Strategy. Hiring the next top performer

A leading FMCG company with a large sales workforce was struggling to balance performance and constant loss of workforce. Productivity was affected since the hiring team was back filling for replacements, who would further take time to settle down.

Analysing data showed that in the sales team, 20 per cent were top performers, 45 per cent were meeting expectations and 35 were average. The parameters which defined a top performer were sales numbers, an above eighty per cent customer satisfaction score, achieving the average order value through up-sell and cross sell numbers.

Most companies with a large sales force do incur hiring costs, training and other managerial processes without assurance of a value add from new hires.

Does the candidate have the interest, attitude, and willingness to perform the job; neither their resume nor the interviews can reveal this. The CEO of this company then used data science to quantify the mindset of the sales guy. A quantified mindset can be used to predict employees who are likely to succeed. Hiring managers use this model to hire candidates with a better fit, decrease attrition and substantially reduce costs.

Several factors have been measured in different ways, but a handful are relevant to the workplace: curiosity, problem solving approach, degree of cooperation, service orientation and aggressiveness.

The underlying concept is that humans have intrinsic attributes that are strengths or weaknesses in different roles. The mindset has been called “attributes” or aptitude, and consists of many factors that can, in fact, be measured.

When we apply these human factors to the performance of a specific job role, in each role, certain combinations of traits do well and others don’t. Attention to detail helps an accountant, but can slowdown a sales guy. Service orientation is often useful for a service role and improves customer satisfaction but might not be effective to make an up-sell.

The focus should be on finding what it takes to achieve goals as set by the CEO. A meta-code called “job fit” with three levels: “good hire,” “bad fit,” “neutral” should be established. Each reason code is assigned to a job fit level. To eliminate bias, modern data science methods need to be used to gather samples, build, evaluate and implement talent models. The data modelling is created with maximum accuracy and sensitivity.

From the existing employees, the management identified a group of 176 top performers based on performance metrics, manager reviews and customer feedback. These top performers were measured with an online talent survey, gathering 10 raw talent metrics that have been validated and calibrated across a wide range.

Next, a mathematical model for success in this role was created. The attribute scores for these top performers were strongly clustered, showing a clear similarity in behavioural and ambition metrics. As a result, a straightforward linear model was created to indicate a percentage match to the scores.

The organisation began to use the match to benchmark its existing top performers as part of its hiring procedure. With predictive power, the system would accurately find more good hires, and accurately reject more of the expensive bad fits.

The writer is CEO, MANCER Consulting

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