--- name: turnover-analytics description: Analyze turnover patterns and develop retention strategies with predictive modeling allowed-tools: - Read - Write - Glob - Grep - Bash metadata: specialization: human-resources domain: business category: HR Analytics skill-id: SK-019 dependencies: - HRIS data - Statistical models --- # Turnover Analytics Skill ## Overview The Turnover Analytics skill provides capabilities for analyzing turnover patterns, building predictive models, and developing data-driven retention strategies. This skill enables comprehensive turnover understanding and proactive intervention. ## Capabilities ### Turnover Calculation - Calculate turnover rates by segment - Differentiate voluntary vs. involuntary - Track regrettable vs. non-regrettable - Compute annualized rates - Compare to benchmarks ### Survival Analysis - Perform survival analysis on tenure - Build tenure curves by segment - Identify critical tenure periods - Calculate hazard rates - Compare cohort survival ### Predictive Modeling - Build turnover prediction models - Identify risk factors - Calculate flight risk scores - Validate model accuracy - Update models with new data ### Risk Identification - Identify high-risk employees and teams - Flag at-risk talent segments - Monitor risk score changes - Alert managers proactively - Track intervention effectiveness ### Cost Analysis - Analyze turnover cost impacts - Calculate replacement costs - Estimate productivity loss - Model cost avoidance - Support business case ### Intervention Design - Generate retention intervention recommendations - Prioritize interventions by impact - Design targeted programs - Track retention program effectiveness - Measure ROI of retention ## Usage ### Turnover Analysis ```javascript const turnoverAnalysis = { period: { start: '2025-01-01', end: '2026-01-01' }, segments: [ 'department', 'location', 'level', 'tenure-band', 'performance-rating', 'manager', 'age-group' ], metrics: [ 'overall-turnover', 'voluntary-turnover', 'regrettable-turnover', 'first-year-turnover' ], benchmarks: { industry: 'technology', internal: 'prior-year' }, analysis: { survivalCurves: true, rootCauses: true, costImpact: true } }; ``` ### Predictive Model ```javascript const flightRiskModel = { target: 'voluntary-termination', predictionWindow: 6, features: [ 'tenure-months', 'time-since-promotion', 'time-since-raise', 'performance-trend', 'manager-tenure', 'commute-distance', 'market-demand-score', 'engagement-score', 'training-hours' ], model: { type: 'logistic-regression', crossValidation: 5, threshold: 0.7 }, output: { employeeScores: true, riskSegments: ['high', 'medium', 'low'], managerAlerts: true } }; ``` ## Process Integration This skill integrates with the following HR processes: | Process | Integration Points | |---------|-------------------| | turnover-analysis-retention.js | Full analysis workflow | | workforce-planning.js | Attrition forecasting | | employee-engagement-survey.js | Engagement correlation | ## Best Practices 1. **Root Cause Focus**: Understand why, not just what 2. **Segment Deeply**: Aggregate metrics hide important patterns 3. **Proactive Action**: Act on predictions before resignations 4. **Manager Enablement**: Equip managers with actionable insights 5. **Privacy Respect**: Handle individual scores carefully 6. **Continuous Learning**: Update models with new data ## Metrics and KPIs | Metric | Description | Target | |--------|-------------|--------| | Overall Turnover | Annual turnover rate | Below industry benchmark | | Regrettable Turnover | High performer departures | <10% | | First-Year Turnover | New hires leaving in year 1 | <15% | | Model Accuracy | Prediction accuracy (AUC) | >0.75 | | Intervention Success | Retention rate of intervened employees | +20% vs. control | ## Related Skills - SK-017: Exit Analysis (departure reasons) - SK-020: Engagement Survey (engagement link) - SK-018: Workforce Planning (attrition forecasts)