Data Analytics and Artificial Intelligence for Improving Employee Retention Strategies

Background: Predictive Decision Support Systems (PDSS) should be highly important if companies wish to preserve a competitive edge over their competitors. On the other hand, human resource professionals are not always able to project based on the statistics under their influence what employees expect. Problem statement: This research intends to address a gap in the body of present information by suggesting novel strategies for employee members to be equipped for occupations in the digital age. One can create a system that forecasts future employee performance needs by aggregating the most current machine-learning approaches with personal data. Real data from many spheres is applied to determine whether the process is efficient and advantageous. Objective: With a novel PDSS technique, Deep Random Forest (DRF), this work attempts to extract currently available data. This research tries to explain how DRF could be able to manage future employment demands by means of an analysis of different datasets including those related to workers hired by the government, companies, and items based on technology. Findings: In terms of accuracy and scalability, the DRF method performs better than numerous other presumably more conventional ways. Human resource professionals can thus utilise this information to direct choices on the design of the digital workforce.