Employee Turnover Prediction
Overview
Turnover prediction uses classification models (logistic regression, random forest, XGBoost) to estimate the probability an employee will leave within a defined period (typically 6-12 months). Features include tenure, compensation, performance, promotion history, and engagement signals.
When to Use
Trigger conditions:
- Identifying employees at high risk of voluntary departure
- Quantifying which factors drive turnover for targeted interventions
- Prioritizing retention budgets toward highest-impact employees
When NOT to use:
- For involuntary termination planning (different process and ethics)
- When headcount is < 200 (insufficient data for reliable modeling)
Algorithm
IRON LAW: Turnover Models Predict RISK, Not Certainty A predicted 80% turnover probability means "employees with similar profiles historically left 80% of the time." It does NOT mean this specific employee WILL leave. Never use model outputs as sole basis for employment decisions — that creates legal and ethical liability.
Phase 1: Input Validation
Collect: employee demographics, tenure, compensation (relative to market), last promotion date, performance ratings, manager change history, engagement survey scores, commute distance. Outcome: voluntary departure within N months. Gate: Minimum 200 turnover events, features available before departure date.
Phase 2: Core Algorithm
- Feature engineering: tenure buckets, comp ratio (salary/market median), time since last promotion, manager tenure, engagement trend
- Handle class imbalance: turnover rate typically 10-20%. Use SMOTE or class weights.
- Train: logistic regression (interpretable, HR-preferred) or GBDT (higher accuracy)
- Output: probability of departure + top risk factors per employee
Phase 3: Verification
Evaluate: AUC, precision-recall (at actionable thresholds). Backtest: did the model correctly flag employees who left in the past 6 months? Gate: AUC > 0.70, precision > 50% at top decile.
Phase 4: Output
Return risk scores with driver analysis.
Output Format
json{ "risk_scores": [{"employee_id": "E123", "turnover_prob": 0.72, "risk_tier": "high", "top_drivers": ["low_comp_ratio", "no_promotion_3yr"]}], "metadata": {"model": "xgboost", "auc": 0.78, "prediction_window_months": 12} }
Examples
Sample I/O
Input: Employee: 4yr tenure, comp ratio 0.85, no promotion in 3yr, engagement score declining Expected: High risk (>0.6). Top drivers: below-market compensation, stalled career progression.
Edge Cases
| Input | Expected | Why |
|---|---|---|
| New hire (< 6 months) | Unreliable prediction | Insufficient behavioral data |
| Top performer, high comp | Still could leave | Non-financial factors (manager, culture) matter |
| Post-reorg period | Model drift likely | Unusual conditions distort patterns |
Gotchas
- Survivorship bias: Training data only includes people who were hired and stayed long enough to observe. Early-stage leavers may be underrepresented.
- Feature leakage: "Started job searching" or "updated LinkedIn" are strong predictors but ethically and legally problematic to use. Stick to internal HR data.
- Self-fulfilling prophecy: If managers treat "high risk" employees differently (less investment, fewer projects), the model prediction becomes self-fulfilling.
- Legal constraints: Using protected attributes (age, gender, ethnicity) directly or via proxies may violate employment law. Audit for disparate impact.
- Retention intervention timing: Identifying risk is only useful if HR acts. Build the model into a retention workflow with specific intervention triggers.
References
- For feature engineering from HR data, see
references/hr-features.md - For ethical AI in HR applications, see
references/ethical-hr-ai.md
