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Leveraging Data Analytics to Enhance Workforce Efficiency and Customer Service in HR-Driven Organizations

Author

Listed:
  • Olufunke Anne Alab

    (Teesside University International Business School, Middlesbrough United Kingdom)

  • Funmilayo Aribidesi Ajayi

    (Department of Corporate Services, Gelose Marine Services Nig. Ltd, Port Harcourt, Rivers State, Nigeria)

  • Chioma Ann Udeh

    (Independent Researcher, Lagos Nigeria)

  • Christianah Pelumi Efunniyi

    (OneAdvanced, UK)

Abstract

This paper explores the transformative role of data analytics in optimizing workforce management within HR-driven organizations, focusing on enhancing workforce efficiency and customer service outcomes. The discussion begins with an overview of HR-driven organizations and the importance of workforce efficiency in maintaining a competitive edge. It then delves into the application of data analytics in HR, highlighting predictive analytics for employee performance, satisfaction, and retention and their direct impact on customer satisfaction. The paper also examines the challenges associated with implementing data analytics in HR, including data privacy concerns, integration with existing systems, skill gaps, and change management. The paper discusses emerging technologies such as AI and machine learning, their potential impact on workforce management, and the evolving customer service expectations. Finally, the paper underscores strategic opportunities for HR leaders to leverage data analytics for workforce planning and overall organizational growth, emphasizing the critical importance of these tools in driving business success in an increasingly data-driven world.

Suggested Citation

  • Olufunke Anne Alab & Funmilayo Aribidesi Ajayi & Chioma Ann Udeh & Christianah Pelumi Efunniyi, 2024. "Leveraging Data Analytics to Enhance Workforce Efficiency and Customer Service in HR-Driven Organizations," International Journal of Research and Scientific Innovation, International Journal of Research and Scientific Innovation (IJRSI), vol. 11(9), pages 92-101, September.
    • Handle: RePEc:bjc:journl:v:11:y:2024:i:9:p:92-101
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