UC21: Compensation Benchmarking & Analysis

The Problem

Annual compensation reviews require matching internal roles to external market survey data. HR collects data from 3 to 5 compensation surveys (Radford, Mercer, Aon Surveys), manually maps internal job titles to survey job families, extracts salary ranges, and calculates compa-ratios (actual salary vs. market). This manual process takes 40 to 60 hours for large organisations and introduces mapping errors. Managers receive incomplete comp data, leading to suboptimal compensation decisions and inconsistent pay equity.

What the Agent Does

Receives request for compensation review for a specific job family, department, or organisation-wide
Maps internal job architecture (job title, level, department, responsibilities) to external survey matches using semantic matching against Radford, Mercer, and Aon survey job families
Retrieves market salary data from integrated survey database: median salary, 25th/75th percentile ranges, bonus targets, benefits data
Calculates compa-ratio for each internal role: actual salary ÷ market median = compa-ratio (1.0 = at market, 0.8 = 20% below market, 1.2 = 20% above market)
Identifies outliers: roles significantly above or below market, roles with pay compression (junior and senior at similar salaries)
Generates compensation review packet for managers with: job family, market data, current incumbent salaries (anonymised), compa-ratios, recommendations (increase to market, hold, monitor)
Flags equity issues: roles with similar responsibilities but different pay, demographic pay gaps (if data available)

Data Requirements

Data Sources:

Compensation surveys (Radford, Mercer, Aon, BLS Occupational Employment Statistics) , market salary data, job family definitions, regional/industry adjustments
HRIS (Workday, PayScale, Salary.com integration) , internal job architecture, job descriptions, employee salary data, tenure, department
PayScale/Salary.com APIs , real-time market data lookups
Internal survey mapping database , historical survey-to-internal-job mappings

Data Classification:

Public: Job families, market salary ranges, compa-ratio benchmarks
Internal Structured: Job architecture, internal salary data (aggregated by role), compa-ratios
Internal Unstructured: Job descriptions
Sensitive/Regulated: Individual employee salary data (must be anonymised in reports)

Data Quality Requirements:

Survey data must be current (within 1 year)
Job descriptions must be accurate and regularly updated
Internal salary data must be current as of latest pay period
Survey-to-job mapping must be reviewed annually by Compensation specialist

Integration Complexity: Medium , Requires API integration with compensation surveys (Radford, Mercer have APIs; Aon typically requires file exchange) and HRIS. PayScale/Salary.com APIs are accessible. Integration is 3 to 4 weeks.

Score Breakdown

Criterion	Weight	Score (1-5)	Weighted
Time Recaptured	15%	4	0.60
Error Reduction	10%	3	0.30
Cost Avoidance	10%	3	0.30
Strategic Leverage	5%	4	0.20
Data Availability	15%	4	0.60
Process Clarity	15%	3	0.45
Ease of Implementation	10%	3	0.30
Fallback Available	10%	5	0.50
Audience (Int/Ext)	10%	4	0.40
Composite	100%		3.75

Why It Scores Well

Compensation benchmarking is a knowledge work task requiring data synthesis from multiple sources. The agent automates manual survey mapping (20 to 30 hours saved per cycle), provides consistent, auditable analysis, and supports data-driven compensation decisions. Process is repeatable and documentable. Fallback is straightforward: Compensation specialist can still manually verify matches.

Regulatory Alignment

Equal Pay Act & EEOC , Compensation analysis should identify gender/demographic pay gaps; agent flags for HR/Legal review
Fair Labor Standards Act , Job classification (exempt vs. non-exempt) affects market comparisons; agent validates classification
State pay transparency laws , Colorado, California, New York require pay range publication; agent generates compliant ranges from market data
GDPR (if international) , Market data may include international salary data; ensure compliance with data use restrictions

Sprint Factory Fit

Sprint 1 (2 weeks) + 1 build sprint (2 weeks)

Scores 3.75. Good use case because: (1) saves significant manual effort (40 to 60 hours per annual cycle), (2) improves compensation decision quality through consistent benchmarking, (3) supports pay equity analysis (strategic value), and (4) data is available in surveys and HRIS. However, market data access and survey integration add some complexity. Business impact is strategic (better compensation decisions) rather than operational.

Comparable Implementations

PayScale: Compensation benchmarking and pay equity analysis , real-time salary data and market comparisons
Radford: Survey data and compensation analysis , enterprise survey integration
Mercer: Compensation benchmarking solutions , market data and gap analysis

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