People analytics (also called HR analytics or workforce analytics) is the practice of applying data analysis and statistical methods to human resources data to improve decisions about hiring, performance, development, retention, and organizational design. According to Deloitte's Global Human Capital Trends report, organizations with mature people analytics practices see 82% higher three-year average profit, 56% better talent retention, and 25% faster time-to-fill open roles than those without. See how Confirm handles performance management.
What Is People Analytics?
People analytics is the systematic collection, analysis, and application of data about people:employees, candidates, and teams:to make better workforce decisions. It spans from basic HR reporting (headcount, attrition rates) to sophisticated predictive modeling (flight risk prediction, performance forecasting) and organizational network analysis (mapping collaboration patterns to identify hidden talent and structural risk).
The field has evolved significantly: what started as basic HR reporting (dashboards showing headcount and turnover) has become a strategic discipline that uses machine learning, network science, and behavioral data to help executives make talent decisions with the same analytical rigor they apply to financial or operational decisions.
Four Levels of People Analytics Maturity
| Level | Type | Description | Example |
|---|---|---|---|
| 1 | Descriptive | What happened? Reporting on historical data. | Monthly headcount, turnover rate by department |
| 2 | Diagnostic | Why did it happen? Root-cause analysis. | Analyzing why turnover is higher in Engineering than Sales |
| 3 | Predictive | What will happen? Statistical modeling of future outcomes. | Identifying employees at risk of leaving in the next 90 days |
| 4 | Prescriptive | What should we do? Recommending specific actions. | Recommending specific managers for retention interventions |
Most HR organizations operate primarily at levels 1-2. Organizations that reach levels 3-4 see the greatest ROI from people analytics:and the talent decisions that matter most (retention, promotion, succession planning) are precisely where predictive and prescriptive analytics deliver the most value.
Core People Analytics Use Cases
1. Turnover and Retention Analytics
Predicting which employees are at risk of leaving:before they've decided to leave:is one of the highest-ROI applications of people analytics. Key predictors of flight risk include: declining engagement scores, reduction in after-hours work, decrease in cross-functional collaboration (measurable via ONA), recent performance rating below expectations, and time-since-last-promotion exceeding role norms.
Organizations with predictive flight risk models reduce voluntary attrition by 15-30% by enabling targeted interventions 3-6 months before employees would otherwise leave. (Bersin by Deloitte)
2. Performance and Potential Assessment
Traditional performance assessment relies on manager ratings:which are subject to recency bias, proximity bias, and the halo effect. People analytics improves performance assessment by adding objective data sources: goal completion rates, peer feedback sentiment, collaboration network position (ONA), 360 feedback patterns, and project outcomes. Organizations using multi-source performance data identify high performers 2.5x more accurately than those using manager ratings alone. (Wharton School of Business)
3. Hiring and Candidate Analytics
People analytics applied to hiring identifies: which sourcing channels produce the best long-term performers, which interview assessments best predict on-the-job success, where bias exists in screening and selection, and which competencies predict performance in specific roles. Companies with mature hiring analytics reduce time-to-hire by 25% while improving quality-of-hire by 38%. (SHRM Research, 2023)
4. Organizational Network Analysis (ONA)
ONA is a specialized form of people analytics that maps the relationships and information flows between people in an organization. Rather than measuring what's in the HR system (org chart, job titles), ONA measures what actually happens: who collaborates with whom, who bridges teams, who are the informal influencers, and where isolation or bottlenecks are forming.
ONA data reveals:
- Hidden high performers who drive cross-functional impact without being visible in traditional reviews
- Flight risk signals when an employee's collaboration network contracts (a 3-6 month leading indicator of attrition)
- Team health issues when a team's internal connectivity is fragmented or over-dependent on a single individual
- Succession gaps when a departing leader's network ties are poorly distributed across potential successors
- DEI patterns when certain demographic groups are systematically excluded from informal networks of influence
5. Learning and Development Analytics
L&D analytics measures which training programs actually improve performance, which employees are most ready for development investments, and where skill gaps pose business risk. The most sophisticated L&D analytics connects training completion to business outcomes 6-12 months later:proving (or disproving) the ROI of development programs.
6. Workforce Planning and Scenario Modeling
Workforce planning analytics models how talent supply and demand will evolve over 2-5 years, enabling proactive hiring and development rather than reactive scrambling. Key inputs: projected business growth, current workforce demographics, skill gap analysis, external talent market data, and historical attrition trends. Organizations with mature workforce planning analytics reduce critical role vacancy time by 40% and reduce hiring costs by 25%. (McKinsey & Company)
Key People Analytics Metrics
| Category | Metric | Why It Matters |
|---|---|---|
| Retention | Voluntary attrition rate (by department, tenure, manager) | Leading indicator of talent health; identifies problem areas early |
| Retention | Flight risk score (predictive) | Enables proactive retention before employees decide to leave |
| Performance | High performer retention rate | Losing A-players costs 150-200% of salary to replace |
| Performance | Promotion accuracy rate | Bad promotions cost more than bad hires; measures calibration quality |
| Hiring | Quality of hire (90-day performance) | Measures how well your selection process predicts success |
| Hiring | Offer acceptance rate by source | Reveals where your employer brand is strongest |
| Engagement | eNPS (Employee Net Promoter Score) | Benchmarkable measure of employee satisfaction and advocacy |
| Managers | Manager effectiveness score | Quantifies leadership quality; predicts team retention and performance |
| DEI | Promotion rate by demographic | Detects systematic bias in advancement decisions |
| Network | Collaboration coverage (ONA) | Reveals cross-functional reach and integration |
How to Build a People Analytics Function
Stage 1: Foundation (Months 1-6)
Establish data infrastructure: audit your HRIS data quality, standardize definitions (who counts as a "voluntary" attrition?), and build a data warehouse or people analytics platform. Develop a small set of core dashboards that leadership trusts and uses. Focus on reliability and accuracy before sophistication.
Stage 2: Insights (Months 6-18)
Move from reporting to insights: start asking "why" questions, not merely "what" questions. Conduct root-cause analyses on turnover hotspots. Segment engagement by manager, department, and tenure. Build your first predictive model (flight risk is usually the best starting point for ROI). Develop relationships with business leaders who will act on your findings.
Stage 3: Prediction (Months 18-36)
Deploy machine learning models for flight risk, performance prediction, and succession readiness. Integrate ONA to add network-level insights to performance and retention decisions. Build feedback loops: track whether interventions driven by analytics recommendations produce the predicted outcomes.
Stage 4: Strategy (Year 3+)
Use people analytics to inform business strategy:not merely HR operations. Connect talent data to business outcomes (revenue per employee, product quality metrics, customer satisfaction). Build workforce planning models that scenario-test business decisions (acquisition, expansion, reorganization) against talent implications.
Common People Analytics Mistakes
- Starting with the data, not the question: The most common mistake. Start with "What decisions do we want to improve?" and work backwards to what data you need:not forward from what data you have.
- Ignoring data quality: Analytics built on dirty data produces wrong answers with false confidence. Invest in data cleaning before building models.
- Neglecting privacy and ethics: People analytics raises significant privacy concerns. Establish clear policies on what data can be used, how it's protected, and how decisions based on analytics will be communicated to employees.
- Building analytics without changing decisions: Analytics only creates value when it changes what people do. Build relationships with decision-makers before building models. Analytics that sits in dashboards nobody acts on is a sunk cost.
- Ignoring manager-level analysis: Aggregate people analytics often obscures the most important story: performance, engagement, and retention outcomes vary enormously by manager. Manager-level analytics often produce the highest ROI interventions.
People Analytics Tools and Platforms
| Tool | Best For | Maturity Level |
|---|---|---|
| Confirm | ONA, performance analytics, AI-powered calibration | Advanced (ONA, predictive) |
| Visier | Workforce analytics, planning, benchmarking | Intermediate to Advanced |
| Workday Prism Analytics | HRIS-native analytics for Workday shops | Foundational to Intermediate |
| One Model | Data warehouse + analytics for complex HR data environments | Advanced |
| Pearson & Tableau / Power BI | Custom dashboards and visualization | Foundational |
How Confirm Uses People Analytics
Confirm applies people analytics through Organizational Network Analysis (ONA):mapping real collaboration patterns from Slack, email, calendar, GitHub, and Jira to produce objective performance insights. This enables:
- Identifying high performers who are overlooked by traditional manager-rating systems
- Flagging flight risk 3-6 months before conventional signals appear
- Running calibration sessions grounded in objective collaboration data, not merely manager opinions
- Measuring manager effectiveness through team network health, not merely surveys
- Detecting DEI patterns in informal networks and promotion pathways
Learn how Confirm's ONA-powered people analytics work.
Frequently Asked Questions About People Analytics
What is people analytics in HR?
People analytics in HR is the practice of collecting, analyzing, and applying data about employees and candidates to improve workforce decisions. It covers everything from basic reporting (headcount, turnover rates) to advanced predictive modeling (flight risk, performance forecasting) and organizational network analysis (mapping collaboration patterns). The goal is to replace intuition-based talent decisions with data-informed ones:reducing bias, improving accuracy, and connecting HR activities to business outcomes.
What is the difference between people analytics and HR analytics?
People analytics and HR analytics are largely synonymous:both refer to the use of data to improve workforce decisions. "People analytics" is the more modern term, used by technology companies and forward-thinking HR organizations to emphasize the human-centered focus. "HR analytics" is the more traditional term, often associated with operational reporting and compliance metrics. Some organizations distinguish them: "HR analytics" covers operational HR (time-to-fill, cost-per-hire) while "people analytics" covers strategic talent decisions (performance, retention, succession).
What is Organizational Network Analysis (ONA) in people analytics?
Organizational Network Analysis (ONA) is a people analytics method that maps the relationships and information flows between employees by analyzing their digital collaboration patterns (email, Slack, calendar, project tools). Unlike org chart analysis (which shows formal reporting relationships), ONA reveals the informal networks of influence, collaboration, and information flow that actually drive how an organization functions. ONA identifies hidden high performers, emerging leaders, flight risk signals (network contraction), team health issues, and DEI patterns in informal networks.
How do you measure ROI on people analytics?
Measure people analytics ROI by tracking: (1) Reduction in voluntary attrition (calculate cost savings using your cost-per-turnover figure), (2) Improvement in quality of hire (measure 90-day performance and 12-month retention rates), (3) Reduction in time-to-fill for critical roles, (4) Improvement in high-performer retention rate, (5) Manager effectiveness improvement scores. Most organizations with mature people analytics programs can demonstrate 3-5x ROI within two years of investment, driven primarily by retention improvements among top performers.
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