On May 18, 2018, Sergio Romo, a lifelong relief pitcher, was the Tampa Bay Rays starting pitcher. He also was the starting pitcher the next day. These were his first starts after 588 appearances as a reliever over his 10 year career. Baseball norm is that a team hopes to have their starting pitcher pitch 6 innings, every 5 days. But in this case, Mr. Romo pitched a scoreless first inning on May 18 and another scoreless inning the next day, and that’s it. And with that, the Rays had used its closer to introduce the role of the “opener” to major league baseball. How did we get here?
It's about the data. The data told the baseball analytics guys that batters have dramatically more success against a particular pitcher the third time they face him. The opener concept is simple – use a pitcher most likely to get the opponent’s toughest hitters out at the beginning of the game, and then hand the ball off to another pitcher who may pitch more innings but will not have to face a batter more than twice. This is just one of many examples of how baseball is taking a data-driven approach to analyzing results, evaluating players and devising new strategies.
DI Lesson 1: Distribution Intelligence (DI) needs a seat at the table. Sabermetricians, i.e., those who use data in the search for objective baseball knowledge, have risen from the windowless back room to the front office and are now often found running teams. Similarly, to harness the full potential of the analytics, DI should not be viewed as a support function, but rather as a key strategic partner and influencer in the Distribution Strategy process. Data scientists are a critical part of the team, but DI is a business function as it is essential that DI leadership understands distribution in order to define the best applications of data insights.
I had the opportunity to speak with Chaim Bloom recently about his transformation experiences. He is the Chief Baseball Officer for the Red Sox and one of the key architects of the Rays exploitation of data. He noted that the Rays did not invent the concept of the opener. What they did do was analyze the data and prepare a compelling case for those who would carry out the novel concept. Getting the manager and pitchers onboard in a sport that has a history of resistance to new ideas was crucial. Bloom also talked about being fortunate that the opener concept was successful when it was first introduced. If it wasn’t, he hopes they would have had the conviction to stay with the concept until an appropriate sample size was attained.
DI Lesson 2: Getting Buy-In is the Art that complements the Science. Crunching the data to derive an insight is often the easy part. Executing on that insight? For those of you who have tried to sell your top producing wholesalers on a new approach, you may know what I mean here. Start small. Pick your proof-of-conceptees wisely – ones open to new ideas, with the savvy to bring ideas into the marketplace and suggest tweaks, and who will serve as an internal advocate of a successful approach. Even consider compensation-neutral assurances. The key is to construct what Olmstead calls a DI3 model – one that turns Data into Insights into Impact using an Iterative learning process.
If you have read the book Moneyball, you know that it is not a coincidence that small market baseball teams are the pioneers of using data and analytics to gain competitive advantage. The Rays total payroll in 2017 was $70M. The Dodgers was $240M. With that said, the success of the small market teams with their data-driven approaches was noticed and now every team invests heavily in the math.
DI Lesson 3: DI can play a role in bending the cost curve. The Asset Management industry laments its profit margin declines from 40% to the low-to-mid-30%s, while forward looking asset managers are strategizing on how to survive and thrive at sub-30% margins, even 25%. The current pandemic has amplified the importance of true Transformation as the end of the long bull market has exposed some unsustainable operating models. Done right, Distribution Intelligence holds the potential to increase the batting average (sorry, couldn’t help myself) of your sellers AND also lower the cost of sale. Self-funding the DI initiative can be explored too. Parallels can be drawn between a baseball team improving its cost structure through reduced reliance on higher-priced starting pitchers and, in our industry, the evolving expectations of the internal/hybrid/external wholesaler model, particularly given recent lessons learned in our new virtual meetings world.
Sergio Romo was returned to his role as closer in June. Not because he wasn’t effective as the opener, but because the data showed that he was more valuable in the closer role and they had other pitchers that could fill the opener role. They realized the end goal was not to win the first inning. Rather the goal was to win the game as a collective team through a combination of new data-inspired tactics and tried and true approaches.
DI Lesson 4: Think horizontally, avoid silo thinking. Distribution wants to be communicating to the right people, with the right message, at the right time, and, now more than ever, in the right ways. This requires a team effort across sales, marketing, servicing, analytics, and product. We see the shiny object syndrome resulting in narrow investments that support a specific business unit’s goals, but not necessarily the broader more strategically important objective of the firm. The distribution architecture should not be about building a stack of leading tools like business intelligence, sales enablement, reporting, CRM, marketing automation, portals and websites. It should be about looking across the sales, marketing and servicing business architecture to determine how an asset manager is going to be great at lead generation, pipeline management, closing, cross-selling, and client management.
Contact the author: