I watched Moneyball last night.
It’s the true story of how baseball started to introduce statistical and scientific methods to build better teams with reduced budgets.
For over 100 years baseball scouts (usually ex-players) picked and discarded players using gut instinct. Their thinking on what makes a great player conformed: technique, grace, beauty, reputation, potential, reliability and creativity.
Scouts looked for what you might call a player’s player. And the received wisdom worked, in the sense that nobody got fired for employing it.
The Oakland Analytics
No sport is an exact science, but baseball gets closer than most. There’s a mountain of data managed by a bunch of geeks who can tell you everything you need to know about players from a numbers perspective: hits, walks, runs, steals…
And these people look at the game differently. They don’t understand potential or grace: they do understand the raw contribution every player makes to the bottom line of winning matches.
And guess what? An analyst’s player isn’t a player’s player. Not even close.
The raw data theory is simple: it says you can assemble a winning team for a fraction of the usual cost if you change focus from subjective feel to objective analysis.
But would anyone have the bottle to take on the establishment and risk their reputation and livelihood? Billy Beane and the Oakland Athletics, with one of the league’s smallest budgets, risking their reputations and livelihoods, finally went for it and recorded the longest winning streak in baseball history with their ugly team of graceless, ageing misfits. And changed the game.
Resistance is Futile
The Oakland management team realized they could only compete by focusing on direct return on investment. They stopped asking how much a player would cost to buy: they started asking how much a run, or even a win, would cost to buy.
They looked at the sport differently and, despite being lampooned by a hostile establishment with everything to lose, they changed the way baseball works.
And it’s the same in B2B marketing. Time to adapt or die. Let’s:
Quit thinking of science as a geek’s preserve and embrace a quantitative approach to marketing.
Stop caring about what our peers think and start finding out which investments actually buy us prospects, customers and revenue.
Cease wasting cash on what’s big and trendy and keep investing cash in whatever tried and tested process keeps bringing in the punters.
Lose reliance on big opinions and take control of the marketing process with big data.
End the fascination with agency beauty parades and get deep and dirty with analysts and data jockeys who truly understand what’s working.
Adjust any hiring processes that rely on portfolios that don’t show impact on the bottom line.
Scrap any marketing campaign that talks costs rather than value and revenue.
Pull creatives out the darkness and give them a fantastic platform to spin their best ideas.
Rethink the idea that growing a marketing budget is more important than delivering more than less.
I could go on. But at the end of the day it’s all about money, whether it’s ball or marketing, and we’d all better get used to it.
I’ll leave you with a quote from “The Win Without Pitching Manifesto” by Blair Enns:
“There are greater causes by which to frame an enterprise, and there are nobler metrics by which to measure the value of effort. But we cannot escape the fact that money is both a necessity in life and the most basic scorecard of success in business. Even if it is not the validation we seek, it is the most basic of test that we must pass.”
And it’s our job as B2B marketers to make sure we pass the test every day.
Killed by the buzz: Why we’re losing words to the buzz effect (and what to do about it)
Here’s a question for you: What do buzzwords and That One Guy You Hate™ have in common? You guessed it. They both sneak into every conversation…
Nur Caplin | 20. 09. 2023
How to break free from the benchmark trap
If you’re turning to industry benchmarks to set your performance goals – make sure you’re asking these two questions.
Agustin Rejon | 06. 09. 2023
The B2B generative AI design shootout: Part 2
We put different models of generative AI to a heftier task in Part 2 of our three-part design test shootout.
Brian Terry | 29. 08. 2023