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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM41917

Utilizing Predictive Analytics for Optimizing Talent Acquisition Strategies in Human Resource Management

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM41917

Volume:

9 |

2 |

Feb 2025

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM41917

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

M

Manisha

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Abstract

The need for effective and data-driven talent acquisition techniques is crucial in the field of human resource management. The impact of data analytics on improving hiring procedures is examined in this study. Organizations can obtain important insights into hiring, sourcing, and candidate screening patterns by organizing both structured and unstructured data. While assessing the function of predictive analytics in decision-making optimization, the study looks at the in-depth analysis of important recruitment variables like hiring time, hiring cost, and hiring quality. This study highlights the sophisticated tools to demonstrate recruitment operations. To achieve more effective results in talent acquisition, this study highlights the critical role that data analytics integration plays in the human resource function. Organizations use data to improve their hiring practices and match them with long-term business objectives. To support HR managers in incorporating data-driven approaches and moving toward precise and comprehensive decision-making that propels corporate performance through practical and implementable solutions.

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Cite This Article

Manisha, "Utilizing Predictive Analytics for Optimizing Talent Acquisition Strategies in Human Resource Management," International Journal of Scientific Research in Engineering and Management, vol. 9, no. 2, Feb 2025. https://doi.org/10.55041/IJSREM41917

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Publication Date

2/28/2025

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Double-Blind Peer Review

Review Rounds: 2

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The authors declare no conflict of interest related to this research work.

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