How to Select The Best Benchmarking Data For Your Organization
Compensation Fundamentals

How to Select The Best Benchmarking Data For Your Organization

Time of posting an article for Barley Compensation Management Software
February 21, 2024
Reading time for Barley Compensation Management Software
13 min read

Surveys from established research firms (like Mercer, Aon Radford, or Willis Towers Watson) have long been the top source of salary benchmarking data for companies looking to build pay bands and compare their salaries to market rates.

However, hiring markets have rapidly changed over the past few years due to massive economic swings and a large migration to remote work. Workforces are more globalized, and once-competitive talent pools have endured mass layoffs—causing companies to seek additional sources of compensation data to supplement traditional surveys and keep up with the market. 

New salary data providers have sprung up in response, fueled by new technologies and increasing access to salary information made available through a rise in pay transparency laws. 

While more choice is often a good thing, the sheer number of salary benchmarking sources has made it increasingly difficult to choose the right option for all of your company’s needs. 

In this post, we weigh the pros and cons of different benchmark datasets and share advice for effectively using multiple sources to guide important pay decisions. 

The Top Salary Benchmarking Data Sources

Understanding the survey benchmark data landscape is the first step to figuring out which will work best for your needs. 

Here are several key types of salary benchmark information sources to consider:

1. Salary Surveys

Salary benchmarking companies (like Mercer, Aon Radford, and Willis Towers Watson) pool information from participating organizations to establish market benchmarks (typically broken out into percentiles) for roles across regions, industries, and company sizes. To gain access to these salary surveys, organizations are expected to “participate” by submitting their employee compensation data in line with the survey’s job function and leveling framework. 

The data from participating companies is then validated, de-identified, aggregated, and released in a searchable database a few months later. 

They often include a variety of compensation details beyond base salary, such as bonuses, equity, benefits, etc. to give participants an idea of total compensation. Some providers also have labor market surveys on larger trends—such as forecasted salary increases, merit pay budgets, transparent pay initiatives, and more.

The Pros: 

  • Data is more consistent, clean, and accurate than other options
  • Generally has a robust, global data set across industries, company sizes, etc.
  • Able to view percentiles and compare oneself to others in a similar percentile 

The Cons: 

  • Salary information can be lagging due to the frequency of survey updates 
  • Participation can be resource-intensive (especially for smaller organizations)
  • Leveling jobs and uploading data is often incredibly manual and cumbersome  
  • May require purchasing multiple surveys for niche roles or industries
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Recommendation: Using at least one aggregate salary survey source is important for increasing the accuracy of your benchmarks. It all comes down to selecting the one that best reflects the talent pool you’d like to recruit from. However, some organizations may not have enough budget or resources to purchase or participate in surveys. It’s worth looking into compensation management tools that make reviewing and submitting benchmarking survey data faster and easier if your team is strapped for time.

2. Self-Reported Salary Database

Websites like and crowdsource salary data from candidates online to create pay ranges for roles. They usually require participants to make an account and share salary details to gain access to pay range information. However, since there is very little structure or guidelines for what a job title means or the work it encompasses, comparing salary ranges can be varied or completely inaccurate compared to your definitions for the role. Outlier data may not be removed from results, causing ranges to be skewed from the market. 

The Pros: 

  • Uses large sample sizes to create average pay ranges 
  • Covers a breadth of industries, company sizes, and geographies
  • Can allow for comparisons against specific companies or competitors

The Cons: 

  • Job level or function definitions not enforced or consistent
  • Self-reporting and a lack of verification leads to inaccurate or broad ranges
  • Usually doesn’t include total compensation details for variable pay or other rewards
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Recommendation: These sources are not leaned on heavily as a best practice since they’re often not very structured and rely on self-reported compensation information, leading to less accurate data. However, since candidates may look at them for salary guidance, it’s useful to be aware of them and be able to speak to why your salary ranges may not match these sources.

3. Candidate Benchmarking

Candidate benchmarking looks at candidate salary expectations and/or candidate offers for a role instead of actual employee salaries to set benchmarks. Offer data is usually captured by integrating with the Applicant Tracking Systems (ATS) of participating organizations before being aggregated to establish market benchmarks.

There are two main sources of candidate data: one is your own talent team’s data, where compensation expectations are recorded on an ongoing basis as your team interviews candidates. (Tools like Barley integrate with your ATS and make capturing and reporting your team’s data easier). The other comes from offer data providers (e.g. Compa) that pool offer datasets from multiple organizations to determine benchmarks.

These benchmarks help companies see if candidate expectations are in line with or above their ranges. While candidate data is far more “transactional” and time-based, it can indicate the direction the market is heading. For example, salary surveys might tell you the 50th percentile for a role is $85K. However, if your talent team has met with 20+ candidates whose expectations are above $100K, this may indicate that the market is seeing a spike in demand.

The Pros: 

  • Gives current insight into candidate salary expectations, which could serve as a stronger market signal for pay
  • Helps HR and compensation teams vet other salary data sources against market expectations
  • Data is more time-sensitive than employee-based compensation data and traditional salary surveys

The Cons: 

  • Building out your own database with a large enough sample size of candidate expectations data can take time
  • Data may not accurately reflect market expectations since recruiting teams may not sanitize their data or remove outliers
  • Salary expectations from candidates may not match role level or job function
  • Candidate expectations and offer data may not reflect actual accepted offers or final pay of candidates
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Recommendation: Candidate salary expectations or offer data can be useful if you have a large enough sample size and are consistently hiring for the same roles over and over again. They can be a good indicator of current market trends or oncoming shifts—but should be used with other data sources before changing internal pay bands. (Increasing offers to meet candidate expectations can lead to wage suppression or inequities for current employees.)

4. Job Posting Ranges

With pay transparency laws forcing more companies to publish their salary ranges, it’s becoming easier for businesses to compare themselves to competitors. 

Companies can have talent or compensation team members do this manually. However, there are freemium solutions (like Squirrel) that aggregate data from career websites—like LinkedIn and Indeed—to make pooling data faster and easier. However, since information is scraped from job listings and not participation-based (like traditional surveys), getting an accurate picture of market benchmarks is difficult. 

The Pros: 

  • Offers timely insight into pay ranges for recently posted roles
  • Allows you to specifically target a competitor’s pay practices and ranges
  • Usually covers a wide breadth of role types and industries

The Cons: 

  • Public salary ranges may be intentionally broad and not match actual internal pay
  • Ranges are not calibrated to a job leveling framework—making comparisons difficult or inaccurate
  • Very manual process, if done without software 
  • Difficult to aggregate, verify, or filter data easily 
  • May not offer insight into other components of pay, like variable/bonus compensation or equity 
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Recommendation: Much like self-reported and candidate salary databases, these data sets should not be your sole benchmarking source since they can be highly inaccurate and inconsistent. However, you may want to be aware of the ranges they suggest for certain roles since potential candidates and current employees may be using them for salary research.

5. Benchmarking Solutions and Other Data Aggregators 

This evolving category of solutions brings together benchmarking survey data with new technology to streamline the reporting process and improve the timeliness of salary information.

For example, Payscale, and—all of which aggregate salary data from multiple surveys and sources—offer benchmarking software that makes comparing market benchmarks to employee pay simpler. Conversely, compensation management tools, like Barley, integrate with survey providers to embed global benchmarking data and give companies a real-time view of how their employee pay stacks up against the most recent industry benchmarks.

 The Pros: 

  • Makes accessing and searching benchmarking data and comparing to employees a much faster and more intuitive process
  • Streamlines the survey participation and submission process 
  • Data is validated, making it more accurate and trustworthy, in addition to being easy to filter through

The Cons: 

  • May be limited to benchmark providers the solution offers or integrates with
  • Must incur cost to have both a software solution and access to benchmarking data
  • Require integrations with HRIS systems to streamline workflows
Barley’s Benchmarking feature seamlessly integrates with Mercer and other survey data
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Recommendation: By connecting data sources into a single solution with actual employee pay data, companies can make the process of salary benchmarking smoother, faster, and more illuminating. These solutions can also help reduce manual errors (which are easy to make across spreadsheets and data sources) and ultimately improve decision-making.

Evaluating Salary Benchmarking Data Sources: A Two-Pronged Approach

Not all salary benchmark sources are created equal. Also, not all sources of data will make sense for your business needs. That’s why evaluating salary data based on these two main factors is important: 1) The quality of the system and its data and 2) its relevance to your organization.

1. Quality of the Data

First, you’ll want to assess the quality of the compensation data and the system of collection as a whole by looking at:

  • Participation style. Is participation in a survey done to gain access to results? Or is data being aggregated from non-participation-based sources? Generally, when participants must contribute information to receive benchmarks, data quality is higher. 
  • Structured role and leveling framework. Are job roles and levels defined well and clearly? And does your internal system mirror them? Systems based on job title alone—and not clear structures for responsibilities or experience level—may not match your pay bands exactly since titles mean different things at different companies.
  • Data validation. Is the collection methodology sound? And is data validated or scrubbed for outliers before being aggregated? Sources that collect information confidentially and have a system for vetting results will be more accurate and reliable.

2. Relevance to Your Organization

Next, you’ll want to assess if a data source makes sense for your needs. Factors to look at include:

  • Roles, industries, locations, and sizes. Does the source provide data for the roles that you have and are recruiting for? Is there sufficient data for the geographics you hire for? Are the industries or company sizes relevant? Ensure any source you choose has a large sample set from companies with similar qualities as yours. 
  • Breadth of pay data collected. Does the survey include data on the types of compensation that you need—like on-target earnings, bonuses, or equity compensation? What about pay for commission-based roles? If you want to understand total compensation, choose a survey that collects more than base pay. 
  • Data freshness. Is the data updated often enough for how quickly the market moves? Remote work, tech workers, or other in-demand roles in fast-paced industries may need to be refreshed frequently to prevent you from lagging. 
  • Ease of use. Is the level of complexity or effort needed to participate high? How easy is it to navigate results? Ensure you have the resources to participate in the survey and implement findings from it.
  • Pricing. What is your budget for accessing pay data? Will you need to pay more for niche geographies or industry surveys? This will limit which sources you can look at.

Choosing compensation data sources isn’t just about finding ones that perfectly match your company’s profile. What matters more is understanding who you’re competing with for talent and what they pay their employees. Also, it’s advisable to use multiple sources to create a composite pay band so you’re never reliant on just one set of data (which may or may not be skewed or outdated). Focus your budget on surveys that provide the most value for your needs based on relevancy and quality. 

Common Challenges With Compensation Data Sources (And How to Overcome Them)

Below, we’re answering frequently asked questions about accessing and using compensation data.

What if I can’t afford to access a formal salary survey? Which source should I choose instead?

See if your compensation solution offers any benchmarking data. Industry groups and associations may also provide salary data if you’re a member. You can also crowdsource pay ranges from free salary benchmarking tools, like LinkedIn and Glassdoor, as a helpful point of reference. Just remember that their data is unstructured and self-reported—and thus not as reliable as a formal salary survey.

What do I do if I’m strapped for time and/or resources to participate in a formal benchmarking survey? (Mapping jobs, completing participation forms, etc.)

Going through a job-leveling exercise can help you prepare to participate in a survey. (Read our step-by-step guide on how it’s done.) However, compensation management tools that are integrated with salary benchmark providers can make participation easier by automating parts of the job matching, formatting, and uploading processes. They also make accessing results, comparing employee pay data to market rates, and visualizing pay bands simpler.

I feel most salary surveys are not specific enough to my industry, region, or company size. Should I even bother with them? 

Even if survey data doesn’t perfectly match your organization’s profile, it may still be relevant to you. Here are a few scenarios that explain why:

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  • There’s less variation between junior employee pay ranges across different company sizes and industries than executive pay (which tends to vary considerably based on company type). General survey data may still apply to a large number of roles in your business. (You don’t need 100% role coverage to benefit from benchmark data.)
  • If a part of your organization functions like another business type, you’ll want benchmark data from that field to attract the right talent. For example, if you’re a financial services company building your first app, you’ll likely want to hire engineers from SaaS companies who have developed apps before—and may have very different pay than IT folks in financial services.
  • If you’re a startup looking to hire experienced developers for a complex solution, you may be competing with large organizations for talent—which may offer better incentives and rewards than you can afford. This is why it’s important to think about who you are competing with for talent vs. who has the same profile as you. Their pay matters more since they’re setting pay expectations with candidates.
  • If a survey doesn’t have information for a specific role within your company, you can build a “hybrid” role range by looking at several different roles that cover the main job responsibilities and blending the salary ranges for those roles into one.
What should I do if I want to use multiple data sources to build pay ranges?

You can aggregate multiple benchmarking sources to create what’s called a “composite.” A composite is a market reference point, and you can make them in two ways: by calculating an average benchmark salary evenly across data points or by creating a weighted average by giving the most relevant, reliable, or robust sources more influence. 

How frequently should I try to get updated salary benchmarks?

It’s best to look for data that is updated quarterly or semi-annually. However, some niche industry surveys may only be updated once per year. 

Remember: many “real-time” salary sources have less stringent data collection, job matching, and data validation methods, which makes them timely—but not as accurate as traditional surveys. While timeliness is important, if the source you’re relying on is mapping similar roles to different levels or functions, then your benchmarks will be inaccurate. Your goal should be to balance getting frequent snapshots of the market with trusted, validated sources that may take a bit longer to access.

Blending Traditional and New Salary Benchmark Sources

Making smart pay decisions isn’t just about which data sources you choose. Having a clear compensation philosophy, a strong salary review process, and a tool that streamlines data management will make your pay practices more strategic and consistent—for both your HR team and employees. 

By integrating multiple data sources (including employee pay data) into a single compensation management platform, you’ll be able to streamline the entire benchmarking process and empower your team to make smarter pay decisions on the fly. 

Learn how Barley’s compensation management software can help you seamlessly analyze salary information from multiple data sources (including Mercer’s global benchmarking survey) in one intuitive platform. Book a demo today. 

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