Dale W. Harrison likes to run a poll: imagine a company about to sign a $5 million annual contract, renewed every year for five years. Who's on the buying committee? Most people guess the CFO, maybe the CEO. Then he reveals the deal: it's Google's contract for toilet paper in the employee restrooms. Nobody's CFO is in that meeting.

In Episode 7, Liam Moroney and Dale W. Harrison flip the usual playbook: can better lead scoring actually define better targeting, instead of the other way around? Dale walks through why lookalike lists built from your closed-won accounts alone are structurally guaranteed to fail, why “buying groups” are an 80-year-old idea being resold as something new, and why the real de-anonymization numbers are nowhere near what most targeting strategy assumes.

Timestamps

0:00 Cold open: massively overshooting who will buy

0:20 Welcome back, and teasing about the solo episode

1:42 Can better lead scoring define better targeting?

5:20 The feedback loop model, explained

10:56 Buying committees, and the summer intern who did the real work

15:21 The 90% de-anonymization era, and what changed

23:31 What Meta's lookalike algorithm actually does

25:07 The vowels-in-the-name story, revisited

29:12 The stock-picking sales pitch analogy

31:25 Glengarry Glen Ross, and the problem with intent

32:47 The $5 million toilet paper deal

35:38 Buying groups: a 1965 Harvard Business Review idea, not a new one

39:48 Why buying committees are confidential, and the HR risk of naming names

41:21 The real de-anonymization numbers

45:18 Reverse-engineering targeting from your own closed-won and closed-lost data

51:09 How to verify it's actually working: MQL-to-SQL rate

52:10 The slot machine analogy

54:21 Home runs vs. the Tour de France: buyer-seller fit in sports

56:47 The roulette wheel: data is not information

59:35 Seventh-grade math, and why the models aren't complicated

1:02:17 Teaser: rising and falling markets, and categories being eaten by AI

Key Topics Discussed

- Why lead scoring data should define targeting, not just measure it after the fact

- How lookalike-list algorithms actually work, and why they keep pointing at your losses too

- Why “buying groups” are an 80-year-old idea, not a new market shift

- Why buying committees are treated as confidential, and what happens if you name one publicly

- The real de-anonymization numbers: ~65% at the account level, 5–7% for named individuals

- Working backwards from your own closed-won and closed-lost history instead of guessing

- How to verify a scoring improvement is real: tracking MQL-to-SQL acceptance rate

- Why data is not information, illustrated with a roulette wheel

Notable Quotes

“The perfect lookalike list would be for us to go out and find more prospects that also have vowels in their name.” — Dale W. Harrison

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

“If you have two slot machines, and one pays off one out of a hundred pulls, and one pays off one out of fifty pulls, which one do you want to put your money in?” — Dale W. Harrison

Resources & Mentions

- Gartner B2B sales funnel benchmark data

- Harvard Business Review, 1965 article on buying committees

- 6sense and Bombora (intent data providers)

- Ocean.io (lookalike list provider)

Next Episode

Next week, Dale and Liam get into rising and falling markets — why a category being quietly “eaten alive” can mean there's no one left in-market at all, no matter how good your targeting is.

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