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 1. **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. 2. **Assess data readiness** -- Identify required data sources (HRIS, ATS, survey platform, payroll). Check for completeness, recency, and quality. Flag any gaps before proceeding. 3. **Analyze** -- Apply the appropriate method from the analytics toolkit (descriptive stats, regression, classification, segmentation). Document assumptions and limitations. 4. **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. 5. **Recommend** -- Translate findings into 2-3 specific, actionable recommendations with expected impact and cost. 6. **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 ```python 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 ```python 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 1. **Calculate response rate** -- Target 80%+ for statistical validity. Flag departments below 60%. 2. **Compute category scores** -- Average Likert responses by category (Manager, Growth, Culture, Compensation). Compare to prior period. 3. **Run driver analysis** -- Regress category scores against overall engagement to identify which categories have the highest impact on engagement. 4. **Segment** -- Break results by department, level, tenure band, and location. Identify where scores diverge most from company average. 5. **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) ## Scripts ```bash # 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