Predictive analytics in recruitment and selection analytics in HR are
increasingly important in a competitive job market. The importance of predictive
measures in recruitment and selection analytics provides practical guidance for HR
professionals looking to implement these measures in their organizations. Predictive
measures involve the use of data-driven methods and statistical analyses to identify and
hire the most qualified candidates for a given job. This approach relies on the collection
and analysis of various data points, such as job requirements, candidate qualifications,
and hiring outcomes, to develop models that predict which candidates are most likely
to succeed in the role. By leveraging this information, HR professionals can streamline
the recruitment process, reduce the risk of making hiring mistakes, and improve overall
organizational performance. This article aims to provide the key predictive measures
used in HR analytics to help organizations make better hiring decisions and an
overview of key concepts and benefits associated with predictive measures in
recruitment and selection analytics in HR, along with the challenges and limitations
associated with the use of predictive measures in HR analytics and recommendations
for overcoming these challenges.
Keywords: Predictive measures, Predictive analytics, Recruitment, Selection.
