Predictive Analytics Makes Sales Rep Hiring a Success

Predictive Analytics Makes Sales Rep Hiring a Success

Predictive Analytics Makes Sales Rep Hiring a Success

Early in my business experience I encountered a curious mystery. A tightly-run inside sales team had an ocean of standardized desks and cubes. Each sales rep had the same scripts to follow, the same goals, the same computer screens and the same incentives for success. They made a lot of outbound calls to sales prospects with little variety. 

Yet the success of these sales reps was remarkably varied. Some were unstoppable, high activity, sales machines. Others were mediocre — they made less calls and less sales. Others provided too much service for the revenue they generated. Another group quickly burned out, had to be pushed to keep making calls and sales, they required the most manager attention, training and coaching. This last group also had the most dissatisfied customers.

Here is what struck me. These sales reps were saying the same words to a wide sample of prospects, but they had very different results. 

This was what economists call a “natural experiment” everything was set to be equal, except for some “it” factor inside of the sales rep themselves. And, that “it” factor consistently delivered results dramatically affecting sales results, activity, compliance, errors, and attrition metrics. What was “it?”

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Other business domains use advanced analytics to find and predict these kinds of factors. They use these factors because they are so predictive of success. 

Marketing, in particular, has made a science of sorting out consumer signals into personas, to optimize outbound messaging and offers. Medicine, finance, elections, and industry all use similar predictive approaches to predict how people will behave.

We can apply the same analytical methods to find and foster this “it” factor for sales representatives. What would this look like?

Most sales organizations spend weeks or months training their reps. Some training, for example in the technical, insurance, pharmaceutical or other industries is complex and lengthy extending into months. Some new reps must complete difficult certification exams before they can begin selling or can make that initial phone call.

Many sales organizations find a disappointing and expensive number of sales reps that terminate earlier in the process. This is a worst-case scenario for the business — all expense, no value.

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A predictive approach would include a standardized way of including aptitude, and other factors for candidates who successfully made it beyond this “three months ramp up time” window, versus those who didn’t. We would build and validate a rigorous predictive model based on these factors that is able to — literally — quantify the “it” factors candidates who repeatedly perform tend to have.

The most powerful way to use a predictive model is for pre-hire candidate selection. Each new candidate would be evaluated against the model, which would calculate a predictive score. In this case, the score would be the probability of staying in a role for more than a year, or the probability of achieving their quota, and the like.

 



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