## **Product History and** **Overview**

In March 2021, Pave launched an innovative approach to compensation benchmarking using data collected directly from automated and persistent (i.e., real-time) connections to human resources (HR) platforms, including human resources information systems (HRIS), applicant tracking systems (ATS), and equity management systems (EMS).

Our real-time model, coupled with the use of sophisticated data science techniques and machine learning algorithms, provides customers with improved compensation benchmarks and a radically streamlined user experience.

Today, more than 8,500 companies—**including more than 1,000 medium and large enterprises**—use Pave's Market Data product as their primary or secondary compensation data source and enjoy significant advantages over companies that rely solely on traditional compensation surveys.

Depending on your company's stage of growth, we currently offer two options when using Pave's Market Data product—our Launch and Pro packages:

* **Market Data Lite** – We recommend our Launch package for business leaders, recruiters and HR generalists looking to access base salary and new hire equity benchmarks in one or two markets to launch and grow a new venture. To join for free today, [sign up here](https://app.pave.com/signup).
* **Market Data Pro** – We recommend our Pro package for organizations with at least one compensation or total rewards professional looking to access detailed cash and equity compensation benchmarks in multiple markets to build formal pay processes and structures. To learn more, please [contact our team](https://www.pave.com/request-demo).

If you would like to learn more about Pave's full compensation management platform, including market pricing, salary range management, compensation cycle planning, and total rewards communication tools, [explore our platform here.](https://www.pave.com/workflows)

## **Pave's Real-Time Model**

### How does Pave's real-time compensation benchmarking model work?

As noted above, Pave's Market Data products deliver compensation benchmarks to customers using data collected directly from real-time connections to HR platforms, including HRIS, ATS, and EMS systems.

Once data is received by Pave, employee records are matched to Pave's job architecture system (i.e., job levels and job families) using a machine learning algorithm. Data for employees with a successful job match is then aggregated and de-identified before entering Pave's overall compensation database. These steps dramatically accelerate the speed with which data is collected, matched, and published, while protecting individual data privacy and maintaining the data confidentiality of participating organizations.

Additional layers of machine learning provide customers with value-added insights, including data quality information (e.g., data consistency labels) and the normalization of benchmarks in cases where raw market data is sparse (e.g., job families with low incumbent counts or markets with smaller concentrations of talent). Overall, this approach provides customers with improved benchmarks and a radically streamlined user experience.

### What makes Pave's real-time model better than traditional compensation surveys?

Pave's Market Data products use data collected directly from real-time connections to HR platforms, which is further enhanced by a world-class data science team and a layer of sophisticated machine learning algorithms. As a result, Pave customers enjoy significant advantages, including:

* **No stale data.** Pave collects information continuously from HR platforms and publishes updated compensation benchmarks monthly. In contrast, traditional compensation surveys provide a point-in-time snapshot of pay that is, on average, six months old before it is published.
* **No survey inputs.** After a customer connects their HR platforms to Pave once—which can take as little as 15 minutes—they never have to complete a survey submission again. Many traditional survey providers ask customers to submit data annually using insecure and overly complex Excel-based input templates.
* **No missing data.** By virtue of collecting data directly from HR platforms, Pave can quickly gather comprehensive information on all employees. In contrast, traditional compensation surveys usually receive pay information covering approximately 70% to 80% of employees.
* **Improved benchmarks.** By virtue of collecting data directly from HR platforms, Pave can not only provide comprehensive base salary, bonus, and equity compensation insights (e.g., unvested equity holdings), but also tens of organizational benchmarks in real-time, including attrition rates, promotion rates, span of control insights, and workforce spend, among many others.
* **Streamlined job matching.** Pave uses a machine learning algorithm to accelerate job matching and allows customers to bypass manual job matching if desired. Our job matching algorithm is reviewed regularly by compensation experts to fine-tune results. This approach can save larger organizations hundreds of hours and tens of thousands of dollars on manual job matching efforts.

To learn more about the disruption of traditional compensation surveys, read our white paper: [A New Era in Compensation Benchmarking is Here](https://www.pave.com/new-era-in-compensation-benchmarking)

### How often is Pave's real-time compensation database updated?

Published compensation benchmarks in Pave's Market Data products are updated monthly, with new data releases typically scheduled for the first Monday of each month. Actual publication dates may vary.

At Pave, we use the term real-time to refer to how often data is collected from customers. Indeed, the automated and persistent connections we have to HR platforms generate new data daily. However, we choose to publish updated compensation benchmarks monthly to ensure the consistency and quality of the data we publish.

### Who can join Pave's real-time compensation database?

Companies of all types—private and public, large and small—can join Pave's real-time compensation database.

Today, our database primarily includes technology companies (e.g., hardware, software, etc.) and technology-adjacent companies (e.g., FinTech, MedTech, etc.), but a growing number of energy, financial services, healthcare, life sciences, and manufacturing firms are joining Pave, among others.

Of the more than 8,500 companies using Pave, a majority are venture-backed private companies. However, more than 1,000 medium and large enterprises now use Pave, and 40% of all employees in our dataset come from companies with more than 1,000 employees. See the [Compensation Database Coverage](https://www.pave.com/market-data-methodology#data-database) section of this guide for more details.

Depending on your company's stage of growth, we currently offer two options when using Pave's Market Data product—our Launch and Pro packages:

* **Market Data Lite** – We recommend our Launch package for business leaders, recruiters and HR generalists looking to access base salary and new hire equity benchmarks in one or two markets to launch and grow a new venture. To join for free today, [sign up here](https://app.pave.com/signup).
* **Market Data Pro** – We recommend our Pro package for organizations with at least one compensation or total rewards professional looking to access detailed cash and equity compensation benchmarks in multiple markets to build formal pay processes and structures. To learn more, please [contact our team](https://www.pave.com/request-demo).

If you have specific questions about the relevancy of Pave's dataset for your company, or the right Market Data package for you, we encourage you to [contact our team](https://www.pave.com/request-demo).

### I already use a traditional compensation survey. Why do I need an additional source like Pave?

If you already use one or more traditional compensation surveys, Pave's Market Data product can still add significant value to your organization. Here's why:

* **Multiple data sources are a feature, not a bug.** Different datasets have unique strengths and weaknesses and can be used to close gaps in data coverage. In particular, Pave's real-time model, coupled with our sophisticated data science and machine learning capabilities, allows us to capture, analyze, and provide certain datasets faster and more comprehensively than others. At the end of the day, compensation is often a company's single largest expense, so maximizing confidence in compensation decisions using multiple sources is time and money well spent.
* **Traditional surveys have limitations.** As noted above, traditional compensation surveys suffer from data lag, antiquated and time-consuming data submission processes, and limited equity compensation insights. Pave's model directly addresses these challenges. If you're going to add a new data source, pick a provider like Pave who makes your workload significantly lighter while closing data coverage gaps.
* **Real-time data and insights will give you an edge.** By virtue of collecting data directly from HR platforms in an automated and persistent fashion, Pave can not only provide comprehensive base salary, bonus and equity benchmarks, but also a wide range of value-added insights. These insights include benchmarks for unvested equity holdings, the ability to normalize annual vs. total equity grant values or toggle between indented (at grant) vs. actual (current) equity grant values, and information on equity burn rates and vesting schedules.

To learn more about the disruption of traditional compensation surveys, read our white paper: [A New Era in Compensation Benchmarking is Here.](https://www.pave.com/new-era-in-compensation-benchmarking)




Market Data Methodology Overview

Advanced Equity

Global Location Insights

Market Data Pro

How to Search for a Benchmark

Market Data Compensation Database Coverage

Market Data 101

Consistency Labels

Job Families

Calculated Benchmarks

Compensation Types

Data Collection and Aggregation

Percentiles

Filters

Pave's Data

Consistency Labels FAQ

Data FAQs

Calculated Benchmarks FAQ

Location and Global Data FAQs

Equity Practices FAQs

Market Data FAQs

Market Data

Market Data Connections Set up

Market Data Connections Overview

Integrations 101

Integrations

Adding Columns

Team View Overview

Team View Column Configuration

Team View Customizations

Re-ordering Columns

Deleting Columns

Team View Settings

Team View

Comp Planner Configurations Overview

Compensation Planner 101

Getting Started - Compensation Planner

Compensation Planner FAQs

Cycle Specific Permissions

Employee Eligibility

Step 1: Initial Cycle Setup

First-Class Columns

Using Handlebars Formula for Calculated Columns

Columns

Step 2: Column Configuration

OTE Planning Configuration Guide

What is OTE Planning?

Step 3: OTE Planning

Am I able to update or change approval chains during a cycle?

Updating Approval Chains

Compensation Cycle Data Upload

Step 4: Data Management

How can I configure market adjustment recommendations for employees?

How can I configure variable raise recommendations for employees?

Recommendation Logic

What are Recommendations?

Step 5: Recommendation Logic

Budgets

Budget Dashboard

Top Down Budgets: In-app Configurations

Step 6: Budget Confirmation

Planning Windows Setup Guide

Planning Windows Overview

Monthly & Hourly Pay

Employee Compensation History Popout

UI Toggles

Filtering & Sorting

Approval Statuses

Step 7: Planner Experience

Smart Flags

Compensation Outliers

Manager Compression Flag

Vesting Drop Flag

Step 8: Controls & Governance

Peers Overview

Cycle Outcomes

Value Rounding in Compensation Planning

In App Reward Letter Editor

Reward Letters

Peers Configuration Guide

Cycle Progress Dashboard Overview

Other Cycle Actions

 Compensation Planner

Logging In

Custom Role Creation Guide

Creating an Account

How to add a colleague to Pave’s Market Data Platform

Market Data Role Management

Why does it matter to get levels and role families right?

SSO Configuration Guide

Account Setup

Contact Us

How much does Pave Market Data access cost?

How does Pave Work?

Intro to Pave

Pave 101

Compensation Philosophy

Total Compensation

Compensation 101

The Stock Option Lifecycle

ISO Tax Implications

The Basics of Stock Options

The Confusing AMT (Alternative Minimum Tax)

ISO Strategies

An Overview of How Stock Options Work

Equity 101

Account Creation Troubleshooting and FAQs

How to Benchmark Executive Compensation

Login Troubleshooting and FAQ

How does company size affect which department, job family and job level I should choose?

How does Pave determine a role level and role family?

What are Job Levels, Departments and Job Families? Why are they important?

Troubleshooting Rippling Zero Records Error

What Market Data Connections does Pave currently support?

FAQs

Getting Started

Survey aging

Edit and apply benchmarks

Set weights and logic

Map data sources to jobs

Supported survey providers

Manage Pave data sources

Manage survey data sources

Benchmarks overview

Step 2: Benchmarking

Band Sets

Pay Types: Calculated TDC, Annual Equity, and Refresh Equity

Manually Create New Band Sets

Bands Set Audit Log

How to duplicate band sets

Managing & Reordering Calculated Columns

Calculated Columns

Unmatched Employees

Upload/Download Bands

View changes

Editing Bands

Employee Distribution in Bands

Pay Zone Manager

Job Architecture 101

Grades

Setting up Company Job Architecture

Company Job Architecture

Step 1: Build Foundation

Generate Bands from Market Data

Bulk & Manually Associate Market Data to Bands

Step 3: Update Ranges

Relative Permissions

Generate Bands for New Jobs in Your Job Architecture

Compare Bands

Advanced Filters

Ongoing Management & Analysis

Market Pricing

Option Driver Deprecation FAQs

Option Driver

Option Impact Deprecation FAQs

Option Impact

Option Impact & Option Driver

Executive Reporting Dashboard

Cycle Monitoring and Reporting

Building Market-Based Compensation Bands

Employee Groups

Cycle Configuration

How does Recommendation Logic work?

Am I able to change another user’s permissions or login credentials while impersonating?

Market Data Methodology Overview

Share

Share

Market Data Methodology Overview

Share

Share