Take every account your company has ever closed-won, upload the list to an ad platform, and ask it to find more companies that look just like them. Here's the uncomfortable part: every one of those accounts almost certainly has vowels in its name too. That's the opening argument in this solo episode, where Dale W. Harrison lays out why most B2B lookalike modelling, lead scoring, and “intent data” never actually worked — and introduces the framework The Insight Collective is calling the NextGen MQL.

With co-host Liam Moroney away this week, Dale walks through the real original sin of lead scoring (mistaking a pattern for a signal), why “data is not information,” and a three-part model — category expansion, buyer-seller fit, and firmographic customer lifetime value — that scores leads on probability and expectation value instead of invented point totals.

Timestamps

0:00 Cold open: marketing is a slot machine, not a vending machine

0:14 Welcome to a solo episode

1:15 Why the MQL still matters

1:45 The flat tyre analogy, again

2:04 The real question: what's worth a sales investment

6:47 The ABM buying-committee fantasy

9:09 The vowels-in-the-name story

9:52 Lead scoring's original sin: subjective, made-up numbers

13:24 Where HubSpot's scoring framework gets it half right

17:15 In an ideal world, what should an MQL mean?

18:20 Buyer fit vs. seller fit

20:58 Customer value fit

26:14 The blood test analogy: data isn't information

27:12 What can we actually know: broad ICP fit

29:29 Why in-market fit is nearly impossible to detect

32:59 Marketing is poker, not chess

39:47 The 95-5 rule, and when it breaks

41:17 The three-part NextGen MQL model

46:25 Vector modelling and buyer-seller fit

50:35 Firmographic CLTV

52:33 Expectation value scoring

55:55 Glass box vs. black box models

58:11 Where to learn more

Key Topics Discussed

- Why the MQL is a structural necessity, not a fad, in any business with a separate sales function

- The vowels-in-the-name story: why a pattern in your closed-won accounts can be meaningless

- Lead scoring's original sin: treating engagement data as if it were intent

- Why “data is not information,” illustrated with a blood test analogy

- Buyer fit, seller fit, and why sales teams are better suited to some accounts than others

- Customer value fit: scoring for lifetime value, not just the next closed-won deal

- Why marketing is poker, not chess — thinking in bets instead of certainties

- The 95-5 rule, and why it breaks down in fast-growing categories

- The three-part NextGen MQL model: category expansion, buyer-seller fit, and firmographic CLTV

- Expectation value scoring: replacing invented point totals with real probability and value

- Why a “glass box” model beats a black-box AI score every time

Notable Quotes

“The most reliable predictor of what you'll find if you look at closed-won accounts is that every one of these accounts has vowels in their name.” — Dale W. Harrison

“Data is not information. Data is the bucket that information comes in.” — Dale W. Harrison

“You're playing poker. Decisions have to be made with incomplete information, hidden variables, and luck. A good decision can lose, and a bad decision can win.” — Dale W. Harrison

Resources & Mentions

- Gartner B2B sales funnel benchmark data

- Forrester and BCG B2B benchmark data

- HubSpot lead scoring framework

- 6sense and Bombora (intent data providers)

- The Insight Collective's NextGen MQL framework

Where to Learn More

If you'd like a copy of the presentation deck, want early access to the NextGen MQL, or have any questions, reach out to Dale W. Harrison directly — by email or by DM on LinkedIn.

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