How to Measure Sales Effort — Or Where's the Sandbagging? | Episode 12 – B2B Effectiveness

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 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 #RevOps #MarketingAnalytics