Case Study: Identifying the Causal Factors for Employee Attrition

Background A company was experiencing a high rate of employee turnover and wanted to address the issue. They provided our team with an employee dataset containing information on employee experience, certifications, commute time, promotions, and more. The company's goal was to identify the reasons behind employee attrition and predict the likelihood of an employee leaving.

Problem Statement Our task was to analyze the employee dataset and identify the causal factors for employee attrition, as well as develop a model to predict the likelihood of an employee leaving the company.

Solution Approach We used PowerBI to visualize the data and identify the most relevant variables for different levels of hierarchy. This helped us to narrow down the important factors for employee attrition. Using this information, we built a prediction model to identify the likelihood of an employee leaving the company.

Results Our solution successfully identified the causal factors for employee attrition and predicted the likelihood of an employee leaving the company. The company was able to take proactive measures to retain their employees and experienced a significant decrease in employee turnover.

Conclusion By using data visualization and predictive modeling techniques, companies can identify the causes of employee turnover and take steps to retain their employees. Our approach using PowerBI and prediction modeling can be applied to similar problems in predictive analytics, providing valuable insights for businesses.