With better predictive power, companies would be able to find better hires and accu-rately reject more of the expensive bad fits
Data science has a long and mature history of measuring the human attributes of consumers to predict advertisement responses, upsell, cross sell and other buying patterns. Employees are equally human and have even more impact on the success or failure of a venture, which makes it meaningful to use data science approach here as well to model and optimize teams and companies. Here’s an illustrative example of how organizations can take benefits from talent analytics:
One of the leading FMCG companies had a large sales workforce struggling to achieve the right balance between performance and the constant loss of people. Productivity was getting hit since the hiring team was back filling for replacements who would further take time to pick up.
On analyzing the data, it was concluded that among the sales team, the performance breakup was: Top Performers-20 per cent; Meeting Expectation-45 per cent; Average-35 per cent.
The parameters for a top performer were: High sales numbers, above 80 per cent customer satisfaction score, achieving average order value through up-sell and cross sell numbers. The company did invest on training all of its sales fleet, however the impact was not substantial.
Most companies with large sales force do incur the cost of hiring, training and other managerial processes without any assurance of the value add to the organization by the new hire. The question is whether the candidate has the interest, attitude and willingness to perform the job since most of these attributes are not clear from his resume and don’t reflect explicitly even during the interviews.
The CEO of this company took the initiative to use data science to answer the question, “Is it possible to quantify the mindset of the sales guy?” The quantified mindset can be used to predict the most likely employees to succeed in performing. Hiring managers use this predictive model to hire better fitting candidates, decrease attrition and substantially reduce costs.
The most important aspect of analytics is predicting human behavior and actions. Several factors have been measured in different ways, but only a handful are relevant to the workplace.
Few attributes that can be measured for the given situation are: Curiosity, problem solving approach, degree of cooperation, service orientation and aggressiveness. Humans have unique personal, intrinsic attributes, different from skills and training that can be either strengths or weaknesses in different roles. The mindset has been called as “attributes” or aptitude here, and consists of many factors that can in fact be measured apart from the above mentioned. It’s interesting to know that when we apply these human factors to the performance of a specific job role, certain combinations of traits do well and others don’t. For instance, attention to detail helps an accountant, but can slow down a sales guy. Service orientation is often useful for a service role and improve customer satisfaction but might not be effective to make an up-sell and so on.
The focus is on finding what it takes to achieve the performance as mentioned by the CEO so a meta-code called “job fit” with three levels: “good hire,” “bad fit,” “neutral” is established. Each reason code was assigned to a job fit level.
To eliminate bias, modern data science methods need to be used to gather samples, then build, evaluate and implement talent models. The data modeling is created with maximum accuracy and sensitivity. It is also worth noting that models are built specific to a given role and do vary for each company.
From the existing employees, 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 of adults.
The next step was to create a mathematical model for success in this role. The attribute scores for these top performers were strongly clustered, showing a clear similarity in behavioral and ambition metrics. As a result, a straightforward linear model to indicate a percentage match to the scores was created. The organization began to use the match to benchmark its existing top performers as part of its hiring procedure.
With better predictive power, the system would be able to find better hires, and accurately reject more of the expensive bad fits. By deploying predictive analytics in business, one can get the right fit with a lasting performance and high tenure.