HR AI Skill

Hr Analytics

Build HR analytics dashboards, workforce reports, people metrics, and data-driven insights for HR decision-making. Use when creating HR reports, analyzing people data, building workforce dashboards, or presenting HR metrics to leadership. Triggers on phrase...

HR Analytics & Reporting

Transform HR data into actionable insights for strategic decision-making.

Workflow

  1. Define reporting requirements: Stakeholder needs, key metrics, frequency, format.
  2. Source and prepare data: Pull from HRIS, ATS, LMS, performance platforms, survey tools.
  3. Clean and validate data: Handle missing values, standardize formats, verify accuracy.
  4. Build dashboards: Interactive visualizations with filters, drill-downs, alerts.
  5. Generate insights: Identify trends, correlations, anomalies, and recommendations.
  6. Distribute reports: Scheduled delivery, self-service access, executive summaries.
  7. Act on insights: Recommend and track HR initiatives based on data.
  8. Iterate: Refine metrics based on stakeholder feedback and evolving needs.

Core HR Metrics

HR METRICS SCORECARD
=====================

CATEGORY 1: WORKFORCE COMPOSITION
  → Total headcount: [Number] (+/- [Number] vs. last quarter)
  → FTE count: [Number] (full-time equivalent)
  → Contractor count: [Number]
  → Headcount by department, level, location, tenure band
  → Diversity metrics: Representation by demographic at each level
  → Org chart depth: Average management span of control

CATEGORY 2: RECRUITING
  → Time-to-fill: [X] days (avg) by department and role type
  → Time-to-hire: [X] days (candidate experience metric)
  → Cost-per-hire: $[X] (total recruiting cost / number of hires)
  → Offer acceptance rate: [X]% (target: > 85%)
  → Source-of-hire: Breakdown by channel (referral, job board, agency, etc.)
  → Quality-of-hire: 90-day performance ratings, 1-year retention
  → Hiring manager satisfaction: Survey score [X]/5.0
  → Pipeline health: Active candidates per open role by stage

CATEGORY 3: ONBOARDING
  → New hire 90-day retention: [X]% (target: > 90%)
  → Onboarding completion rate: [X]% (all tasks completed by Day 30)
  → New hire satisfaction: Survey score [X]/5.0
  → Time-to-productivity: Days until first major deliverable

CATEGORY 4: PERFORMANCE
  → Performance distribution: % in each rating bucket
  → Review completion rate: [X]% (on time)
  → Goal completion rate: [X]% of goals achieved
  → Performance by department, demographic, tenure
  → Calibration variance: Consistency across managers

CATEGORY 5: COMPENSATION
  → Avg salary by role, level, department, location
  → Compa-ratio: Actual salary / midpoint of band (target: 0.90–1.10)
  → Salary equity metrics: Controlled pay gaps by demographic
  → Merit increase avg: [X]% (by performance rating)
  → Bonus payout avg: [X]% of target
  → Total labor cost: $[X] ([X]% of revenue)

CATEGORY 6: LEARNING & DEVELOPMENT
  → Training hours per employee: [X] hours/year
  → Training completion rate: [X]% of assigned courses
  → L&D investment: $[X] per employee
  → Certification rates: [X]% of eligible employees certified
  → Promotion rate: [X]% internally promoted (vs. external hire)

CATEGORY 7: RETENTION & TURNOVER
  → Overall turnover rate: [X]% (voluntary: [X]%, involuntary: [X]%)
  → Regrettable turnover: [X]% (top performers leaving)
  → Turnover by department, manager, tenure, demographic
  → Tenure distribution: % in each tenure band
  → Early turnover (< 1 year): [X]% — indicates hiring/onboarding issues
  → Stay interview completion: [X]%
  → Exit interview completion: [X]%

CATEGORY 8: ENGAGEMENT
  → Engagement survey score: [X]/5.0 (trend over time)
  → eNPS (employee Net Promoter Score): [X]
  → Pulse survey scores: By category and department
  → Absenteeism rate: [X]% (unplanned absences / total workdays)
  → PTO utilization: [X]% (are people actually taking time off?)

Dashboard Design

EXECUTIVE HR DASHBOARD
=======================

Layout: Single-page summary with drill-down capability

ROW 1: KEY HEADLINE METRICS (big numbers)
  [Headcount] [Turnover %] [Open Roles] [Engagement Score] [Time-to-Fill]
    1,247       12.3%        23           3.7/5.0          42 days

ROW 2: WORKFORCE TRENDS (line charts)
  → Headcount trend (last 12 months)
  → Turnover trend (last 12 months, voluntary vs involuntary)
  → Engagement score trend (last 8 quarters)

ROW 3: RECRUITING PIPELINE (funnel)
  Applied → Screened → Interviewed → Offered → Hired
  2,340    1,120      456         89        67

ROW 4: DEPARTMENT BREAKDOWN (bar chart)
  Turnover by department (flag > 15%)
  Engagement by department (flag < 3.5)

ROW 5: ALERTS AND ACTIONS (table)
  ⚠ Engineering turnover at 18% (3-month avg) — investigate
  ⚠ Sales hiring pipeline below target — 14 roles > 60 days open
  ✓ Onboarding satisfaction improved to 4.2 (from 3.8)
  ⚠ Q3 engagement pulse declining in Operations — action needed

INTERACTIVE FEATURES:
  → Click any metric to drill down (by department, location, demographic)
  → Filter by date range, department, level, location
  → Export to PDF or Excel
  → Subscribe to scheduled delivery (weekly/monthly)
  → Alert configuration: Set thresholds for automatic notifications

Report Templates

MONTHLY HR REPORT — [Month, Year]
===================================

EXECUTIVE SUMMARY:
  [3–5 bullet points highlighting key changes, concerns, and wins]

WORKFORCE SNAPSHOT:
  Headcount: [Current] ([Change] from last month)
  New hires: [Number] | Separations: [Number] | Net change: [Number]
  Open positions: [Number] ([X] critical, [X] backfill, [X] growth)

RECRUITING UPDATE:
  Offers extended: [Number] | Accepted: [Number] | Declined: [Number]
  Avg time-to-fill: [X] days ([Trend] vs. last month)
  Top sources: Referrals ([X]%), Job boards ([X]%), Agencies ([X]%)
  Critical hires needed: [List roles and status]

TURNOVER ANALYSIS:
  Voluntary separations: [Number] ([X]% of headcount)
  Top exit reasons: [Reason 1], [Reason 2], [Reason 3]
  Regrettable losses: [Number] ([Names/Roles if appropriate])
  Departments above avg turnover: [Department 1] ([X]%), [Department 2] ([Y]%)

ENGAGEMENT:
  Latest pulse score: [X]/5.0 ([Trend])
  Key themes: [Positive theme], [Concern theme]
  Action items in progress: [List]

COMPENSATION:
  Total labor cost YTD: $[X] ([Variance] vs. budget)
  Avg merit increase: [X]% (budget: [Y]%)
  Bonus pool status: [On track / At risk / Over budget]

LEARNING & DEVELOPMENT:
  Training completed this month: [Number] courses
  Completion rate: [X]% (target: [Y]%)
  Upcoming: [Key training programs]

NEXT MONTH PRIORITIES:
  → [Priority 1]
  → [Priority 2]
  → [Priority 3]

Predictive Analytics

PREDICTIVE HR ANALYTICS
=========================

FLIGHT RISK MODEL:
  → Predicts likelihood of employee resignation within next 90 days
  → Input variables: Tenure, recent promotion, comp vs market, engagement score,
    manager changes, PTO usage pattern, internal job applications, commute distance
  → Output: Risk score (0–100%) per employee
  → Action: Managers flagged for high-risk employees; proactive stay interview

PERFORMANCE PREDICTION:
  → Predicts future performance based on hiring data, onboarding, early reviews
  → Input variables: Pre-hire assessment scores, referral source, onboarding completion,
    30-day feedback, training completion
  → Output: Predicted performance rating range
  → Action: Early intervention for at-risk new hires

TURNOVER FORECASTING:
  → Predicts quarterly turnover by department
  → Input variables: Historical turnover, seasonality, hiring volume, engagement trends,
    market conditions, compensation competitiveness
  → Output: Expected turnover rate and headcount impact
  → Action: Proactive hiring, retention strategies, workload planning

SKILLS FUTURE-CASTING:
  → Predicts which skills will be in highest demand in 12–24 months
  → Input variables: Business strategy, technology roadmap, market trends,
    current skills inventory, industry benchmarks
  → Output: Skills gap forecast by department
  → Action: Targeted L&D investment, hiring strategy adjustment

IMPLEMENTATION:
  → Data requirements: Minimum 24 months of historical data
  → Model validation: Back-test against known outcomes
  → Privacy: Employee-level predictions used for proactive support, not punitive action
  → Ethics: Regular bias audits on predictive models

Integration Points

Edge Cases