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Predictive Analytics: “To Know Our Employees as well as We Know Our Customers.”

If you own or manage a business, one thing occupying your mind must be to find and add great employees to your team. Hiring good employees is not a major problem but retaining them is most certainly one of the things that keep owners and managers up at night. What if there was a way to predict the future of your work force? Who among your star performers will stick around? Which people you can trust with major long term responsibilities? Answers to these questions can help you in working with confidence to achieve the business objectives. Predictive HR Analytics tries to give you the answers to such questions.

Have you heard of Predictive HR Analytics?

These are software for generating models that predict how likely a company’s employees are to stay with their employer. The software uses various factors obtained from the data stored by the employer and uses it to perform sophisticated analyses such as logistic regression, discriminate analysis, neural networks and tree analysis, to predict the picture of employee retention in the near future.

Armed with this information, the employers can focus on the vulnerable employees and find ways to retain them.   Investing time, effort and funds in predictive analytics is a worthwhile investment by all means because the cost of hiring new employees is much higher than retaining the existing ones.

Predictive analytics give executives insight into who is most likely to resign, along with reasons they’re at higher risk…With these insights, executives can take targeted actions designed to retain specific employees, thereby focusing their limited resources on the individuals who are most at risk and whom they most want to keep,” says John Houston, a principal in Deloitte Consulting LLP’s Actuarial, Risk and Advanced Analytics practice.

How Predictive Analytics Help in Decreasing Employee Turnover?

The software uses various pieces of data including the role of an employee, tenure of the employee, age, salary, marital status, number of sick/casual leaves, results of performance appraisals, the amount of the salary increments, vacation time, how far they live from the office and several other factors. Using specific algorithms, a risk score is calculated for the employees, which shows the likelihood of an employee leaving the company in the near future.

The employers can benefit from this information by:

  • Performing a SWOT analysis to identify the vulnerabilities and try to mitigate them by proactive planning and actions.
  • Assessing the turnover risk on an organization wide level and build the learning in the strategic business plans to ensure that the projects and deliverables will not be compromised due to unexpected departure of the key resources.
  • Not only have the figures of turnover at hand but also become able to understand its causes and calculate its probability. This information can be used to develop employee retention programs, which address the real issues and not just work on the cosmetic aspects.
  • Using the information acquired to predict the critical skill sets, which may be at risk and devise strategy to maintain the required number of resources for the identified skills.

Great talent is scarce, hard to keep and in high demand. Given the well-known direct relationship between happy employees and happy customers, it becomes of utmost importance to understand the drivers of employee dissatisfaction. In doing so, predictive analytics can be a core strategic tool to help facilitate employee engagement and set up well targeted employee retention campaigns, “said Professor Dr. Bart Baesens, associate professor at KU Leuven University (Belgium).

A businesses’ most important asset is its people, so the employers should use the available tools to manage their most important asset in the best possible way.

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Sharon Stuart offers expert writing guidance to students via Dissertation Mall, a well-established firm that offers assistance with Dissertation Proposal Writing all over the UK and beyond.

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