HR Analytics: Data as a Key to Getting to Know Employees

Data are a highly valuable source of information and firms should use them regardless of whether they are tackling a certain issue or not. HR analytics can deal with the whole lifecycle of an employee. It is of assistance even before the firm hires the employee – during the selection procedure or in setting up processes and searching for and selecting talent, it identifies employees with the greatest potential who will fit in with the team, be successful at the firm and remain in it for a long time.

Analytics processes data in a way that enables us to gain a better understanding of employees’ behaviour, adjust the working environment to them or forecast events before they occur. Besides traditional information about the selection procedure, the employee’s placement, his or her career and day-to-day life, it makes it possible to assess, based on the collected data, how happy the specific employee is at work or what factors affect his or her performance. Data also serve as a basis for efficiency, occupational health and safety, sickness rate and other predictive models. Last but not least, HR analytics is helpful in predicting employee churn and generally in structurally addressing retention.

The first step towards knowledge? You must be aware of the problem and understand its context. In respect of the former, you can make use of smart reporting or data visualisation with an emphasis on the context of the submitted information. In the subsequent step, advanced analytics addresses what the optimal course of action/solution for the identified problem should be.

RETENTION ANALYTICS BESTS EMPLOYEE CHURN

You may know in advance that a key employee will hand in their notice but the news may also come as a bombshell for the firm. In any event, you may make use of retention analytics, which has a clear objective: telling which employees are at the greatest risk of leaving and what the reasons for this are. What is the result? Improved targeting in recruiting new employees, work with the existing ones and elimination of the high costs associated with undesirable employee churn.

What is Retention Affected by?
6 Key Examples

1. Personal time off (a consultancy) – Long-term holiday, unpaid leave or maternity leave are stabilising factors having a highly positive impact on employee loyalty.
2. Salary and promotion (a consultancy) – Employees are motivated by a salary increase to a much greater degree than by promotion; promotion without a salary increase is a negative factor.
3. Fast-track and skip-level promotion (a consultancy) – Many HR managers are under pressure from the new generation, which is more dynamic and seeks to change the terms of promotion to make them more flexible and give millennials the chance to grow fast. However, the results of our analysis show that fast-track and skip-level promotion is, for the most part, a destabilising factor, with the newly promoted employees tending to leave soon.
4. Shift work (Shared Service Centre) – Shift work is time-consuming and mainly organisation-intensive and it has been shown that employees working shifts prefer a certain regime: a reasonable number of shift types per month and, ideally, a minimum of evening and night shifts. If night and evening shifts are inevitable, their volume should not grow over time.
5. Diversity (Shared Service Centre) – Contrary to the current trends, data often show that employees are more satisfied, and thereby tend to leave at a lower rate, if the team is homogeneous in terms of both age and nationality. More than three different nationalities on a single team are a clearly destabilising factor.
6. Team Size (telephone operator) – Employee churn may also highly correlate with the team’s size, whereby it has been shown that the optimal team size is approximately 5-10 members. Smaller or larger teams tend to be riskier in retention terms.

If your firm has 200 employees, of which only five have left in the past two years, it will be difficult for us to design the model and the results will most probably be statistically insignificant. Therefore, not only the overall number of employees is important, but so is the frequency of the phenomenon being observed. These two things are highly interrelated; therefore, if the frequency is low, a longer data history is necessary and, ideally, a greater number of employees. In respect of a high frequency, a short history and a small group of employees is sufficient. In most cases, the bare minimum is at least one year of data with at least 100 employees.

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