A B2B SaaS company once found that accounts on Amazon FBA generated ten to twenty times more lifetime value than accounts on Etsy or eBay — even though the sales effort to close either one was statistically identical. If your lead qualification can't tell the difference between those two prospects, what exactly is it qualifying them for?

In Episode 2, Liam Moroney and Dale W. Harrison move from last episode's argument — the MQL is a structural necessity, not a fad — into the harder practical question: what would a good MQL actually need to know? Dale lays out five distinct things a useful qualification should answer (broad ICP fit, in-market fit, consideration set eligibility, conditional win probability, and lifetime value fit), explains why checklist frameworks like MEDDIC don't solve the problem, and makes the case for thinking in bets instead of chasing false certainty.

Timestamps

0:00 Cold open: what a good MQL should tell you

0:24 Welcome back (and a smart home tangent)

0:58 Recap: last week's argument that the MQL isn't dead

2:04 Why marketing can never know for certain

4:33 Thinking in Bets: the book behind the mental model

16:18 Why the MQL gets so politically corrupted internally

18:49 The poker analogy: raise, fold, or walk away

21:03 Why hand raisers are a better bet, not a sure one

26:28 Thinking in bets, applied to poker itself

31:31 Broad ICP fit vs. in-market fit

31:52 The fantasy of intent data, and the 95-5 rule

32:29 Why MEDDIC-style checklists don't solve this

34:55 Hand raisers, revisited

41:56 HubSpot vs. Salesforce: the Fortune 1000 problem

43:15 Conditional win probability

46:13 Lifetime value fit

47:01 The Amazon FBA vs. Etsy story

49:07 The five things a useful qualification needs to know

1:00:27 Why tweaking HubSpot's scoring system won't fix it

1:00:47 Teaser: Episode 3, why your lead scoring is broken

Key Topics Discussed

- Why a good MQL is a bet, not a certainty

- The five things a useful qualification needs to answer: broad ICP fit, in-market fit, consideration set eligibility, conditional win probability, and lifetime value fit

- Why checklist frameworks like MEDDIC don't solve the qualification problem

- The procurement bid-padding story: why every box can be ticked and the deal still isn't real

- Why hand raisers are a better bet than cold outbound, but still not a sure thing

- The HubSpot vs. Salesforce Fortune 1000 example, and why market fit matters

- The Amazon FBA vs. Etsy lifetime value story

- Thinking in bets: why a good decision can still lose, and a bad decision can still win

Notable Quotes

“A good MQL should tell you what's the likelihood of taking this name and putting it into a sales process and getting a good return on that investment.” — Dale W. Harrison

“A good decision can still lose, and a bad decision can still win.” — Dale W. Harrison

“Just because you're a hand raiser doesn't mean there is a single iota of actual buying intent there.” — Dale W. Harrison

Resources & Mentions

- Thinking in Bets (book)

- MEDDIC sales qualification framework

- Gartner B2B sales funnel benchmark data

- HubSpot and Salesforce market share data

Next Episode

In Episode 3, Dale and Liam dig into why your lead scoring is broken — the specific things that are guaranteed not to work, and what a better approach actually looks like.

Subscribe & Follow

Catch every episode of B2B Effectiveness: Evidence-Based Marketing Ideas for B2B Practitioners on your favourite podcast platform, and subscribe here on YouTube for future episodes.

#B2BMarketing #DemandGeneration #LeadScoring #MQL #MarketingAnalytics

“The MQL is dead” has been a B2B marketing clickbait headline for a decade. This episode makes the contrarian, evidence-based case that it never actually died — it just never worked the way we scored it.

Liam Moroney sits down with data-driven marketing strategist Dale W. Harrison to unpack why the entire industry conflated engagement data with intent data, the infamous story of a RevOps team who proved their lead scoring model performed no better than a random number generator, and why more data almost never means more information. They close with a reframe: MQL scoring isn't about certainty — it's about improving your odds.

Timestamps

0:00 Cold open: “The sales team needs leads”

0:36 Welcome back after a year off

1:04 Is the MQL dead, or just pining for the fjords?

3:55 A brief history of the MQL, from Glengarry Glen Ross to SiriusDecisions

6:55 The Zoho CRM random-number-generator story

8:12 The “original sin”: engagement data isn't intent data

9:38 The ebook-download absurdity

13:09 What Gartner's benchmark data actually shows

16:10 Why sales just wants better odds, not certainty

17:18 The lead-nurturing “goose” and the ice-cream-stand fallacy

24:16 HubSpot vs. Salesforce: why market share changes everything

27:57 The ABM challenge: should marketing target accounts, not leads?

34:08 Data isn't information: the roulette-wheel test

38:55 Why hand-raisers aren't the golden ticket

39:35 Brand vs. performance marketing and the “consideration set”

47:38 Thinking in bets: the poker-hand analogy

56:31 Wrapping up part one

58:04 So… is the MQL dead? (No.)

58:56 What's coming in Episode 2

1:03:05 Sign off

Key Topics Discussed

- Why the “MQL is dead” narrative misses the actual structural need it serves

- A brief history of the MQL, from the SiriusDecisions demand waterfall to today

- The Zoho CRM story: when a random number generator scored leads as well as a real model

- The “original sin” of lead scoring: treating engagement data as if it were intent data

- Why more data doesn't automatically mean more information

- What Gartner's long-term B2B sales funnel benchmarks actually show

- The HubSpot vs. Salesforce market-share example, and why “buyer-seller fit” matters

- Why Marketing Qualified Accounts (MQAs) repeat the same scoring flaw at a bigger scale

- Brand vs. performance marketing, and getting into the buyer's “consideration set”

- “Thinking in bets”: reframing lead scoring as a probability game, not a certainty machine

Notable Quotes

“The problem is in the queue. What are we doing to qualify the lead?” — Dale W. Harrison

“Data is a bucket — an empty bucket that may or may not contain some amount of information.” — Dale W. Harrison

“Just because you have a flat tyre doesn't mean it's time to haul the car to the junkyard. You just fix the tyre.” — Dale W. Harrison

Resources & Mentions

- Glengarry Glen Ross (film)

- SiriusDecisions demand waterfall model

- Forrester Research

- Gartner B2B sales funnel benchmark data

- HubSpot and Salesforce market share data

- Zoho CRM RevOps lead-scoring anecdote

- Ehrenberg-Bass Institute (mental availability research)

Next Episode

In Episode 2, Dale and Liam dig into which factors can realistically move the odds of a lead converting — and which ones the data simply can't tell us, no matter how much of it we collect.

Subscribe & Follow

Catch every episode of B2B Effectiveness: Evidence-Based Marketing Ideas for B2B Practitioners on your favourite podcast platform, and subscribe here on YouTube for future episodes.

#B2BMarketing #DemandGeneration #RevOps #MQL #MarketingAnalytics #LeadScoring