Introduction

Employee turnover can impact morale, engagement, and company culture, and may have a domino effect and cause more employees to leave. For this project, I took on the role of a BI analyst to solve the business task of increasing employee satisfaction and retention. I used SQL joins and aggregate functions to perform data exploration and uncover KPIs and other metrics and Python to perform data wrangling, exploratory data analysis, correlation analysis, and modeling building. I also built machine learning classification models in Python to predict employee attrition. Finally, I loaded employee data into Power BI for visualization and analysis.

This study resulted in data-driven insights and recommendations for IBM to retain their employees, increase employee satisfaction and predict employees at risk of churning.

SQL code

Python code

Dashboard

Article Explaining this Project

Data-driven Insights

KPIs and Metrics

There are 1470 employees at IBM. 1233 of those employees have stayed with the company while 237 have churned. Of the 1470, 31% of employees are very satisfied with their jobs.

The attrition rate is 16.12%. Males are churning the most at IBM. Employees between the ages of 30 to 39 are churning the most. The second group is aged between 20 and 29.

By department, the Research & Development department has the most churners. The Sales department is close behind it. Lab Technicians, Sales Executives and Research Scientists are roles experiencing the most churning. These roles are in the Research & Development and Sales departments.

Satisfaction on the Job

Each satisfaction rating is on a scale with 1 being low and 4 very high (1: Low, 2 :Medium, 3 :High, 4 :Very High). Employees are between Medium and High for satisfaction with environment, relationships, work life balance and their overall job. Job satisfaction is the highest among employees in the sales department and in the age group of <20. This shows that job satisfaction isn’t indicative of employee churn.

Correlations & Other Relationships

There is a strong correlation between years at company and years with current manager and years at company and years in current role. The scatter plots show that the longer an employee stays at IBM, the longer they stay with the same manager in the same position. This is indicative that lack of promotion may be causing attrition. When we click on the group that has the most churning (30–39), the correlation is even stronger (~80%).

There isn’t much opportunity for growth or promotion, especially for those between the ages of 30 and 39. This same age group received the least training time. Males also received the least training time and these two groups experience the most attrition. This is indicative that lack of professional training may be a key driver of employee churn.

Recommendations

From this analysis, I found that the Logistic Regression classifier is the best predicting the risk of employees churning when compared the the Decision Tree and Gaussian NB models that my team suggested. To improve accuracy I could try different models or use more training data.

The correlation analysis told us that the key driver of employee churn is lack of promotions.

To increase satisfaction on the job, the company can begin with improving work to life balance. They can implement changes such as offering flexibility in work schedules, location or by allowing remote work. They can encourage work break and consider opportunities where employees can receive time off. Support for parents such as accommodating child care needs or parental leave benefits, may also improve work to life balance.

IBM can host events or create more team work opportunities to foster good relationships between employees. Smarts hires, best practices for conflict resolution and diversity training may improve environment satisfaction.

Our key drivers for churn seem to be lack of promotion and opportunities for training and growth. By implementing opportunities for professional training, growth, promotion or recognition, we may see improvement in employee retention. These efforts should be focused on groups such as males and those between ages 30 to 39. It may also be a smart move to develop great managers for the Research & Development and Sale departments since they are the ones experiencing the most attrition.

Overall, IBM should ensure that their employees are heard. Moving forward they should consider an employee survey to gauge satisfaction with their jobs and the company, and to learn their thoughts, needs, and suggestions. They should conduct annual reports on attrition after implementing changes and continually monitor satisfaction among employees.