Top performers in sales roles can sell over 10 times more than low performers, according to a new study. But what makes a top sales performer and can recruiters really identify which applicants are more. One of the most exciting developments in talent management is the use of predictive talent analytics, as these can help you make better talent decisions. David Barrett explains how predictive talent analytics can help HR teams to make better talent decisions.
Nearly every organisation will have a wealth of talent and performance data highlighting the effectiveness of each individual in their role. This could be data and metrics about their productivity, their sales performance (if relevant), their engagement, their line manager and peer ratings, their customer satisfaction ratings, their workplace behaviour, their safety record, their punctuality, their attendance rate and their disciplinary record.
Many employers will have also used psychometric assessments to collect data about their employees, in terms of their abilities, their competency levels, their values, their motivations and their personality traits. By putting these two sets of data together, HR teams can gain valuable and actionable insights - called predictive talent analytics – which can be used to identify, engage and recruit people with the highest potential.
Take, for example, Swiss company localsearch, which produces online, mobile and printed directories of business listings and local area information. To grow its revenues, the company needs high performing sales representatives. However, many of the candidates who apply for sales jobs at localsearch have little or no sales experience, so how could they identify which applicants had the most potential?
To answer this, our team at cut-e (now Aon) undertook a study of localsearch’s high and low performing sales representatives. The high performers were those with the 25% highest commission; the low performers had the 25% lowest commission. We examined the results of a personality questionnaire completed by these employees and compared the data accordingly.
The results revealed that seven key characteristics ‘predict’ sales success at localsearch. In other words, the study showed that it is possible to identify specific personality characteristics and competencies that will make someone a successful sales representative at the company. For instance, top performers at localsearch were found to be:
- Less sociable than low performers. They prefer to work alone and they’re less likely to be distracted by networking and keeping in touch with other people.
- Higher achievers than low performers. They’ll set themselves ambitious goals and will strive to accomplish and exceed sales targets.
- Less ‘imaginative’. They’re more likely to stick with conventional, tried and tested solutions rather than look at ways to change.
- Less autonomous. They’re likely to adopt a consensus approach before implementing new projects.
- They’re more driven by recognition and are more motivated by the acknowledgement of their achievements.
Armed with these insights, localsearch has changed its recruitment process and is now selecting candidates who fit this ‘success profile’. The benefits have been significant. They’ve halved the time it takes to recruit new sales people and this alone is saving them €5,300 per person; they’re saving time and money by not recruiting and training people who are likely to be low performers; their new staff are not only able to contribute to the business much faster, they’re also consistently making more sales than the low performing staff – and this has increased the revenues of the business.
Juerg Gabathuler, localsearch’s Head of Management Development, said: “Using a psychometric assessment with proven predictive value means that HR is able to support our line managers much better. cut-e’s personality questionnaire automatically generates an interview guide which helps our line managers to probe for the relevant competencies. This is a further step that helps us identify whether an applicant is likely to be a top or middle performer.”
The lesson here is that predictive talent analytics can help HR teams to make better talent decisions. What’s more, the data that’s required for this is either right there in front of you or it can be easily found. All you need is help to uncover it, to piece it together and to utilise it effectively.
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