Price New Jobs

Pricing new jobs is an end-to-end workflow that takes you from adding jobs to your band set through configuring market data mappings, applying data rules, and finalizing benchmark values.

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Table of Contents

1. Overview
2. The Complete Workflow
3. Step 1: Add Jobs to Band Set
4. Step 2: Configure Market Data Mappings
5. Step 3: Apply Data Rules
6. Step 4: Review and Finalize Benchmarks
7. Common Use Cases
8. Best Practices
9. Related Articles

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Overview

By the end of this workflow, your new jobs will have:

• ✅ Bands created in the band set (rows added)
• ✅ Market data mappings configured (job family → survey families)
• ✅ Data rules applied (how to calculate benchmarks)
• ✅ Benchmark values loaded (ready to inform compensation ranges)

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The Complete Workflow

Full end-to-end flow:

1. Add jobs → Create bands for new jobs in selected pay zones
2. Configure mappings → Map to survey job families and locations
3. Apply data rules → Set up benchmark calculation rules
4. Review benchmarks → Validate market data loaded correctly
5. Set band ranges → Use benchmarks to price the jobs (see Edit Bands articles)

This guide covers steps 1-4. After completing these steps, you'll be ready to set compensation ranges based on your benchmark data.

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Step 1: Add Jobs to Band Set

First, add the jobs you want to price to your band set. You can create jobs directly in the app or upload.

Create New Ladder In-App

When to use: Adding a brand new job ladder that doesn't exist yet (e.g., expanding to a new function or creating a new family).

From Job Manager:

1. Navigate to Jobs tab (Job Manager)
2. Click Create Job
3. Enter the pay zone and ladder details:

• Function - Department (e.g., Engineering, Sales)
• Family - Specialization (e.g., Backend Engineering, Field Sales)
• HRIS Matcher (based on your matchers)

4. Pave pre-populates levels based on standard job architecture structure

• Typically creates P1, P2, P3, P4, P5, etc.
• You can edit, add, or remove levels on this page

5. For each level, configure:

• Display title (optional)
• HRIS matchers (HRIS level, HRIS title, HRIS job code)
• Display levels and ranks

Example:

Creating Finance > AI > IC ladder:
1. Function: Finance
2. Family: AI
3. Track: IC
4. Pave pre-populates: P1, P2, P3, P4, P5
5. Select pay zones: Brazil, San Francisco
Result: 10 new bands (5 jobs × 2 pay zones) ready for pricing

See Add Bands article for details on the pre-populate workflow.

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Step 2: Configure Market Data Mappings

After adding jobs, map them to survey data sources so Pave knows the survey job codes to pull benchmarks.

Navigate to Mappings

From your band set:

1. Click the breadcrumb or navigate to Mappings tab
2. Click Ladders in the left sidebar

Accessing Mappings from Job Creation Flow

After creating jobs in-app or activating a ladder, you can move directly into mapping:

1. From Add Job flow - After creating the ladder, select "Move to benchmarking"
2. From the band set - Click Add Mapping button which directs you to the specific ladder that needs the mapping added

Map each ladder:

1. Find your ladder in the list
2. For each survey source (Mercer, Pave, etc.):

• Click the Label field
• Select the matching survey job family
• Set the Weight (typically 1)

3. Repeat for all survey sources
4. Click Save and regenerate benchmarks

Mapping Levels

After mapping ladders, map levels:

1. Click Levels in the left sidebar
2. Map your job levels/ranks to survey seniority levels
3. Save

Mapping Pay Zones

Map your pay zones to survey geographies:

1. Click Pay Zones in the left sidebar
2. For each pay zone, map to survey location cuts
3. Example: San Francisco → "San Francisco Bay Area", "United States - West Coast"
4. Save

See Job Mapping article for detailed mapping instructions.

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Step 3: Apply Data Rules

Data rules control how benchmark data is calculated from your survey sources. Associate your new jobs with data rules.

Navigate to Benchmarking Tab

1. Go to Benchmarking tab in your band set
2. Navigate to the new jobs you just added
3. Click Apply data rules button in the toolbar

Choose Data Rule Option

Option 1: Add to existing rule (Recommended)

If you already have a data rule for similar jobs:

1. Select Add to existing rule
2. Choose the rule from the list (e.g., "Data Rule 3")
3. Click Apply

The new jobs will use the same benchmarking configuration as other jobs in that rule.

Option 2: Create new rule

For jobs that need different benchmark logic:

1. Select Create new rule
2. Configure:

• Name the rule: e.g., "Account Management - Brazil"
• Select survey sources: Choose which surveys to use
• Choose percentile targets: P25, P50, P75, or custom
• Configure fallbacks: What to do when survey data is missing

3. Click Apply

Option 3: Duplicate existing rule

Copy an existing rule's configuration and modify:

1. Select Duplicate existing rule
2. Choose which rule to copy
3. Modify any surveys or percentiles
4. Click Apply

See Data Rules article for detailed configuration options.

Regenerate Benchmarks for New Jobs Only

This feature allows you to regenerate matches only for added/updated jobs, so you don't lose previous benchmark edits.

From the Benchmarking tab:

1. After adding new jobs and configuring mappings/data rules
2. Click Regenerate benchmarks
3. Choose regeneration scope:

• Only for updated jobs (recommended) - Preserves existing benchmark edits
• All jobs - Full refresh

This prevents accidentally overwriting manual benchmark adjustments you've made to existing jobs while ensuring new jobs get the latest data.

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Step 4: Review and Finalize Benchmarks

After applying data rules, review the benchmark values to ensure they're correct.

Check Benchmark Status

In the Benchmarking tab, look for status indicators:

✅ Has Benchmark (Green)

• Benchmark loaded successfully
• Shows benchmark value (e.g., R$227,949.95)
• Shows progression percentage (e.g., 14%, 33%)

⚠️ No Data (Red/Gray)

• Survey doesn't have data for this job
• Need to adjust mapping or use different survey

⚠️ Missing Mapping

• Job family mapping incomplete
• Go back to Step 2 and complete mappings

⚠️ Missing Data Rule

• Data rule not applied
• Go back to Step 3 and apply data rule

Use the Benchmarking Sidepeek

Click any job to open the sidepeek and review:

Benchmark value:

• The composite benchmark (weighted average)
• Visual range showing P25/P50/P75

Contributing data sources:

• Each survey source with its value
• Sample size for each
• Weight applied
• How the composite is calculated

Auto-smoothing indicator:

• If auto-smoothed, shows "Auto-smoothed" badge
• Link to "Revert this job to Composite to adjust weighting"

Quality checks:

• Are sample sizes adequate? (>20 is good, <20 is questionable)
• Are values consistent across surveys?
• Does the benchmark make sense for this role?

Fill Data Gaps (If Needed)

See Auto-Smoothing and Benchmarks Edits articles for details.

What's Next?

After pricing new jobs with benchmark data, you're ready to:

1. Set band ranges - Use benchmarks to define min/mid/max compensation (see Edit Bands articles)
2. Assign to grades - If using grades, slot jobs into your grade structure (see Edit Bands & Assign Jobs to Grades)
3. Review impact - See how many employees fall within the new bands
4. Publish - Push bands live or to a merit cycle (see Publish articles)

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