A Structured Recruitment Analytics Framework for Candidate Screening and Talent Pool Utilization in SAP SuccessFactors Recruiting
Large-scale hiring environments often struggle to convert applicant data into consistent and reusable recruitment decisions, even when supported by mature platforms such as SAP SuccessFactors Recruiting. Screening outcomes may vary when recruiters depend on manual résumé review, keyword filters, and individual interpretation of candidate suitability. At the same time, talent pools may lose practical value when previously assessed candidates are stored without structured ranking, role alignment, or availability indicators. This study presents a structured recruitment analytics framework designed to improve candidate screening and talent pool utilization in SAP SuccessFactors Recruiting. The framework uses normalized candidate attributes, weighted scoring criteria, and role-specific evaluation logic to assess experience alignment, skill relevance, application history, and candidate availability. It further supports dynamic talent pool classification, enabling qualified candidates to be organized and reused for future requisitions. The proposed approach is compared with manual screening and rule-based filtering across multiple hiring scenarios. The results show improvements in shortlisting accuracy, hiring precision, screening efficiency, candidate reusability, and time-to-fill reduction. The findings demonstrate that structured candidate scoring and active talent pool management can improve recruitment consistency, reduce repetitive sourcing effort, and support more reliable hiring decisions within enterprise HR systems.
Keywords: SAP SuccessFactors Recruiting, Candidate Selection, Talent Pool Management, Recruitment Analytics, Candidate Scoring Model, Hiring Decision Consistency, Applicant Tracking Systems, Talent Acquisition Strategy, Data-Driven Recruitment, Candidate Shortlisting, Recruitment Process Optimization, HR Systems, Workforce Planning, Recruitment Efficiency
