Predictive Performance Analytics: a Strategic Framework for Enhancing Employee Productivity through an Integrated Business Analytics and Machine Learning Approach

EasyChair Preprint 12189

Abstract

This paper introduces a comprehensive framework, leveraging integrated business analytics and machine learning, to predict and enhance employee productivity. By amalgamating advanced analytical techniques, our strategic approach provides organizations with predictive insights into employee performance. The study employs a methodology that integrates historical data, business metrics, and machine learning algorithms to forecast individual and team productivity. The results showcase a significant improvement in predictive accuracy, enabling proactive management interventions for enhanced productivity. The discussion explores the challenges associated with implementing such a framework and proposes effective treatments to mitigate these obstacles. In conclusion, the paper emphasizes the transformative potential of predictive performance analytics in shaping a more productive and resilient workforce.

Keyphrases: Business Analytics, Employee Productivity, Predictive Analytics, Strategic Framework, machine learning, performance management

@booklet{EasyChair:12189,
  author    = {Battle Hurry and Wejy Jay},
  title     = {Predictive Performance Analytics: a Strategic Framework for Enhancing Employee Productivity through an Integrated Business Analytics and Machine Learning Approach},
  howpublished = {EasyChair Preprint 12189},
  year      = {EasyChair, 2024}}