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turnover-analytics

// Analyze turnover patterns and develop retention strategies with predictive modeling

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stars:384
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updated:March 4, 2026
SKILL.mdreadonly
SKILL.md Frontmatter
nameturnover-analytics
descriptionAnalyze turnover patterns and develop retention strategies with predictive modeling
allowed-toolsRead,Write,Glob,Grep,Bash
metadata[object Object]

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

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

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:

ProcessIntegration Points
turnover-analysis-retention.jsFull analysis workflow
workforce-planning.jsAttrition forecasting
employee-engagement-survey.jsEngagement 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

MetricDescriptionTarget
Overall TurnoverAnnual turnover rateBelow industry benchmark
Regrettable TurnoverHigh performer departures<10%
First-Year TurnoverNew hires leaving in year 1<15%
Model AccuracyPrediction accuracy (AUC)>0.75
Intervention SuccessRetention 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)