Like most of my thoughts, this blog starts with baseball. It will ultimately lead us to sales organizations, analytics and the implications of next-best-action recommendations, but indulge me for a minute first.
Swing changes are all the rage today in baseball. Within the past several years, new data has come to light that tracks the movement of the baseball on the field at a minute level of detail, and with this detail has come insight. Among the most important insights is the relationship between the angle at which a batter strikes the ball and the outcomes it generates. And from this, we get the launch angle revolution, with dozens of ballplayers reworking their swings in order to launch baseballs at precise, 25-degree angles.
Within a few years this thinking has become common, and analytic-minded sports websites now have periodic reports tracking the swing changes made by star hitters whose salaries eclipse $10 million annually. But this wasn’t always the case.
The early days of swing changes date back four or five years, and the first converts were not young superstars like Mookie Bets or Francisco Lindor. Instead, who were the first players to rebuild their swings from scratch in an attempt to generate more lift? It was the desperate, the has-beens, and a bunch of ex-New York Mets. Just as in the book (and movie) Moneyball, when the analysts came calling with new insights and advice, it wasn’t the superstars who listened—it was the people who had nothing to lose.
Which brings me to your sales organization.
Nearly every sales organization I speak with is attempting to improve sales performance through data and analytics. Whether it be next-best-action guidance, customer segmentation, activity planning or all of the above, there’s a universal hunger to apply new data and analytic techniques to sales in an effort to generate more purposeful activity, better conversations and better results.
At a recent MFEA conference, Bob Cunha of investment management firm Eaton Vance noted that his organization had struggled to drive change through its early analytic efforts. He said it was his poorly performing salespeople who were most receptive to new ideas. After some experimentation, Bob and team adapted their tactics and their ambitions: They worked more closely with poorly performing salespeople, and they shrunk the size of the change they were seeking. I think his experience is instructive.
A question I would ask of any team looking to change sales behaviors through analytics is, who is bearing the risk in making a change?
In most examples I’ve seen, the salesperson bears nearly all of the risk. If a salesperson is asked to reprioritize her customers based on new segment insights, who pays the price for getting it wrong? Not the marketer or data analyst; it’s the salesperson who runs the risk of wasting her time and losing sales (and pay). If the ask is to change the topic of a meeting, the salesperson faces the embarrassment of pursuing a topic that doesn’t resonate with her client. Following a different course of action? It’s the salesperson whose routine is disrupted, whose time is being spent.
None of this is to say that salespeople shouldn’t change, or should be allowed to resist change without cause. Coming back to baseball for a moment, J.D. Martinez, Daniel Murphy and Justin Turner would not be the stars they are today had they not changed. Rather, organizations can be more thoughtful about how they pursue analytics-driven change.
What if you pursued behavioral change first with salespeople who have the least to lose through experimentation, as Bob Cunha and Eaton Vance have done? Or better yet, what if you looked to reduce the risk through guarantees, or by explicitly rewarding risk-taking? Perhaps with the right start, you can build to your own version of the fly-ball revolution and elevate the performance of your entire team.