Salary surveys are one of the most widely used tools for compensation benchmarking. But while they’re often treated as a reliable source of market pay data, the reality is far more nuanced.
This guide breaks down how salary surveys actually work, what data they provide, where they fall short, and when they’re useful. You’ll also learn how teams turn survey data into usable benchmarks – and which alternatives exist for fast-moving tech companies.
If you’re making pay decisions and want clarity, not assumptions, this guide is for you.
Salary surveys are the traditional method for collecting compensation data from large enterprises across different industries, levels, and regions.
Conducted by large global HR consultancies like Mercer and Radford, salary surveys give People and HR teams the market pay data they need to understand how companies generally pay for specific roles, seniority levels, and industries.
You can either purchase the data as a full dataset or a custom peer-group report, tailored to specific locations, industries, or company sizes.
Salary survey data is often delivered in spreadsheets. However, most consultancies today offer salary survey software to make it easier to view and analyse market data.
The exact data a salary survey gives you depends on the provider, but it typically includes:
You get compensation data across multiple target percentiles and per job role and job level, depending on the provider’s job catalogue and level framework.
Traditional compensation surveys rely on a give-to-get model where participants must manually submit their employee compensation data, usually once or twice a year, to access the dataset.
Because this process requires significant time and resources, contribution is dominated by large enterprises rather than smaller, fast-growing companies.
For instance, participation from global organisations in legacy industries like manufacturing, oil and gas, pharmaceuticals, and financial services is common, so you’ll often see industry-specific surveys for these types of companies.
Most global salary surveys update once or twice a year, with the specifics depending on the provider.
Given their large-scale nature, collecting and validating survey data takes months. So by the time the results reach you, it's often long after the data was captured – making it more of a snapshot in time than fresh market insights.
Salary surveys are especially useful for:
When selecting a salary survey, make sure you review the following:
Salary survey data supports a range of compensation tasks, from benchmarking to band design, to help you make informed, competitive decisions. Here are the most common ways teams use them:
As you’ve seen above, salary survey data is immensely useful in making compensation decisions, from creating salary bands to adjusting pay during annual reviews. However, these decisions require current, relevant market data to get right – an area where traditional salary surveys fall short.
Essentially, salary surveys run on long manual data collection cycles, meaning the data reflects pay levels from months earlier rather than what companies are offering today. And because market conditions shift quickly – especially in competitive or fast-moving talent markets – this lag makes it harder to rely on traditional surveys alone as your only source of truth.
Not to mention, the lengthy submission process also introduces challenges. Between manually extracting, formatting, and inputting data – and aligning it with the survey provider’s job and level frameworks – there’s plenty of room for human error.
Example salary survey output: Salary survey data delivered in spreadsheets that you need to manually map against your internal roles
Any unintentional data entry mistakes, like misclassifying a role or mistyping a number, can skew the dataset and impact quality.
This is why fast-growing tech teams don’t use survey data alone. Instead, they pair it with real-time benchmarks from modern benchmarking software providers to stay on top of market trends and improve compensation decision-making accuracy.
There are three leading ways to access salary survey data, depending on how much detail you need and whether you have the resources to contribute your own compensation information:
Participation typically follows quarterly or annual submission cycles, where you provide detailed, up-to-date data on your job titles, levels, base pay, variable pay, and benefits – all organised according to the provider’s predefined job catalogue.
These formats are often rigid, with specific templates and rules to follow so the provider can standardise input across all participants. Once submissions are complete, the survey provider then aggregates all data – making it available for purchase for participants.
Some providers, like Willis Towers Watson (WTW), allow you to purchase salary survey data without having to participate in the salary survey. However, there are only limited datasets accessible without directly participating in surveys.
Some HR platforms provide access to salary survey data through partnerships with survey providers. For example, HR tool, HiBob, offers Mercer data with its compensation module, delivering data as a PDF – without requiring you to participate directly in the survey.
You can also access survey data through providers that aggregate multiple salary surveys, such as Payscale.
Salary surveys are helpful for high-level, point-in-time comparisons, especially for large, established roles and industries. However, they’re less reliable for fast-moving or competitive markets because the data is typically updated infrequently, relies on manual submissions, and may not reflect current pay trends or niche roles accurately.
Here’s more on the strengths and limitations of salary surveys:
Dig deeper: The pros and cons of salary surveys
Companies have plenty of choices when it comes to salary survey providers – from Culpepper and Salary.com to Empsight, Altura, and Brightmine (previously XpertHR and Cendex). Here’s a closer look at four of these salary survey providers:
Radford is the HR arm of global consultancy firm Aon, offering:
Who is Radford salary data ideal for? Global organisations, especially those needing deep expertise in executive pay, equity, or pre-IPO compensation.
Korn Ferry is a global consulting firm that helps organisations hire, engage, and retain talent, and sells salary survey data:
Who is Korn Ferry salary data ideal for? Large enterprises and multinational organisations, particularly those in regulated industries such as finance, pharmaceuticals, and manufacturing.
Mercer is a global HR consultancy that helps enterprises design and implement strategic compensation practices. It offers:
Who is Mercer salary data ideal for? Large enterprises and multinational organisations in regulated industries such as banking, pharmaceuticals, and manufacturing.
Willis Towers Watson is also a global organisational consultancy that offers the following for large, multinational enterprises:
Who is WTW salary data ideal for? Global enterprises looking for broad coverage in core markets like the US and UK.
Croner Reward is the pay and benefits arm of Croner, a UK HR advisory business.
It offers:
Who is Croner salary data ideal for? UK SMBs and HR generalists in traditional industries – particularly those already using Croner for employment law and HR advisory support who want pay data within the same ecosystem. Less suited to organisations that need global coverage, real-time data, or benchmarks for tech and high-growth roles.
Traditional salary survey providers typically give you raw compensation data – the responses to their salary survey collected, aggregated, and shared with you.
But raw data isn’t instantly usable for making pay decisions.
Firstly, you need to feel confident that:
Given how strenuous this work is, converting the voluminous raw data into benchmarks is a statistical exercise that a provider is responsible for doing using a trusted methodology – but there’s little information on how traditional salary survey providers validate data and turn it into a statistically robust benchmark.
So it’s a question worth asking a provider when you evaluate them.
Then, secondly, you also need to be able to seamlessly map the salary benchmarks you’re provided against the internal reality of your company – the job titles, roles, and levels that you actually use.
If you aren’t able to accurately match up your ‘Lead Engineer’ with the benchmark that best reflects that employee’s role scope and seniority level, then the benchmarks won’t give you the reference point you need.
So the question now is, how can you turn salary survey data into usable compensation benchmarks?
You either put in the manual work that goes into sifting through, mapping, validating, and making sense of all that overwhelming pool of data, or you upload the data into modern benchmarking software to access usable benchmarks.
Here’s how both options work:
This involves taking all the salary survey data, whether accessible via an online platform or a PDF, and manually mapping it to your job titles, levels, and functions.
Besides being time-consuming, this process still relies on raw salary data – not statistically validated benchmarks – which limits accuracy.
And because HR and Rewards teams can’t independently validate or standardise that data at scale, manual mapping often introduces inconsistencies, especially when roles don’t neatly match predefined survey categories.
As a result, while manual mapping makes survey data usable, it doesn’t make it reliably accurate for compensation decisions.
This involves uploading your salary survey data to a real-time benchmarking software provider to improve benchmark usability and context.
While this doesn’t turn your raw salary survey data into usable benchmarks in itself, it lets you combine salary survey data you’ve mapped with benchmarks from a real-time provider so you can:
For this though, you’ll need a provider that lets you add and use additional datasets. Ravio, for instance, lets you upload your salary survey data into the platform so you can easily – compare reliable benchmarks across roles, levels, or locations.
This approach gives HR and Reward teams a more well-rounded view of compensation levels – without having to do manual work and getting reliable benchmarks.
Bolt takes the same approach by pairing salary survey data with Ravio to build more reliable and relevant benchmarks, which helps them make faster compensation decisions and stay ahead of market trends with real-time benchmarks.
“Because most providers also have their own job levelling and job families structure, it’s a huge effort to then convert ours to theirs. It requires keeping up with all the changes they might have implemented during a year. [But] for someone to take that task away is a huge benefit.”
Evert Kraav
Senior Compensation Manager at Bolt
Modern benchmarking software integrates with companies’ HR systems to give you continuously updated compensation benchmarks in real-time.
Depending on the provider you select, these tech-first benchmarking solutions either offer AI-led job mapping or have human teams that map your job roles during onboarding.
Several providers also apply consistent data validation and cleaning methodologies to give you reliable benchmarks.
Pros:
Cons:
Compensation benchmarking aggregators combine data from multiple salary surveys into a single platform, giving you a consolidated view of compensation data rather than relying on one provider’s dataset.
Some aggregators, like Payscale, also offer tools that support salary survey participation, helping simplify data submission.
Pros:
Cons:
User-reported salary data comes from publicly available sources, including:
While these sources are easy to access and often free, the data is self-reported and not validated or standardised, which limits their reliability for compensation decision-making.
Pros:
Cons:
Based on the alternatives of traditional compensation survey companies above, it’s clear that real-time benchmarking tools are the strongest alternative for reliable pay data.
They easily overcome the shortcomings of salary survey data by automating job mapping and giving you continuously updated benchmarks that reflect current market conditions.
Here’s a quick comparison between traditional salary surveys and real-time benchmarking tools – with more details covered here:
Traditional salary surveys | Real-time salary benchmarking tools |
|---|---|
Out-of-date data. |
Real-time data from HRIS integration. |
Error-prone, time-consuming manual data submission. |
Data direct from source, no lengthy manual submissions. |
Broad, misaligned peer group, as data comes from multinational companies mainly. |
Deeper insights for fast-growing industries and niche locations with data filters available. |
Limited visibility into data validation, confidence levels, or outlier handling. |
Benchmarks generated using consistent validation and confidence thresholds (depending on the provider). |
Manual job matching. |
Job level mapping done for you. |
One-off data snapshot with no additional tools to analyse and use the data. |
Comprehensive compensation management functionality. |
Salary surveys still play a significant role in compensation planning – but they’re not the only universal solution anymore.
As for whether they’re right for you depends on your company’s size, structure, and how critical pay competitiveness is to your hiring strategy.
With that, salary surveys are a good fit if:
But salary surveys are not the best fit for you if:
In short, salary surveys can provide useful context, but on their own, they’re often too static and manual to use for modern, fast-moving organisations.
Teams that need reliable, current benchmarks typically look beyond surveys and combine survey data sources with real-time salary benchmarking software like Ravio to support more accurate compensation decisions.
A salary survey for benchmarking is a tool used to compare your company’s pay levels against the wider market. It collects compensation data from large organisations and aggregates it by role, level, and region. Companies use this data to understand typical pay ranges.
Salary surveys can be worth the cost for large, enterprise organisations that need broad, industry-wide benchmarks and have the resources to manage manual survey submissions and analysis. For fast-growing or tech-led teams that need up-to-date, role-specific benchmarks, the cost often outweighs the value unless surveys are combined with more current data sources.
Startups can use salary surveys, but they’re often not the best fit on their own. Surveys tend to give you market pay data from larger, established companies, not your peers. They also update infrequently, which can misalign with startup roles and fast-changing markets. Most startups use salary surveys only as background context, pairing them with more current, flexible benchmarking data.
Salary surveys lag reality because they rely on manual data submissions collected on quarterly or annual cycles. After data is gathered, it takes additional time to validate, aggregate, and publish the results. By the time the data reaches users, it often reflects pay levels from months earlier rather than current market conditions.
Many companies upload salary survey data into compensation management software that supports custom market imports. These tools accept Excel or CSV files, and some, like Ravio, let you automatically map job codes to internal roles so you can compare benchmarks across roles, levels, or locations.
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