HR analytics are reported HR data after gathering and analyzing. It allows organizations to improve talent selection, workforce processes, the employee experience, and other HR-related activities.
Why are HR analytics important?
HR analytics help companies improve processes related to payroll, benefits, hiring, employee onboarding, employee performance, company culture, and more. When HR teams collect and analyze this data, they gain a better understanding of the company’s performance and assess if they’re using their resources wisely.
HR analytics can indicate employee satisfaction levels, if they feel valued, are meeting performance standards, whether they are happy with their managers, and so on.
What are the four types of HR analytics?
- Descriptive analytics- HR professionals analyze data patterns or inconsistencies, such as behaviors, abnormalities, strengths and weaknesses.
- Diagnostic analytics- Diagnostic analytics provides an explanation for the data patterns.
- Predictive analytics- This gives employers an idea of future events. In this stage, professionals look at past and
present data to identify patterns and come up with models for what could happen in the future.
- Prescriptive analytics- This stage uses predictive analytics to come up with ideas for how to move forward and achieve success.
What are some examples of HR analytics?
HR teams can use analytics to assess several processes. One example of such a process is the job offer acceptance rate or the hiring team’s success rate when it comes to signing talent. If the company has a low offer acceptance rate, it could mean they need to re-evaluate their hiring process or offers to candidates.
Example: If the hiring team received 20 job offer acceptances during a certain year out of 40 offers, the offer acceptance rate would be 50%.
Another example is retention rate.
Retention rate is important for businesses because it shows how well they are able to keep their talent. To calculate retention rate, companies divide the total number of employees who stayed with the company through a certain time period by the headcount the company started with. Then, multiply that number by 100 to get the employee retention rate.
Example: If a business had 250 employees at the beginning of Q1 and 175 by the end of Q2, the employee retention rate would be 70%: (175/250) x 100 = 70.
Employers may also want to examine revenue per employee, which measures how much money the business brings in for each employee (factoring in salary, benefits, and so on). In this calculation, businesses can see whether they’re generating revenue for each hire. The calculation for revenue per employee is: Total revenue in a given period / Current number of employees in the same period.
Example: If a business brings in $20 million in revenue and had 200 employees, their revenue per employee would be 100,000.
What are some best practices for HR analytics?
- Align HR analytics with business goals- collaborate with leadership to understand how HR analytics can drive business outcomes (ex. if company is struggling with low retention rates, HR’s goal may be to focus on employee advancement).
- Get the right tools- the right tools can help organizations efficiently organize and manage all kinds of employee data, from engagement levels to performance reviews.
- Encourage data-based decision-making- encourage management to make data-driven decisions to ensure HR and management are aligned on measuring, gathering insights, and coming up with action items based on grounded, reliable sources.
- Transform data into action- Collecting data and reporting on it is only one step of this process. The next step is to take those insights and determine the right course of action.
- Gather feedback and evolve- Get feedback on the data gathering process from employees and tweak the approach as needed.