So far we have we have met and learned a bit about Jenny Dearborn and her book, Data Driven. That was in the first installment of this blog series. In part two we dug a little deeper into data analytics itself and explored its different stages or types. In this third post, we want to focus on one of the primary benefits of data analytics—performance coaching.
Actually we said that incorrectly. Performance coaching is not really a benefit of data analytics. Rather, analyzing data yields the insights that bring coaching to life and make it effective. Unless reps understand their performance gaps, they have little chance of doing anything to correct them. Data analytics gives reps (and their coaches) a window into what’s happening performance-wise—and why.
While data analytics may seem cold, impersonal and detached, the impact can be just the opposite: warm, personal and highly engaged. Or as Jenny Dearborn says, “The Holy Grail of data analytics, at least with respect to sales, is to increase the effectiveness of the individual… . The real power comes when big data gets personal.” (1)
In Data Driven, Dearborn tells the story of how an imaginary chief sales officer (Pam Sharp) is hired to turn around the lagging sales of an imaginary company (Trajectory Systems). A key player on the task force Sharp sets up to accomplish her objective is Cathie Martinez, who directs sales enablement and training for Trajectory.
As data analytics begins to take hold and the whole effort gains traction, we see how Martinez reshapes her training efforts and adds performance coaching to the mix. Analytics gives her insights and informational ammunition to fight apathy and inertia. See for yourself.
“A few months ago I started implementing individualized training plans based on the deficiencies that our analytics revealed, but as usual the reps have been resisting. They keep saying that they don’t need more training. But with this data I can point to exactly who needs what, as well as why he needs it. We can individually coach reps in the specific areas where they most need to improve.” (2)
What she doesn’t say, except between the lines, is “Why try to shoehorn all reps into one-size-fits-all training when data analytics can pinpoint for us precisely what each rep needs to address performance gaps on an individual basis?”
Stepping outside of the imaginary sales enablement manager she created, Dearborn explains how data analytics can put so much power behind coaching. “Because their relationships with the people they coach are built on trust, coaches can be particularly helpful to a predictive analytics initiative, which also depends to a significant degree on trust. It takes a great deal of trust, for example, for reps to change longstanding behaviors based on the recommendations of a data analytics model rather than on their own intuition and experience. People are much more likely to accept and act on data-based input when they have a coach in their corner to reassure and encourage them.” (3)
Dearborn also makes the point that data builds confidence and provides motivation. Because it is a living link between an ailing rep’s potential success with other reps’ actual success, data analytics helps build hope. It provides a mirror in which a sluggishly performing rep can see him or herself sharing the success that fellow reps are enjoying by following where data leads. What’s more, the input is based on hard, objective facts extracted from the selling environment—not someone else’s gut feeling or hunch.
The importance of coaching in Data Driven culminates in a chapter devoted to the transformation of another fictional character, this time a bottom-tier sales reps named Hunter Cooke. The chapter relates how Cathie Martinez personally coaches Cooke, by sharing data that suggests he is spending too much time on the wrong products and the wrong customers. It’s a new data analytics-powered take on an old story—being busy doing the wrong things.
Obviously, she shares a lot more input to turn Cooke around. But what needs underscoring here is not just the volume of information data analytics provides but also the nature of the facts—real and irrefutable telemetry from how Cooke has been selling. In short, embedded within the data snapshot of how he has been selling are all the answers to how he should be selling. All he needs is someone to point them out and explain them.
Building a data analytics capability and integrating it successfully into an organization has the power to transform, but it’s a daunting task—just as it was for the fictional Pam Sharp. In our final post of this series, we’ll share Jenny Dearborn’s suggestions for how to go about it.
(1) Dearborn, Jenny. Data Driven. Hoboken, NJ: John Wiley & Sons, Inc., 2015. P. 188.
(2) Ibid, pp. 158-159.
(3) Ibid, p. 168.
Before You Start Crunching Numbers
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