Frequently Asked Questions in relation to Talent Acquisition & Retention

By Alex Hellemons

When an organization embarks on data-driven talent acquisition and retention journey, there's a lot to take in. Below, we address some frequently asked questions.

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How to calculate employee retention?


Retention rate = (Number of employees at the end of the period – Number of new hires during the period) / Number of employees at the beginning of the period

For example, if a company had 100 employees at the beginning of the year, hired 10 new employees during the year, and had 92 employees at the end of the year, their retention rate would be: (92 – 10) / 100 = 0.82 or 82%

This means that 82% of the employees who were employed at the beginning of the year were still employed at the end of the year.

This formula can be used to calculate retention rate over any period of time, such as monthly, quarterly, or annually.

It’s worth noting that measuring retention rate helps you understand the percentage of employees who stayed with the organization over a period of time. It’s a good way to understand how much employee turnover your organization is experiencing, but it doesn’t necessarily tell you the reasons behind why employees are leaving, or how to improve employee retention. To understand these factors, onboarding, engagement and exit interviews can be used in combination with data science techniques.

How to calculate employee attrition?

Attrition rate = (Number of employees who left during a period / Average number of employees during that period) x 100

For example, if a company had 100 employees at the beginning of the year, and 15 employees left during the year, the attrition rate would be: (15 / 100) x 100 = 15%

This means that 15% of the employees who were employed at the beginning of the year left the company during that year.

This formula can be used to calculate attrition rate over any period of time, such as monthly, quarterly, or annually.

It’s worth noting that measuring attrition rate helps you understand the percentage of employees who left the organization over a period of time. It’s a good way to understand how much employee turnover your organization is experiencing, but it doesn’t necessarily tell you the reasons behind why employees are leaving, or how to reduce employee attrition. To understand these factors, you should use other metrics such as employee engagement and satisfaction, exit interviews, and employee turnover costs.

Is it allowed to predict employee flight risk at an individual level?

It is possible to predict employee flight risk at an individual level. Flight risk can be predicted by analyzing data such as employee engagement, job satisfaction, turnover history, and other factors that indicate an employee’s likelihood of leaving. But, it’s important to be aware of the legal and ethical considerations when doing so.

From a legal perspective, employers must comply with laws and regulations related to discrimination, data privacy (GDPR), and equal opportunity. From an ethical perspective, employers must consider the potential negative impact on employees’ privacy and well-being. Employers must consider the potential impact on employee trust and engagement and ensure that they are transparent about the process and their intentions.

If predicting flight risk at an individual level is not preferred, predicting flight risk at a higher level (e.g. team), can be considered.

Is it allowed to use employee survey response data for data science purposes related to employee retention and attrition?

Employee survey response data is a valuable source of information to understand employee engagement, job satisfaction, and other factors that impact employee retention and attrition.

It is generally allowed to use employee survey response data for data science purposes related to employee retention, as long as the data is collected and used in compliance with laws and regulations related to data privacy, discrimination, and equal opportunity.

When collecting employee survey response data, employers must ensure that they have the appropriate legal basis for collecting and processing the data, such as obtaining employee consent or relying on a legitimate interest. They must also ensure that the data is collected in a fair and transparent manner, and that employees are informed about how the data will be used.

It is also important to ensure that the data is used in a way that does not discriminate against any protected groups, and that appropriate measures are in place to prevent unauthorized access to or sharing of the data.

In addition to legal and regulatory compliance, employers must also consider the ethical implications of using employee survey response data for data science purposes. Employers should be transparent about their intentions and the methods used. Employers should also ensure that the data is used in a way that respects employees’ privacy and well-being.

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