Mark Deacon shares practical analysis of B2B SaaS go-to-market strategy, including pricing, sales and marketing, and AI-driven revenue workflows. The LinkedIn posts also promote interviews with business leaders on the GTM Playbook podcast.
Mark Deacon is most useful for practical B2B SaaS go-to-market analysis, especially pricing and AI-driven revenue workflows.
Best for
Series B B2B SaaS workflow software teams needing fractional GTM and pricing advice.
Not for
Paid sponsorship packages with guaranteed reach, media kit, and standard usage rights.
Key takeaways
Price on a unit of value, with a usage meter under the seat.
Defensibility is accumulated context, not the underlying model.
An agent automates government tender discovery, HubSpot recording, alerts, and draft preparation for human review.
Check first: Confirm client results, self-reported figures, and audience analytics before contracting.
Main ideas
Price on a unit of value, and put a meter under the seat — not the other way round · Defensibility is accumulated context, not the underlying model · AI is pulling twenty years of sales specialisation back into full-cycle roles
Mark Deacon shares practical analysis of B2B SaaS go-to-market: pricing, sales and marketing, and AI-driven revenue processes.
Their posts pair arguments about how businesses should adapt with examples of processes being rebuilt, including an agent for government-tender work and an AI-assisted prospecting process.
They also present interviews on the GTM Playbook podcast, covering how operators approach growth, hiring and customer retention.
Follow their work for concrete explanations of how AI may change revenue teams and SaaS business models, alongside tactical examples of applying automation to everyday GTM work.
Mark Deacon connects strategic claims to operational choices.
In their pricing post, they argue that companies should measure the value their product creates before deciding what to charge: a seat can provide a base, with a usage meter tied to agent work.
They warn that measuring actions may be easier but harder to defend at renewal than measuring outcomes.
In their government-tender example, they lay out the process before and after automation: finding opportunities across portals, recording them in HubSpot, alerting the team and preparing a draft for human review.
The contrast makes the proposed change easier to understand and assess.
Their posts also vary in evidence: the prospecting example gives before-and-after time and contact-rate figures, but carries a vendor partner hashtag, so check the results against the underlying test.
Together, these examples make their work useful to readers seeking both a point of view on AI-era GTM and specific processes to examine.
Mark Deacon's posts argue that AI-era B2B growth rewards companies that rethink how they price, why their product is hard to remove, and how the manual work of selling gets done.
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The recurring position is that agents are not a tool bolted onto an existing revenue process but a reason to rework it: meter value instead of seats, accumulate context that makes a product costly to switch, and hand tender discovery and prospect-list building to agents wired into the CRM. They also use the GTM Playbook podcast and revenue-leader events to carry other operators' arguments to a wider audience, naming whose experience each framework comes from rather than presenting it as their own.
Price on a unit of value, and put a meter under the seat — not the other way round
Asked how they would build a pricing model from scratch in 2026, Deacon rejects seat-based pricing as the starting point — not because seats are dead, but because they answer the wrong first question.
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Headcount and logins are poor proxies for value once agents do the work.
The sequence they set out is: choose the unit of value before the unit of pricing, instrument it before a single contract is signed because retrofitting telemetry into live billing is a mess, then layer a small seat base on top of a variable meter so the customer gets a predictable floor and the vendor keeps upside.
The stated tension is what to meter: actions are easy to count and easy for procurement to argue down, while outcomes are hard to count but much easier to sell.
They leave that as "the harder call" rather than resolving it.
The rollout recommendation is deliberately narrow — a hybrid model on new logos only, since migrating existing seat contracts is a separate, painful project.
The failure mode named is customers quietly renewing the same seat count while doing three times the work through agents: same ARR, half the value capture.
Deacon's named units of value are "resolved tickets.
Qualified leads.
Deals moved forward.
Messages sent that actually got a reply" — and the test is whether each can be measured cleanly before it touches a billing system.
Defensibility is accumulated context, not the underlying model
Reading Anthropic's roughly $965 billion valuation against about $47 billion of run-rate revenue as a 20x multiple, Deacon argues the market is not betting on benchmark performance but on context.
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Once a company wires a model into procurement, code review, customer operations and finance workflows, that integration learns how the business operates — a point they credit to Sequoia's Alfred Lin — and accumulated context makes removal feel like a re-organisation.
They apply the same curve to SaaS generally: products that accumulate workflows, decisions, institutional memory and messy judgment calls get harder to switch every quarter, while products that wrap a model and re-skin a user interface get cheaper to replace every quarter.
One side compounds, the other commoditises.
The line the argument turns on: "If a customer could swap you out next Tuesday with a weekend of work, you don't have a moat.
You have a feature."
Deacon closes by asking operators whether anyone would say the same about what they are building.
AI is pulling twenty years of sales specialisation back into full-cycle roles
This argument comes from a named guest, not Deacon: Michael Hartman, who leads go-to-market at Anthropic, said in Boston that AI is undoing twenty years of sales specialisation.
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Deacon relays and expands the point, tracing how one account executive role split into SDRs, BDRs, AEs, solutions engineers, RevOps, CSMs and AMs.
They argue that AI removes much of the manual work inside those roles — research, prospecting, account planning, follow-up, CRM administration, proposal preparation, finding information, analysing customer data — while the responsibilities themselves remain.
The result they point to is a generation of full-cycle sellers judged on outcomes rather than task completion, with human context and judgment still in the loop.
They emphasise a reframed leadership question: instead of asking which jobs AI can replace, ask what a person could become capable of if AI removes half the work from the role.
Deacon lists the manual work that has to happen regardless — research, prospecting, account planning, follow-up, CRM administration, proposal preparation, information finding and customer-data analysis — and argues one person can increasingly orchestrate far more of it.
Replace the manual revenue process with an agent, not a better checklist
Deacon's most concrete build example is government tendering.
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Eighteen months after expanding into public sector as a vertical, the old approach was someone logging into portal after portal every day, reading every new listing, deciding fit, hand-creating Company, Contact and Deal records in HubSpot and assembling a 20-plus page submission from a blank page.
The replacement agent runs daily across Australian, New Zealand, UK, EU and US portals, pulls each new tender at the source, judges relevance, creates HubSpot records with the portal and source URL stamped on them, posts to Slack when something looks promising, and drafts the submission so the team only reviews and submits.
The post sets the old and new approaches side by side and asks other people selling into government what they have learned.
The before-and-after framing: "Old way.
Team logs into portals, reads listings, copy-pastes into HubSpot, writes the submission from a blank page."
In the new way, the draft submission is waiting and Slack tells you when to look.
Outbound list building becomes one command, with the plumbing handled by a data waterfall
Deacon describes years of assembling prospect lists across Apollo for emails, ZoomInfo or Lusha for mobile numbers the others missed, and Sales Navigator for LinkedIn URLs — enriching, exporting, finding the gaps and enriching again into what the post calls a Frankenstein CSV built over hours or days.
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The replacement is a Claude skill plus FullEnrich connected through its MCP: a /find-contacts command with a target account list attached waterfalls through more than twenty data providers, and the enriched contacts push into HubSpot and into outreach sequences.
On a 400-account list the reported result is roughly half a day down to about twelve minutes, a mobile and email hit rate above 86%, and around double the coverage of any single provider.
The post carries a partner hashtag for the enrichment vendor but gives no payment terms.
Ask to see the underlying test before relying on the vendor-aligned figures.
The specific claim to interrogate: a 400-account list moving from "~half a day" to "~12 minutes", with an 86%+ mobile and email hit rate.
Platform other operators' experience through interviews and revenue-leader events
A large share of what Deacon publishes is a named operator's framework, packaged for an audience, rather than their own operating result.
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Interview posts follow a consistent shape — the guest's backstory, then an arrow-list of takeaways, then a link to the full episode.
Varun Bhandarkar, Stripe's head of SMB and mid-market for Southeast Asia, is presented through why outbound is the last pipeline lever to pull and what must be in place first, the standardise-then-localise framework Stripe uses across very different Asian markets, how to spot expansion signals before a customer mentions budget or renewal, an interview question for identifying coachable hires, and the three moments where churn is actually decided rather than at renewal.
Nicholas Holland, HubSpot's former SVP of product and head of AI, is presented on scaling Marketing Hub from $400 million to $1.6 billion ARR, Content Hub from zero to $130 million, the free CRM launch, and what HubSpot learned when customers objected to paying for AI they previously received free.
Alongside the podcast, Deacon promotes and reports from revenue-leader events, including a Chief Revenue Officer Summit in Sydney and UNBOUND 2026 in Boston, where the recurring theme they identify is AI-first GTM: the companies getting ahead are rethinking how the whole revenue engine operates rather than adding AI tools to old processes.
Deacon's own framing of the format: "Some of the best conversations don't happen in the sessions. They happen at the executive dinners, side events, and random catch-ups outside the main event" — where operators compare what is actually working and what is not.
Answers the question most people searching this name arrive with: what does Mark Deacon argue about pricing and AI-era business models?
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This is the clearest single statement of their position.
Deacon argues against starting with seat-based pricing.
First, pick a unit of value — resolved tickets, qualified leads, deals moved forward, messages that got a reply — and instrument it before signing a single contract; retrofitting telemetry into live billing is a mess.
A seat can then provide a predictable floor, with a variable meter for agent work.
The harder call is what to meter: actions are easy to count and for procurement to argue down, while outcomes are hard to count and easier to sell.
The recommendation is to run a hybrid model on new logos only, since migrating existing seat contracts is a separate project.
The named failure mode: customers quietly renew the same seat count while doing three times the work through agents — same ARR, half the value capture.
For brands planning a campaign
For a SaaS brand scoping a pricing advisory, this post shows the analysis on offer: a sequenced recommendation and a named renewal risk. It frames the advisory and helps establish whether the engagement is about repricing new business or migrating the installed base.
Shows how Deacon reasons about defensibility, the question behind most SaaS positioning work: what makes a product hard to remove?
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Deacon reads a roughly 20x multiple on $47 billion of run-rate revenue as a bet on accumulated context rather than model quality.
The argument runs through Fortune 100 companies wiring Claude into procurement, code review, customer ops and finance workflows, so the integration learns how the business operates — a point they attribute to Sequoia's Alfred Lin.
They apply the same curve to SaaS generally: products that accumulate workflows, decisions, institutional memory and messy judgment calls get harder to switch every quarter, while products that wrap a model and re-skin a UI get cheaper to replace.
The post turns on this line: if a customer could swap you out next Tuesday with a weekend of work, you do not have a moat, you have a feature.
For brands planning a campaign
Gives a brand a differentiated message frame — institutional memory and switching cost rather than benchmark performance. Useful as a positioning test for a product whose differentiation currently rests on the underlying model.
Shows what “I built an agent” means in practice, spelling out the old and new processes — a concrete version of a claim that is usually left abstract.
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Deacon describes expanding into government roughly 18 months earlier, into slow procurement cycles, compliance work and opportunities buried across tender portals.
The old process was manual: logging into portal after portal, reading every new listing, deciding fit, hand-creating Company, Contact and Deal records in HubSpot, then assembling a 20-plus page submission from a blank page.
The replacement agent runs daily across Australian, New Zealand, UK, EU and US portals.
It pulls new tenders at the source, judges relevance, creates the HubSpot records with the portal and source URL stamped on them, posts to Slack when something looks worth pursuing, and drafts the submission for the team to review and submit.
The post sets the old and new workflows side by side and asks others selling into government what they have learned.
For brands planning a campaign
A reusable proof-of-work example for a fractional engagement scoped on revenue ops or outbound automation: capture at source, create the CRM record with provenance, alert, then draft. The pattern is specific enough to copy and interrogate.
Shows how Deacon handles the unglamorous plumbing of outbound — list building — with before-and-after numbers rather than a principle.
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Deacon describes years of stitching together Apollo for emails, ZoomInfo or Lusha for mobile numbers the others missed, and Sales Navigator for LinkedIn URLs, then enriching, exporting, finding the gaps and enriching again.
The result was what they call a Frankenstein CSV, built over hours or days.
The replacement is a Claude skill plus FullEnrich: the /find-contacts command with a target account list attached connects to FullEnrich through its MCP, which waterfalls through more than 20 data providers.
The enriched contacts push into HubSpot and outreach sequences.
On a 400-account list, the claim is roughly half a day down to about 12 minutes, with a mobile and email hit rate above 86% — around double the coverage of any single provider.
The post carries a partner hashtag for the enrichment vendor but states no payment terms.
For brands planning a campaign
Useful when a brand wants measured throughput claims for an outbound build. Ask for the raw list and hit-rate data before repeating the figures in a campaign; the partner tag makes that verification especially important.
Shows Deacon's interview format and the argument they platform: a named operator describing how AI changes the shape of a revenue team, not just its tooling.
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Deacon relays an observation from Michael Hartman, who leads go-to-market at Anthropic, at an event in Boston: AI is undoing 20 years of sales specialisation.
The post traces the split of one account executive role into SDRs, BDRs, AEs, solutions engineers, RevOps, CSMs and AMs, then argues that AI removes much of the manual work inside those roles — research, prospecting, account planning, follow-up, CRM administration, proposal preparation, information finding and customer-data analysis — so one person can orchestrate far more of it.
The possible result: full-cycle sellers judged on outcomes rather than task completion.
The post closes with a reframed leadership question: if AI removes half the work from a role, what could that person become capable of?
For brands planning a campaign
A ready-made framing device for a brand selling sales enablement or revenue-team redesign, and evidence that Deacon can carry a named operator's argument to a general audience without flattening it.
Answers what the podcast exists to do and who it features — the best entry point for anyone deciding whether the interview work is worth following or pitching to.
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Deacon builds the episode around the guest's backstory: arriving in Australia with two months left on a student visa, rewriting a CV every evening to apply for five jobs a day, and tweeting a hiring manager to get noticed.
The episode then covers why outbound is the last pipeline lever to pull and what has to be in place first; the standardise-then-localise framework Stripe uses across very different Asian markets; how to spot expansion signals weeks before a customer mentions budget or renewal; an interview question used to identify coachable high performers; and why churn is rarely decided at renewal, with three earlier moments when it actually is.
A companion post previews the same episode and cites Stripe's $1.9 trillion in annual payments as context.
For brands planning a campaign
Shows the podcast as a distribution channel, with a defined guest profile and topic menu a brand can pitch against for an episode slot or co-marketing. The episode's framework list also works as an outline a brand can borrow for its own content.
Pricing architecture: start with a meter, not a seat1
Context accumulation as the real moat1
AI is pulling sales specialisation back into full-cycle roles1
Replacing outbound list building with a single AI-assisted workflow1
An agent for public-sector tender discovery and first-draft submissions1
Pricing architecture: start with a meter, not a seat
Mark Deacon lays out how they would build a pricing model from scratch in 2026.
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Start with a meter, not seats: headcount and logins are shrinking as agents do more of the work.
Choose the unit of value before the unit of pricing—resolved tickets, qualified leads, deals moved forward or messages that got a reply—and instrument it on day one, before any contract is signed.
Then add a small seat base to the variable meter to give customers a predictable floor.
The harder call is what to meter: actions are easy to count and easy for procurement to argue down; outcomes are hard to count but easier to sell.
Roll out the new model to new logos only rather than migrate existing seat contracts.
The warning: companies whose customers renew the same seat count while doing three times the work through agents risk ending up with the same ARR and half the value capture.
Choose the unit of value before the unit of pricing; instrument telemetry before it touches billing; use a hybrid seat-plus-meter structure; roll it out to new logos only.
For brands planning a campaign
The most relevant piece for a fractional pricing advisory engagement.
A company evaluating Mark Deacon can test the proposal against its own product: Is there a measurable outcome to meter?
Can it be instrumented now?
Would a seat-plus-meter model work for new logos while existing contracts stay untouched?
The post presents an approach, not a documented client engagement; ask for the underlying analysis and any case work.
Mark Deacon reads Anthropic's roughly $965 billion valuation against about $47 billion of run-rate revenue as a bet on context rather than model quality.
Read more
Once a company wires a model into procurement, code review, customer operations and finance workflows, the integration learns how the business operates.
That accumulated context makes removal feel like a re-organisation.
The lesson for SaaS operators: products that accumulate workflows, decisions and institutional memory get harder to switch every quarter, while products that wrap a model and re-skin a user interface get cheaper to replace.
Their line: “if a customer could swap you out next Tuesday with a weekend of work, you do not have a moat, you have a feature.”
For brands planning a campaign
Useful for positioning and retention messaging: it offers a concrete test of whether a product's integration depth is real or cosmetic. This analogy draws on a public valuation and a quoted investor, not proprietary research into the buyer's market; test the argument against the prospect's own data.
AI is pulling sales specialisation back into full-cycle roles
Reacting to a talk by Anthropic's GTM lead Michael Hartman in Boston, Mark Deacon relays the observation that AI is undoing twenty years of sales specialisation.
Read more
That trend split the customer journey among SDRs, BDRs, AEs, solutions engineers, RevOps, CSMs and AMs.
AI removes much of the manual work inside those roles, not the responsibilities themselves.
Research, prospecting, account planning, follow-up, CRM administration, proposal preparation and customer data analysis still need doing, but one person can increasingly orchestrate more of it, opening the door to a generation of full-cycle sellers focused on outcomes.
Their recommended leadership question: not which jobs AI can replace, but what a person could become capable of if AI removes half the work from the role.
Reframe the AI question from job replacement to role expansion: what does this person become capable of if half the work disappears?
The core argument is another operator's observation from an event, quoted and expanded rather than original research by Mark Deacon.
For brands planning a campaign
Relevant to org design, sales enablement and productivity messaging.
For a campaign about rebuilding a revenue team around AI, this is the clearest statement of the thesis.
The supporting authority is the quoted source; pair it with the buyer's own role-level data before making a recommendation.
Replacing outbound list building with a single AI-assisted workflow
Mark Deacon describes years of building prospect lists with Apollo for emails, ZoomInfo or Lusha for mobile numbers, and Sales Navigator for LinkedIn URLs, then enriching, exporting and stitching the gaps into a manual file over hours or days.
Read more
They replaced that workflow with a Claude skill and FullEnrich connected through an MCP: type a command, attach a target account list, and FullEnrich automatically waterfalls through more than twenty data providers, pushing enriched contacts into HubSpot and straight into outreach sequences. Reported result on a 400-account list: about half a day reduced to about twelve minutes, a mobile-and-email hit rate above 86%, and roughly double the coverage of any single provider.
Single-command enrichment through a data-provider waterfall connected to the CRM, replacing multi-tool manual stitching.
The post carries a partner hashtag for FullEnrich. The timing and coverage figures are Mark Deacon's own account of their workflow and have not been independently verified.
For brands planning a campaign
Directly applicable to an outbound automation proposal. Before adopting it, ask for the workflow definition behind the hit-rate claim, how many providers were in the waterfall, which contact types counted toward coverage, and whether the partner relationship affects the recommendation.
An agent for public-sector tender discovery and first-draft submissions
Eighteen months after expanding into government as a new vertical, Mark Deacon describes automating a process that had a team member logging into tender portals one by one every day, reading listings, deciding fit, creating company, contact and deal records in HubSpot by hand, and writing twenty-page submissions from scratch.
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The replacement agent runs across government portals daily in Australia, New Zealand, the UK, the EU and the US.
It pulls every new tender at source, decides which are relevant, creates HubSpot records stamped with the portal and source URL, alerts Slack when something looks promising, and drafts the submission for the team to review and submit.
The post presents a side-by-side of the old manual process and the new automated one.
For brands planning a campaign
The strongest operational example in the posts: public-sector pipeline automation with a specific vertical, named geographies, a CRM record structure and an output format.
Useful for assessing hands-on build capability and developing a comparable plan for a buyer selling into government.
The post describes the workflow, not measured win rates or ROI; ask for those separately.
Mark Deacon previewed an episode with Varun Bhandarkar, Stripe's head of SMB and mid-market for Southeast Asia, framing Stripe as processing $1.9 trillion in payments a year.
The follow-up lists what the episode covers: why outbound is the last pipeline lever to pull and what must be in place first; Stripe's standardise-then-localise framework across Asian markets; how to spot expansion signals before a customer mentions budget or renewal; an interview question for identifying coachable hires; and the three moments when churn is actually decided, rather than at renewal.
A separate post covers Nicholas Holland, HubSpot's former SVP of product and head of AI, on scaling Marketing Hub from $400 million to $1.6 billion ARR, Content Hub from zero to $130 million, the free CRM launch, and what HubSpot learned when customers objected to paying for AI features they had previously received free.
Episode-driven posts with arrow-list takeaways and links to the full episode in comments.
For brands planning a campaign
These posts show Mark Deacon convening named operators from Stripe and HubSpot and packaging their frameworks, a fit for co-marketing, sponsored episodes, webinars or interviews. The takeaways summarise guests' experience rather than Mark Deacon's operating results; match them to the buyer's segment and market before treating them as a playbook.
Mark Deacon promotes and reports from revenue-leader events.
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One post announces their return to the stage at the Chief Revenue Officer Summit in Sydney, hosted by Revenue Operations Alliance for CROs and senior revenue leaders.
It lists the day’s topics: AI reshaping the revenue engine, building more predictable pipeline and growth, aligning sales, marketing and customer success, GTM productivity, and what is actually working versus AI hype.
Another post closes out UNBOUND 2026 in Boston, arguing that the best conversations happen at executive dinners, side events and informal catch-ups rather than in sessions.
Its recurring theme: AI-first GTM.
Companies getting ahead are rethinking how the whole revenue engine operates, not bolting AI tools onto old processes.
For brands planning a campaign
Points to event-led and community-led partnership options, including sessions for invite-only CRO and revenue-leader audiences. The posts promote and reflect on events rather than document a tested method; confirm the format, audience and any speaker relationship before planning around them.
The posts show one explicit partner disclosure: FullEnrich, tagged #FullEnrichPartner.
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Anthropic and Claude, HubSpot, the GTM Playbook Podcast, and Revenue Operations Alliance appear as subjects, tools, or platforms. Ask Deacon about any employment, ownership, client, sponsorship, or investment relationships not stated in the posts.
FullEnrich
Disclosed partnership
Mark Deacon disclosed a partnership with FullEnrich in a post about replacing prospect-list building with a Claude skill and FullEnrich's MCP.
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The post carries #FullEnrichPartner and says enriched contacts push into HubSpot and outreach sequences.
Mark Deacon discusses Anthropic and Claude across multiple posts: analyzing Anthropic's valuation and embedded-context moat, interviewing Anthropic's GTM lead about AI and sales, and describing a Claude skill in an outbound workflow.
HubSpot appears as a tool and subject: Deacon says the team created HubSpot records by hand before automating government-tender intake, pushes enriched contacts into HubSpot, and interviews HubSpot's former SVP of Product and Head of AI on the GTM Playbook Podcast.
Mark Deacon's posts are worth following for AI-first go-to-market arguments tied to concrete decisions rather than slogans: what to charge for, what makes a product hard to remove, and which parts of the selling job an agent should take over.
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Start with the pricing post, where they explain why a value meter beats a seat as the starting point and what has to be instrumented before any contract is signed.
The workflow build posts — public-sector tender discovery and outbound list enrichment — apply the same idea to real processes.
Interview posts and event notes round out the feed: they package named operators' frameworks, from Stripe's expansion playbook to HubSpot's AI rebuild, and use revenue-leader events to track where AI-first GTM practice is heading.
Follow the account for operator-level arguments about pricing, defensibility and automation; guest posts summarize someone else's experience, not their own results.
Shimon makes the same core move Deacon does: rather than buying another vendor tool, build a thin layer with AI on top of the CRM and data tools the team already runs.
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Both write from firsthand build experience, connect the build back to pipeline visibility, and treat the working system as the argument rather than a slide about one.
Shimon's examples stay inside one go-to-market team's own tooling — an internal pipeline app and a shutdown-verification loop for its bugs — and the lessons are about engineering discipline and how a fix is confirmed live. Deacon's builds target processes outside the CRM, including government tender discovery and prospect-list enrichment, and sit alongside wider arguments about pricing design and product defensibility that Shimon does not cover.
Hill frames the change in her field the way Deacon frames it in revenue: AI alters the nature of the buying decision, not just the tools used to execute the existing task, and the people closest to supply and spend are positioned to see it first.
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Both writers push readers past tool selection toward the questions that change once AI is in the picture.
Hill writes about construction procurement, where the questions are about supplier structures, data and IP ownership, and where risk sits; Deacon writes about B2B SaaS revenue teams, where the questions are what to meter, which workflows to automate and how to keep a product hard to switch. Different industries, different buying decisions: Hill's posts sit closer to training and function-level strategy than Deacon's pricing and pipeline builds.
Danishevskiy asks the same practical question Deacon keeps returning to — build it or buy it — and answers it with costed comparisons rather than opinion, much as Deacon asks operators to prove a pricing model or a moat with something measurable.
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Both write for revenue and operations leaders deciding how their stack and partners should work.
Danishevskiy works the build-versus-buy decision from the buyer's side, with three-year total-cost comparisons and a checklist for choosing a RevOps partner; their material is about evaluating vendors and engagements.
Deacon's is about designing the pricing model and automated workflows themselves.
They also bring in guest operators' frameworks through podcast interviews, which Danishevskiy does not.
Mark Deacon’s posts target B2B SaaS founders and go-to-market leaders, with practical material on pricing, outbound, sales operations, and AI-enabled workflows.
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Their content suits a brief about GTM or pricing advisory. Before booking, request recent LinkedIn audience analytics and, if the brief requires specific roles or industries, documented survey results or another source showing those professional attributes.
Mark Deacon's ten LinkedIn posts show three possible ways for a brand to work with them, though none is presented as a published offer.
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First, a post walking through a contact-enrichment workflow carries a partner hashtag naming FullEnrich and reports a specific result: a 400-account list built in roughly 12 minutes, with an 86%-plus mobile and email hit rate.
Second, the GTM Playbook Podcast, which they promote and use to interview revenue leaders, including Stripe's Varun Bhandarkar and HubSpot's former head of AI Nicholas Holland.
The posts don't describe a sponsor slot or host-read ad.
Third, event appearances: they promoted the invite-only Chief Revenue Officer Summit in Sydney and pointed readers to a registration link in the comments, suggesting a speaking or attendance role rather than a paid placement.
LinkedIn is the only contact route visible in the posts.
Ask directly about payment, exclusivity, approval, usage rights, sponsor slots, deliverables, disclosure wording and pricing.
Strong fit, with one condition before contracting.
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Brief
A Series B B2B SaaS company selling workflow software to revenue and operations teams wants a fractional GTM and pricing advisor for one quarter. Deliverables: a repricing proposal for a product where AI agents now do much of the work, and a rebuilt outbound list-building process connected to its CRM.
Reasons
Mark Deacon's published framework answers the pricing brief directly. It starts with a meter, not a seat: choose a unit of value first (resolved tickets, qualified leads, deals moved forward, replies), instrument it before signing a contract, then add a small seat base for a predictable floor. The buyer can test that sequence against its own product in the first meeting.
The post names the risk the company is likely worried about: customers renewing the same seat count while doing three times the work through agents, producing the same ARR against half the value capture. It recommends a hybrid model for new logos only and treats migration of the installed base as a separate project. If the company wants one plan covering both, it should expect a no—useful to know when scoping the work.
The outbound brief has a concrete proof of work. The government-tender post describes the process in operational detail: capture listings at the source across named geographies, judge relevance, create CRM records stamped with the portal and source URL, alert a channel, then draft the submission for a person to review and submit. The same pattern transfers to commercial outbound and shows what "I built an agent" means in practice.
The prospect-list post covers the underlying plumbing: replace a multi-provider stitch of Apollo, ZoomInfo or Lusha and Sales Navigator with a single command that waterfalls through more than 20 providers and pushes enriched contacts into HubSpot. Reported figures are roughly half a day down to about 12 minutes on a 400-account list, with a mobile-and-email hit rate above 86%, about double the coverage of any single provider.
A fourth post offers a way to brief the company's leadership: AI removes much of the manual work inside SDR, BDR, AE, solutions engineer, RevOps, CSM and AM roles without removing the responsibilities, while one person orchestrates more of the journey. That framing is directly useful for a pricing engagement that will change how the sales team is measured.
Condition before contracting
Every result figure is self-reported, and one post carries a partner hashtag for the enrichment vendor. Treat the 12-minute time and 86% hit-rate figures as vendor-aligned until the underlying list and test are shared.
The posts show no client outcomes, case studies, win rates or ROI from a completed advisory engagement. The engagement is being bought on demonstrated method, not an advisory track record; ask for evidence of relevant client results before contracting.
If distribution through the account is part of the brief, request recent LinkedIn audience analytics before scoping that work. Follower count alone does not show who follows the account.
When to choose a specialist
Poor fit based on the posts. The mismatch is between what the campaign assumes exists and what the posts show.
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Brief
A B2B fintech brand wants a paid sponsorship package booked now: a fixed number of sponsored LinkedIn posts plus a host-read segment on a podcast, priced against a guaranteed reach target, with a media kit and standard usage rights.
Reasons
The posts offer no sponsorship policy, rate card, media-kit page or partnership inquiry route. The only disclosed commercial marker is a partner hashtag on the contact-enrichment post, naming the vendor. It does not state payment, exclusivity, approval steps, usage rights or price, so ask for a confirmed offer before treating this as a standard sponsorship package.
The brand's guaranteed-reach target cannot be assessed from the posts: they include no follower count or audience-demographic data. Request audience analytics to check whether followers match the target roles.
The strongest interview and event material is editorial, not advertising inventory. The Stripe and HubSpot interviews are interviews; nothing describes a sponsor slot. The invite-only Chief Revenue Officer Summit appearance reads as a speaking or attendance role, pointing readers to a registration link rather than a paid placement. Ask whether any of these formats are actually available as ad slots.
There is also a volume mismatch. Of the ten posts reviewed, none uses the short-form, high-frequency promotional format the brand wants. The observed pattern is long argumentative posts, before-and-after workflow write-ups, and episode promotion.
What would change the verdict
A confirmed offer from Mark Deacon covering deliverables, disclosure wording, exclusivity, usage rights and price.
Audience analytics or a documented audience survey showing the roles and industries the brand needs.
A named sponsor slot or host-read arrangement described in the material itself, rather than inferred from event or podcast promotion.
A worked campaign example
Angle
Lead with the failure mode the company's finance and revenue leaders both recognise: same ARR, half the value capture. Most pricing conversations start with packaging and discounting; this one starts with what the product produces in the world and whether that can be measured cleanly today.
Brief
The same Series B company as in the good-fit case wants a two-part engagement over one quarter: redesign pricing for an agent-assisted product, then rebuild outbound list building.
How to judge it worked
The chosen unit of value can be read cleanly from production data, not modelled from estimates.
A new-logo contract is signed on the seat-plus-meter structure without discounting the meter to close.
The rebuilt list workflow produces documented time savings and coverage figures from the company's own accounts, not the vendor-aligned figures quoted publicly.
The team can state the three moments when churn is decided before renewal, drawing on the Stripe episode material, and show where those moments are handled in its own process.
Open items to request
Ask for the analysis or case work behind the pricing post, and whether it reflects a documented client engagement or an approach.
Ask how the 86%-plus hit rate was defined: how many providers were in the waterfall, which contact types counted toward coverage, and whether the partner relationship shaped the recommendation.
Ask for the Stripe and HubSpot episode recordings to verify the episode-level frameworks; only promotional posts were available.
Ask for recent LinkedIn audience analytics if the campaign relies on distribution through the account.
Sequence
Week 1-2, diagnostic. Pick the unit of value with the product and data teams. Test three candidate metrics against the post's filter: is it easy to count, or easy to sell? Actions are easy to count and easy for procurement to argue down; outcomes are hard to count and easier to sell. Decide which the company is prepared to instrument.
Week 2-4, instrumentation check. Confirm telemetry exists before anything reaches billing. The published argument identifies retrofitting measurement into a live billing system as the failure path, so scope any gap as an engineering task, not a modelling one.
Week 4-8, model design. Use a small seat base for access and administration, plus a variable meter for what the agents do. This gives customers a predictable floor and the vendor upside as usage grows. Run it on new logos only; treat migration of existing seat contracts as a separate project with its own plan.
Week 8-12, outbound rebuild. Replace the manual multi-provider list workflow with a single enrichment command connected to the CRM, then push enriched contacts straight into outreach sequences. Mirror the tender-agent pattern the company can inspect: capture at source, judge relevance, create the CRM record with provenance stamped on it, alert the owner, then draft for human review.
Ongoing, narrative. Use the full-cycle seller framing to explain to the sales team why roles are changing shape. Close on the reframed question from the Anthropic talk: if AI removes half the work from a role, what could this person become capable of?
What the company gets
A pricing model tied to a metric it can defend in procurement, with a named boundary around the installed base.
An outbound process with a measurable before-and-after, built on a pattern shown in a different vertical rather than asserted.
A leadership narrative that connects the pricing change to how the revenue team is structured and measured.
Choose Ori Shimon when the blocker is an unreliable pipeline view, not uncertainty about pricing.
Read more
The posts show how to build an internal GTM layer on top of the CRM, call recordings and enrichment tools, giving each rep a view of their open pipeline, surfacing bottlenecks and enriching every deal from the same sources.
The second post covers verification: an issue does not close when a fix is pushed; it moves to a checking stage, where a real browser runs the scenario against production.
If the check crashes or cannot tell, the outcome is unknown, not fixed.
That discipline matters to a company selling agent-assisted workflows: it answers the operational question behind the pricing need—how do we know a shipped agent change actually altered behaviour before we charge for it?
The posts focus on tooling and verification, not pricing, unit of value, seat-versus-meter choices or outbound list-building.
A company engaging Ori Shimon for a CFO-focused campaign would need a separate pricing workstream; the repricing proposal central to Mark Deacon’s fit case has no counterpart here.
Choose Sergey Danishevskiy when the live question is build versus buy and the company needs a costed answer and a way to assess a RevOps partner.
Read more
One post models three-year total cost of ownership for HubSpot versus building and maintaining a custom CRM, assuming AI-assisted development: 8 seats at $1M ARR, $61,000 versus $225,000; 45 seats at $15M ARR, $480,000 versus $1.85M; 250 seats at $100M ARR, $2.8M versus $6M.
The conclusion is to buy the core and build only the modules that give the business an edge.
The second post offers five tests for selecting a RevOps partner: diagnose before quoting, build rather than advise, plan their own exit so workflows sit in the client’s CRM, cover the full revenue loop, and tie accountability to outcomes rather than hours.
Both address the buyer’s task, from the procurement side.
The posts address CRM economics and vendor selection, not product pricing.
They offer no meter-versus-seat framework, unit-of-value discussion or agent-era monetisation argument.
They also do not cover the outbound rebuild: list building, enrichment waterfalls or CRM handoff of enriched contacts.
Consider Suzanne Hill when the company is deciding what category of thing to buy, rather than how to charge for what it sells.
Read more
The posts frame AI-era procurement as a shift in the buying decision: the choice may be between buying technology, outsourcing a capability, partnering, building internally, procuring a managed service or simply buying the outcome.
The questions change accordingly: what is being bought, who owns the data and IP, where risk sits, how dependent the business becomes on the supplier, and what happens if the commercial model changes.
The other post argues that scattered qualitative signals across tenders and supplier conversations can now be captured and used rather than lost in someone’s memory.
For a Series B company being asked to restructure how its agents are bought and integrated, this framing is useful on the client side of the table.
The posts are grounded in construction procurement, not SaaS pricing or outbound list building.
They offer no seat-plus-meter structure or product monetisation advice.
The transferable value is a framework for build-versus-buy-versus-partner decisions and questions of ownership and dependency.
Ask for separate guidance on repricing and the outbound rebuild.
This profile is based on ten complete LinkedIn posts from Mark Deacon's account, covering pricing model design, SaaS defensibility, AI and sales team structure, agent-built workflows for government tendering and outbound prospecting, podcast interviews, and revenue-leader events.
The posts do not carry consistent publication dates.
No episode transcripts or recordings, client outcomes or advisory case work, sponsorship rate card, or audience demographics were available.
Figures in the outbound post are self-reported, and the post carries a partner hashtag, so they are presented as reported rather than verified.
