# Matt Doyon

Matt Doyon works on sales and revenue enablement, using call data and AI to help teams improve discovery, coaching, and performance.

- Profile: https://b2binfluencer.marketing/creators/matt-doyon/
- LinkedIn: https://www.linkedin.com/in/matt-doyon
- LinkedIn followers: 27,955
- Topics: artificial-intelligence, sales, revenue-operations, customer-success, ai-in-sales, sales-coaching
- Based on 19 dated posts, latest 2026-10-08

## Summary

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.
- Check first: Ask how call data supports broad claims and whether audience data exists.

### 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.

## Main ideas

- **Qualification is a process, not a checklist**: Doyon argues that sales frameworks should guide questions across several conversations. 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.
- **Find the cost of inaction, not just the buyer’s problem**: Doyon argues that identifying pain is not enough to create urgency. 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.
- **AI should turn saved time into better performance**: Doyon distinguishes efficiency from improvement. Automating notes, CRM updates and email writing can save time, but teams need coaching actions and useful reporting to improve how people sell.
- **Coaching deserves measurable outcomes**: Doyon treats sales coaching as a significant business investment that should be measured, not managed through intuition and scattered notes. They ask practical questions: what each representative needs to work on, whether coaching affects revenue, and which managers coach effectively.
- **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. Customer success now owns more expansion and renewal work, while sales must consider long-term fit, adoption and retention before closing.
- **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. Instead, they should teach, challenge assumptions and explain why the status quo no longer works. A prospect’s disagreement can help establish fit early.

## Work to read

- [What 17,000 SPICED discovery calls reveal about stalled deals](https://www.linkedin.com/posts/matt-doyon_salesstrategy-spiced-revenueoperations-activity-7457432916902526976-sB6i)
- [11,000 MEDDICC calls reveal the qualification gap that surfaces late](https://www.linkedin.com/posts/matt-doyon_we-scored-more-than-11000-meddicc-calls-activity-7437151913885749248-SjvX)
- [500,000 calls reveal the role swap between sales and customer success](https://www.linkedin.com/posts/matt-doyon_we-assessed-500k-aircall-calls-mostly-cs-activity-7431744267032739840-3EZR)
- [The $72,000 a year in sales coaching nobody measures](https://www.linkedin.com/posts/matt-doyon_when-companies-consider-buying-a-72000-activity-7490427282944278528-TdNb)
- [The three legs of AI: automation, action plans, and reporting](https://www.linkedin.com/posts/matt-doyon_gartner-just-reported-on-an-ai-symptom-the-activity-7475571401295319040-6xp9)
- [Stages 1, 2, and 3: a maturity model for AI in go-to-market teams](https://www.linkedin.com/posts/matt-doyon_last-week-i-delivered-a-keynote-to-a-room-activity-7495823161297465345-NKHM)
- [Half of AI: efficiency without performance](https://www.linkedin.com/posts/matt-doyon_just-spoke-with-another-sales-leader-getting-activity-7482798089368686592-8Oal)

## Brand relationships

- **Quo** (disclosed_partnership)
- **Triario** (disclosed_partnership)
- **Winning by Design** (mention)
- **Aircall** (mention)
- **HubSpot** (mention)

## Partnerships

Matt Doyon's visible partnership activity runs through co-hosted webinars, conference talks, and data-led content rather than a published sponsorship package. 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.

## Alternatives

- Kate Redfern: Pick Kate Redfern when the buyer's problem is building capability inside a client's team rather than presenting a public data study. 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.
- Doug Davidoff: Choose Doug Davidoff when the campaign is about how reps run and qualify deals, not about scoring discovery calls at scale. 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.
- Ross Keating: Consider Ross Keating when the campaign needs an AI-native product story rather than a research or coaching story. 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.

---
AI-assisted summary of public posts. Audience demographics are not available. Engagement appears only where we checked recent posts. Method: https://b2binfluencer.marketing/methodology/