HubSpot as first with live two-way connectors across Claude and ChatGPT, or describing a HubSpot release as their most significant product release in years.
That works for HubSpot-aligned demand generation but conflicts with a competitor's request for an independent comparison.
The posts provide no campaign results, post-level engagement figures or conversion data, and publish no media kit, rate card, deliverable specification or official sponsorship inquiry route.
The profile gives a follower count but no demographics, so there is no verified targeting basis for a campaign requiring a specific geography, seniority band, industry or company size.
A consumer lifestyle or short-form video campaign is also a poor fit: the posts are written LinkedIn posts, with no video, transcript or other format information.
A competitor or performance-focused buyer should treat the fit as unverified and request campaign results, audience demographics and commercial terms before committing budget.
A worked campaign example
Worked example: a three-post LinkedIn thought leadership series for a CRM platform, designed to get sales and marketing leaders thinking about data quality before they buy another AI tool.
Campaign angle: 'Context before automation.' Post one, 'The stale-data test': open with the concrete failures in the generic-context post, such as AI recommending discontinued products, analysing pipeline against the wrong numbers and reaching out to buyers who left six months ago.
Ask readers to check three records before trusting any AI output: a deal stage, a contact role and a product line.
End with one question, not a product pitch.
Post two, '90% versus 6%': use the UNBOUND keynote gap to argue that the differentiator is not AI adoption but focus on demand, deals and customer service.
If the brand cannot cite the original source for the adoption figures, use the structure without repeating the numbers.
Post three, 'Where AI lives in the stack': use the Boston versus San Francisco setup, with marketers in Claude, sales in ChatGPT, ops in Copilot and Gemini elsewhere, and make the practical point that value depends on whether customer data travels with the tools teams already use.
Explain connectors as a workflow, state their limits and close with a short checklist: which AI tools the team uses, whether they can read and write CRM data, and who owns data quality.
CTA: a one-page data-quality checklist or a workflow review, not a hard demo request.
Measurement: treat this as a pilot; no creator performance baseline is published.
Track post engagement, checklist downloads and demo requests separately, and compare them against the brand's own channel benchmark.
Conflict check: this angle works for a HubSpot-adjacent brand or a company comfortable with a HubSpot-perspective creator.
A direct HubSpot competitor should not run it as neutral thought leadership.
Before production, request follower demographics, post-level engagement or past campaign results, sponsorship terms and an inquiry route; these determine the budget, not the follower count.
Choose Yamini Rangan when a campaign needs an executive-stage voice describing what a CRM platform now does, rather than a method the reader can run alone.
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The UNBOUND post walks through three named releases in order: Marketing Studio for planning and running omnichannel campaigns, a CRM that updates itself and moves deals to the next stage, and a rebuilt Breeze Assistant.
It then gives one worked prompt for a marketer and one for a sales rep, a usable structure for a brand telling its own product story role by role.
The vibe-coding post covers the same data-quality ground from the opposite direction: it argues that a hand-built CRM still needs 2000+ integrations (their figure), domain depth and a security audit, and anchors the point in a customer conversation about delivering forecasts to a board.
If the campaign's job is to make a platform's direction feel set and answer the 'we could build this ourselves' objection, this is the stronger choice.
The tradeoff is dependence on one vendor's announcements and stage-sourced claims.
The UNBOUND post is a release recap: its examples, figures and product names belong to the platform that shipped them, so a third-party brand cannot reuse its substance as its own thought leadership without making the same product claims.
It places the argument inside a company's news cycle instead of offering a repeatable method.
Ask for engagement figures, audience detail and commercial terms to assess reach and cost for the same campaign need.
Choose Gabriel Marguglio when the buyer task is agency-side: helping a company adopt a CRM and connect that adoption to AI-era visibility.
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The AEO agency post names three capabilities buyers should look for (CRM expertise, SEO and AEO strategy, and the ability to connect AI, marketing, sales and service through CRM data) and shows the writer's own ladder from website and SEO work through inbound, growth, AI-powered inbound and AEO growth, with two dated claims (24 years in business, more than 13 years on HubSpot) and a client onboarding review as proof.
The agents post supplies the complementary framing: internal context (CRM data, processes, customer history, sales conversations, tickets) and external context (what AI systems know about the brand), plus a three-step progression from Google search to LLMs to agents that research and recommend on the human's behalf.
If the campaign ends in 'who do we hire to do this' rather than 'why does context matter', this is the closer match.
The tradeoff is that the material doubles as agency positioning.
The agency post closes on a client testimonial and a team shout-out; the agents post ends on a question inviting replies off the back of a recap webinar.
Both are shaped to bring inbound to the writer's business rather than stand alone as editorial.
A brand that wants distance from a seller-to-buyer pitch, or wants one data-quality example opened up in detail, gets less of it here: the posts offer one client review and one framing model, not a close reading of how a specific campaign performed.
Choose Jasper Meurs when the campaign is about helping practitioners prepare for and use a conference, or when the adoption case is assembled from outside data.
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The game plan post sets a specific problem (12 days out, 200+ sessions, how to leave with something that changes work at home) and answers it with a named nine-speaker lineup, a set of planning questions and a live goal-polling agenda for a free session that already had 30+ sign-ups.
The adoption post takes KPMG's Q3 AI Pulse finding (44% reporting significant workforce AI adoption, up from 23%) and a keynote line that 'AI with bad context is worse than no AI at all', then turns them into two questions a team can answer: is AI pointed at the work where context matters most, and has the business built the systems that feed it clean CRM data, a documented ICP, logged call notes and a written brand voice?
For a demand-generation campaign aimed at a live event or a survey release, this is the more usable pattern.
The tradeoff is provenance.
Both posts lean on material the writer did not produce: other people's sessions, a KPMG survey, a keynote quote.
That works for curation and framing, but a campaign that wants the creator's own analysis as its main evidence gets less of it; the subject's context post, for instance, lists specific AI failures.
These posts cover conference preparation and adoption messaging rather than a consistent CRM-context series.
Ask for engagement, audience and commercial data to compare performance.
This profile draws on four complete LinkedIn posts written by Crevan O'Malley: a recap of the DMEXCO conference in Cologne, a post comparing HubSpot's UNBOUND with Salesforce's Dreamforce and the two companies' AI connectors, a post on HubSpot's UNBOUND product release built around keynote figures, and a post arguing that generic or stale business context degrades AI output.
The posts are available in full, not just as titles or snippets.
The posts carry no publication dates, so no order or development over time can be shown, and the posts here cover written LinkedIn posts only — not talks, video or transcripts.
The LinkedIn profile shows 7,161 followers as of 8 October 2026.
Similar-creator comparisons in the discovery section use dated LinkedIn posts from Yamini Rangan, Gabriel Marguglio and Jasper Meurs.
The posts here do not mention a job title, a media kit, sponsorship terms, rates, or an official inquiry route, so these remain gaps; the profile does not claim that O'Malley never accepts sponsorship work.
