Compensation Benchmarking Tools: How Accurate Are They?

Compensation Benchmarking Tools: How Accurate Are They?

Most companies put significant effort into pricing and benchmarking their products. It’s simply good business to understand how your product compares to others in your industry and whether you’re charging appropriately for it.

Similarly, it’s just as important to price and benchmark the cost of your talent. The salaries you choose will ultimately attract (or turn away) the talent you want and can have a huge effect on churn and employee satisfaction down the line. Worse, if your salary choices appear misleading to prospective candidates, you could run afoul of salary transparency laws.

Compensation benchmarking tools allow you to research how your compensation packages stack up against competing businesses hiring for the same positions. At least, that’s what they would do in a perfect world. In reality, there are a number of issues with the compensation benchmark data produced by popular algorithm-based tools.

Since accurate data is of the utmost importance — the last thing you want to do is base your salary packages on little more than a guess — let’s dissect why the compensation benchmarking data you’ll get from an algorithm isn’t necessarily as trustworthy as you might think. Luckily, even if you find yourself running into these issues, there is a solid alternative that can help you get around these problems.

Or you could skip the hassle and ask the experts at MorganHR for help instead. Schedule a call here to see how we can assist you in determining the best compensation for your employees.


Are compensation benchmarking tools your best bet — or is there something better?

First of all, what are we talking about when we refer to compensation benchmarking tools?

Some of the most common tools you’ll encounter while doing your research include websites like Payfactors, CompAnalyst, and

These tools are intended to be a one-stop-shop for companies researching compensation for a given position. You could simply type “entry level accountant” into the tool and receive what certainly looks like a plausible industry average for that position.

Essentially, you’re paying for access to a proprietary algorithm that crunches data gathered from survey houses. These survey houses, behind the scenes, collect data from companies — usually by asking HR reps directly — about their salaries and benefits packages.

You can even input your company’s data to receive a more customized result for your situation. If you’re a small business, for example, it’s far more helpful to receive results taken from other, comparable small businesses in your area than from mega-corporations and international businesses. Even if you’re technically hiring for the same position, you probably can’t keep up with those giants, and you shouldn’t base your compensation package too heavily on what they’re doing.

However, there are some problems with the results generated by algorithm-based compensation benchmarking tools.

In theory, the data these algorithms use comes from multiple survey houses — not just one — and should therefore be more accurate than the data points gathered from a single source. And since every major compensation benchmarking tool claims to have purchased data from the same global survey houses, you might expect the results they generate to be roughly on par with each other, even if their algorithms are slightly different. This should increase accuracy even further.

In practice, however, this usually isn’t the case.

Let’s say you set up a standard baseline for a job description and searched for salaries within that industry, company size, and region. As a handful of examples, you might find that the results for Marketing Assistants vary by only 3%, but Fork Lift Operators were off by 15% and IT Directors have a staggering triple-digit percentage variance.

The same is true for almost every job position. For example, you could search for salary information on entry level Accountants — people who have degrees but are just out of college, with no prior experience in the industry — using one algorithm and see salaries around $50,000, while another tool would show salaries around $70,000. Even if all custom inputs are the same — for example, the location, industry, and job scope and requirements — the results are often wildly different.

Considering Accountant is the benchmark job all compensation experts use as a baseline for comparing compensation structures in nearly every company, this 35% variance is concerning.

Base Salary 50th Percentiles from Four Tools Revenue / Industry Minimum Reported 50th Maximum Reported 50th Variance

Level 1

10M All Industry 52,200 70,320 35%
1B Transportation Industry 55,100 67,201 22%
Human Resources Business Partner

Level 1

10M All Industry 62,200 71,040 14%
1B Transportation Indusry 67,990 79,000 16%
Marketing Assistant

Level 1

10M All Industry* 42,993 44,400 3%
1B Transportation Industry 41,515 47,200 14%
Forklift Operator

Level 1

10M All Industry 34,700 40,000 15%
1B Transportation Industry 35,251 39,400 12%

*Of note, the variance of the 50th percentiles for total cash was only 1% for the Marketing Assistant.


This begs the question: Why are the results so different when these tools claim to have pulled data from the same sources?

Since these algorithms are proprietary, we don’t actually know where their data comes from. You’ll subscribe to their service under the impression that they’ve purchased data from all the major survey houses and combined this information into the most helpful, condensed averages and representations. But there’s no way to tell if that’s actually the case.

If you only use one tool, you might be setting yourself up to pay far too little or too much because the source of information is unknown. It could be a compilation of self-reported salaries from places like GlassDoor (notorious for gathering the disgruntled opinions of workers who didn’t leave on the best of terms) or a quick web scrape of the job description on LinkedIn (which might include data from people in wildly different locations with varying levels of experience).

To make matters more confusing, the algorithmic results don’t appear to reflect real-world fluctuations in the market. Even through the pandemic and lockdowns and subsequent economic struggles of the last few years, the major algorithms have shown a smooth, even increase on most salaries.

There’s simply no way to know for sure why these discrepancies are happening. All we know is that algorithm-based compensation benchmarking tools are disappointingly inaccurate.

How can you get more accurate data if your chosen benchmarking tool falls short? Are there better tools out there?

In this case, it’s not necessarily a matter of finding the best compensation benchmarking tool.

In most cases, you might be better served by avoiding the algorithm-based tools altogether and opting to purchase your data directly from one (or a handful) of the major survey houses that gather information on salaries and benefits packages.

Survey houses collect the data that algorithms claim their tools are based off of. This information comes directly from the companies that pay salaries for the specific positions in question, so it reflects real-world data.

There are a number of well-known, highly regarded global survey houses that measure compensation. These include but are not limited to:

  • Mercer
  • Culpepper
  • Towers
  • Aon Hewitt

Working with any of these sources will get you the most relevant and accurate data, along with a guarantee that the results have been validated by human experts who prioritize the accuracy of their work.

These survey houses can also take into account things like vacation time and 401(k) matching when generating results. These factors can dramatically alter a final salary number, and are therefore important to include in your considerations when deciding on how to compensate your employees.

Additionally, when working with a survey house, you can request the basis for the data they present and understand the context surrounding their results. In contrast, an algorithm is a black box you can’t peer into if you want more information.

In other words, the quality of data you’ll receive from a survey house is a far cry from what you’ll get out of an algorithm that might as well be based on a random number generator.

Is it ever a good idea to work with algorithmic tools?

It can be a reasonable choice, depending on your situation.

Whereas algorithms claim to provide real-time results because they can scrape from many different sources, survey houses must conduct surveys to gather their data, and therefore they are slower to produce up-to-date information. Survey house results will be far more accurate, but you’ll have to wait for them to release new data if you want the most current numbers.

If you’re a larger company that can comfortably wait to receive the most accurate data possible — and eke out the most competitive edge with your compensation packages — it’s worth your time to be patient and work with a survey house.

If, however, you’re a smaller company with a position that urgently needs to be filled, you may need to make do with algorithms. It can help to buy access to multiple algorithms and use a combination of their results — along with your own expertise — to make your final salary decision.

Need help finding a compensation level that works for your job candidates and your company?

Even with accurate, up-to-date data at your fingertips, it isn’t always easy to choose the right salary for a new position. There are a lot of variables and individual circumstances that can affect your outcomes. Our expert team at MorganHR has over 30 years of experience with solving complex challenges such as this. Using in-depth consulting and accurate benchmarking methods, we can help you research, build, and implement a compensation strategy that will keep you competitive in your field.

Schedule a call with a MorganHR sales consultant to get started today.


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About the Author: Laura Morgan

As a founder and owner of MorganHR, Inc., Laura Morgan has been helping organizations to identify and solve their business problems through the use of innovative HR programs and technology for more than 30 years. Known as a hands-on, people-first HR leader, Laura specializes in the design and implementation of compensation programs as well as programs that support excellence in the areas of performance management, equity, wellness, and more.