Useful for call-data backed content on discovery, coaching, and revenue-team AI.
Best for
B2B SaaS revenue enablement brands selling coaching or discovery improvement.
Not for
Consumer brands seeking broad awareness, lifestyle framing, or impression-based reach.
Key takeaways
Reps often find customer pain but fail to quantify its impact.
MEDDICC call study names Decision Process as the weakest qualification category.
AI should turn saved time into better performance, not just faster admin.
Check first: Ask how call data supports broad claims and whether audience data exists.
Main ideas
Qualification is a process, not a checklist · Find the cost of inaction, not just the buyer’s problem · AI should turn saved time into better performance
Matt Doyon works on sales and revenue enablement, using call data and AI to examine how teams discover customer needs, coach sellers, and improve performance.
Their posts turn large call analyses into practical arguments: for example, they report that reps often identify customer pain but fail to quantify its impact, leaving buyers without a clear reason to act.
They also write about connecting AI insights to coaching and measurable improvements, rather than stopping at faster administrative work.
Doyon links findings, diagnosis, and a proposed next step.
In posts about more than 17,000 SPICED calls and 11,000 MEDDICC calls, they use specific scoring patterns to show why checking framework boxes is not the same as understanding a deal: reps need to quantify impact and learn how decisions will actually be approved.
Their AI writing applies the same practical lens, distinguishing time-saving automation from action plans and reporting intended to improve performance.
The result is useful for readers who want concrete questions to ask about discovery, coaching, or revenue-team AI—not just a framework name or a technology forecast.
They also make broad claims, such as that sales and customer success roles are changing; buyers should ask how the call data supports those conclusions.
Matt Doyon’s posts argue that revenue teams should use call data and AI to improve performance, not just save time.
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They emphasize deeper qualification, measurable coaching and connected AI systems, and urge sellers to lead with a clear point of view before presenting a product.
Qualification is a process, not a checklist
Doyon argues that sales frameworks should guide questions across several conversations.
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Recording a category alone does not qualify a deal: sellers need specifics, such as a metric’s baseline, its financial impact and who owns it. Decision and paperwork steps can also stall deals late.
In a post about more than 11,000 MEDDICC calls, Doyon says first discovery conversations usually covered only two or three categories. They cite “we’re trying to improve CAC” as a response that checks a Metrics box but lacks useful detail.
Find the cost of inaction, not just the buyer’s problem
Doyon argues that identifying pain is not enough to create urgency.
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Sellers need to connect a problem to measurable impact and a reason to act now; otherwise, later steps such as decision-making and next meetings lack substance.
In an analysis of more than 17,000 SPICED discovery calls, Doyon reports stronger scores for Situation (84%) and Pain (83%) than for call openings (38%) and Impact (53%). They link weak impact discussions to vague critical events and follow-ups that fail to build momentum.
Automating notes, CRM updates and email writing can save time, but teams need coaching actions and useful reporting to improve how people sell.
Doyon describes an AI system that uses call analysis to score conversations and prompt coaching, deal guidance, roleplay and pipeline review. In another post, they frame this as moving from AI that helps people do the same work faster to AI that helps them produce better outcomes.
Doyon treats sales coaching as a significant business investment that should be measured, not managed through intuition and scattered notes.
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They ask practical questions: what each representative needs to work on, whether coaching affects revenue, and which managers coach effectively.
Doyon estimates that one-to-one coaching for an eight-person team costs about $72,000 a year in staff time, then compares that with the scrutiny a software purchase of the same amount would receive. They point to the P.A.R. coaching methodology as a way to make coaching more repeatable.
Sales and customer success share more commercial responsibility
Doyon argues that the old division—sales closes while customer success retains—no longer fits how revenue teams operate.
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Customer success now owns more expansion and renewal work, while sales must consider long-term fit, adoption and retention before closing.
Drawing on an assessment of 500,000 Aircall calls, mostly from customer success, and 500,000 video calls, mostly from sales, Doyon describes customer success as a growth partner and sales as responsible for setting up durable customer value.
A clear point of view should come before the product pitch
Doyon argues that sellers lose deals when they sound interchangeable and wait for buyers to define the conversation.
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Instead, they should teach, challenge assumptions and explain why the status quo no longer works. A prospect’s disagreement can help establish fit early.
Doyon criticizes generic questions such as “What keeps you up at night?” and argues that teams should state what they believe before buyers spend months comparing features.
It answers a newcomer’s first question: does Matt Doyon work from real call data, and what do they say is the most common discovery failure?
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Matt Doyon says the team scored more than 17,000 discovery calls against the SPICED framework.
Scores were strong at the start and weak later: Situation at 84% and Pain at 83%, but call opening at 38% and Impact at 53%.
They argue reps find problems without putting numbers on them, so nothing creates urgency and the later stages weaken as a result.
They name sales coaches Jen Allen-Knuth and Keenan as people who keep returning to the topic, and credit Winning by Design and Jacco van der Kooij with the methodology.
For brands planning a campaign
A ready-made hook for a discovery or quantification webinar: a large dataset, a memorable finding about missed cost of inaction, and a familiar framework.
It shows how Doyon turns a scoring dataset into a training argument and states the belief behind most of their work: qualification depth matters more than covering a framework.
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Across more than 11,000 calls scored against MEDDICC, Matt Doyon reports that first discovery conversations usually cover only two or three categories, with Metrics and Pain appearing early and Decision Criteria and Decision Process emerging later.
They argue that many reps technically cover MEDDICC without capturing useful detail, and that Decision Process is consistently the weakest category.
That is one reason organisations have moved to MEDDPICC, which separates out the paper process of procurement, legal review, and vendor onboarding.
The sample spans several geographies, languages, and company sizes.
For brands planning a campaign
Useful evidence for a coaching offer aimed at deal approval steps and paperwork delays, rather than better question lists.
This is the broadest claim in the set and the clearest statement of how Doyon thinks revenue roles are changing.
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Matt Doyon reports assessing about 500,000 Aircall calls, mostly in customer success, and 500,000 video calls, mostly in sales.
They conclude the two functions have swapped role requirements.
In their account, customer success now carries expansion quotas tied to net revenue retention, negotiates renewals, builds financial cases for executives, and runs quarterly reviews as growth planning rather than recaps.
Sales, meanwhile, is expected to qualify for long-term fit, agree on success metrics before the deal closes, and accept pay linked to retention.
They sum it up as customer success moving from relationship manager to strategic growth partner.
For brands planning a campaign
Supports a joint webinar or article aimed at revenue leaders, not just enablement, because the argument crosses sales and customer success budgets.
The clearest example of how Doyon packages a commercial argument with a downloadable resource.
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Matt Doyon puts the salary cost of one-to-one coaching for an eight-person team at roughly $72,000 a year, counting rep time, manager time, and the opportunity cost of selling.
They contrast that spending with the scrutiny a $72,000 software purchase would receive: multiple demos, security review, procurement, ROI analysis, executive approval, and success metrics after implementation.
Most organisations, they write, could not say which rep should be coached on what, what coaching earns, or which managers coach well.
They introduce a P.A.R. coaching methodology and offer a 13-page guide in exchange for a comment.
For brands planning a campaign
A template for a co-branded gated guide: quantify a familiar cost, compare it with accepted procurement rigour, then offer a named framework.
A concise statement of the framework Doyon speaks about, and evidence that they present it to large rooms, not just in posts.
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After a keynote to a room of 700, Matt Doyon writes that most go-to-market teams are at stage 2, where AI is built into individual workflows that do not talk to each other, so overlapping work gets rebuilt from scratch.
Stage 1 is manual AI use for research or summaries.
Stage 3 is an intelligence layer where one call analysis feeds coaching, pipeline review, deal guidance, customer-facing material, and roleplay before the next meeting.
They say stage 3 is hardest to reach because it needs executive sponsorship and RevOps, enablement, and revenue leaders working from the same plan.
For brands planning a campaign
A maturity model can anchor a webinar or self-assessment for buyers working out where their team sits.
One screen that captures Doyon's priority, and a useful contrast to the tool-led AI pitches a reader will have already seen.
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Doyon describes sales leaders getting half of AI: rep efficiency but not rep performance.
Writing emails, updating the CRM, and summarising meetings all save time, but if that time goes back into the same work, they argue, the larger opportunity is missed.
The second half is turning every sales call into a coaching session, every lost deal into an improvement plan, and strong call moments into shared training material or roleplay.
For brands planning a campaign
Sharp framing for a launch or webinar headline, with a contrast that sets an enablement pitch apart from an automation pitch.
AI maturity and an integrated AI operating system5
Call-data studies built on large samples3
Making sales coaching measurable3
Discovery and qualification frameworks2
Sales and customer success roles converging1
Point of view before product1
What Doyon actually uses, and what "cheap" means1
Call-data studies built on large samples
Doyon's most repeated format is an original analysis of a large body of sales or customer calls.
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They report scoring more than 17,000 SPICED discovery calls and more than 11,000 MEDDICC calls, and separately assessing 500,000 Aircall calls alongside 500,000 video calls. Each post leads with the sample size, reports which parts of the framework held up and which collapsed — for example, weak discovery openings at 38% and quantified impact at 53% — then interprets the gap for the reader.
Scores calls against a named qualification model (SPICED, MEDDICC), reports a percentage for each category, then explains what low scores cost in the deal.
For brands planning a campaign
A brand with conversation data can commission a study in the same format: sample size first, framework-by-framework results, and a ranked list of weaknesses tied to business consequences. The format gives a partner citable numbers and a ready-made hook for a webinar, report or sales-facing content series.
Across the call analyses, Doyon argues that frameworks like SPICED and MEDDICC work as roadmaps unfolding over several conversations, not checklists to complete in one call.
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Their MEDDICC review found Decision Process consistently scored lowest and that many reps technically tick a box without qualifying anything.
A prospect saying "we're trying to improve CAC," for example, fills the Metrics field but captures no baseline, financial impact or internal owner.
They also explain why teams move from MEDDICC to MEDDPICC, adding the paper process for procurement, contract redlining, legal approvals and vendor onboarding.
For brands planning a campaign
Directly useful for a revenue enablement or sales training brand: the frameworks are widely recognised, the failure points are specific, and the material supports co-branded content on discovery quality, call scoring or coaching around qualification detail.
Doyon argues that most AI spending goes to automation — meeting summaries, CRM updates, email drafting and task creation — and that time saved does not translate into performance gains without two other elements: AI-generated action plans (coaching plans, deal conversion playbooks and objection handling) and AI reporting that explains why something happened, not just what happened.
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Their stage model moves from AI used manually as a general helper, to scattered point solutions that overlap and duplicate each other, to an integrated intelligence layer where a single call analysis triggers coaching, pipeline review, deal guidance, customer-facing collateral and practice roleplays. They present a "5 Pillars of an AI Operating System" workshop and say reaching the integrated stage requires RevOps, enablement and revenue leaders to align under executive sponsorship.
For brands planning a campaign
An enablement or revenue-AI vendor can use the maturity model as a joint diagnostic or webinar spine: ask prospects which stage they are at, then show what the next stage requires. Doyon's emphasis on integration over tool count suits a platform-positioned partner better than a single-point tool.
In a post built around a single number, Doyon puts an eight-person team's one-to-one coaching at roughly $72,000 a year in salary and opportunity cost, then contrasts that with how closely companies scrutinise a $72,000 software purchase: multiple demos, security review, procurement, ROI analysis, executive approval and success metrics after implementation.
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They say most organisations cannot answer which topic a rep should be coached on, what coaching has done to revenue or which managers coach well, and offer a 13-page guide to a P.A.R. coaching methodology. Related posts make the same case: every sales call should become a coaching session, and every lost deal a personalised improvement plan.
For brands planning a campaign
A coaching, conversation-intelligence or enablement brand can build a campaign around measurement — topics per rep, manager effectiveness and revenue impact — using the $72,000 comparison as the opening argument and P.A.R. as a framework a partner can co-brand or extend.
Drawing on the combined call analysis, Doyon argues that Sales and CS have swapped expectations.
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CS, in their account, is now commercially accountable: it owns nets tied to net revenue retention and expansion revenue, negotiates renewals and pricing, builds CFO-ready ROI cases, uses MEDDICC-style qualification and stakeholder mapping, and runs quarterly reviews as growth conversations.
Sales, meanwhile, is told to qualify for long-term fit, define adoption milestones during the sales cycle, hand off through continuity sessions rather than calendar invites, and factor retention into commission.
Their summary flips the traditional division: sales nurtures the first revenue, CS hunts for more.
For brands planning a campaign
Useful for a partner selling to both CS and sales leadership, or for a webinar that brings the two functions into one room. The role-swap thesis is a debatable, discussion-driving premise for a panel or roundtable rather than a quiet product pitch.
In a more opinion-led post, Doyon argues that most deals are won or lost before the product conversation — before discovery, before problem assessment — because the seller arrives without a differentiated point of view.
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Commodity questions such as "what keeps you up at night" produce commodity outcomes, while the strongest teams teach, challenge assumptions and frame where the market is going. They treat prospect disagreement as useful qualification in real time: win or lose on the premise, not after six months of feature comparison and fake pipeline.
For brands planning a campaign
Relevant to a brand whose differentiation rests on a thesis, category stance or contrarian market view rather than a feature list. Doyon makes the case for leading with a belief, giving such a partner an on-message angle.
Doyon recounts a customer calling their company "cheap" and takes it as a compliment, defining cheap as the client getting far more value than they paid for.
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They list tools they use and consider cheap: Aircall for telephony and rep performance tracking at under $100 per rep, HubSpot as a CRM at under $100 per user, Pilot.com for accounting and tax filing under $10,000 a year, and Vanta for compliance and security review under $10,000 a year.
The tool list is a statement of personal use and satisfaction; it is not evidence of a paid relationship with any named vendor.
For brands planning a campaign
A value-framing angle for a software brand: Doyon names the tools they use and the price threshold they consider justified, which suits a cost-benefit or stack-consolidation message. Treat the mentions as a description of their own tooling, not as sponsorship.
Doyon's publishing pattern is consistent: numbered LinkedIn posts that open with a headline statistic or cost figure, break a framework into labelled parts, and sometimes offer a longer guide in the comments.
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They also speak live: a keynote to a room of 700, a workshop at a RevOps summit in Brazil on the five pillars of an AI operating system, and a planned appearance at Inbound Summit 2026 in Medellín.
They join partner webinars, including one with Quo on pairing call data with revenue enablement AI.
They name collaborators, hosts and methodology sources rather than absorbing credit: Winning by Design, Jen Allen-Knuth, Keenan, GAP Selling, Sebastian Mejia, and the teams at Triario and Quo.
Short, data-led LinkedIn posts, conference keynotes, workshops and partner webinars; a longer downloadable guide is offered as a follow-up asset.
For brands planning a campaign
Doyon suits co-branded programming as a webinar guest, conference speaker or workshop host. They regularly credit partners and sources by name, easing the path to a first collaboration.
Matt Doyon's posts show a public collaboration, two speaking engagements with CRM consultancy Triario, credit to the organization behind the SPICED sales methodology, and software they say is used to run the business.
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They describe Quo as a partner for a webinar on pairing call data with revenue-enablement AI, teach and keynote at Triario-hosted events, and name Aircall, HubSpot, Pilot.com, and Vanta as tools considered cheap relative to the value they deliver.
The posts do not state a sponsorship, affiliate arrangement, or investment.
Ask who runs the business behind the newsletter-style posts.
Quo
Disclosed partnership
Announced a webinar with Quo on using call data with integrated revenue-enablement AI to improve go-to-market efficiency and rep performance, and credited Quo team members Katie Bentz, Jennifer Keepes, and Daryna Kulya.
Spoke at Inbound Summit 2026 in Medellin, hosted by CRM and AI consultancy Triario, and delivered an event keynote, thanking the Triario team for hosting.
Credited Winning by Design and co-founder Jacco van der Kooij with the SPICED discovery methodology measured in the call analysis, calling it the go-to framework for SMB and mid-market sales motions.
Matt Doyon writes about how sales teams get better at discovery, qualification and coaching, and about what AI should change in a revenue organisation.
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The large-sample call studies offer the easiest entry point: Doyon reports what thousands of scored conversations show about where deals stall — weak openings, thin impact stories, unclear decision and paper processes.
The AI posts set out a maturity argument: automating notes and CRM updates saves time, but the gains only compound when analysis turns into coaching plans, deal guidance and reporting leaders act on.
The coaching material applies the same idea, treating one-to-one coaching as a measurable investment rather than a matter of intuition.
The three creators below approach this territory through fractional enablement, rep-level deal management and an AI sales-agent product, respectively.
Both write for the same buyer — commercial and enablement leaders trying to lift sales performance — and treat methodology as something to be rolled out deliberately rather than recited.
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Redfern's post on how executive buyers have already done their research before a seller arrives lands squarely on Doyon's argument that sellers should lead with a point of view instead of a discovery checklist.
Redfern presents the work as a fractional enablement service, listing programmes, methodology rollouts and outcome percentages she says she has delivered. Doyon tackles the same problems through original call data at scale and an AI operating-system argument, publishing analysis rather than a service menu.
Davidoff's territory overlaps Doyon's qualification work: both argue that reps must decide whether a deal deserves their time, assess pain intensity rather than the mere existence of a problem, and map the whole buying committee instead of only the economic buyer.
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Both also write about coaching reps to work the right deals rather than every deal that appears in the pipeline.
Davidoff stays at the level of the individual rep running a deal — process ownership, deal scoring, navigating obstacles — and writes in a coaching voice. Doyon starts from aggregate call data, then asks how AI and enablement systems should change team-wide behaviour.
He describes a wide spread of AI maturity across go-to-market teams, argues that giving reps a chatbot licence is not enough, and stresses building a context layer and agentic capability — the same gap Doyon describes between automating work and improving performance. Both also repeat that a CRM on its own never made anyone sell better.
Keating is launching and promoting an AI sales-agent product, so his posts read as a founder's argument for agentic software. Doyon analyses other companies' calls, speaks and runs workshops, framing the same maturity argument as a system design problem rather than a product launch.
For a B2B SaaS revenue-enablement company improving coaching and discovery, Matt Doyon's posts support a practitioner-facing placement: a webinar or content collaboration built around their call-data studies and coaching frameworks, rather than a broad-reach brand campaign.
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The only published audience measurement is their LinkedIn follower count: 27,955 as observed on 9 October 2026.
Their posts show who the content is built to attract, not who actually reads it.
The published work targets sales and revenue leaders and enablement practitioners.
Long-form posts examine call-data studies — 17,000+ SPICED discovery calls, more than 11,000 MEDDICC calls, and 500,000 Aircall and video calls — and identify where discovery and coaching break down.
Another strand covers AI maturity in go-to-market teams, arguing that most teams stop at efficiency instead of improving rep performance.
A third covers the cost and structure of coaching, including a P.A.R. coaching method pitched at teams buying coaching tools.
That mix suits a buyer whose product helps teams coach reps and run better discovery.
Reach information is event-based rather than demographic.
Matt reports delivering a keynote to a room of 700, speaking at Inbound Summit 2026 in Medellín, hosting a workshop at the RevOps Summit in Brazil, and running a webinar with Quo on call data and revenue enablement.
These appearances put the work in front of revenue-operations and sales audiences.
The posts provide no attendance figures beyond the 700-person keynote and no breakdown of the online audience by geography, seniority, or job function.
Examples of the practitioner framing: the SPICED post tells reps to quantify the cost of inaction; the MEDDICC post names Decision Process as the weakest qualification area; the coaching post compares $72,000 of annual coaching salary time against a software purchase that would require KPIs.
Each is written for someone who runs or buys sales coaching.
The posts do not verify followers' employers, occupations, or seniority, and contain no audience survey or platform demographic data.
If a campaign needs the share of directors-and-above or the industries represented, ask for recent results from posts comparable in topic, depth, and format — for example, a call-data deep dive versus an AI-maturity opinion post — and whether audience survey data on job titles and industries is available.
LinkedIn followers
27,955
LinkedIn profile follower count for Matt Doyon · 2026-10-09
Keynote audience (self-reported in post)
700 attendees
Matt Doyon's LinkedIn post describing a keynote delivered to a room of 700 · 2026-08-19
Matt Doyon's visible partnership activity runs through co-hosted webinars, conference talks, and data-led content rather than a published sponsorship package.
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The posts show them alongside software and services vendors, presenting call-data findings on their stages and channels.
Because their material is built from aggregated call recordings — analyses of 11,000 MEDDICC calls, 500,000 Aircall calls, 500,000 video calls, and 17,000 SPICED calls, for example — a partner's plan generally needs to supply a data source, an event host, or a distribution channel rather than simply buy a mention.
No rate card, sponsorship policy, or booking page is published.
Ask directly whether paid partnerships are available and settle pricing, exclusivity, and lead ownership.
Co-hosted webinars with platform partners
Buyer requirement
A partner that brings its own audience and registration flow.
In the Quo webinar, Matt is joined by partners from the hosting vendor's side, and access is distributed through that partner's signup.
Matt supplies the call-data framing on GTM efficiency and revenue performance; expect the host to own registration, attendance data, and follow-up unless you agree otherwise in advance.
Conference keynotes and workshops
Buyer requirement
An inviter with a venue, program, and audience.
Matt delivered a keynote to a 700-person room hosted by Triario and ran a workshop at the RevOps Summit in Brazil on invitation from local organizers, with a further speaking slot at Inbound Summit in Medellin.
Travel, an agenda slot, and the host's attendee list are prerequisites; the host controls the program.
Data-led research collaborations
Buyer requirement
Access to a call corpus or platform analytics, plus clarity on rights.
The findings Matt publishes come from aggregating recorded calls across geographies and company sizes.
Before commissioning this format, settle who owns the recordings, who may quote or republish the results, how the sample is defined, and whether the partner's own call data can be folded in.
Comment-gated guides as list-building assets
Buyer requirement
A live post and a process for handling comment leads.
The 13-page P.A.R. coaching guide is distributed by requesting a keyword in the comments, so a partner relying on it needs to collect, follow up on, and measure those responses rather than point to a hosted download page.
Delivery quality depends on comment moderation, not a landing-page setup.
In-content tool and vendor mentions
Buyer requirement
The posts disclose no payment terms.
Matt's references to Aircall, HubSpot, Pilot.com, and Vanta are first-person preference statements about tools they use.
Confirm the commercial basis separately rather than treating a positive mention as an agreed or paid placement.
A B2B SaaS revenue enablement company wants to sharpen how it sells coaching and discovery, and test a webinar or content collaboration with Matt Doyon built on their call data and coaching methods.
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It’s a strong fit in three ways.
First, the subject matter is a direct match.
Their most developed work scores large call datasets against the qualification frameworks this buyer’s customers already use.
The SPICED study covers more than 17,000 discovery calls and reports the call opening at 38% and Impact at 53%.
Its argument: reps find pain but don’t put numbers on it, so urgency never forms.
The MEDDICC study covers more than 11,000 calls and identifies Decision Process as consistently the weakest category, explaining why teams separate out a paper process for procurement, legal review and vendor onboarding.
For a company selling coaching and discovery improvement, that’s message and evidence in one bundle, backed by citable numbers rather than opinion.
Second, the commercial formats suit a campaign to sharpen how a B2B SaaS revenue enablement company sells coaching and discovery.
Their pattern is data-led analysis or a cost argument, published as a numbered post and sometimes followed by a longer guide offered through the comments.
The $72,000 coaching post does exactly what a revenue enablement vendor needs: it prices one-to-one coaching for an eight-person team at about $72,000 a year in salary and opportunity cost, contrasts that with the demos, security review, procurement and ROI analysis a $72,000 software purchase would face, then introduces a named P.A.R. coaching methodology and a 13-page guide.
That structure is a ready template for a co-branded asset.
They also have a live-event track record: a keynote to a reported 700-person room, a workshop on the five pillars of an AI operating system at the RevOps Summit in Brazil, a slot at Inbound Summit 2026 in Medellín, and a co-hosted webinar with Quo pairing call data with revenue enablement AI.
A webinar is an established format for them, not a speculative one.
Third, the audience aligns with the buyer’s customers.
The posts target sales and revenue leaders and enablement practitioners.
The follower count recorded on 9 October 2026 is 27,955.
The AI-maturity thread — automation, action plans and reporting as three legs, and stages one to three from manual AI use to an integrated intelligence layer — gives the vendor a diagnostic spine for a webinar and a position against point-solution competitors: the argument favors integration over tool count.
Before committing, ask for the rate card, sponsorship policy, exclusivity and lead-ownership terms; the posts disclose none, and a positive mention of a tool is not necessarily a paid placement.
Ask for audience seniority, industry and geography breakdowns, too: beyond the follower count and the self-reported keynote room, those figures aren’t available.
If the follow-up asset is the comment-gated guide, assign someone on the partner side to collect, answer and measure those responses.
When to choose a specialist
A consumer brand — say, a direct-to-consumer fitness or home-electronics launch — wants broad awareness placements, a large general readership and a lifestyle framing, with reach measured in impressions rather than qualified sales conversations.
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It’s a poor fit for specific reasons.
The audience details point the other way.
The only published audience figure is a follower count of 27,955 on a professional network, recorded on 9 October 2026, with no breakdown by age, geography, seniority or job function.
The content targets people who run or buy sales coaching: it scores calls against MEDDICC and SPICED, identifies Decision Process and cost-of-inaction as failure points, and examines qualification roadmaps unfolding across several meetings.
Readers without responsibility for a revenue team have little reason to follow, and a consumer brand would be buying access to an audience whose purchasing decisions it can’t influence.
The reach record doesn’t make the case either.
The posts feature conference keynotes and vendor webinars — a 700-person keynote room, the RevOps Summit in Brazil, Inbound Summit 2026 in Medellín, and a Quo webinar — all venues for revenue practitioners, not consumer channels.
No engagement rates, view counts or demographic breakdowns are published, so there’s no basis for a mass-reach case.
The format also clashes.
Almost every strong example in the posts depends on a partner supplying a call corpus, an event stage or a distribution channel; the value comes from a joint dataset, diagnostic or audience.
A consumer awareness campaign seeking a straightforward paid post with a lifestyle image has no natural fit with the data-led format, and the frameworks that distinguish the work would read as jargon to a general audience.
A better fit would be a sales, coaching, conversation-intelligence or revenue-AI company, or a consumer brand willing to run a practitioner-facing B2B campaign aimed at revenue leaders rather than end consumers.
A worked campaign example
A campaign angle for the revenue enablement vendor, using the co-hosted webinar and joint-study formats already in use.
Campaign idea: “Where discovery actually breaks — a data-led webinar on the two gaps that kill deals.” The case rests on two findings the vendor can co-brand without inventing anything new: the call opening scored 38% and quantified Impact 53% in the SPICED study, while Decision Process scored lowest in the MEDDICC study. Together, they support one actionable claim: reps open weakly and don’t put a number on the problem, leaving both urgency and approval paths unclear.
Format: a co-hosted live webinar followed by a guide, modeled on the existing webinar structure, in which the host platform supplies booking and registration while Matt Doyon provides the data framing. Agree up front who owns registration data, attendance reporting and lead follow-up; the posts set no default.
Sequence: 1. Pre-webinar post. Lead with the sample size and two scores, then pose the question the webinar will answer: if openings and impact are weak points, which should a manager coach first? Keep it short and data-first, in line with the existing post pattern. 2. Live session. Open with the cost framing the vendor can use: coaching an eight-person team costs roughly $72,000 a year in rep time, manager time and lost selling time, yet most organisations can’t say which rep to coach or on what. Walk through the two datasets, then show what the same call analysis could trigger in an enablement setup — a coaching plan for the weak opener and a deal-specific plan for the missing impact figure. 3. Joint asset. Create a downloadable guide pairing the vendor’s coaching method with the P.A.R. structure introduced in the $72,000 post. Confirm rights before republishing any part of the existing 13-page guide, and decide who handles requests if the asset is offered through comments rather than a download page. 4. Follow-up. Publish a short recap post with the headline numbers again; the analysis posts are the format that travels.
Roles: Matt Doyon provides the data and framework; the vendor supplies the method and product context and owns follow-up; the host or partner platform handles registration and attendance if the webinar runs on vendor or platform infrastructure.
Measure what the available reporting supports: registrations and attendance from the host’s own reporting, guide requests and their conversion into conversations, and clicks or leads attributed to the two recap posts. Don’t set targets based on audience demographics, which aren’t published. Don’t treat past mentions of tools such as Aircall, HubSpot or Vanta as paid placements. Confirm commercial terms, exclusivity and usage rights directly; the posts disclose none of them.
This angle uses two of the strongest pieces of original work, reuses an established partner-webinar format and gives the vendor a claim it can make only with this data. The trade-offs are dependence on the partner’s registration and reporting, and a data-rights conversation that has yet to happen.
Pick Kate Redfern when the buyer's problem is building capability inside a client's team rather than presenting a public data study.
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Her posts describe Omni Enablement, a fractional service that supplies revenue enablement leadership, sales leadership and coaching, BDR/SDR roadmaps, manager development, methodology rollouts such as MEDDPICC, Challenger and Value Selling, onboarding and ramp programmes, QBR design, and AI-powered coaching systems.
The same post lists outcomes including a 33% improvement in conversation rates, a 23% uplift across a 500-person sales and customer success organisation, and a 43% improvement in sales capability across 350+ commercial professionals.
Her second post makes the buyer-centric case directly: executive buyers have already read reviews, spoken to peers and formed a view before they ever speak to a seller.
The question for a sales leader is whether conversations are designed around how the team likes to sell or how customers prefer to buy.
For a revenue enablement vendor, that is a ready-made argument for a buying-journey redesign offer.
Redfern can lead a workshop on process and coaching design rather than simply present findings.
The tradeoff is the evidence behind the claims.
Both posts draw on practitioner experience and outcome percentages, but cite no named dataset, sample size or method.
Matt Doyon's strongest work, by contrast, is built on declared corpora — more than 17,000 SPICED discovery calls and more than 11,000 MEDDICC calls.
If the webinar promises “here is what the call data shows,” Redfern is the weaker lead voice; she fits better where the promise is “here is how to build the capability.”
Her posts do not mention a named partner webinar or co-published study, so a co-hosted format would be new ground rather than a repeat of an existing pattern.
Ask for audience figures, rates and availability before booking.
Choose Doug Davidoff when the campaign is about how reps run and qualify deals, not about scoring discovery calls at scale.
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His posts frame the same revenue problem from the deal side.
The first names a lost skill — running a deal — and breaks it into six jobs: owning the process, assessing deal quality, managing the buying journey, coordinating resources, controlling the timeline, and navigating obstacles.
The second turns pipeline into a portfolio that needs active management: assess pain intensity rather than pain existence, map the whole buying committee including who holds veto power, be honest about competitive position, and score every deal monthly.
He states that reps who do this have 30-40% higher win rates because they are better at choosing which deals to work.
For a campaign about coaching and discovery, that supplies the deal-inspection and qualification layer that sits after the discovery call, plus a strong point of view on pipeline quality a vendor can build a session around — for example, coaching managers on which deals deserve the hours.
The tradeoff is that his claim rests on framework and coaching practice rather than measured call analysis.
The 30-40% win-rate figure appears in the post without a stated sample, dataset or method, so it works as a coaching claim but not as a citable statistic — unlike the role Doyon's 17,000-call and 11,000-call studies play.
If the webinar is marketed on a headline data finding, Davidoff should be the second voice or the deal-execution follow-up, not the data source.
His posts mention no named partner event or co-authored asset.
Ask for audience numbers, fee and availability before booking.
Consider Ross Keating when the campaign needs an AI-native product story rather than a research or coaching story.
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His posts launch Overpath and its AI teammate Molly, aimed at proactive deal execution, CRM hygiene and coaching delivered in Slack, Teams, WhatsApp, web and voice, with the claim that no CRM ever made a salesperson sell better.
The second post describes a spectrum of AI maturity across go-to-market teams, contrasting teams with AI-native solutions and a context layer against teams still updating the CRM by hand.
It argues that giving a team a Claude licence is not enough.
For a revenue enablement vendor selling AI-assisted coaching, that is close to the buyer's own language.
A collaboration could be framed as an AI-in-enablement conversation or a launch-style session instead of a data presentation.
The tradeoff is that he is a vendor founder speaking on behalf of his own product.
A co-marketing pairing with another enablement vendor risks reading as competing product messaging, and gives the buyer less of an independent, citable asset than a joint study would.
The posts offer no research dataset, sample sizes or measured outcomes; the AI-maturity framing is an argument, not an analysis.
Ask for reach, rates and availability before booking.
Twelve LinkedIn posts from Matt Doyon's own account cover call analysis against the SPICED and MEDDICC qualification frameworks, AI adoption in go-to-market teams, sales coaching, and the changing division of work between sales and customer success.
The posts have no publication dates and are not a complete posting history, so the page describes recurring positions rather than a timeline of how their views developed.
A follower count of 27,955 was recorded on 9 October 2026.
No engagement figures, audience demographics, rates, sponsorship terms or off-LinkedIn long-form work are available.
Tools and events named in the posts are treated as mentions unless a post itself describes a collaboration, and no payment is inferred from any of them.
Similar-creator comparisons rest on the comparator posts considered alongside the posts here.
