Salary Benchmarking

It’s not the gender gap. Not the race gap. Not even the gap between startup and enterprise salaries.

It’s the blind spot. The one that happens when businesses decide what someone’s worth… by guessing.

And they don’t mean to guess. But without a structured approach, that’s exactly what’s happening, salary decisions built on gut feel, gut checks, or gut-wrenching comparisons to your cofounder’s brother’s last pay slip.

Welcome to the underworld of pay, murky, subjective, and quietly eating away at trust, retention, and budgets.

That’s where salary benchmarking steps in. Not as a magic fix, but as a mirror. It forces you to stop pretending you “just know what the market pays.” It shows you, brutally and mathematically, what’s out there, what you’re paying, and where the disconnect is.

And if you do it right, it becomes one of the most powerful tools for attracting great people, avoiding lawsuits, and scaling with sanity.

What Is Salary Benchmarking, Really?

Strip away the spreadsheets, and salary benchmarking is simple: it’s the process of comparing your company’s pay for specific roles to the market rates for similar roles elsewhere.

That’s it.

But behind that simplicity is a mess of decisions: What data do you trust? What counts as a “similar” role? Do you care more about internal fairness or external competitiveness? What if you can’t afford the going rate?

Done well, benchmarking helps you:

• Build salary bands that make sense
• Avoid awkward “why do they earn more than me?” conversations
• Make offers that don’t get laughed out of inboxes
• Keep your early hires from feeling short-changed
• Avoid creeping salary inflation that tanks your runway

Done badly? It makes your compensation structure even more confusing, now with added false confidence.

Why Free Data Isn’t Free (And Might Cost You More)

You Google “mid-level software engineer salary.” You land on Glassdoor, PayScale, or LinkedIn. Boom. Free data. Case closed?

Not quite.

Most of those platforms rely on self-reported data. That means anyone, from a summer intern to a CEO’s cousin, can submit numbers. And those numbers might be inflated, outdated, or just plain wrong.

Is that data useless? No. But it’s risky as a foundation. Think of it like building your house on YouTube tutorials instead of hiring an architect. It might look fine, until the storm hits.

That’s why serious benchmarking leans on multi-source validation. Paid surveys from firms like Mercer, Radford, and WTW gather verified salary data across thousands of roles, segmented by geography, seniority, and industry. Yes, they’re expensive. But the cost of paying someone 20% over (or under) market for three years? Much higher.

Why Job Titles Are Lying to You

“Product Manager” means wildly different things at different companies. One might manage a $10M budget. Another might just manage Jira tickets.

That’s the problem.

Benchmarking doesn’t care what you call the job. It cares what the person does. So if your Head of Marketing also writes content, runs paid campaigns, and manages freelancers, you need to benchmark against multiple roles. That’s called job matching, and it’s more art than science.

Some companies blend benchmark data (e.g., take 60% of a Marketing Manager’s median, 40% of a Content Lead’s) to get an accurate number. Others anchor to the highest-responsibility area. Either way, title ≠ reality. And if you benchmark off the wrong job? You’re back to guessing, just with fancier charts.

The Internal Equity vs. Market Rate Tug-of-War

Here’s a classic dilemma:

You’ve hired a new backend dev at R70K/month, fair, according to benchmarks. But your existing dev, who joined 18 months ago, still earns R55K.

Now what?

If you bump the veteran’s pay, you blow your budget. If you don’t, they eventually find out, and leave.

Welcome to the friction between internal equity (paying fairly inside your team) and market alignment (paying fairly vs. the market).

Good benchmarking helps you navigate both. It gives you ranges, not absolutes. You can decide: do we lead the market? Lag behind? Pay at median but with generous equity? There’s no perfect answer, but being consistent, and transparent about your logic, is what matters.

And no, “We just like them more” isn’t a logic you want on record.

Pay Transparency: Enlightenment or Chaos?

In theory, salary transparency is great. It keeps everyone honest. It reduces discrimination. It forces managers to justify decisions.

But in practice? It’s messy.

Suddenly, people start comparing their pay to each other. They don’t see the context, tenure, impact, negotiation skills, just the numbers. And that can lead to confusion, resentment, and a Slack thread you really didn’t want on the company record.

That’s where benchmarking earns its keep. If you have to disclose pay ranges (and in some countries, you legally do), being able to say “Here’s how we set these based on independent, verified data” goes a long way. It’s not just what you pay, it’s how you defend it.

When Annual Surveys Aren’t Fast Enough

Imagine benchmarking salaries in January 2020, and then trying to use that same data in April 2020.

Yikes.

The market moves fast, especially in tech, where salaries spike (or collapse) in months, not years. Traditional surveys often lag by 6–12 months. And in the middle of a hiring war, that lag can cost you top candidates.

Enter “real-time” benchmarking platforms like Pave, Ravio, or Figures. They pull anonymized salary data directly from payroll systems, updating weekly or even daily. That’s great if you’re hiring aggressively and need live intel.

But here’s the trade-off: smaller data sets, less vetting, and more reliance on peers entering good data. Use these tools to sense-check traditional sources, not to replace them outright.

Startups, Remote Teams, and the Salary Location Debate

Do you pay your Cape Town engineer the same as your London one?

Some say yes, work is work, no matter where it’s done. Others say no, cost of living matters, and paying globally leads to inefficiencies and resentment.

There’s no universal rule. But benchmarking helps you make the decision consciously.

You might anchor to one city (e.g. “All salaries are benchmarked against London rates”), then apply location multipliers (e.g. 80% for Johannesburg, 110% for Zurich). Or you might pay fully remote rates, then sweeten the pot with local perks.

The mistake? Making no decision at all. If you don’t have a position, the default is inconsistency. And inconsistent pay is the fastest way to burn trust, and top talent.

When Benchmarking Backfires

Here’s a dirty little secret: benchmarking can make things worse.

If you apply market data without context, or without a clear compensation philosophy, you might overpay for junior roles and underpay for critical ones. You might compress your salary bands. You might reinforce existing bias baked into the market.

Benchmarking doesn’t absolve you of judgment. It sharpens it.

It gives you reference points, not commandments. Use it to guide conversations, not replace them. And never forget the question behind all this data: What kind of company do we want to be?

So, Where Do You Start?

• Define your compensation philosophy. Do you want to lead, lag, or match the market?
• Choose your benchmarking sources wisely, a mix of trusted paid surveys and real-time data works best.
• Match roles accurately, titles lie, job descriptions don’t.
• Communicate ranges, not absolutes. And document your logic.
• Review regularly. Salaries age fast, and so do market rates.

Salary benchmarking isn’t a checkbox. It’s an ongoing, messy, context-rich process. But done right, it’s not just about numbers. It’s about trust.

And trust, more than any perk or ping pong table, is what keeps people around.