When was the last time you checked whether your salaries are keeping up with the market? If you’re relying on outdated reports or gut feeling, chances are you’re already behind. Today’s candidates have more visibility than ever into pay ranges, and your competitors are adjusting their offers in real time.
That’s where salary benchmarking comes in. Done right, it helps you see the big picture: what roles are in demand, what companies are paying for them, and how compensation trends are shifting across industries. The secret ingredient? Jobs data.
By analyzing job postings at scale, you get a live pulse of the market, which is far more accurate than static surveys or once-a-year reports.
In this article, I’ll walk you through how to use jobs data for salary benchmarking, spot emerging market trends, and make smarter decisions about compensation that keep you competitive and attractive to top talent.
What is salary benchmarking?
Compensation benchmarking is the process of comparing your company’s compensation packages against the wider market to ensure they’re competitive. In practice, it means looking beyond your internal payroll data and evaluating what similar roles pay across industries, regions, and company sizes.
Definition and evolution
Traditionally, salary benchmarking was about maintaining parity with peers. HR leaders would buy annual compensation surveys, compile peer group data, and use it as a reference point for setting pay ranges. While helpful, these snapshots quickly became outdated. In fast-moving labor markets where demand for certain skills shifts quickly, stale benchmarks can lead to overpaying for some roles while underpaying for others.
The evolution of salary benchmarking has been toward agility and precision. Instead of relying on once-a-year survey reports, forward-thinking companies now use dynamic datasets such as job postings, employee profiles, and firmographic insights to track real-time salary trends. This shift allows businesses to stay in step with market changes, not play catch-up.
Traditional methods vs. data-driven salary benchmarking
Traditional salary benchmarking methods often rely on:
- Annual or semi-annual compensation surveys
- Peer benchmarking through informal networks
- Internal historical pay scales
While these methods provide a baseline, they miss the nuance of rapidly changing job markets.
Data-driven benchmarking, on the other hand, uses internal and publicly available web data such as job postings to capture salaries, benefits, and market demand at scale. With this approach, companies can:
- Detect emerging roles and adjust pay before competitors do
- Understand salary variation across geographies and industries
- Identify trends in benefits packages that attract top talent
In short, data-driven compensation benchmarking moves the process from reactive to proactive. Instead of asking, “Are we paying enough?” you can ask, “How can we use compensation as a competitive edge?”
Popular salary benchmarking methods
There’s no single way to benchmark salaries. Companies often combine multiple ways to get a clearer view of compensation trends. Let’s take a look at the most common approaches and see why job data is quickly becoming the most powerful option.
Salary surveys
Salary surveys are one of the oldest benchmarking tools. Vendors collect pay information from participating organizations and publish aggregated results by job title, industry, and location.
They’re reliable for establishing baselines, but there are two downsides: surveys are expensive, and by the time results are published, the data is often out of date.
Government and public data sources
National statistics offices and labor departments publish wage data by occupation and region. In the U.S., for example, the Bureau of Labor Statistics (BLS) provides datasets on median wages and employment trends.
These sources are authoritative, but they’re also broad. They won’t always capture niche roles, emerging skills, or the latest pay shifts in fast-changing industries like tech.
Internal data analysis
Your own payroll data is a valuable starting point. Looking at compensation by role, tenure, and performance can help you spot internal inequities and ensure pay transparency.
However, internal data only tells you how you are paying, not whether those salaries are competitive in the market.
Online compensation databases
Websites like Glassdoor and Payscale crowdsource salary information directly from employees. These platforms offer more granularity than government reports and are widely used by job seekers to check pay fairness, however, it’s not the most reliable source for optimal market salary analysis.
The challenge here is reliability. Self-reported data can be incomplete or skewed, and not all industries are equally represented.
Job posting data
Job postings have become a powerful new lens for salary benchmarking. With pay transparency laws spreading and more companies listing compensation ranges in job ads, you can now see real-time salary signals directly from the market.
Analyzing job posting data at scale allows you to:
- Track how competitors are adjusting pay in real time
- Spot regional and industry-specific pay variations
- Identify in-demand skills that command higher salaries
- Benchmark not only base pay, but also advertised benefits and perks
In short, job posting data makes compensation benchmarking more dynamic and actionable. Instead of waiting months for reports, you can adapt compensation strategies in step with market demand.
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How to benchmark salaries using job data (step-by-step)
With job postings data, salary benchmarking becomes more precise and proactive. Instead of relying on outdated surveys, you can analyze fresh compensation signals as they appear in the market.
- Collect job posting data at scale
First off, you'll need to get fresh job posting data across roles, industries, and geographies. You might scrape it yourself or buy a premade dataset. With Coresignal’s jobs data, you can access millions of postings from multiple sources that can be filtered by title, location, industry, or company. This ensures you’re working with up-to-date data, not cherry-picked samples.
- Extract salary ranges from postings
Many postings now include salary ranges due to transparency laws. Use the dataset to capture minimum, maximum, and midpoint salaries. Coresignal’s structured data makes it easy to calculate averages or distributions for specific roles.
- Normalize job titles for comparison
Different companies label roles differently; one firm’s “Software Engineer” might be another’s “Mid-level Developer.” Normalizing titles into standardized categories allows for apples-to-apples comparisons.
- Segment by geography, seniority, and industry
Compensation varies widely by market and role. With Coresignal, you can slice the dataset to see salary benchmarks by 20+ filters, including:
- Geography (e.g., San Francisco vs. Austin)
- Seniority (entry-level, mid-level, senior)
- Industry (tech, healthcare, finance, etc.)
- Employment type (full-time, part-time, volunteer)
This segmentation helps you spot patterns that would be invisible in aggregated averages.
Compare to internal payroll or employee survey data
Finally, benchmark external data and market salary analysis against your internal payroll and employee survey results. This lets you answer critical questions:
- Are we paying competitively in high-demand roles?
- Are there geographic markets where we’re underpaying?
- Do our compensation bands align with employee expectations?
By combining internal and external data, you gain a full picture of how your salaries measure up and where adjustments can give you a competitive edge.
Salary benchmarking insights from job postings
Analyzing job postings at scale unlocks a new layer of salary intelligence. Instead of relying on static reports, you can extract live signals about how companies are setting pay and adapting to market shifts.
Challenges in salary benchmarking
Salary benchmarking can unlock powerful insights, but it’s not without its hurdles. Even with access to fresh data, companies often face issues that make benchmarking less straightforward than it sounds. From incomplete salary disclosures to inconsistent job titles and fast-changing market conditions, these obstacles can skew results if left unaddressed.
Salary benchmarking can have several issues:
- Incomplete data: Not all postings disclose pay ranges.
- Title inconsistencies: “Data Analyst” at one firm may mean “Data Scientist” at another.
- Rapid changes: Salary trends shift quickly, so you need to set up infrastructure that ensures data is constantly updated.
- Bias in sources: Crowdsourced or niche datasets may not reflect the whole market, you need to use multi-source data to get the best results.
By being aware of these challenges, HR leaders, product managers, and executives can approach benchmarking with realistic expectations and build safeguards into their process.
To overcome these challenges and extract meaningful insights:
- Combine job posting data with HR survey data for a balanced view.
- Use rolling averages to smooth out short-term fluctuations.
- Refresh benchmarks quarterly or biannually to stay current.
- Segment by region, role, and industry rather than relying on broad averages.
- Leverage APIs and structured datasets for accuracy and scale.
When executed thoughtfully, salary benchmarking helps you move beyond compliance and use compensation as a genuine competitive advantage.
Salary benchmarking best practices
Overcoming the challenges of salary benchmarking is about building a process that is consistent, repeatable, and rooted in multiple perspectives. One of the most effective approaches is to combine job posting data with HR survey data, creating a balanced view that captures both real-time market signals and established industry benchmarks.
To account for short-term volatility, it’s best to use rolling averages, which smooth out sudden spikes or dips in advertised pay.
Because salary trends evolve quickly, especially in competitive industries, benchmarks should be refreshed quarterly or biannually or even more frequently rather than relying on annual updates. Segmenting by region, role, and industry ensures that insights are meaningful, since broad averages often mask important differences: what’s true for a software engineer in San Francisco won’t necessarily apply to one in Berlin or Austin.
Finally, leveraging APIs and structured jobs datasets like Coresignal’s helps automate the process, scale data collection, and improve accuracy across large volumes of postings.
When executed thoughtfully, salary benchmarking moves beyond a compliance exercise. It becomes a strategic tool for workforce planning, helping you attract top talent, retain high performers, and use compensation as a genuine competitive advantage.
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Get jobs data with Coresignal
Our new multi-source jobs data is a game changer for salary benchmarking, making it easy to check millions of fresh jobs postings. No duplicates, no extra work – focus on salary analysis instead of cleaning the data.
You could also sign up for a free trial with Coresignal to receive 400 free search credits, enough to start exploring job postings data and testing salary benchmarking queries.
What you’ll get:
- Access to more than 1 billion company, employee, and job records
- Up to 200 company profiles to download, free of charge
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- A 14-day free trial to explore data in more depth
Whether you’re testing salary benchmarking for the first time or validating data for a specific use case, you can explore freely before making any decisions.
Ready to explore salary data?
Our multi-source jobs dataset contains jobs listings from multiple sources, making it ideal for salary benchmarking. Reach out and I would be glad to help you get fresh, ready-made data.
