turnover-analytics

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

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:

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)