Improving Time-to-Hire and Quality-of-Hire Metrics Using Predictive Analytics in Indian Tech Recruitment

Abstract

In the evolving landscape of India’s tech-driven economy, efficient recruitment processes have become imperative for sustaining innovation and organizational performance. The study investigates the role of predictive analytics, specifically using SmartPLS, in improving two critical recruitment metrics: Time-to-Hire and Quality-of-Hire within product-based IT organizations in India. While traditional hiring practices often rely on subjective judgment and fragmented data, this research explores how data-driven decision-making can optimize hiring outcomes. A quantitative research design was adopted, involving 54 HR professionals across 18 Indian tech firms. Primary data were collected through surveys and interviews, while secondary data were extracted from Applicant Tracking Systems (ATS) and historical hiring records. Descriptive statistics, correlation analysis, and structural equation modeling using SmartPLS were employed to analyze the relationships among key recruitment variables such as Recruiter Efficiency, Resume Accuracy, and Interview Score. The results revealed that higher SmartPLS adoption was significantly associated with shorter Time-to-Hire and improved Quality-of-Hire. Resume Accuracy and Recruiter Efficiency were significant predictors of post-hire performance, while Interview Score emerged as the strongest (albeit not statistically significant) predictor of faster hiring. The findings also identified practical bottlenecks, including inconsistent recruiter assessments and limited analytics integration in ATS platforms. The study concludes that predictive analytics, when effectively integrated, enhances recruitment outcomes by enabling data-informed and strategic decision-making. The research offers practical recommendations for HR leaders and lays a foundation for extending analytics frameworks to other industries and geographies.