Retaining Your Top Talent Through Analytics

Retaining Your Top Talent Through Analytics
Last Updated: June 17, 2022

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Employee turnover has a huge impact on organizational success. When an employee leaves the organization, the effects are felt in all departments. Directors and HR leaders are trying their best to solve this problem. But, before they try to solve it, they have to understand clearly with an unbiased mind what is really causing employee turnover within their organisation. Employee turnover is the proportion of the employees who leave an organization over a set period. It’s also referred to as churn and includes both voluntary leavers (those that resign or retire) and involuntary leavers (as in the case of redundancies, poor performance, or other cases where the employee was forced to leave the organization).


We can all agree that the success of many businesses relies on positive employee retention. When companies experience high employee turnover, their budget and operational capabilities are affected. Turnover costs involve recruiting, selecting and training new hires. When vital positions remain unfilled for extended periods, current staff become taxed and productivity suffers.


When key employees that other employees rely on to get help or work done leave, it disrupts all social and communication structures. This disruption results in loss of productivity. A research done by Sagie and team in 2002 found that a high-tech firm lost 2.8 million US dollars or 16.5% of before-tax annual income because of employee turnover. These researchers also found that turnover reduced profits, increased the organization’s total risk, and triggered more turnover among the organization’s other employees.



One of the surveys we did at a workforce planning workshop pointed out that many organisations do not use analytics to reduce employee attrition. This is because they do not know-how. There are some steps that can be followed to carry out an employee attrition project.


First, you have to identify relevant data from within your organization. This data is data concerning your employees. The data may include employee position, experience, age, income, marital status, maternal leave, number of sick days taken, and performance reviews. This combined data should include both employees who left and those still within the organization. Clean and make sure the data is ready for analysis to avoid the issue of garbage in garbage out.


Second, discover what happened. We call this descriptive analytics. The first step in solving most problems is figuring out what’s going on. Did into the data and try to find out who exactly is resigning: Is it your top performers? Senior managers? How much revenue did we gain or lose? Amount of costs incurred by the department or location? When many of the employees who leave are your best and brightest, they take all their skills, knowledge and connections with them, putting your organization at a disadvantage.


Third, discover why it happened. We call this diagnostic analytics. Using the information you discovered above (descriptive analytics), you might be able to reach some conclusions about the situation. For example, from the above analysis, you may discover that contract employees are leaving the organization more than permanent employees. The conclusion might be because contract employees are not getting overtime pay.


Forth, discover what will happen. We call this predictive analytics. Here you will be trying to find out what will happen if one factor is changed. You will be trying to be proactive by seeking our future trends.

For example, offering overtime payment will positively impact retention. As with many companies, you don’t have to make all the changes once. You need to implement the changes logically manner and use the resources wisely. It also helps individuals accept and embrace change.


Fifty, discover what you should do. At the prescriptive analytics stage, decisions are made. You have to consider all the options as an organization. All the action plans provided at this stage should be data-backed. For example, an overtime rate of two times the normal hours will increase tenure.


Many studies have shown that employee turnover significantly affects an organization’s performance. It changes the organisation’s direction when a top executive leaves. For example, when Steve Jobs was fired from Apple, the organization changed direction and lost its actual cause. The same happened to Starbucks when Howard Schultz. With all the benefits and savings that come with applying analytics, it’s time for organisations to apply this technique in managing their employees.


Benjamin Sombi is a Data Scientist, Entrepreneur, & Business Analytics Manager at Industrial Psychology Consultants (Pvt) Ltd a management and human resources consulting firm.


Benjamin Sombi
This article was written by Benjamin a Consultant at Industrial Psychology Consultants (Pvt) Ltd

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