There is a reason this newsletter is called Moneyball CMO.
In 2002, Billy Beane was the General Manager of the Oakland Athletics, a baseball team with one of the smallest budgets in the league. The Yankees were spending $125 million on player salaries and Beane had $ 41 million, so he couldn't afford the players everyone else wanted. He had to find a different way to win.
Billy Beane | Taylor Richards Glenn
He partnered with Paul DePodesta, a young Harvard economics graduate, and started digging through decades of player data. What they found changed the sport forever: baseball had been measuring the wrong thing.
For 150 years, scouts evaluated players on batting average, which is basically how often a guy gets a hit when he steps up to bat. It was the number everyone understood, the number careers were built on, the number that decided who got paid.
Beane found that a different stat predicted winning far better: on-base percentage. This measures how often a player reaches base by any means, whether that's a hit, a walk, or even getting hit by a pitch. It asks one question: did you avoid making an out? Turns out a player who walks four times in a game is more valuable than a player who hits one home run and strikes out three times, because outs are what lose baseball games.
Maths Was Clear - But the Scouts Were Following an Ancient Framework
When Beane showed them the data, they revolted. These were experienced baseball men who'd spent decades evaluating players by watching them swing and run and throw. They didn't reject the numbers because the numbers were wrong. They rejected them because the numbers contradicted the only framework they'd ever known. One of them said it in a meeting: "You're discounting what scouts have done for 150 years."
The old framework said to measure the batting average - i.e. how often a player gets a hit. The number everyone understood for last 150 years and the number careers were built on.
As per Beane and DePodesta On-Base Percentage was the better metric that actually predicted wins. It measured how many times a player avoid making an out. A walk is as valuable as a hit, outs are what lose games.
Data was right but the framework was wrong.
Billy Beane with Brad Pitt, who portrayed him in the film The Moneyball.
But the part of that story that I keep coming back to is this: Beane's genius wasn't finding the data. It was understanding that giving correct data to people without the framework to interpret it would have killed his strategy before it started. He didn't democratize the spreadsheet, rather he sequenced the adoption.
I keep seeing the exact same pattern play out with marketing data. Correct data given to people without the framework to interpret it will kill your strategy before it starts.
Pipeline vs. Booking: Why This Distinction Matters
You already know the difference between pipeline and booking, but the way this story plays out depends on understanding why these two numbers create completely different reactions inside a marketing team.
Pipeline tells you whether a campaign helped create an open deal. Someone engaged with your content, entered the funnel, and eventually a sales opportunity was created. It's the number most of us grew up on. Your Salesforce source field. "My campaign generated X pipeline." Familiar territory.
Booking tells you something harder. Did this campaign help close a deal that actually turned into revenue? Not an open opportunity, but a signed contract where money actually changed hands.
Most marketing teams live entirely in pipeline because it's comfortable and it maps to how they've always reported results. The ones who figure out booking are the ones who stop losing budget battles with their CFO, because booking is the language finance actually speaks.
The Dashboard She Deliberately Broke
One of our customers was setting up RevSure's Budget Reallocation feature on a live call with our team. This feature needs three inputs to work: pipeline volume, booking volume, and spend. You give it those three numbers and it tells you which campaigns are wasting money and where to move that budget instead.
She filled in pipeline and spend without hesitation. But when our team got to booking, she stopped.
"I don't think our team has quite oriented themselves to booking yet. I know that's a little bit counter to the point of this, but I don't think we're ready."
She left it empty on purpose, knowing the feature would break without it, and worked around the gap.
Her company runs a six-month sales cycle with dozens of campaign touching per deal. The booking model divides credit equally across every single touch, so a campaign that ran in month 1 of a 40-touch deal gets 1/40th of the booking credit.
That number looks small, but the campaign wasn't failing. That's just what honest attribution looks like when credit is shared across a long journey.
The First-Touch Problem
Her team had spent their entire careers on first-touch attribution, where a campaign either gets 100% of the credit or nothing at all.
If They See 1/40th
They see failure and want to cut the budget on a programme that's actually working.
If They See a Surprisingly High Number
They see a winner and want to double down without understanding what's driving it.
Either Direction
Leads to a wrong call - not because the data is wrong, but because the framework is wrong. She knew this, and she had the discipline to act on it. Beane would have recognised her move instantly.
The Data Trap You Already Know
Every marketing leader has a version of this problem right now, even if it has nothing to do with booking attribution.
1.
The AI Tool Nobody Questions
Maybe you've rolled out a tool that gives your team instant answers from your CRM data, and you've noticed that nobody on the team knows how to question what it spits out.
2.
The Attribution Switch That Caused Panic
Maybe you switched from last-click to multi-touch attribution and your paid media manager came to you panicking because their numbers dropped 60% overnight, even though nothing actually changed in performance.
3.
The Board Deck Moment
Maybe you're putting together a board deck where you're showing influenced pipeline for the first time and you already know someone is going to ask "which campaign actually closed this deal?" - and the honest answer, that all of them contributed a little, is going to sound like you're dodging the question.
Every one of these situations is the same pattern. A new metric or tool arrives, it produces numbers that are correct but unfamiliar, and the people receiving those numbers don't have enough context to understand why they look the way they do. So they either reject the data entirely or they make a fast, confident, wrong decision based on it.
This is why analytics investments fail. Not because the data is bad or the models are wrong, but because of the gap between what the data says and what the organisation is actually equipped to hear.
The Question Worth Asking To Your Team
Think about the most important metric or dashboard you've rolled out to your team in the last year, and ask yourself this: if you pulled any person on your team aside and showed them the number that worries you most, could they explain why that number is what it is? Not just read it off a screen, but actually tell you what methodology produced it, what assumptions sit behind it, and what would change if those assumptions shifted.
If they can - You're in good shape.
If they'd just react to whether the number looks big or small - You have a framework problem. And you cannot fix a framework problem by adding more data or buying another tool.
You fix it the way Beane fixed it, and the way our customer fixed it. You sequence - start with the metrics your team already understands, build trust in the system using numbers that feel familiar, and let them see the platform get it right on things they can verify with their own eyes. Then, once that trust exists, you introduce the harder metrics.
That's the difference between analytics programmes that actually change how a company operates and analytics programmes that just generate arguments in Slack.
P.S. - A Note on Sequencing with RevSure
RevSure gives you pipeline and booking attribution across every campaign, channel, and touch. But the smartest customers we work with don't flip everything on day one. They sequence it.
If you want to see what that rollout looks like, you can mail me at: [email protected]
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