People Analytics

The agent operates as a senior people analytics partner, translating workforce data into actionable insights using statistical modeling, segmentation analysis, and data governance best practices.

Workflow

Frame the question -- Clarify the business question with the HR or business stakeholder. Examples: "Why is Sales attrition 2x the company average?" or "Are we paying equitably across gender?" Define the success metric for the analysis.
Assess data readiness -- Identify required data sources (HRIS, ATS, survey platform, payroll). Check for completeness, recency, and quality. Flag any gaps before proceeding.
Analyze -- Apply the appropriate method from the analytics toolkit (descriptive stats, regression, classification, segmentation). Document assumptions and limitations.
Validate findings -- Sense-check results with domain experts (HRBPs, managers). Test for statistical significance and practical significance. Check predictive models for bias across protected groups.
Recommend -- Translate findings into 2-3 specific, actionable recommendations with expected impact and cost.
Deliver and monitor -- Present insights using the dashboard framework. Set up ongoing monitoring for key metrics with alert thresholds.

Checkpoint: After step 2, confirm that all data has been anonymized or aggregated to comply with privacy policy before analysis begins.

Analytics Maturity Model

Level	Name	Capabilities	Typical Questions Answered
1	Operational Reporting	Headcount, compliance, ad-hoc queries	"How many people do we have?"
2	Advanced Reporting	Dashboards, trends, benchmarking, segmentation	"How has attrition changed by quarter?"
3	Analytics	Statistical analysis, correlation, root cause	"What drives attrition in Sales?"
4	Predictive	Turnover prediction, performance modeling, risk scoring	"Who is likely to leave in the next 6 months?"
5	Prescriptive	Automated recommendations, real-time interventions	"What should we do to retain this person?"

Core HR Metrics

Workforce Metrics

Metric	Formula	Benchmark
Turnover Rate	(Separations / Avg HC) x 100	10-15%
Retention Rate	(Retained / Starting HC) x 100	85-90%
Time to Fill	Days from req open to offer accept	30-45 days
Cost per Hire	Total recruiting cost / Hires	$3-5K
Regrettable Turnover	Regrettable exits / Total exits	< 30%

Performance Metrics

Metric	Formula	Benchmark
High Performers	% rated top tier	15-20%
Goal Completion	Goals achieved / Goals set	80%+
Promotion Rate	Promotions / Headcount	8-12%

Engagement Metrics

Metric	Formula	Benchmark
eNPS	Promoters % - Detractors %	20-40
Engagement Score	Survey composite (1-100)	70%+
Absenteeism	Absent days / Work days	< 3%

Turnover Prediction Model

import pandas as pd
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report

def build_turnover_model(employee_data: pd.DataFrame) -> dict:
    """
    Build and evaluate a turnover prediction model.

    Input: DataFrame with columns for features + 'left_company' (0/1).
    Output: dict with model, feature importance, and evaluation metrics.
    """
    features = [
        'tenure_months', 'salary_ratio_to_market', 'performance_rating',
        'months_since_last_promotion', 'manager_tenure', 'team_size',
        'engagement_score', 'training_hours_ytd', 'projects_completed'
    ]

    X = employee_data[features]
    y = employee_data['left_company']

    X_train, X_test, y_train, y_test = train_test_split(
        X, y, test_size=0.2, random_state=42, stratify=y
    )

    model = RandomForestClassifier(n_estimators=100, random_state=42)
    model.fit(X_train, y_train)

    y_pred = model.predict(X_test)
    report = classification_report(y_test, y_pred, output_dict=True)

    importance = (
        pd.DataFrame({'feature': features, 'importance': model.feature_importances_})
        .sort_values('importance', ascending=False)
    )

    return {'model': model, 'importance': importance, 'evaluation': report}


def score_flight_risk(model, current_employees: pd.DataFrame) -> pd.DataFrame:
    """
    Score current employees for flight risk.

    Returns DataFrame with employee_id, flight_risk_score (0-1), and risk_level.
    """
    probabilities = model.predict_proba(current_employees[model.feature_names_in_])[:, 1]

    risk_levels = pd.cut(
        probabilities,
        bins=[0, 0.25, 0.50, 0.75, 1.0],
        labels=['Low', 'Medium', 'High', 'Critical']
    )

    return pd.DataFrame({
        'employee_id': current_employees['employee_id'],
        'flight_risk_score': probabilities.round(3),
        'risk_level': risk_levels
    }).sort_values('flight_risk_score', ascending=False)

Example: Sales Attrition Root-Cause Analysis

QUESTION
  Sales voluntary turnover is 22% vs 12% company average. Why?

DATA
  Source: HRIS + engagement survey + exit interviews (n=45 exits, trailing 12 mo)

ANALYSIS
  Segmentation by tenure band:
    < 1 yr: 35% of exits (onboarding/ramp issues)
    1-2 yr: 40% of exits (comp dissatisfaction + career path)
    2+ yr: 25% of exits (manager relationship)

  Regression on exit survey scores (n=38 respondents):
    Top drivers of intent-to-leave:
      1. "I am paid fairly" (beta = -0.42, p < 0.01)
      2. "I see a career path here" (beta = -0.31, p < 0.01)
      3. "My manager supports my development" (beta = -0.28, p < 0.05)

  Compensation benchmark:
    Sales IC3 compa-ratio: 0.88 (12% below midpoint)
    Sales IC2 compa-ratio: 0.91 (9% below midpoint)
    Rest of company average: 0.98

FINDINGS
  1. Sales comp is significantly below market, especially at IC2-IC3
  2. No defined career ladder for Sales ICs beyond IC3
  3. New hires (< 1 yr) leaving due to unrealistic ramp expectations

RECOMMENDATIONS
  1. Market adjustment: Bring Sales IC2-IC3 to 95th percentile compa-ratio ($180K budget)
  2. Publish a Sales career ladder through IC5 with clear promotion criteria
  3. Redesign onboarding: extend ramp period from 30 to 90 days with milestone targets

EXPECTED IMPACT
  Reduce Sales attrition from 22% to 14-16% within 12 months
  ROI: $180K adjustment saves ~$450K in replacement costs (10 fewer exits x $45K/hire)

Pay Equity Analysis

import pandas as pd
import statsmodels.api as sm

def analyze_pay_equity(employee_data: pd.DataFrame) -> dict:
    """
    Conduct pay equity analysis controlling for legitimate pay factors.

    Returns raw gap, adjusted gap, model fit, and employees flagged for review.
    """
    # Raw gap
    avg_by_gender = employee_data.groupby('gender')['salary'].mean()
    raw_gap = (avg_by_gender['Female'] - avg_by_gender['Male']) / avg_by_gender['Male']

    # Adjusted gap (control for level, tenure, performance, location)
    controls = pd.get_dummies(
        employee_data[['job_level', 'tenure_years', 'performance_rating', 'department', 'location']],
        drop_first=True
    )
    controls = sm.add_constant(controls)
    controls['is_female'] = (employee_data['gender'] == 'Female').astype(int)

    model = sm.OLS(employee_data['salary'], controls).fit()
    adjusted_gap = model.params['is_female']

    # Flag outliers (residual > 2 std dev)
    employee_data['predicted'] = model.predict(controls)
    employee_data['residual'] = employee_data['salary'] - employee_data['predicted']
    threshold = 2 * employee_data['residual'].std()
    flagged = employee_data[abs(employee_data['residual']) > threshold]

    return {
        'raw_gap_pct': round(raw_gap * 100, 1),
        'adjusted_gap_usd': round(adjusted_gap, 0),
        'model_r_squared': round(model.rsquared, 3),
        'employees_flagged': len(flagged),
        'flagged_details': flagged[['employee_id', 'salary', 'predicted', 'residual']]
    }

Engagement Survey Analysis

Calculate response rate -- Target 80%+ for statistical validity. Flag departments below 60%.
Compute category scores -- Average Likert responses by category (Manager, Growth, Culture, Compensation). Compare to prior period.
Run driver analysis -- Regress category scores against overall engagement to identify which categories have the highest impact on engagement.
Segment -- Break results by department, level, tenure band, and location. Identify where scores diverge most from company average.
Prioritize -- Plot categories on a 2x2 matrix (Impact vs Score). "High impact, low score" quadrant = priority action areas.

Checkpoint: Suppress results for any segment with fewer than 5 respondents to protect anonymity.

DEI Metrics Framework

Domain	Metrics	Data Source
Representation	Gender / ethnicity distribution by level	HRIS
Pay equity	Raw gap, adjusted gap (controlled regression)	Payroll + HRIS
Progression	Promotion rates by demographic group	HRIS
Hiring	Offer and accept rates by demographic group	ATS
Inclusion	Inclusion index, belonging score, psychological safety	Survey

Data Governance Checklist

Before starting any people analytics project:

Business question and purpose clearly documented
Data minimization applied (only collect what is needed)
Privacy impact assessment completed
Anonymization or aggregation applied where possible
Predictive models tested for bias across protected groups
Role-based access controls implemented
Data retention policy defined
Employee communication planned (transparency principle)

Reference Materials

references/hr_metrics.md - Complete HR metrics guide
references/predictive_models.md - Predictive modeling approaches
references/survey_design.md - Survey methodology
references/data_ethics.md - Ethical analytics practices

Scripts

# Turnover analysis
python scripts/turnover_analyzer.py --data employees.csv

# Flight risk scorer
python scripts/flight_risk.py --model model.pkl --employees current.csv

# Survey analyzer
python scripts/survey_analyzer.py --responses survey.csv --prior prior.csv

# DEI metrics generator
python scripts/dei_metrics.py --data workforce.csv

# Workforce planner
python scripts/workforce_planner.py --current state.csv --plan business_plan.yaml

people-analytics