Solving the Sales Hiring Mystery with Predictive Analytics


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Early in my business experience, I encountered a mystery. A tightly run inside sales team had an ocean of standardized offices with 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.

Sales success was remarkably varied; from unstoppable sales machines to those who were mediocre, making less calls and less sales. Others provided too much service. Yet another group burned out, had to be pushed to keep making calls and required huge manager attention, training and coaching. This last group also had the most dissatisfied customers.

Here is what struck me. These sales reps were largely doing the same things with a wide sample of prospects, but with radically different results.

Economists call this a “natural experiment” – everything was set to be equal, except for some “it” factor inside of the sales rep. themselves. That “it” factor consistently delivered results dramatically affecting sales, activity, compliance, errors, and attrition. What was “it?”

Predicting Human Behavior is Well Documented
Other business domains use advanced analytics to find and predict these kinds of factors, using these factors because they are so predictive of success. Marketing 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 human behavior. We apply the same analytical methods to find this “it” factor in sales representatives. Here’s what it looks like.

Example: Preventing Early Attrition
Most sales organizations spend weeks or months training reps. Technical, insurance, and pharmaceutical training can be complex and lengthy extending into many months. Sometimes difficult exams are required before selling or making that initial phone call. Sales organizations find a disappointing and expensive number of reps terminate early, a worstcase scenario for the business – all expense, no value. A predictive approach includes a standardized way of measuring Aptitude, and other factors for successful candidates who made it beyond “ramp up time”, versus those who didn’t. We build and validate a rigorous predictive model based on these factors that is able to – literally – quantify the “it” factors of top performing employees.

Predict Performance and Attrition Pre-Hire
The most powerful place for predictive models is pre-hire, during candidate selection. Each candidate is evaluated against the model with a predictive score. The score could be the probability of staying in a role for a year, or the probability of achieving quota, and the like. One candidate might have a 73% probability of staying in the role for a year, and another 32%. (Or we could predict a candidate’s sales call activity, or product mix sold – all pre-hire). These predictions guide the recruiting team, along with other factors.

Sales candidates more likely to stay in their role for more than a year, are typically sales reps that move beyond ramp up and into selling where they generate revenue and employer value, leading to less candidates wasting time in a career they ultimately don’t want. The sales organization spends less money recruiting, acquiring, training and coaching sales reps. Performance goes up. Attrition goes down. Customers feel it. Stockholders see results.

Everybody wins.

Machine Learning Models
The predictive modeling process, by design, provides ongoing feedback to the recruiting and hiring process. It connects hiring with sales results. Math and modeling methodologies keep everyone honest; if something is predictive, it stays in the model. If it is random and doesn’t predict, it’s gone.

When new products or solutions are added, if a merger occurs, or the economy improves or gets worse, the model detects these differences and learns. (Interestingly this is a major disadvantage of using an industry model that is static.)

A quality model informs recruiters of the qualities that matter and others that don’t. This approach can additionally be useful for entry level candidates with little job experience – there isn’t much to go on except for that “it” factor.

Multiple Predictive Models
We have bigger goals beyond staying in a role for a year. We want sales reps that exceed their quota, sell the right mix of products, have a sense of urgency about closing the quarter and year end and to stay on the job for years. Your sales organization – can – have – more – of – these.

Models can, and should be built to address these KPIs. The goal is to use predictive tools to screen in both a) high potential candidates, and b) to screen out low potential candidates. Your approach could vary with location, job market conditions, and changing business imperatives.

The smartest users of predictive models have a portfolio of predictors for each candidate, so that hiring professionals can intelligently balance potentials and business needs.

High volume sales organizations are ideal for this kind of work because of the sales performance data they gather today. Almost all needed data exists today and can be easily found. It’s gold waiting to be mined – so you can solve the Sales Rep hiring mystery.

Greta Roberts
Greta Roberts is the CEO & Co-founder of Talent Analytics, Corp. She is the Program Chair of Predictive Analytics World for Workforce and a Faculty member of the International Institute for Analytics. Follow her on twitter @gretaroberts.


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