PREDICTIVE ANALYTICS FOR EMPLOYEE TURNOVER: A COMPARATIVE STUDY BETWEEN INDUSTRIES
DOI:
10.54443/morfai.v5i6.4454Published:
2025-11-24Downloads
Abstract
Employee turnover poses a significant challenge across industries, yet its drivers are often assumed to be universal. This study challenges that assumption through a comparative analysis of predictive analytics in the technology, healthcare, and manufacturing sectors. Utilizing human resources data and machine learning models, we identified profoundly industry-specific predictors and model performances. Results revealed distinct turnover dynamics: career-centric in technology, well-being-driven in healthcare, and structurally transactional in manufacturing. Consequently, no single predictive algorithm was universally superior. The discussion concludes that effective turnover prediction and mitigation require tailored, context-aware models aligned with the unique operational and psychological realities of each industry, rendering one-size-fits-all HR strategies obsolete and advocating for a decentralized analytical approach.
Keywords:
Predictive analytics employee turnover industry comparison machine learning retention strategiesReferences
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