A Glossary of Key Terms in HR Analytics & People Science

In today’s data-driven world, HR professionals are increasingly leveraging advanced analytics and people science to make informed decisions, optimize talent strategies, and drive organizational success. Understanding the core concepts and terminology is crucial for any HR or recruiting leader looking to harness the power of data. This glossary provides essential definitions, tailored for HR and recruiting professionals, explaining how these concepts apply in practical, often automated, contexts.

HR Analytics

HR analytics refers to the systematic collection, analysis, and interpretation of human resources data to improve an organization’s performance. Unlike traditional HR reporting, analytics focuses on uncovering patterns, trends, and causal relationships within the data to predict future outcomes and guide strategic decision-making. For recruiting, this might involve analyzing the sources of hires to optimize marketing spend or correlating specific interview scores with post-hire performance. Automation plays a critical role in HR analytics, as tools can automatically collect data from ATS, HRIS, performance management systems, and engagement surveys, then feed it into dashboards for real-time insights, eliminating manual data compilation and ensuring data accuracy.

People Science

People Science is a more holistic, interdisciplinary approach that applies scientific methods, including psychology, sociology, economics, and statistics, to understand and improve human behavior and outcomes in the workplace. It goes beyond mere data analysis to explore the “why” behind HR metrics, aiming to develop evidence-based HR policies, programs, and interventions. In recruiting, people science might inform the design of unbiased assessment methods or the structure of onboarding programs based on psychological principles of motivation and belonging. Automation supports people science by providing robust, clean datasets for research and by facilitating the deployment and measurement of scientifically designed interventions at scale, such as automated feedback loops or personalized learning pathways.

Workforce Planning

Workforce planning is the strategic process of anticipating and meeting an organization’s future talent needs. It involves analyzing current workforce capabilities, forecasting future demand for skills and roles, identifying potential gaps, and developing strategies to close those gaps through recruitment, development, or retention initiatives. Effective workforce planning is critical for ensuring the right people with the right skills are in the right places at the right time. Automation can significantly enhance workforce planning by integrating data from HRIS, financial planning, and market trends to create dynamic forecasts, model different scenarios, and even suggest talent acquisition strategies. For example, AI-powered tools can predict future skill requirements based on business objectives and industry shifts.

Predictive Analytics

Predictive analytics in HR uses statistical algorithms and machine learning techniques to forecast future outcomes or behaviors based on historical data. Rather than just understanding what happened (descriptive analytics) or why it happened (diagnostic analytics), predictive analytics aims to answer “what will happen?” Common applications include predicting employee turnover risk, identifying high-potential candidates who are likely to succeed, or forecasting future hiring needs. In a recruiting context, automation platforms can ingest applicant data (e.g., resume keywords, assessment scores) and use predictive models to rank candidates most likely to be a good fit, thereby streamlining the screening process and reducing time-to-hire. This shifts HR from reactive to proactive decision-making.

Descriptive Analytics

Descriptive analytics is the most fundamental form of data analysis, focusing on summarizing and describing the characteristics of a dataset. In HR, this involves calculating key metrics like average time-to-hire, employee headcount, compensation ratios, or demographic breakdowns. It answers the question “what happened?” or “what is happening?” While simpler than predictive or prescriptive analytics, it forms the foundation for all other forms of analysis, providing a clear picture of the current state of the workforce. Automation ensures that descriptive HR reports and dashboards are accurate, up-to-date, and accessible. Automated reporting systems can generate weekly or monthly summaries of recruiting metrics (e.g., pipeline stages, source effectiveness) without manual intervention, saving HR teams significant time.

Employee Lifetime Value (ELTV)

Employee Lifetime Value (ELTV) is a metric adapted from customer lifetime value, representing the total net benefit an employee brings to an organization throughout their tenure. It considers an employee’s productivity, contributions, innovation, and impact on team morale, offset by the costs associated with their recruitment, training, compensation, and eventual departure. ELTV helps organizations understand the long-term return on investment for their talent acquisition and development strategies. Calculating ELTV is complex but can be supported by analytics platforms that integrate performance data, salary, benefits, and even 360-degree feedback. Understanding ELTV can drive strategic decisions in recruiting, encouraging investment in candidates who demonstrate higher potential for long-term contribution, even if initial hiring costs are higher.

Time to Hire

Time to Hire measures the duration from when a job requisition is opened or a candidate applies to when the candidate accepts the job offer. It is a critical efficiency metric in recruiting, reflecting the speed and agility of the hiring process. A shorter time to hire often correlates with a better candidate experience and a reduced risk of losing top talent to competitors. Automation significantly impacts time to hire by streamlining repetitive tasks: automated initial screening, scheduling interviews, sending offer letters, and even managing background checks. Integrating an ATS with CRM and communication tools can create a seamless candidate journey, reducing delays and accelerating the hiring cycle without sacrificing quality.

Cost Per Hire

Cost Per Hire (CPH) is a key recruiting metric that calculates the average expense incurred to fill one open position. This includes internal costs (recruiter salaries, interviewers’ time, employee referral bonuses) and external costs (job board fees, advertising, agency fees, assessment tools, background checks). Monitoring CPH helps organizations understand the financial efficiency of their recruitment efforts and identify areas for cost reduction. Automation can lower CPH by reducing manual labor through automated candidate sourcing, initial outreach, and interview scheduling. It can also optimize job ad spend by providing data on which channels yield the most cost-effective hires, ensuring that resources are allocated to the most efficient recruitment sources.

Attrition Rate

Attrition rate, also known as turnover rate, measures the percentage of employees who leave an organization over a specific period. It is a vital HR metric for understanding employee retention challenges and their impact on productivity, morale, and recruitment costs. A high attrition rate can signal issues with company culture, management, compensation, or career development opportunities. HR analytics helps identify patterns and root causes of attrition by correlating departure data with factors like performance reviews, tenure, manager, department, or compensation. Automation can monitor key indicators that might predict attrition (e.g., declining engagement scores, prolonged inactivity in development programs) and trigger interventions, such as automated stay interviews or personalized retention programs, before an employee decides to leave.

Retention Analytics

Retention analytics focuses specifically on understanding why employees stay with an organization and what factors contribute to their longevity and commitment. It uses data to identify key drivers of retention, predict which employees are at risk of leaving, and evaluate the effectiveness of retention strategies. This often involves analyzing data points like tenure, performance, engagement scores, compensation, benefits utilization, and manager effectiveness. For example, analytics might reveal that employees who complete a specific internal training program within their first year have significantly higher retention rates. Automation facilitates retention analytics by aggregating data from various HR systems into a unified view, enabling real-time dashboards that highlight at-risk groups and even trigger automated personalized communications or professional development opportunities to improve retention.

Employee Experience (EX) Analytics

Employee Experience (EX) Analytics involves collecting, analyzing, and interpreting data related to an employee’s journey within the organization, from hire to retire. This includes interactions with HR, technology, the physical workspace, culture, and leadership. The goal is to understand and improve every touchpoint to create a more engaging, productive, and satisfying work environment. Data sources include engagement surveys, pulse surveys, exit interviews, internal social media activity, and performance data. In recruiting, EX analytics might examine the candidate’s journey from application to onboarding, identifying friction points. Automation is crucial for EX analytics, enabling automated feedback collection at various points (e.g., after onboarding, after a project), personalized communications, and the analysis of sentiment data from open-ended feedback to identify systemic issues and opportunities for improvement.

Organizational Network Analysis (ONA)

Organizational Network Analysis (ONA) is a methodology used to map and understand the formal and informal relationships and communication patterns within an organization. It visualizes how information flows, who the key influencers are, and which individuals or teams are isolated. ONA can reveal hidden leaders, knowledge silos, and potential bottlenecks in collaboration. For HR, ONA insights are valuable for leadership development, change management, identifying high-potential employees, and improving team effectiveness. While traditionally manual, advanced automation tools can now analyze digital communication data (e.g., email, collaboration platforms) to generate ONA maps, providing insights into collaboration patterns and identifying critical connectors who can be leveraged in talent or change initiatives. This helps recruiters understand the social capital within a team they’re hiring for.

Skill Gap Analysis

Skill gap analysis is the process of identifying the difference between the skills an organization currently possesses (or a candidate has) and the skills it will need in the future to achieve its strategic objectives. This analysis is crucial for strategic workforce planning, learning and development, and targeted recruitment. It helps organizations pinpoint critical skill deficiencies that could hinder growth or innovation. Automation plays a key role by integrating skills data from performance reviews, learning management systems, and resume parsing tools, then comparing it against evolving industry standards or future business requirements. AI-powered platforms can automatically identify skill gaps at individual, team, and organizational levels, and even suggest relevant training or targeted recruitment campaigns for specific skill sets.

Sentiment Analysis

Sentiment analysis, or opinion mining, is the process of computationally identifying and categorizing opinions expressed in a piece of text, especially to determine whether the writer’s attitude towards a particular topic is positive, negative, or neutral. In HR, sentiment analysis is applied to unstructured data sources such as employee survey comments, feedback platforms, internal communication channels, and even candidate reviews. This helps HR gain insights into employee morale, specific pain points, or overall sentiment towards new policies. For recruiters, sentiment analysis can gauge candidate reactions to the application process. Automation, particularly through natural language processing (NLP) and AI, makes sentiment analysis scalable, allowing HR to quickly sift through thousands of comments to extract actionable insights that would be impossible to do manually.

Candidate Experience Analytics

Candidate experience analytics involves systematically collecting and analyzing data related to a job candidate’s perceptions and feelings throughout the entire recruitment process, from initial awareness to onboarding (or rejection). The goal is to identify pain points, optimize the candidate journey, and enhance the organization’s employer brand. Data sources include candidate surveys, feedback forms, social media reviews (e.g., Glassdoor), and even analysis of application drop-off rates at different stages. Automation facilitates candidate experience analytics by automatically sending feedback surveys after key touchpoints (e.g., interview, offer), tracking candidate progress through the ATS, and centralizing feedback for analysis. This enables recruiters to quickly identify and address issues that might deter top talent, ensuring a positive and engaging experience for all applicants.

If you would like to read more, we recommend this article: HR’s 2025 Blueprint: Leading Strategic Transformation with AI and a Human-Centric Approach