Twenty years ago, Dale W. Harrison built a system that de-anonymized roughly 90% of website traffic down to a named individual, then checked his scoring model against what actually happened next. The result: each of his two buckets — “likely to buy” and “not likely to buy” — was only about 60% correct. A small, genuine improvement over a coin flip, not the confident precision the intent data industry has been selling ever since.

In Episode 10, Diego Sosa joins Dale again for the most detailed breakdown yet of what should actually replace the broken MQL. They cover why you don't need to know why something works, only that it reliably does; the two-sided idea of buyer-seller fit; why thinking in slot machines beats thinking in vending machines; and why the data you need has been sitting in your CRM the whole time.

Timestamps

0:00 Cold open: intent data vendors are lying to you

0:20 Welcome back

0:33 Setting up today's topic: what replaces the old lead scoring model

2:53 20 years, still a 99%+ failure rate

5:34 It's just a slot machine, not a vending machine

6:07 Causality without correlation: the biotech and drug analogy

6:47 Viagra vs. antidepressants: two kinds of “we don't know why it works”

9:16 Defining buyer-seller fit

9:50 HubSpot vs. Salesforce in the Fortune 1000

10:39 Why Exxon won't buy from a six-month-old company

11:13 Seller fit: the biotech researcher-vs-government-lab example

17:26 Thinking in slot machines, not vending machines

19:32 The ROAS story, and “this is where marketers get stupid”

21:55 Why win rate and sell cycle are both the wrong things to optimise

23:27 Rolling buyer fit and seller fit into one metric: revenue per unit of sales effort

44:06 The Pampers analogy: reverse-engineering demographic targeting

47:26 Why the data you need is already sitting in your CRM

48:15 The vowels-in-the-name story, one more time

50:35 Diego's first sales job, and the deals that looked too easy

52:26 The notion of opportunity cost

55:48 Dale's own 20-year-old experiment: de-anonymizing 90% of traffic

59:47 Click monkeys, and the people who show up ready to buy with no warning

1:01:00 Naming it: Dark Social

Key Topics Discussed

- Why you don't need to know why something works, only that it reliably correlates with the outcome

- Buyer-seller fit: why it's not enough that a buyer would buy from you — your sales team has to be equipped to close them too

- The HubSpot vs. Salesforce Fortune 1000 gap, and why tenure and trust matter more than product quality

- Thinking in slot machines: why win rate and payout size can't be judged in isolation

- Why win rate and sell-cycle length are both the wrong things to optimise for

- Revenue per unit of sales effort: the one metric that rolls buyer fit and seller fit together

- Reverse-engineering targeting from your own CRM data instead of buying generic intent scores

- Click monkeys and dark social: the two failure patterns any real scoring model has to account for

Notable Quotes

“What we're really looking at here are slot machines, not vending machines.” — Dale W. Harrison

“You cannot work backwards from success. You have to look at how the winners are different from the losers, not how the winners are similar to one another.” — Dale W. Harrison

“Three minutes on the phone with someone will give you more information than 1,000 touch points from your intent data provider.” — Dale W. Harrison

Resources & Mentions

- Gartner B2B sales funnel benchmark data

- HubSpot and Salesforce Fortune 1000 market share data

Next Episode

Back next week with more detail on the buyer-seller fit model.

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#B2BMarketing #DemandGeneration #LeadScoring #MarketingAnalytics

For roughly five hundred years, bloodletting with leeches was standard medical treatment — and even decades after doctors realised it was probably killing people, they kept doing it, because patients demanded it. That's Dale W. Harrison's analogy for the intent data industry today: happy customers and glowing reviews that prove nothing about whether the product actually works.

This week, Diego Sosa joins Dale in Liam Moroney's usual seat for a deep, methodical breakdown of the idea the show keeps returning to: data is not information. Diego and Dale walk through why ten ebook downloads don't mean ten times the certainty, the name-tags-at-a-networking-event analogy for how information actually works, and why nobody in the intent data industry has ever produced a single graph proving their product does what it claims.

Timestamps

0:00 Cold open: data is not information

0:26 Welcome back, with a new guest in Liam's seat

0:34 Meet Diego Sosa

1:08 The core question: why do we think more data is the answer?

2:07 What “information” actually means

6:59 The ebook download example, and why more never means better

8:18 Why marketers keep falling for this anyway

14:22 Nate Silver's The Signal and the Noise, and rebranding touch points as signals

16:06 The classic failure: intent data

20:59 A real correlation graph, built from actual sales data

21:27 The name tags analogy: how much information is really in the data

26:46 The iceberg: what you don't see still matters

27:20 Dark social, and why the two buckets end up identical

36:48 Working backwards from success doesn't work, and the measles analogy

38:44 The real closed-won vs. closed-lost numbers on LinkedIn engagement

42:07 The billboard story: proving impact without a click

44:35 The Google ads scam agencies run on branded search

46:17 Diego's first data analytics job, and asking the question before the data

48:36 Intervention vs. no intervention: the umbrella and headache examples

49:06 “They're lying to you”: why intent data doesn't exist

Key Topics Discussed

- Why data is not information, and why more data usually isn't more information

- The ebook download example: why the information curve flattens long before the points curve does

- The name tags analogy: how prior knowledge determines how much a data point can tell you

- Why no intent data vendor has ever produced a real correlation graph

- Working backwards from closed-won accounts doesn't work, illustrated with a measles analogy

- The real numbers: closed-lost deals were more likely to visit the LinkedIn company page than closed-won deals

- The billboard story, and the branded-search scam some agencies run on Google Ads accounts

- Why you have to ask the question before you look at the data

Notable Quotes

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

“What you see is not all there is, and you don't know how much there isn't.” — Diego Sosa

“They're lying to you. It's a straight lie when they tell you they're selling you intent data, because intent data does not exist.” — Dale W. Harrison

Resources & Mentions

- The Signal and the Noise by Nate Silver

- Bombora and 6sense (intent data providers)

- Forrester, Gartner, and BCG B2B benchmark data

Next Episode

Back next week with a new episode of B2B Effectiveness.

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 #MarketingAnalytics #IntentData

For years, LastPass dominated the password manager category. Then it suffered a string of serious data breaches, trust collapsed, and competitors like Bitwarden absorbed the fallout. Bitwarden's marketing team could be forgiven for thinking their campaigns did that. Almost none of it was marketing.

In Episode 8, Liam Moroney brings real branded-search data to back up an argument Dale W. Harrison has been building for weeks: marketing very rarely grows a business — the category does. Using a live three-slide case study of the password manager category (plus a second, more mature category for contrast), Liam and Dale walk through how to actually read share of branded search, why market share barely moves except after one of two specific shocks, and why AI is now eating entire software categories the same way email once ate the fax machine.

Timestamps

0:00 Cold open: marketing doesn't grow the business, markets do

0:15 Welcome back (Dale's in the UK this week)

1:04 Setting up today's topic: rising and falling markets

2:22 Why 99.99% of growth comes from riding the category, not beating competitors

3:54 The disruption myth, and why tech clings to it

5:04 Disruption happens in engineering, not marketing

6:22 Bitcoin, NFTs, and fads that look like marketing genius

7:57 Welcome to the life of a fax machine salesman, circa 2005

9:20 Marketing automation platforms as a case study

11:06 Categories that self-disrupt through over-promising

12:37 Why intent data and de-anonymization providers are collapsing

15:16 How AI is uniquely positioned to eat interactive demo software

16:14 Liam's data: a tale of three slides (the password manager category)

17:32 Bitwarden vs. LastPass: the branded search view

19:17 What “share of branded search” actually measures

20:50 Why campaign changes show up in the data almost instantly

22:59 The LastPass data breach, and what it cost them

25:11 Defining “category”: Tide detergent vs. Tide Pods on TikTok

31:36 Dale's own hypergrowth story: 100x growth, same market share order

32:50 The collective view: how the category grew even as LastPass fell

39:09 A mature category case study: content management platforms

41:03 What an accelerating category decline looks like

43:40 Rolodexes, CRMs, and where declining categories' customers go

47:23 Optimizely's rebrand, and repositioning into an adjacent category

47:47 Why categories almost never fully vanish

57:50 The tool behind today's data, and where to learn more

Key Topics Discussed

- Why 99.99% of business growth comes from riding a category, not beating competitors

- The disruption myth: why tech assumes a great product creates its own momentum

- Fads vs. real category shifts: Bitcoin, NFTs, and fax machines

- Why AI is uniquely positioned to eat categories like interactive demo software

- How to actually read share of branded search as a proxy for market share

- The LastPass/Bitwarden case study: a real market-share transfer, and why it wasn't about marketing skill

- Why market share is remarkably sticky, and the two things that actually move it

- What a declining category looks like from the inside, using a mature CMP category example

Notable Quotes

“Marketers live in this fantasy world where they think it's their marketing that's growing the business.” — Dale W. Harrison

“You're not growing the category. This is another one of these silly myths that people love to blather on about.” — Dale W. Harrison

“It is these category-level and market-level dynamics that are really driving things — the fish are jumping out of the water into the boat by themselves.” — Dale W. Harrison

Resources & Mentions

- Ehrenberg-Bass Institute research

- Bitwarden, LastPass, 1Password, Dashlane, and NordPass branded search data

- Optimizely, AEM, Contentful, and Sitecore (content management platform category)

- Kantar market research

- Liam Moroney's category-tracking tool (Storybook)

Next Episode

The show is taking a short break for the Fourth of July and the following week while Dale's still travelling in Europe — back with a new episode on the 18th.

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 #BrandMarketing #MarketingStrategy #MarketingAnalytics