Retaining Your Top Talent Through Analytics

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.