B2B Effectiveness

Most B2B marketers have heard that share of search tracks share of market. Far fewer have asked where that idea came from, or whether it survives contact with a B2B market. It was drawn from a handful of B2C examples, and it only holds when products, prices and routes to purchase are close to uniform — which, as Dale W. Harrison puts it, is the difference between a tub of yogurt and a multimillion-dollar ERP system.

In Episode 14, Dale W. Harrison and Liam Moroney ask what brand-aware search actually measures. They cover the conditions under which share of search does reflect market share, the price spreads, basket sizes, free trials and indirect sales that break it in B2B, why Buffer can hold nearly half the search volume in its category and about 1% of the revenue, how Sprout Social grew while its branded search fell, and why the metric is far more useful as a measure of category demand.

Timestamps

0:33 Back for another episode — Dale and Liam reunite

0:47 This week's topic: what does brand-aware search actually measure?

1:08 Liam's starting point: years of branded search data

1:53 How a few B2C examples became a universal rule

2:25 A tub of yogurt versus a multimillion-dollar ERP system

3:01 You can't outsource your thinking — to research or to AI

3:45 The original claim: share of search reflects share of market

4:50 Share of search as a stand-in for share of voice

5:29 Brands do not grow: market share barely moves

6:30 When a leader stumbles: the password management example

7:53 It's actually measuring a lot of other useful things

8:24 Slides: when share of search does track market share

8:54 Product parity, price parity and how things are sold

10:37 The supermarket with an unwatched back door

11:41 No door-to-door detergent salesmen

12:24 Price discrepancy: $20 versus $200,000

13:15 Volume: one search, a thousand licenses

14:16 Microsoft Office, Exxon and 100,000-fold basket sizes

15:10 Drinks, houses and boxes of Tide: why B2C variance is small

16:31 Ehrenberg's own caution about uniform basket size

17:01 Light and heavy buyers in B2B search data

17:46 Free trials: noise that inflates search volume

18:35 Penetration is not revenue

20:43 Partners, procurement and cross-selling: revenue that never searches

21:42 Liam's in-house story: the vendor that sold itself in

22:29 Ideal conditions versus the typical B2B picture

23:35 Real data: the social media management category

23:59 Buffer versus Sprout Social: volume versus value

25:54 Nearly half the search volume, about 1% of the revenue

27:16 Sprout Social: branded search down, revenue up

28:17 Two stories: the company picture and the category demand picture

29:03 Growth from bigger contracts, not more customers

31:08 Detergent for families with 12 kids

32:37 Penetration and market share are two different things

33:06 HubSpot's users versus Salesforce's money

33:43 “Our revenue's never been better”

34:37 Hightouch and the declining CDP category

35:51 Defining penetration: the Tide example

36:55 HubSpot: 50% greater penetration, a fraction of the revenue

38:00 The Fortune 1000: 85% versus 0.4%

39:34 Where a brand's revenue actually comes from

41:28 Audience question: can you segment around the variances?

42:14 Branded search as a measure of demand

43:37 Fax machines and Ricoh: share up, category collapsing

46:02 Category demand: missing from B2B conversations

46:22 The bowtie and trying to outrun a lack of new business

47:50 The 95:5 rule and stationary markets

48:33 Why laundry detergent grows at about 2% a year

50:26 Dynamic tech categories: fax machines and CRM

51:45 Conditional win probability and the odds a lead is in market

52:37 Why a hot category makes you look like a genius

53:01 Televisions and the 8K flop

54:14 When good enough is good enough

54:59 Marketing automation and mechanical lead scoring

55:16 The lessons: active demand and category health

56:25 B2B demo software and a 99:1 market

57:11 NFTs and the crypto bros

58:04 Wrapping up

Key Topics Discussed

- Where the idea that share of search reflects share of market came from, and why it doesn't generalize to B2B

- The conditions that make the proxy work: product parity, price parity and a single way to buy

- Price discrepancy, seat-based volume and 100,000-fold differences in basket size

- Free trials, partner ecosystems, procurement and installed bases: revenue that never shows up in search

- Buffer versus Sprout Social: share of search against share of revenue

- Two stories in one decline: company growth versus category demand

- Penetration is not revenue: HubSpot versus Salesforce

- Branded search as a gauge of category health, and what it means for the 95:5 rule

- Why growing and shrinking categories make marketers look like geniuses or failures

Notable Quotes

“Nobody wants 100% of a market of which there are no customers.” — Liam Moroney

“Market penetration is not the same thing as revenue, certainly not in B2B.” — Dale W. Harrison

“HubSpot have lots and lots and lots of users, and Salesforce have lots and lots and lots of money.” — Liam Moroney

“It is not because you're bad at sales and marketing or you're some kind of a sales and marketing genius. It's because the category is either growing or shrinking.” — Dale W. Harrison

Resources & Mentions

- How Brands Grow, and the light and heavy buyer model

- Andrew Ehrenberg's original work and the Ehrenberg-Bass Institute

- Share of search data for the social media management category, including Buffer, Hootsuite, Sprout Social and Sprinklr

- Sprout Social's quarterly earnings reports

- Hightouch and the CDP category

- The 95:5 rule and the NBD model of purchase frequency

- The bowtie model

Next Episode

TBC

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#B2BMarketing #ShareOfSearch #MarketingEffectiveness #BrandMarketing

Most marketing teams can tell you what their best customers have in common. Very few can tell you how those customers differ from the ones they lost — and that second question is the only one that carries any information. Looking at your winners in isolation is, as Dale W. Harrison puts it, probably the single biggest failure point in marketing's attempt to use data.

In Episode 13, Dale W. Harrison and Diego Sosa take on the habit of working backward from success. They cover lookalike lists that could just as easily have been built from your losing accounts, a vendor report that mistook a company-page visit for a revenue signal, why a random number generator matched an elaborate lead scoring system, how averages hide your losses, and why LinkedIn-influenced pipeline says more about the platform's incentives than about your buyers.

Timestamps

0:32 Back for another episode — Diego, a split of three

1:16 This week's topic: differences, not similarities

1:35 Why working backward from success is marketing's biggest failure point

2:21 The classic example: target account and lookalike lists

3:23 Diego: the same reasoning in the signal debate

4:16 The attribution report and the LinkedIn company page

4:58 The question nobody asked: what about closed-lost?

5:51 Vowels in your name — a 100% correlation

6:47 Diego: when a data point actively misleads you

7:58 The number was right. The conclusion was wrong.

8:24 Why “data-driven” is a warning sign

9:00 Data is the bucket, not what's in it

9:16 Points-based lead scoring counts buckets

10:04 The roulette wheel: lots of data, no information

10:40 Decisions as discrimination between options

11:36 What an MQL actually is

13:33 Zoho's lead scoring versus a random number generator

15:28 Diego: information as the reduction of uncertainty

16:05 Betting under uncertainty: the coin toss

17:18 Beating a random pull from ZoomInfo

17:53 Two slot machines that look identical

19:22 A flawed sales-cycle analysis from a RevOps podcast

20:20 Measure against your opposite, not your average

21:39 What is your average actually averaging?

23:05 Diego: averages and non-normal distributions

24:02 Why we do it: “I blame it on martech”

24:25 Dashboards full of dancing monkeys

25:04 LinkedIn-influenced pipeline

26:10 Closed-lost deals get more engagement

27:38 The foundational question: compared to what?

28:41 Diego: touch the whole market, count only the winners

30:45 Grading their own homework

31:00 Not accidental: incentives behind the dashboards

31:47 LinkedIn's own advice on company pages

32:51 The dashboard, the CFO and your credibility

34:02 Two key questions for every piece of data

34:35 Why common characteristics mean nothing

35:50 The value of uncommon characteristics

37:11 What AI gets wrong when it mines your CRM

38:04 Diego: put in the dirty work and test the platforms

38:55 Be driven by information, not data

41:15 One article, ten articles, a hundred articles

42:41 Diminishing returns on every additional data point

44:35 Diego: gaming engagement scores

45:33 Driving people through the company page

45:58 Ice cream sales and drownings

46:25 Lead nurturing and the evil genius

47:06 Wrapping up

Key Topics Discussed

- Why a description of your winners contains no usable information on its own

- Lookalike and target account lists, and what happens when your losers look the same

- Revenue signals that look strong among closed-won deals and are stronger still among closed-lost

- Data versus information: information is whatever reduces your uncertainty

- Marketing decisions as discrimination, and the random number generator as the benchmark to beat

- How averages hide your losses — and what “average revenue per deal” usually leaves out

- LinkedIn-influenced pipeline and the incentives behind martech dashboards

- Why AI tends to surface common characteristics, and why uncommon ones carry the signal

- Diminishing returns: why the hundredth data point tells you almost nothing new

Notable Quotes

“Data is not what should be driving your decisions. Information should be doing it.” — Dale W. Harrison

“The number was right, but the conclusion was completely wrong.” — Diego Sosa

“If all you do is keep a tally of all your wins, the two slot machines look identical.” — Dale W. Harrison

“Data is not the problem. It's just that it shouldn't be your stopping point. It should be a starting point for you.” — Diego Sosa

Resources & Mentions

- A marketing attribution vendor's report on LinkedIn company page visits among closed-won accounts

- Zoho CRM's RevOps test of lead scoring against randomly selected leads

- LinkedIn's own marketing guidance on investing in company pages

- LinkedIn-influenced pipeline reporting

- Previous episodes in this series on why information is not data

Next Episode

“What does Brand-Aware Search Actually Measure?”

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#B2BMarketing #MarketingAnalytics #RevOps #MarketingAttribution

Ask a revenue operations team how long the average deal takes to close and you will get an answer. Ask how long it takes to lose one and the room goes quiet. In most organisations it is three times longer — and across roughly 90% of the companies Dale W. Harrison has examined, somewhere between three and seven times longer. That gap is not a quirk of the reporting. It is the reporting being wrong, in ways that flow into every other number on the dashboard.

In Episode 12, Dale W. Harrison and Diego Sosa take on sales effort — the input that pipeline velocity, cost per acquisition and segment efficiency all depend on, and the one number in the CRM that can be 700% wrong. They cover why dead accounts stay on the books, how a maintenance drip feed exposes a rep who has already given up, why deals held out of the system make your win rates look spectacular, and how to read a win-rate chart without drawing exactly the wrong conclusion from it.

Timestamps

0:30 Back for another episode — Diego standing in again

0:52 Where sales effort sits inside the Buyer-Seller Fit Model

1:08 The metric that never appears on a dashboard but feeds most of it

2:44 The traditional proxy: days active in the CRM

3:29 The perfect measure nobody will ever build — a 24/7 rep diary

4:20 Not 10% wrong. 700% wrong.

4:59 Data is not information: context is what creates meaning

5:45 Diego on the rep's own incentive to make the data look good

6:50 High-side and low-side errors, often in the same organisation

8:17 The one metric Dale has never seen a rev ops team produce

9:08 Why it takes three times longer to lose a deal than to win one

9:48 90% of companies land between 3x and 7x

10:10 Zombie accounts, and why reps keep touching them

10:54 The never-say-die rep — and why you want that instinct

12:38 Diego: conversion rate as a quarterly performance measure

13:30 Pipeline value, bragging rights and adverse incentives

16:52 The maintenance drip feed

17:16 Plausible deniability: “still, quote, working the account”

18:18 “Nobody is going to tell you I'm sandbagging my numbers”

18:21 Diego: when does a rep actually create the deal?

18:50 Low-side errors: 80% of the work done before it hits the CRM

20:23 CRM discipline, and the trigger point that forces a deal into the system

22:36 Diego: why effectiveness collapses if this number can't be trusted

23:30 What an ICP should actually be

24:17 Biotech: a P-card in a university lab versus three months with Pfizer

27:26 Deal value and win rate are unambiguous. Sales effort is not.

28:29 Diego: the slot machine, and treating accounts as a portfolio

30:01 Why optimising for a single metric produces confidently wrong answers

31:41 Bureaucratic inertia on the buyer side, and what you cannot control

33:44 Real sales data, across multiple years, from a real company

34:11 Activity density explained

35:36 The unweighted activity measure, and the fallacy inside it

37:01 Reading the chart: 4.5% at one end, 82.1% at the other

38:24 The same mistake everybody makes with lead scoring

39:09 Why the 24% bar is the honest one

40:01 Wittgenstein's ruler: is the win rate real, or is the data wrong?

40:49 Late entry, and the burst of activity around a priced proposal

42:20 The 4.5% end: stretching a deal that is already lost

43:28 Two touches a month is not a pipeline, it is cover

45:18 Both distortions running inside one sales organisation

45:53 Backing it out: weighted activity and days with activity

47:59 The 97% rule and the 81-day cutoff

49:10 Run it weekly, and close by default

50:26 Why the true believers are the more expensive problem

52:15 Diego: using data to raise better questions

53:30 What AI lacks is the context around the process that made the data

54:25 The real danger: turning your brain off and outsourcing the thinking

55:23 Multiple versions of the world, one set of data

Key Topics Discussed

- Days active as a proxy for sales effort, and why the error can reach 700% rather than 10%

- The closed-won to closed-lost ratio: a five-minute calculation almost no rev ops team runs

- Zombie accounts, and the difference between a true believer and a rep buying cover

- Adverse incentives that appear even when management never mentions the numbers driving them

- Low-side errors: deals held out of the CRM until the outcome is already clear

- Wittgenstein's ruler applied to a win-rate chart — measuring the table, or testing the ruler

- Why the middle of the distribution is usually the honest part of it

- Better proxies: weighted activity, days with one or more activities, and weighted days

- The 97% rule: closing zombie deals by default and making the rep argue to keep them open

Notable Quotes

“This metric, this proxy for sales effort, can be massively wrong. And when I say massively, I mean not 10% wrong — I'm talking about like 700% wrong.” — Dale W. Harrison

“There's nobody is going to tell you I'm sandbagging my numbers.” — Dale W. Harrison

“I'm also not incentivized, so to speak, to represent my work as directly relatable to reality, but just in the way that I can look better for my manager.” — Diego Sosa

“If you do not know the context that created your data, then you have no idea what the data means.” — Dale W. Harrison

Resources & Mentions

- Gartner B2B sales funnel benchmark data

- Win rate against activity density — several years of anonymised CRM data from a single company

- Activity data pulled from HubSpot; the same analysis runs on Salesforce or any other CRM

- The Buyer-Seller Fit Model, covered across this series

Next Episode

Back next week, including a closer look at weighting activity data by the effort each activity actually represents.

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#B2BMarketing #DemandGeneration #RevOps #MarketingAnalytics

HubSpot holds roughly 7% of the CRM market. Inside the Fortune 1000 it holds 0.4%, while Salesforce holds about 85%. Same category, same buyers on paper, and a gap that has almost nothing to do with product quality — large companies are averse to buying from small ones, and small ones would rather not be Exxon's smallest account. No volume of intent data will ever tell you that.

In Episode 11, Dale W. Harrison goes solo for the most technical instalment of the series so far, opening up the back end of the Buyer-Seller Fit Model. He covers why the team committed to a glass box rather than a black box; why nobody above sales and marketing cares about your close rate; the vowels-in-the-name problem sitting inside every lookalike list ever built; and the maths that turns a CRM full of wins and losses into a ranked view of where sales effort actually pays.

Timestamps

0:23 Solo episode this week — no co-host

0:33 Why this one is a technical dive into the Buyer-Seller Fit Model

1:43 Glass box, not black box: why you should be able to see the working

3:32 Defining buyer-seller fit

3:54 HubSpot vs Salesforce: 7% of the market, 0.4% of the Fortune 1000

5:58 The biotech example: same product, completely different customers

9:05 The CFO's black box

10:40 Nobody at corporate level cares about your closed-won rate

11:15 The 20% benchmark, and paying for the 80% you lose

13:45 Siloed ICPs and the 100 dream accounts problem

14:59 “They will, in fact, crush your dreams”

17:31 The vowels-in-the-name fallacy

18:35 Lead scoring and MQL designation

19:36 25 years of intent data, and a process that fails 99% of the time

20:21 There is no such thing as intent data

22:46 HubSpot's scoring model: 90% made-up intent, 10% firmographic fit

23:46 Sales effort, and why it dwarfs what marketing spends

26:32 Why pipeline velocity on its own tells you nothing

28:47 Slot machines, not vending machines

31:31 One unified model, from ICP through to sales allocation

33:05 Why great reps can smell a win

36:05 Unicorns, goats, whales and workhorses

39:21 The maths: mismatched units, benchmarks and log efficiency space

45:40 Beyond industry: revenue band, headcount, geography and tech stack

49:14 Reading the output: the efficiency and scale quadrant

57:56 Why no AI model can show you its work

1:05:17 Whatever you were bad at last quarter, you're still bad at

Key Topics Discussed

- Glass box versus black box: why a model you cannot audit is a commercial risk, not just a technical one

- Buyer-seller fit as two separate questions — will they buy from someone who looks like you, and are you equipped to sell to someone who looks like them

- Why the business cares about revenue per unit of sales effort, not close rate, deal size or pipeline velocity in isolation

- Paying for your losses: any honest model has to account for the 80% of deals that produce no revenue

- The vowels-in-the-name problem: why a lookalike list built only from closed-won data carries no information

- What intent data actually measures, and why digital touchpoint data is not intent

- Unicorns, goats, whales and workhorses: separating sales efficiency from commercial scale

- Closing the loop: one model running from ICP definition through campaign targeting, lead scoring and sales resource allocation

Notable Quotes

“Just because someone is buying something that looks like what you sell doesn't mean they will ever buy from you. And just because they're potentially willing to buy from you doesn't mean you're really prepared to sell to them.” — Dale W. Harrison

“Your guesses, and your hopes and wishes are not the same thing as an evidence-based approach.” — Dale W. Harrison

“Just because someone is showing intent doesn't mean they're showing intent toward you. Nor will they ever show any intent toward you.” — Dale W. Harrison

Resources & Mentions

- Gartner B2B sales funnel benchmark data

- HubSpot and Salesforce Fortune 1000 market share data

- Slide deck from this episode — link to follow in the comments

Next Episode

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

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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 #MarketingAnalytics

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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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 #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.

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#B2BMarketing #BrandMarketing #MarketingStrategy #MarketingAnalytics

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.

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#B2BMarketing #DemandGeneration #LeadScoring #MQL #MarketingAnalytics #ABM

Run the numbers on almost any B2B company's accounts, and a strange pattern shows up: the top 10% of your top 20% highest-revenue accounts tend to be your absolute lowest-profit accounts. They cost a fortune to land, a fortune to service, and know they can make unreasonable demands you can't refuse.

In Episode 5, Liam Moroney and Dale W. Harrison take on Ideal Customer Profile — why most B2B “dream account” lists are built on a B2C-shaped mistake, why buyer-seller fit has to work in both directions, and why you can't just hire your way into a new market. Dale also lays out the layered framework the series has been building toward: broad ICP fit, in-market fit, consideration set eligibility, conditional win probability, and CLTV.

Timestamps

0:00 Cold open: your best accounts might be your worst

0:18 Welcome back

0:35 Setting up today's topic: ICP

1:19 The marketing guru who couldn't define ICP

2:12 What ICP means in B2C vs. B2B

3:37 Your “dream accounts” list, and the Swedish supermodel problem

5:24 Why B2B buyers prefer buying from other large companies

5:56 HubSpot vs. Salesforce, and the Fortune 1000 problem

12:35 Defining buyer-seller fit

13:20 Why sales teams develop lopsided skills (“leg day, arm day”)

16:22 The rep who could only sell to banks

17:12 The distributor rep who only sold refrigerators

19:53 Can you just hire your way into a new market?

21:22 The Salesforce 15-person team story

26:19 The sober reality, and how to be more optimistic about it

33:29 Brand marketing's slow payoff vs. today's performance needs

40:37 Consideration set eligibility and conditional win probability

41:34 The Apollo moon suit story

44:14 Highest CLTV expectation value

51:08 Market orientation, and grounding leadership in reality

55:22 How static is an ICP, really?

57:14 Why relative market share barely changes over time

Key Topics Discussed

- Why B2C-style ICP thinking breaks down in B2B

- The “dream accounts” problem: wanting a buyer isn't the same as being wanted back

- Why the Fortune 1000 almost never buys from the smaller player, no matter the product

- Buyer-seller fit: why sales teams get good at selling to some accounts and bad at others

- Why hiring experienced enterprise sellers rarely fixes a buyer-seller fit problem on its own

- The Apollo moon suit story: when the best product and the best seller are two different companies

- The five-layer framework: ICP fit, in-market fit, consideration set eligibility, conditional win probability, and CLTV

- Why relative market share is remarkably stable, even in fast-growing categories

Notable Quotes

“Your dream accounts are the accounts you're dreaming of selling to, but it's not necessarily the accounts that are interested in buying from you.” — Dale W. Harrison

“For most sales teams, it's like every day is leg day, or every day is arm day at the gym. And you end up with some pretty distorted-looking physiques as a result.” — Dale W. Harrison

“It has to be a buyer who is likely to buy from someone who looks like you, and it has to be a buyer that your sales organisation has a reasonably good chance of selling to.” — Dale W. Harrison

Resources & Mentions

- Gartner and Boston Consulting Group B2B benchmark data

- Ehrenberg-Bass Institute research

- HubSpot, Salesforce, Zoho, and Pipedrive market share data

Next Episode

Next week, Dale goes solo to get into the messy technical details of what building this kind of scoring system actually looks like in practice.

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 #ICP #MarketingStrategy #MarketingAnalytics