Enhanced Methodology for Boosting Employee Retention Through Various ML and Data Engineering Methods

Undeniably one of the biggest threats the economy has experienced post COVID-19 is the heightened resignation rates. An optimistic outlook among workers regarding their prospects for securing higher-paying positions, a low unemployment rate, and an ample supply of job opportunities are all indications of a robust labor market. The American labor market encountered a novel peril due to the emergence of the COVID-19 pandemic. The nation has experienced an unprecedented “quit rate” in the past quarter-century, which has occurred within the last half-year. Labor markets tightened in 2021 due to significant resignations, which followed significant employment losses in the early months of the epidemic. As the pandemic progressed in 2020, 8.6% and 7.2% of the workforce was laid off in March and April, respectively, and the ensuing resignation rate in the United States fell to a seven-year low of 1.6%. An initial pattern emerged in the resignation rates. On the contrary, the advent of predictive analytics has furnished organizations with a potentially invaluable asset for managing any type of data. Analytics presents an exceptional opportunity for organizations to acquire knowledge in areas where they are deficient. Through vigilant observation of trends and patterns, HR specialists and management teams have the potential to assist the organization in preparing for forthcoming challenges. Our framework gives 360-degree insights on employee behavior and predicts employees likely to quit. This framework is assisted by multiple drivers like employee feedback and past data on employee attrition, designation, year in service, and benefits. We have observed that classification models are performing better than the logistic regression and SVM. Random forest gives an accuracy of 90% to predict attrition rate is selected as preferred model. As a part of this study, we came up with FPA methodology (feedback, predict, and act) which will contribute tremendously to reduce attrition rate as backbone of HR data platform.