A ‘Moneyball’ Approach to Marketing and Big Data
Remember the movie (and book) "Moneyball"? The story depicts baseball's budget-challenged Oakland A's. Outmuscled by richer teams when it came to signing the most coveted players, the A's changed the game by pioneering a new approach to data.
That probably rings some bells for marketers. Marketing stands at a similar crossroads as it grapples with the right approach to big data.
In baseball, conventional wisdom held that to win games, you need to field players who deliver big numbers for home runs, RBIs, batting average and other traditional metrics. Those players are expensive, however. A's General Manager Billy Beane saw that he'd be unable to keep up if he played by rich-team rules. Ahead of the 2002 season, Beane zeroed in on then-peripheral metrics — chiefly on-base percentage — to unearth players who produced more than their modest price tags would indicate. What could an entire team of undervalued performers do?
The 2002 A's won a league-best 103 games — as many as the free-spending New York Yankees. Now data drives baseball.
In marketing, spending big money for TV and other "heavy hitters" was common sense for 50 years. Marketers today have many more ways to "get on base" and connect with customers, yet old-school mind-sets persist. And while at first glance big data promises revolutionary transformation, in practice it can be overwhelming. Marketers are actually making fewer data-driven decisions.
Inspired by the Oakland A's intentional focus on a few meaningful metrics, you can begin to optimize your marketing and avoid getting stuck in the data weeds.
Workhorses over bling
The just-released catcher who could barely throw wasn't a hot commodity, but he had a history of getting on base. The A's picked him up, converted him to first base, and he delivered. In the "Moneyball" mind-set , you don't just emphasize TV and high-concept campaigns, you focus on what drives results.
Cost: The great normalizer
You can rank players by how often they get on base. Or you can rank players by how often they get on base divided by how much they cost. Like the A's, marketers are under pressure to do more with less, so divide outcomes by cost to ensure you're choosing the right tactics.
Don't just swing for home runs, get on base
The A's didn't optimize based on home runs or even runs scored. They prioritized on-base percentage, because they understood that the more often players get on base, the more often they score — and the more the team wins.
In marketing, the end goal is revenue. Sure, attributing revenue directly to marketing is murky. But you certainly won't get revenue if few are aware of your brand. You need to engage those who are aware of you. These are factors you can cost effectively optimize around.
Choose KPIs beforehand
The A's decided up front what their key performance indicator to assess players was. Consequently, every decision was straightforward. Likewise in marketing, whatever you do — email, event, TV, etc. — decide before you start how you'll judge performance.
The perfect is the enemy of the good
After you know what you're optimizing for, get going. Did Billy Beane waver because certain signings might not work out? Did he wait for a year-end performance report before taking any action to win more games? Not at all. General managers monitor performance and make personnel moves all the time.
Similarly, optimizing your marketing is an ongoing process via a hundred small optimizations. Monitor your marketing mix and shift budget from underperformers to overperformers.
It all boils down to taking an intentional approach to big data.
Jennifer Zeszut is the CEO of Beckon, a provider of SaaS marketing solutions.