Leveraging Predictive Analytics for Strategic Compensation Forecasting

This article explores the transformative role of predictive analytics in modern compensation management and forecasting. The article examines how organizations leverage advanced analytics capabilities to enhance their compensation strategies while maintaining market competitiveness. Through a comprehensive analysis of implementation methodologies, the article demonstrates significant improvements in talent retention, employee engagement, and organizational performance through data-driven approaches. The article investigates various aspects, including predictive modeling techniques, time series analysis, multivariate regression models, and machine learning applications in compensation planning. It further explores strategic implementation considerations, highlighting the importance of data quality, model maintenance, and stakeholder engagement in successful analytics deployment. The article reveals substantial enhancements in forecasting accuracy, budget allocation efficiency, and cost management through the integration of advanced analytical capabilities.

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Publication Details

Journal
Journal of Computer Science and Technology Studies
Published
2025-05-14
DOI
https://doi.org/10.32996/jcsts.2025.7.4.32
Primary Topic
Forecasting Techniques and Applications
Type
article
Field-Weighted Citation Impact
0.00
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article

Leveraging Predictive Analytics for Strategic Compensation Forecasting

Shraddha Verma
Journal of Computer Science and Technology Studies
Forecasting Techniques and Applications
article

Leveraging Predictive Analytics for Strategic Compensation Forecasting

Shraddha Verma
article en

Abstract

This article explores the transformative role of predictive analytics in modern compensation management and forecasting. The article examines how organizations leverage advanced analytics capabilities to enhance their compensation strategies while maintaining market competitiveness. Through a comprehensive analysis of implementation methodologies, the article demonstrates significant improvements in talent retention, employee engagement, and organizational performance through data-driven approaches. The article investigates various aspects, including predictive modeling techniques, time series analysis, multivariate regression models, and machine learning applications in compensation planning. It further explores strategic implementation considerations, highlighting the importance of data quality, model maintenance, and stakeholder engagement in successful analytics deployment. The article reveals substantial enhancements in forecasting accuracy, budget allocation efficiency, and cost management through the integration of advanced analytical capabilities.

Journal of Computer Science and Technology StudiesVol. 7(4)
University of North Carolina at Charlotte (US)
Openalex Percentile: Top 88%
Forecasting Techniques and Applications
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