SOBRE ESTE EPISÓDIO
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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