Can a ‘Moneyball Strategy’ Help You Grow Your Business?

All the data you need to create separation from your competitors is there; are you using it?

I am Nick. I have several passions that drive me: Family, tennis, making beer, and analyzing data to help companies make better decisions. Mistakes in life and business often result from failing to see patterns in the data.

The decline of craft beer

A few years ago, you might have considered brewing beer if you wanted to own a business. The craft beer revolution in the United States began a little over two decades ago, and until 2020 or so, it was, for many who got involved, a commercial and professional win.

For a while, it was simple: Brew a decent IPA, develop a cool name and colorful packaging, and devote time to expanding the product offerings. The business side of things was always important, but it didn’t require an MBA from an elite school to figure out that much of the world’s biggest economies had reached a saturation point for standard, mass-brew beer. Drinkers wanted a more artisanal approach and a complex flavor offering.

Those days now look to be in the past. In a backlash to the explosion of styles, everything from IPAs to fruity, sour, wheat, rye, stouts, Belgian style, and so many more, consumers today are either moving away from beer or want simple, straightforward ones like the lagers against which they initially rebelled.

But does this mean you should shutter your craft brewery or not follow through with your dream of being a small-batch brewer of excellent beers? No, not at all. It means you need to invest as much time on the business intelligence side as brewers formerly did on the taste and quality side.

In 2023, if you want to get into the craft beer business or currently have a small brewery, consider the role that business analytics play in not just improving your market position but dynamically increasing the profitability of your brewery.

Try the Moneyball Strategy for making your business buzz

Many might recall the six-time Oscar-nominated movie Moneyball, starring Brad Pitt and Jonah Hill. The strategy of Moneyball was simple. Paul DePodesta, working for the general manager, Billy Beane, of the Oakland A’s, created a formula showing that for the A’s to get to the playoffs, they needed to win 95 games.

Through the use of extensive analysis of data like runs scored, on-base percentage (OBP), runs-batting-in (RBI), steals, errors made on defense, batting average with runners in scoring position, and de-emphasizing the traditional big-ticket items like home runs and batting average, DePodesta was able to load the A’s with players most other teams had given up on. It was those players, though, that DePodesta showed could make the data sets sing, thus helping the A’s win.

The traditional metrics the A’s management used had the team most likely winning 80 games for the 2004 season. Beane gave DePodesta the green light to apply his evolutionary approach to baseball data. Despite waves of criticism from baseball insiders, the manager of the A’s, and the fans, the struggling A’s went on to win 20 games in a row and a total of 103 for the year. The A’s made the playoffs but lost to the New York Yankees.

The Moneyball strategy so evolutionized the approach to how baseball teams were put together that within two years after the DePodesta experiment, the front offices of many teams, led by 20 and 30-somethings with economic and statistics degrees and little or no experience in baseball, created similar data strategies.

Three teams that applied the evolutionary use of data analytics to assemble their teams, the Boston Red Sox, the Chicago White Sox, and the Chicago Cubs, went on to win their first World Series in 86, 88, and 108 seasons, respectively.

Nowadays, most companies amass gigs of raw, unprocessed data (data lakes) and structured, processed data in “data warehouses.”

However, collecting data is often not the problem that companies have. Similar to the DePodesta experiment and the Oakland A’s, which was then applied to many other teams, it isn’t a lack of data but a failure to properly analyze what they possess.

The Moneyball strategy is nothing more than a clear articulation of the problem. The A’s needed to score more runs than they allowed. To do that, they needed to get players who, despite not being superstars, had a unique habit of getting on base and scoring.

Big Beer aggressively uses data

The moment craft breweries started cutting into the bottom lines of the big commercial brewers, the bees in the hive were roused. Emerging into the light, they were angry and determined to punish.

Some of the biggest name craft brews in the United States are no longer small-batch craft. Through precise and careful data analysis, the marketers at AB InBev, Carlsberg, Miller-Coors, Heineken, Diageo, Kirin, and others assessed which brand would fit perfectly with their on and off-trade portfolios and then acquired them by making offers that craft brewers couldn’t refuse — let us buy your brand, or we will crush you with data analysis.

Walking into a cool bar in Manhattan’s Lower East Side, you notice a slew of coolly-named craft beers and then next to them, the mass-produced Heineken, Kronenburg, Moretti, and other tasteless, Heineken-style international lagers. Playing off of the emotions and hipness of those formerly craft-marketed beers, the Heineken monolith increases the image of its commercially-oriented beer.

Heineken is now in a partnership with Walmart, using data from how much time consumers spend standing before the beer displays in their stores. This creative and astounding use of data permits the brewer to manipulate shelf and in-store positioning to urge consumers to buy its beers instead of competitors. Small, true-craft brewers don’t have access to such data, and if they did, would they even be able to use it?

Try the Moneyball Strategy

The Moneyball strategy of data analytics is something that business owners at both the early stages of their company’s operations can utilize as well as for marketers at growth level stages. Simplifying the task at hand permits a more clearly articulated understanding of the company’s fundamental problem.

Potential problem: The data point to a need for increasing beer sales at the point of purchase where profitability is highest.

Solution: Beer with the highest margins is served at the brewpub. How do we increase sales here?

Next, try to move beyond the data traditionally culled for business intelligence purposes and take a new look with a fresher perspective. Data shows that people don’t come to the brewpub as much because they don’t want to drink and drive.

Business considerations: Can a shuttle from a sizeable nearby neighborhood be run to pick people up? Or, a deal can be made with Uber or Lyft drivers to give discounts so customers can leave cars at home. Can a beer delivery truck circulate through neighborhoods, delivering fresh tap beer in growlers for people while they remain home?

The data is there, folks.

It always comes down to letting trained analysts sift through it to help you grow your business. Everything you need to know to overcome growth doldrums is locked away in your computers. Saying, “Ah, we got a good handle on our data collection,” is not the same as actually using it.

Unlock it, get into and grow your business.