“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.

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