Agent skill
turnover-analytics
Analyze turnover patterns and develop retention strategies with predictive modeling
Install this agent skill to your Project
npx add-skill https://github.com/a5c-ai/babysitter/tree/main/library/specializations/domains/business/human-resources/skills/turnover-analytics
Metadata
Additional technical details for this skill
- domain
- business
- category
- HR Analytics
- skill id
- SK-019
- dependencies
-
[ "HRIS data", "Statistical models" ] - specialization
- human-resources
SKILL.md
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)
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