Strategies for Implementing HR Predictive Analytics to Reduce Voluntary Turnover in Technology-Based Companies
DOI:
https://doi.org/10.62872/zj5w6744Keywords:
burnout , employee engagement , HR Predictive Analytics, retention strategy, voluntary turnoveAbstract
Voluntary turnover in technology-based organizations has continued to escalate, resulting in operational disruption and significant loss of digital talent. This study aims to explore the role of HR Predictive Analytics in developing retention strategies to reduce voluntary turnover. A qualitative descriptive–exploratory approach was applied using thematic analysis of academic literature and organizational practices related to data-driven human resource management. The findings reveal that the primary drivers of turnover include burnout, career stagnation, low employee engagement, and weak leadership interaction. HR Predictive Analytics serves as a reflective mechanism to identify patterns in employee work experiences that contribute to dissatisfaction and increased resignation risk, enabling organizations to formulate precision-based retention interventions. Recommended analytics-driven retention strategies emphasize workload regulation, structured career development, meaningful job design, and leadership capability enhancement. This study concludes that HR Predictive Analytics supports preventive and sustainable talent stability strategies within the technology industry by aligning predictive insights with targeted retention initiatives.
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