Executive Salary Benchmark 2025: How to Read Real Data (Not Bullshit Studies)

Every January, a wave of salary studies floods LinkedIn. Headlines promise that "executives earn an average of €58,000" or that "data scientists now command €75,000." Most of these numbers are useless for one simple reason: an average that mixes a junior in Limoges with a director in Paris-La Défense tells you nothing about your own market value.

If you're negotiating a package, deciding whether an offer is fair, or just trying to set your expectations before a search, you need a range — for your role, your seniority, and your location. Here's how to build one in about 30 minutes, and how to spot the studies that exist only to generate press coverage.

Why most salary studies mislead you

The problem isn't that the data is fake. It's that the headline number hides too much variance to be actionable.

Averages crush the distribution. A €60,000 average can come from a tight cluster (most people between €56k and €64k) or a wide spread (€40k to €90k). The headline is identical; the reality is completely different. Always look for the median and the quartiles, not the mean.

National numbers ignore geography. The same role can vary by 20–30% between Paris, Lyon, and a regional city. Brussels and Geneva have entirely different baselines — Swiss gross salaries in particular look inflated until you factor in cost of living and the absence of employer-side social charges comparable to France.

Self-reported data skews high. People who voluntarily declare their salary on a platform are not a random sample. High earners tend to share more readily, and a few rounding-up entries pull the figure upward.

Vendor studies have an agenda. A recruitment firm's annual "salary guide" is partly a marketing asset. The numbers aren't invented, but the framing ("talent is scarce, salaries are rising") serves their business. Read them — just don't treat them as gospel.

The fix is not to find the one perfect source. It's to triangulate several flawed sources until they converge.

The five sources worth crossing

No single dataset is reliable on its own. Used together, they bracket the truth.

1. Apec (France) — the structural baseline

Apec publishes detailed studies by function, sector, region, and experience level. It's the most methodologically serious source for French executives because the sample is large and stratified. Use it to anchor your range and understand how seniority moves the number. The downside: data is often a year old by the time it's published, so treat it as a floor in a rising market.

2. Glassdoor — self-reported, with context

Glassdoor gives you company-specific and role-specific figures, often with a breakdown of base versus bonus. The data is self-reported, so widen your reading: look at the range, not the single "average base pay" headline. It's most useful when you already have a target company in mind.

3. HelloWork and other job boards — the live market

Job boards show what employers are actually offering right now, which matters more than any retrospective study. The catch: many French postings hide the salary. When they do show it, treat the displayed range as the negotiation envelope, not a promise. We've written a full guide on how to find a job's salary before applying — it's worth reading before you waste time on roles that can't match your number.

4. Recruitment firm guides — the directional signal

Firms like Robert Half, Michael Page, or Hays publish annual guides with role-by-role ranges. Their value is the trend and the breadth of roles covered. Read the methodology footnote: a guide based on "placements we made" is more grounded than one based on "market sentiment."

5. Actual job ads in your niche — the ground truth

The single most reliable signal is what current openings for your exact role, in your exact region are paying. Pull 15–20 live postings, note every disclosed range, and you'll see a real distribution emerge. This is slower than reading a study, but it reflects today's market for your specific profile — not a national average from eighteen months ago.

The 30-minute method

Here's a sequence that gets you a defensible range without spending a weekend on it.

Minutes 0–5: Define your exact profile. Write down your role title, seniority (years and scope, not just a label), sector, and region. "Marketing manager, 8 years, B2B SaaS, Lyon" is a query you can benchmark. "Marketing person" is not.

Minutes 5–15: Anchor with Apec + one firm guide. Find the median for your function and experience band. Note the regional adjustment if one is published. This gives you a center of gravity.

Minutes 15–25: Pull live job ads. Search HelloWork and one or two boards for your role in your region. Record every disclosed range. Discard outliers (a startup offering equity-heavy comp, or a role with a misleading title). You should see most figures cluster.

Minutes 25–30: Reconcile. Place your anchor and your live-ad cluster side by side. Where they overlap is your reliable range. Where the live market sits above the structural baseline, the market is moving — and you should aim toward the upper half.

The output isn't a single number. It's a sentence you can defend: "For my role and region in 2025, the credible range is €62k–€72k, and live postings cluster around €68k."

Reading the numbers like a skeptic

A few habits separate a useful benchmark from a comforting illusion.

• Always convert to the same basis. Compare gross-to-gross, and always include variable pay. A €60k base with a €15k bonus is a different conversation from €70k flat. In Belgium and Switzerland, double-check whether figures are gross, net, or include the 13th month.
• Distrust round numbers in studies. "€65,000" with no quartiles is a press release, not data.
• Weight recency. In a fast-moving function, a six-month-old job ad beats a year-old study.
• Separate base from total compensation. Recruiters negotiate the package; you should benchmark the package.

Why this matters before you apply

Knowing your range changes how you search. It stops you from applying to roles that structurally can't pay you, and it stops you from underselling yourself on the ones that can. It also means that when an offer arrives, you're negotiating from data instead of hope.

The same logic applies to which roles you pursue. A precise salary expectation is one more filter that keeps your search focused — the same way a good fit score keeps you from spreading yourself thin. If you tend to apply broadly, our piece on applying to a job you match 70% pairs well with this one: combine a realistic salary band with a realistic fit assessment, and your shortlist gets dramatically shorter and better.

Build the range, then move

A salary benchmark isn't a trophy number to quote at parties. It's a working range that helps you decide where to spend your effort and how to hold your ground in a negotiation. Thirty minutes of triangulation beats thirty seconds of reading a headline.

Once you know your number, the next step is making sure you only apply to roles that fit it — and that your CV is tailored to each one rather than blasted out generically. That's exactly the kind of focus Yeepl is built for: surfacing relevant offers, scoring fit, and leaving the decision (and the application) in your hands.

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