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B2B Effectiveness

B2B Effectiveness

Written by: The Insight Collective
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B2B Effectiveness: Evidence-Based Marketing Ideas for B2B Practitioners is a podcast for marketers who want evidence, not opinion. Marketing Scientist and host, Dale W. Harrison sits down with B2B marketing strategists to challenge the assumptions B2B marketing keeps repeating without checking — from lead scoring and demand generation to brand building and attribution — replacing conventional wisdom with research, real benchmark data, and hard-won practitioner experience. Each episode takes one persistent industry belief, tests it against the evidence, and leaves you with a sharper, more honest way of thinking about the problem.Copyright © 2026 The Insight Collective. All rights reserved. Economics Management Management & Leadership Marketing Marketing & Sales
Episodes
  • What Does Brand-Aware Search Really Measure? | B2B Effectiveness - Episode 14

    What Does Brand-Aware Search Really Measure? | B2B Effectiveness - Episode 14
    Oct 15 2026
    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.Timestamps0:33 Back for another episode — Dale and Liam reunite0:47 This week's topic: what does brand-aware search actually measure?1:08 Liam's starting point: years of branded search data1:53 How a few B2C examples became a universal rule2:25 A tub of yogurt versus a multimillion-dollar ERP system3:01 You can't outsource your thinking — to research or to AI3:45 The original claim: share of search reflects share of market4:50 Share of search as a stand-in for share of voice5:29 Brands do not grow: market share barely moves6:30 When a leader stumbles: the password management example7:53 It's actually measuring a lot of other useful things8:24 Slides: when share of search does track market share8:54 Product parity, price parity and how things are sold10:37 The supermarket with an unwatched back door11:41 No door-to-door detergent salesmen12:24 Price discrepancy: $20 versus $200,00013:15 Volume: one search, a thousand licenses14:16 Microsoft Office, Exxon and 100,000-fold basket sizes15:10 Drinks, houses and boxes of Tide: why B2C variance is small16:31 Ehrenberg's own caution about uniform basket size17:01 Light and heavy buyers in B2B search data17:46 Free trials: noise that inflates search volume18:35 Penetration is not revenue20:43 Partners, procurement and cross-selling: revenue that never searches21:42 Liam's in-house story: the vendor that sold itself in22:29 Ideal conditions versus the typical B2B picture23:35 Real data: the social media management category23:59 Buffer versus Sprout Social: volume versus value25:54 Nearly half the search volume, about 1% of the revenue27:16 Sprout Social: branded search down, revenue up28:17 Two stories: the company picture and the category demand picture29:03 Growth from bigger contracts, not more customers31:08 Detergent for families with 12 kids32:37 Penetration and market share are two different things33:06 HubSpot's users versus Salesforce's money33:43 “Our revenue's never been better”34:37 Hightouch and the declining CDP category35:51 Defining penetration: the Tide example36:55 HubSpot: 50% greater penetration, a fraction of the revenue38:00 The Fortune 1000: 85% versus 0.4%39:34 Where a brand's revenue actually comes from41:28 Audience question: can you segment around the variances?42:14 Branded search as a measure of demand43:37 Fax machines and Ricoh: share up, category collapsing46:02 Category demand: missing from B2B conversations46:22 The bowtie and trying to outrun a lack of new business47:50 The 95:5 rule and stationary markets48:33 Why laundry detergent grows at about 2% a year50:26 Dynamic tech categories: fax machines and CRM51:45 Conditional win probability and the odds a lead is in market52:37 Why a hot category makes you look like a genius53:01 Televisions and the 8K flop54:14 When good enough is good enough54:59 Marketing automation and mechanical lead scoring55:16 The lessons: active demand and category health56:25 B2B demo software and a 99:1 market57:11 NFTs and the crypto bros58:04 Wrapping upKey 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 failuresNotable 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....
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    59 mins
  • You Cannot "Work Backward From Success" | B2B Effectiveness - Episode 13

    You Cannot "Work Backward From Success" | B2B Effectiveness - Episode 13
    Oct 8 2026
    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.Timestamps0:32 Back for another episode — Diego, a split of three1:16 This week's topic: differences, not similarities1:35 Why working backward from success is marketing's biggest failure point2:21 The classic example: target account and lookalike lists3:23 Diego: the same reasoning in the signal debate4:16 The attribution report and the LinkedIn company page4:58 The question nobody asked: what about closed-lost?5:51 Vowels in your name — a 100% correlation6:47 Diego: when a data point actively misleads you7:58 The number was right. The conclusion was wrong.8:24 Why “data-driven” is a warning sign9:00 Data is the bucket, not what's in it9:16 Points-based lead scoring counts buckets10:04 The roulette wheel: lots of data, no information10:40 Decisions as discrimination between options11:36 What an MQL actually is13:33 Zoho's lead scoring versus a random number generator15:28 Diego: information as the reduction of uncertainty16:05 Betting under uncertainty: the coin toss17:18 Beating a random pull from ZoomInfo17:53 Two slot machines that look identical19:22 A flawed sales-cycle analysis from a RevOps podcast20:20 Measure against your opposite, not your average21:39 What is your average actually averaging?23:05 Diego: averages and non-normal distributions24:02 Why we do it: “I blame it on martech”24:25 Dashboards full of dancing monkeys25:04 LinkedIn-influenced pipeline26:10 Closed-lost deals get more engagement27:38 The foundational question: compared to what?28:41 Diego: touch the whole market, count only the winners30:45 Grading their own homework31:00 Not accidental: incentives behind the dashboards31:47 LinkedIn's own advice on company pages32:51 The dashboard, the CFO and your credibility34:02 Two key questions for every piece of data34:35 Why common characteristics mean nothing35:50 The value of uncommon characteristics37:11 What AI gets wrong when it mines your CRM38:04 Diego: put in the dirty work and test the platforms38:55 Be driven by information, not data41:15 One article, ten articles, a hundred articles42:41 Diminishing returns on every additional data point44:35 Diego: gaming engagement scores45:33 Driving people through the company page45:58 Ice cream sales and drownings46:25 Lead nurturing and the evil genius47:06 Wrapping upKey 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 newNotable 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 SosaResources & 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 dataNext Episode“What does Brand-Aware Search Actually Measure?”Subscribe & FollowCatch every episode of B2B Effectiveness: Evidence-Based Marketing Ideas for B2B Practitioners on your favorite podcast platform, and subscribe here on YouTube for future ...
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    48 mins
  • How To Measure Sales Effort - Or Where's The Sandbagging? | B2B Effectiveness - Episode 12

    How To Measure Sales Effort - Or Where's The Sandbagging? | B2B Effectiveness - Episode 12
    Oct 1 2026
    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.Timestamps0:30 Back for another episode — Diego standing in again0:52 Where sales effort sits inside the Buyer-Seller Fit Model1:08 The metric that never appears on a dashboard but feeds most of it2:44 The traditional proxy: days active in the CRM3:29 The perfect measure nobody will ever build — a 24/7 rep diary4:20 Not 10% wrong. 700% wrong.4:59 Data is not information: context is what creates meaning5:45 Diego on the rep's own incentive to make the data look good6:50 High-side and low-side errors, often in the same organisation8:17 The one metric Dale has never seen a rev ops team produce9:08 Why it takes three times longer to lose a deal than to win one9:48 90% of companies land between 3x and 7x10:10 Zombie accounts, and why reps keep touching them10:54 The never-say-die rep — and why you want that instinct12:38 Diego: conversion rate as a quarterly performance measure13:30 Pipeline value, bragging rights and adverse incentives16:52 The maintenance drip feed17:16 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 CRM20:23 CRM discipline, and the trigger point that forces a deal into the system22:36 Diego: why effectiveness collapses if this number can't be trusted23:30 What an ICP should actually be24:17 Biotech: a P-card in a university lab versus three months with Pfizer27:26 Deal value and win rate are unambiguous. Sales effort is not.28:29 Diego: the slot machine, and treating accounts as a portfolio30:01 Why optimising for a single metric produces confidently wrong answers31:41 Bureaucratic inertia on the buyer side, and what you cannot control33:44 Real sales data, across multiple years, from a real company34:11 Activity density explained35:36 The unweighted activity measure, and the fallacy inside it37:01 Reading the chart: 4.5% at one end, 82.1% at the other38:24 The same mistake everybody makes with lead scoring39:09 Why the 24% bar is the honest one40:01 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 proposal42:20 The 4.5% end: stretching a deal that is already lost43:28 Two touches a month is not a pipeline, it is cover45:18 Both distortions running inside one sales organisation45:53 Backing it out: weighted activity and days with activity47:59 The 97% rule and the 81-day cutoff49:10 Run it weekly, and close by default50:26 Why the true believers are the more expensive problem52:15 Diego: using data to raise better questions53:30 What AI lacks is the context around the process that made the data54:25 The real danger: turning your brain off and outsourcing the thinking55:23 Multiple versions of the world, one set of dataKey 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 openNotable 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, ...
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    58 mins
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