Imagine a medical test that tells a patient they have cancer, but is wrong 19 times out of 20. No regulator would ever allow it near a hospital. Yet for roughly twenty years, that's been about the accuracy of the average B2B lead scoring system — long-running Gartner benchmark data puts MQL acceptance rates by sales at around 5–6%.

In Episode 3, Dale W. Harrison and Liam Moroney dig into why every proposed fix for the MQL — MQAs, AI-qualified leads, more signals, better points — keeps the exact same broken scoring math in place. Dale explains why data is not information, why patterns that show up equally in your wins and your losses tell you nothing, and why any real qualification system has to measurably beat a coin flip — something most lead scoring never actually does.

Timestamps

0:00 Cold open: the failed system, guaranteed

0:30 Welcome back

0:52 Why this topic is personal for Dale

3:31 The Gartner benchmark: 5-6% MQL acceptance rate

4:39 The false positive problem, explained

5:24 The cancer test analogy

12:20 Data is not information

17:53 Liam pushes back: doesn't more signal data help?

21:49 The false negative problem

23:06 Measuring both false positives and false negatives

24:14 SQOs, and the click monkeys who ruin every model

27:03 Dark social: invisible engagement

32:48 The Harry Potter sorting hat problem

33:44 Thinking in bets, revisited

35:35 MEDDIC, BANT, and why checklists don't help

35:51 The buyer is scoring you too

37:47 The parallel universe you can't see

40:11 Why identical products can have wildly different close rates

44:24 Liam's camera store story

47:03 Teaser: how growing markets break the 95-5 rule

Key Topics Discussed

- Why every proposed MQL replacement keeps the same broken points-based math

- The cancer test analogy: why a 5% accuracy rate would never pass regulatory approval

- Why data is not information, and why more signals make the problem worse, not better

- Why a pattern has to differ between closed-won and closed-lost to mean anything

- Measuring false negatives as well as false positives, and the “click monkey” problem

- Dark social: why the best buyers sometimes show zero digital engagement

- Why MEDDIC and BANT don't solve the qualification problem on their own

- The buyer is scoring the vendor too — and most of their signals are invisible to you

- Why identical products can have wildly different close rates depending on who's selling them

Notable Quotes

“No matter how you choose your signals, how you weight the signals, or how you gather up the signals, the moment you start adding up your data points, you have a fundamentally failed system.” — Dale W. Harrison

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

“Just because there's something that characterises all of the closed-won deals doesn't mean that same thing doesn't also characterise all the closed-lost deals.” — Dale W. Harrison

Resources & Mentions

- Gartner B2B sales funnel benchmark data

- MEDDIC and BANT sales qualification frameworks

- Mark Ritson (marketing commentator and academic)

Next Episode

In Episode 4, Dale and Liam take a detour that turns out to matter more than it sounds: how rising and falling markets break the well-known 95-5 rule — not by a little, but massively.

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