Analysis of Employee Performance and Job Satisfaction

Human Resource (HR) Managers devote long hours to generating descriptive reports that provide insight into employees’ performance and job satisfaction. However, they were unable to answer a question as to why employee performance and job satisfaction are low. HR managers tend to rely on intuition to apply HR strategies in their companies. HR managers would be able to better understand their employees using HR analytics that can then be turned into interactive dashboards to further answer the question. This paper applied exploratory data analysis and predictive modeling to gain insights into a company’s employee performance and job satisfaction data and suggest potential HR management strategies. SAS Enterprise Miner and Power BI were used to complete the research. The results show that Decision Tree was concluded to be the most optimal model with a cumulative lift of 2.258 for the performance rating and 1.307 for job satisfaction.