In parts I, II and III of this blog series about Jenny Dearborn’s Data Driven, we explored data analytics, its stages and its ability to generate information and insights to drive powerful game-changing performance coaching. In this last post, we want to let Jenny Dearborn summarize the steps in this Predictive Action Model (PAM) as a step-by-step guide to building your own data analytics effort.
(You will recall that in the storyline Dearborn uses to explain the potential of data analytics, she relates the story of Pam Sharp, the fictional chief sales officer of the imaginary company, Trajectory Systems. Therefore, PAM is both an accurate description of the analytics process created and a compliment from the “team” to Sharp for turning the company’s sales around.)
Before ever diving into data analytics, though, Dearborn lays out at the very beginning how the smart use of data can lead to better decision-making. And in the end, that means improved performance in the marketplace. “In this age when companies are competing on a global scale at electronic speed, maintaining a competitive advantage is increasingly difficult… . That’s the kind of advantage data analytics can give you.” (1)
Data Driven takes us on a year-long journey with Pam Sharp and her imaginary team of visionaries who harness data to change the direction of their company. Now, just as the Trajectory team is beginning to see the fruits of their labor, seems like the ideal time to share a summary of how they accomplished their objective.
“1. Collect Sales Rep Data
Brainstorm all the variables that drive sales rep success. There are often called key performance indicators (KPIs).
Collect as many variables (KPIs) as you can.
Gather metrics. For example, from your HRIS, gather the number of years the rep has been with the company, and the number of years the rep has been in sales. From the CRM, gather the rep’s average deal size and … typical product mix.
“2. Analyze the Data
Use statistical techniques to understand what differentiates above-average performers from average and below-average performers… .
Perform … (1) descriptive analytics, to gather intuitive input about how the KPIs are related to attainment, and (2) diagnostic analytics, to determine which KPIs are most important to attainment.
Use the top KPIs and different rep groups (top, middle and bottom) to come up with profiles of performers. Use these profiles to help set KPI benchmarks. (This helps identify which metrics are the major predictors of success in order of priority. From this information, models can be built that will predict what attainment group each rep will fall into.)
Analyze the performance of each sales rep against these metrics to identify gap areas and suggested personalized enablement plans.
“3. Forecast Performance
Measure past sales rep performance against goals to train your model to predict which reps will not meet their quotas. This helps create a shortlist of reps to focus on.
Test the outcome of the predictions for accuracy to modify the model as needed.
Apply the same techniques to identify which deals may or may not close, and which prospects may or may not buy.
“4. Recommend Action
After identifying gap areas through a trained model, make recommendations to each rep. for example, recommendations might include (1) which products to focus on, (2) what percentage of the pipeline should have partner engagement, (3) what percentage of the pipeline should be generated via bundled product deals, (4) how much focus to put on new business, (5) which training courses to attend, etc.
Create personalized action plans and coach reps to understand the data and follow the recommendations… .
Gain insights to improve operations, product development, lead generation, and other activities. Opportunities for continuous improvement exist in all groups that support sales.” (2)
Jenny Dearborn is data driven. And you can’t read her book without beginning to wonder if everyone in business, or at least in sales, isn’t also. After all, you can’t really opt-out. You can’t just choose to be unaffected by data because if you don’t embrace it, tame it and aim it, someone else will—possibly to your disadvantage. That’s why the first five words of Data Driven work equally well as a summary and closing: “A sales revolution is coming!” (3)
(1) Dearborn, Jenny. Data Driven. Hoboken, NJ: John Wiley & Sons, Inc., 2015. P. xiii (Preface).
(2) Ibid, pp. 207-209.
(3) Ibid, p. xi (Preface).
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