turnover-analytics

Business

Analyze turnover patterns and develop retention strategies with predictive modeling

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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

Root Cause Focus: Understand why, not just what
Segment Deeply: Aggregate metrics hide important patterns
Proactive Action: Act on predictions before resignations
Manager Enablement: Equip managers with actionable insights
Privacy Respect: Handle individual scores carefully
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)