Identifying the most effective recruitment metrics using analytical hierarchy process

Abstract Recruitment in contemporary IT organizations is largely data-driven. Organizations often lack a systematic and theoretically sound approach for prioritizing recruitment metrics. While few indicators are commonly used in practice, the available literature offers limited insights into how these metrics can be evaluated and compared systematically to facilitate methodical strategic recruitment decision-making. Most previous studies treat recruitment metrics merely as tools for operational reporting, rather than as strategic constructs that necessitate correct validation and prioritization, resulting in disjointed, subjective, and context-dependent practices for selecting metrics. The present research aims to fill this void by creating a comprehensive prioritization framework for pre-hire recruitment metrics, focusing on three key dimensions: academic relevance, industry relevance, and measurement feasibility. These dimensions illustrate the theoretical validity of metrics in academic research, their practical significance in organizational hiring systems, and their operational viability within HR analytics settings. Six frequently utilized pre-hire metrics—source per hire, time to fill, time to hire, cost per hire, application completion rate, and offer acceptance rate are methodically assessed within this framework to determine their relative strategic significance for recruitment effectiveness in the IT industry. By connecting academic research with industry practices and measurement practicality, the study enhances recruitment analytics from mere descriptive metric application to a more organized approach to metric prioritization. The results equip HR leaders and talent acquisition professionals with a research-backed basis for creating recruitment measurement systems that are strategically relevant, operationally significant, and decision-focused, thereby enhancing workforce planning analytics.