The Moneyball Moment – Adaptive Modelling and the End of Marketing’s Scout Culture
Glenn Granger, CEO of marketing analytics company marketingQED offers his thoughts on what marketers can learn from the movie Moneyball and the inner workings of sporting stars' salaries.
What does Brad Pitt’s 2012 Oscar nomination for a film about baseball have to do with the challenges currently facing consumer marketing departments? A lot. Let me explain.
I’m a Maths graduate, so let’s start with some statistics. In 2002 the Oakland Athletics baseball team spent $41 million on players’ salaries, and the Boston Red Sox spent $108 million. Yet the A’s finished first in the American League West with 103 wins, setting a record by winning 20 consecutive games, while The Red Sox finished second in the East Division, winning 93.
It was a feat nobody saw coming and it was entirely down to one thing: the Oakland A’s young general manager Billy Beane started playing Moneyball.
Beane abandoned the scout-led approach based on opinions and collective knowledge that had ruled baseball drafting. Instead, he took a quantitative approach called “Sabermetrics,” which applies statistical analysis to baseball records in order to evaluate individual players’ regular performances. In 2002, the A's stopped looking for ‘star’ players and began looking for more effective ones. The subjective knowledge of scouts watching players took a backseat to measuring players’ statistical contribution through specific measures, such as how often he reaches a base.
Ten years later, marketing is having its own Moneyball moment. For too long, marketers have been their own scouts, making emotional decisions about the marketing mix that are just as ‘hit and miss’ as scouting in baseball. In reality, like the scouts, marketers still look for a ‘star’ rather than effective marketing mix.
Since the 1960s, marketers’ gut feel and experience have been underpinned by various forms of statistical analyses to accurately link spend and media choice to market share. The weakness these approaches share, beyond just cost, is the inability to accurately predict consumer responses. The inflexible, laborious processes they use yield a non-dynamic, infrequent and increasingly irrelevant ‘snapshot approach’.
A Beane-like revolution in the analysis of marketing is underway: adaptive modelling. Using rapid, continuous aggregation and analysis of mass segments of data, and an understanding of the context of individual campaigns, marketers can begin to accurately predict individual consumer behaviour.
Through adaption, marketers can make faster, better and dramatically cheaper judgements. Analysis is now an essential part of the everyday decision-making process, so marketers can achieve much, much more, with a lot less.
Consumer marketers are under pressure to demonstrate return on investment from their marketing decisions. And like baseball, the stakes are high. A percentage point movement in market share can result in the gain or loss of hundreds of millions of pounds. This pressure will grow as economic uncertainty and the proliferation of online and offline channels combine with new business models to increase the challenge of managing an ever-more complex marketing mix, against tight budgets. This kind of pressure has to drive change, and it is.
Sabermetrics was a watershed moment for baseball, just as adaptive modelling is for marketing. Moving from art to science - by drawing on the objective metrics behind the past performances – marketers can focus their budget and time on those campaigns that are proven to deliver results.
In the wake of Billy Beane’s breakthrough, the New York Mets, New York Yankees, San Diego Padres, St. Louis Cardinals, Boston Red Sox, Washington Nationals, Arizona Diamondbacks, Cleveland Indians, and the Toronto Blue Jays hired full-time Sabermetric analysts. Teams which resist Moneyball are falling behind and similarly, marketers that ignore adaptive modelling risk finding themselves knocked out of the park too.