As made famous in the best-selling book and Oscar-nominated film Moneyball, sabermetrics, an advanced analytical approach used to measure a player’s in-game performance, helped launch the Oakland Athletics to the playoffs in 2002 and 2003. This approach gave the team a competitive advantage over larger MLB teams like the New York Yankees, despite the A’s having a much smaller payroll.
This made for a fascinating and compelling story, but it barely scratches the surface of the power that this type of analytical approach can provide for normalizing performance.
Sabermetrics statistics analyze various drivers of baseball player performance. Ballpark factors such as dimensions, elevations, and temperatures influence varying degrees of player performance. For example, a player who plays regularly at Coors Field in Denver, CO (elevation above sea level 5,183 ft.) will naturally hit the ball farther than a player who regularly plays at Citizens Bank Park in Philadelphia, PA (elevation above sea level 9 ft.). These factors are now universally used to normalize player performance provide a standard baseline of value over a replacement player.
Yet, in the business world, similar analytical approaches and principles are only now beginning to be used to measure sales performance. In order to do this, companies must isolate sales territory and/or account factors that drive performance. Does the location (urban, suburban, rural), territory demographics, account tenure or potential account size influence who is performing well and who is not? Using “sabermetrics”, style regression based approaches, companies can now normalize performance for these variables and begin to evaluate actual sales performance vs. the expected performance of an average salesperson. This type of approach equips sales leadership with more prescriptive insight and can often lead to more strategic growth discussions at the individual sales rep level as volume, price, product penetration and account retention goals can now be compared to “expected” for that specific account.
For example, a publishing client of ours that uses over 250 independent sales reps to sell books to schools and students across the country struggled to understand if their commission structure was truly driving the right behaviors and rewarding the actual best sales reps. When we began to probe into this issue analyzing the last 3 years of all sales transactions a host of dimensions and factors, such as: what is the total enrollment at the school, is the school private or public, the median income of the zip code that the school is in, and the number of awards the school has won for their books all began to bubble up as statistically significant drivers of sales volume and achieved pricing.
When we began to normalize for these driver variables using a regression based sabermetrics style approach, we developed a more accurate expectation of sales rep performance, whether they were in rural Georgia, Manhattan, or Des Moines we could now compare actual performance to an expected performance that normalize the account for all significant demand drivers.
Once that is complete, we aggregated the associated school level performance on volume, price, product penetration and account retention vs. the expected rep’s performance for their portfolio of schools, our client could quickly and enabling an apples to apples performance metric to compare rep performance across the entire country.
The lessoned learned was even though a sales rep in Des Moines isn’t generating as much total volume as their peer in Manhattan, that does not mean they are a deficient sales rep. Rather, by using a sabermetrics style approach, our client realized that many middle of the road volume reps are actually high sales performers when you look at their ability to outperform normalized expectations for their portfolio on price, volume, product penetration, and account retention.
It is clear sabermetrics has a lot more to offer beyond baseball. Ultimately, this approach can and should be applied to inform corporate organizational growth and determine how companies can best reward members of their own big league team.