Work

Leveraging Advanced Analytics to Enhance Employee Retention and Engagement

Year: 2025

Type: article

Abstract: : The increasing complexity of workforce dynamics poses challenges for organizations striving to retain skilled employees. High turnover rates lead to operational inefficiencies and financial burdens. Advanced analytics provides a proactive approach by leveraging machine learning, artificial intelligence (AI), and statistical modelling to predict attrition risks and improve engagement strategies. This study explores the effectiveness of predictive analytics, sentiment analysis, and personalized engagement methods in fostering employee retention. Findings indicate that flexible work arrangements, leadership quality, and career development opportunities significantly influence employee loyalty and satisfaction. Organizations that integrate data-driven retention strategies experience reduced turnover and enhanced workforce stability. This study underscores the transformative impact of analytics in shaping a resilient and engaged workforce. Keywords: Employee Retention, Workforce Engagement, Advanced Analytics, Predictive Modelling, AI in HR JEL Classification Number: M12, M54

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.15281213

Authors: Balasubramanian Annamalaichamy, B. Shanmuga Priya, C. Sathiskumar

Institutions: Symbiosis International University, Hindustan Institute of Technology and Science

Language: English

Topic: AI and HR Technologies

Subfield: Organizational Behavior and Human Resource Management

Field: Business, Management and Accounting

Domain: Social Sciences

SDG: Decent work and economic growth

Open Access status: green

OpenAlex ID: W6893261996

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