Employee Attrition and Performance Analytics Dashboard

Why are Employees leaving?

Every organization wants its valuable employees to be a part of their organization for a long period. Still, when many employees start leaving the organization, it will be a concern for the organization. The organization should know the possible reasons why their employees are leaving.

Solution

The organization will collect data of all the employees who have left and who are still working there and, with the help of analysis, try to find out the possible reasons for employee attrition so that the organization can make the required changes in its policies and environment retain its best employees.

Employee Attrition

The organization will list out the possible factors that could be the reason for attrition. Some of them are

• Less job involvement can be tracked by the number of projects an employee worked on.

• Career growth includes the factors like 

• Promotions,

• Salary hike

• Performance

• Job satisfaction

The above dashboard will give organizations deep insights into the factors that could be the possible reasons for the employee to leave the organization. 

Here, you can get the information about the distribution of employees' salaries and compare those who have left the organization and those who are still working. Also, you can get an insight into the performance of the employees and the job involvement, which is depicted from the number of projects done by the employees in the organization.

User Story

The first two graphs represent the distribution of salaries.

• It can be observed that the salary of most of the employees who have left the organization falls in the range of 60,000 - 80,000.

• Also, the box plot signifies that the salary range of employees who have left and are still working are almost the same. It can be concluded that salary may not be the reason for attrition. 

• From the pie chart, It can be observed that most of the employees have performed average and good and only 7% of the employees were excellent. Most of the employees did not perform very well.

• In the last bar chart, it is observed that employees who have left the organization are involved in fewer projects than the employees who are still working there. Less job involvement will be a cause of employee attrition.

With the help of the dashboard, the organization can learn more factors that would help the organization find the reasons for attrition. Here, you can get the information about the promotion score, i.e., whether the employees are getting enough appreciation and compensation for their good work or not from the organization. 

You can also get an idea about the satisfaction level of employees of the organization and the number of accidents they have met with. 

User Story

• It is observed that the count of employees who were promoted and who have left the organization is very low. Even the Internal Promotion Rate of the organization is also very low.

• The average satisfaction level of employees who have left is very low than that of employees who are still working in the organization.

• We can observe that workplace accidents are high, and 27% of employees who met with accidents left the organization. 

End Customer Value

Here, the analysis will help the organizations deepen their understanding of the causes of employee attrition. It will also help the organization make appropriate decisions based on the data collected and analyzed and determine which steps the organizations should take to improve for the betterment of the organization.

Why XenonStack?

Xenonstack gives you the demo on employee attrition and performance analytics to analyze and find out the reasons for attrition for your organization. The organization will get insights related to Employees promotion rate, performance score, salary information, work accidents, etc. The dashboards will provide you with all the necessary graphs and information according to your needs.

Xenonstack can also provide you with the prediction of future employee attrition rates based on past data by using machine learning models and factors we analyzed that make your organization proactive while making decisions for its employees.