by Jesse Wolfersberger | July 14, 2017

An old sports cliché goes: "Games aren't played on paper." In other words, games often unfold differently than you would have expected at the outset. That is an apt metaphor for my career. 

Considering my current role as a data scientist, leading Decision Sciences at Maritz Motivation Solutions, you might not believe that I have an English degree and started out my career as a sports writer. I covered high school sports for The Clovis News Journal in Clovis, NM. Not a bad gig, all things considered, but it wasn't destined to last. 
Two things happened at about the same time: my realization that the newspaper industry was dying and the release of Michael Lewis' Moneyball about how the Oakland A's used rigorous statistical analysis to find undervalued players. It showed me the hidden side to my favorite game that I never knew existed. There was a deeper truth to baseball, beyond batting average and earned run average, that better explained who won and who lost. If that was true about baseball -- a 100-year-old, highly scrutinized institution -- what deeper truths hid in the data in other realms? This notion lit a new fire in me, which led to a career change and a new direction. I went back to school for my Masters in Economics and the quantitative side of me was born.

From there, I landed a job in digital media, and eventually found my way to Maritz. Through the years, I never let the sports writer side of me go, but now I had new analytical tools in my arsenal that I didn't have back in Clovis. Outside of my day job, I crunched baseball statistics and wrote for stat-oriented baseball sites. Those led me to make connections with a few MLB teams, and now am a contracted consultant for one of them.

The overlap of these two worlds -- baseball and the incentive industry -- is bigger than you might think. In both, the first step is to just track what happened. In baseball, they have box scores (the first appearing in 1859!). In incentives, we have reporting and dashboards. In both cases, those are good starts, but it won't answer the real questions you need to answer. In baseball, that might be "Who should I trade for? When should I bunt? When should I go to the bullpen?" In incentives, they are, "What is the ROI of my program? How much should I spend on this promotion? What should my rule structure be? How do I know what my participants would have done without the incentive?"

These questions are why the field of analytics has exploded across the baseball and business world in the past 20 years. The answers to those questions (and more) are there, but it takes good analysts and the right tools to uncover them.

There is another parallel between the baseball world and the incentive world: there is more data today than ever. In baseball, the Statcast system has given analysts new toys to play with -- exit velocity, launch angle, spin rate, and route efficiency. For incentive programs, we have more ways to track participants through web, mobile, and social channels, than ever before. At the same time, new methodologies are hitting the mainstream as well, with machine learning and artificial intelligence. 

In both worlds, baseball and incentives, those who make the best use of these new data sources and methodologies will win most often over the next decade. This is why Maritz Motivation Solutions has made Decision Sciences such a big part of what we do. We are more than just analytics, because we have to be. We are designing programs for human beings, which means the results will always turn out a little differently than you expected. After all, games aren't played on paper.

In the columns to come, I'll share examples of how we are using this methodology and technology to gain insights and explain why decision sciences should be a CMO's best friend. 

So how are you using data in your programs to make them smarter? I'd enjoy hearing from you in the comments or reach out to me at

Jesse Wolfersberger leads the Decision Sciences team for Maritz Motivation Solutions. Jesse is a data scientist who specializes in merging the fields of behavioral science and artificial intelligence. In his free time, Jesse enjoys using his data skills in the baseball world, where he has written for The Hardball Times and, and consults for a Major League team.