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.

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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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#B2BMarketing #DemandGeneration #LeadScoring #ABM #MarketingAnalytics

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.

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 #LeadScoring #MQL #MarketingAnalytics #ABM