People Analytics

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.

Clarify First

Before generating the analysis, confirm these inputs. If any is unknown or vague, ASK — do not assume:

• [ ] Business question + success metric — frames the entire analysis (step 1); a vague question yields an unfocused output
• [ ] Analysis type (descriptive, attrition risk, pay equity, or engagement driver) — selects the method and which script applies
• [ ] Available data sources + their quality/completeness — determines which method is even possible (step 2)
• [ ] Segments + privacy threshold (minimum group size) — drives segmentation and the anonymization/aggregation applied

Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact.

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

# Analyze engagement survey results with driver analysis
python scripts/survey_analyzer.py --file survey_results.csv
python scripts/survey_analyzer.py --file survey_results.csv --prior prior_survey.csv --json

# Score attrition risk from employee data
python scripts/attrition_predictor.py --file employees.csv
python scripts/attrition_predictor.py --file employees.csv --threshold 0.7 --json

# Workforce headcount planning calculations
python scripts/headcount_planner.py --file workforce.csv --growth 0.15 --attrition 0.12
python scripts/headcount_planner.py --file workforce.csv --growth 0.15 --attrition 0.12 --json

Troubleshooting

| Problem | Root Cause | Resolution |

|---------|-----------|------------|

| Low survey response rate (< 70%) | Survey fatigue, lack of trust in anonymity, or no visible action from prior surveys | Shorten survey to 15-20 questions max; communicate anonymity safeguards clearly; publish and act on top 3 findings from prior survey before launching next one |

| Attrition model produces too many false positives | Overfitting on historical data, missing key features, or class imbalance | Add regularization; use SMOTE or class weights to handle imbalance; validate with cross-validation not just train/test split; include manager quality and comp-ratio as features |

| Stakeholders distrust analytics findings | Results contradict lived experience, or methodology is opaque | Present methodology transparently; validate findings with HRBPs before publishing; use confidence intervals not point estimates; start with descriptive analytics to build trust before predictive |

| Data quality issues across HRIS sources | Inconsistent coding, missing fields, stale records, or duplicate entries | Establish data governance council; define data owners per field; run quarterly data quality audits; build automated validation checks at ingestion |

| Privacy concerns block analysis | Insufficient anonymization, no consent framework, or regulatory gaps | Apply k-anonymity (minimum group size of 5); conduct privacy impact assessment before each project; engage Legal early; use aggregated data when individual-level is not required |

| Engagement scores are flat despite interventions | Measuring wrong drivers, action plans not executed, or survey is too generic | Run driver analysis to identify high-impact low-score areas; assign action owners with quarterly check-ins; customize survey questions by department or function |

| Leadership does not act on insights | Insights are too academic, lack business framing, or arrive too late | Lead with business impact (revenue, cost, risk); limit recommendations to 2-3 with clear owners and timelines; deliver insights within 2 weeks of data collection |

Success Criteria

| Dimension | Metric | Target | Measurement |

|-----------|--------|--------|-------------|

| Data Quality | HRIS data completeness | > 95% of required fields populated | Quarterly data audit report |

| Data Quality | Data freshness | All records updated within 30 days | HRIS last-modified timestamps |

| Adoption | Stakeholder usage of dashboards | > 70% of HRBPs and VPs access monthly | Dashboard analytics / login tracking |

| Adoption | Insight-to-action rate | > 60% of recommendations result in initiatives | Quarterly tracking of recommendation outcomes |

| Accuracy | Attrition prediction precision | > 70% precision at 50% recall | Model evaluation against actuals (6-month lag) |

| Accuracy | Survey driver analysis validity | Top 3 drivers validated by qualitative data | Cross-reference with exit interviews and focus groups |

| Impact | Regrettable attrition reduction | 10-20% reduction within 12 months of intervention | HRIS voluntary termination data, regrettable flag |

| Impact | Time from question to insight | < 2 weeks for standard analyses | Request-to-delivery tracking |

| Compliance | Privacy incidents | Zero breaches of anonymity thresholds | Audit log of all queries; minimum group size enforcement |

| Maturity | Analytics maturity level progression | Advance 1 level per 12-18 months | Self-assessment against the Analytics Maturity Model |

Scope & Limitations

In Scope:

• Workforce descriptive analytics: headcount, turnover, retention, demographics, tenure distribution
• Engagement survey design, analysis, driver identification, and benchmarking
• Attrition risk scoring using rule-based and statistical methods (standard library only)
• Pay equity analysis: raw gap, controlled gap, outlier flagging
• DEI metrics: representation, progression rates, hiring funnel equity
• Workforce planning: headcount forecasting, scenario modeling, gap analysis
• Dashboard design and KPI framework recommendations

Out of Scope:

• Real-time predictive models requiring ML frameworks (scikit-learn, TensorFlow) -- scripts use rule-based scoring for portability
• Sentiment analysis of free-text survey responses (requires NLP libraries)
• Individual employee profiling or surveillance -- all analysis uses aggregated or anonymized data
• HRIS system administration, data pipeline engineering, or ETL development
• Legal interpretation of pay equity findings (requires Employment Law counsel)
• Organizational network analysis requiring email/calendar metadata

Known Limitations:

• Attrition risk scoring in scripts uses weighted heuristics, not trained ML models; accuracy depends on feature quality and weight calibration
• Pay equity analysis in the SKILL.md examples requires statsmodels (external dependency); scripts use standard-library approximations
• Survey analysis assumes Likert scale (1-5) responses; other formats require preprocessing
• Small population segments (< 30) produce unreliable statistical results; flag these in reporting
• Historical data biases (e.g., biased performance ratings) propagate into predictive models if not addressed

Integration Points

| System / Skill | Integration | Data Flow |

|----------------|-------------|-----------|

| HRIS (Workday, BambooHR, HiBob) | Employee master data, tenure, compensation, performance ratings | HRIS -> analytics data lake; analytics insights -> HRBP workforce plans |

| ATS (Greenhouse, Lever) | Hiring funnel data, source-of-hire, time-to-fill | ATS -> hiring analytics; quality-of-hire scoring feeds back to TA strategy |

| Survey Platform (Culture Amp, Qualtrics, Lattice) | Engagement survey responses, eNPS, pulse check data | Survey platform -> survey_analyzer.py; driver analysis -> action planning |

| Talent Acquisition skill | Hiring funnel metrics, source effectiveness, quality of hire | TA pipeline data -> analytics models; analytics insights -> sourcing optimization |

| HR Business Partner skill | Workforce planning inputs, org health scoring, retention strategy | Analytics insights -> HRBP recommendations; HRBP questions -> analytics projects |

| Operations Manager skill | Headcount forecasting, capacity planning, productivity metrics | Ops demand forecast -> headcount_planner.py; workforce metrics -> ops capacity models |

| Finance skill | Compensation budgets, cost modeling, headcount budget vs actual | Finance comp data -> pay equity analysis; headcount plan -> Finance budget model |

| Payroll (ADP, Gusto) | Compensation actuals, bonus payouts, overtime data | Payroll -> comp analysis; pay equity findings -> comp adjustment recommendations |

| BI Platform (Tableau, Looker, Power BI) | Dashboard hosting, self-service analytics, scheduled reporting | Analytics outputs -> BI dashboards; BI usage metrics -> adoption tracking |