People Metrics and Key Performance Indicators Questions

Covers the selection, definition, measurement, reporting, and governance of people and human resources metrics and key performance indicators that align to business strategy and people priorities. Includes metric design for different audiences, distinguishing leading and lagging indicators, target setting, and translating measurement into people programs and interventions. Core metric categories covered include recruitment and talent acquisition measures such as hiring velocity, time to hire, time to fill, cost per hire, quality of hire, offer acceptance rates, and applicant conversion rates; onboarding and productivity measures such as time to productivity and new hire retention; retention and attrition measures including voluntary and involuntary turnover, regrettable loss, retention curves, and tenure distribution; engagement measures such as employee net promoter score, engagement survey scores, and survey participation rates; workforce and cost measures such as headcount, full time equivalent counts, and human resources cost per employee; promotion and internal mobility rates; training and compliance metrics such as training hours and completion rates; and diversity equity and inclusion representation and compensation equity metrics. Discusses data sources and analytics approaches including human resources information system data, payroll, applicant tracking and recruitment systems, learning management systems, performance management systems, time and attendance data, engagement surveys, and external benchmarks. Analytical techniques include cohort analysis, retention survival analysis, segmentation by role or location, normalization and rate calculations, rolling averages and seasonality adjustment, and experimental or quasi experimental methods to measure program impact. Covers dashboard and balanced scorecard design for senior leaders, people leaders, and managers, how to avoid vanity metrics by linking measures to outcomes, approaches to setting targets and thresholds, and operational reporting cadence. Emphasizes data governance, single source of truth and clear metric definitions, metric ownership, data quality checks, and privacy and ethical considerations such as anonymization and aggregation thresholds, access controls, legal compliance including the General Data Protection Regulation, and minimizing bias when using demographic information.