When people talk about benchmarking OTE, the conversation often starts with a fairly simple question:

What is the market paying for this role?

That is obviously useful to know. If you are hiring an Account Executive in London, a Customer Success Manager in Stockholm or an SDR in New York, you probably want some idea of what comparable companies are offering.

But I would not stop there.

Two companies can offer exactly the same €120,000 OTE and still have very different compensation plans underneath it. One might offer €80,000 base salary and €40,000 variable pay. Another could use a 60/60 split. One salesperson may need to close €600,000 to earn the target variable amount, while another needs to close €1.2 million.

The market benchmark tells you something about compensation. It does not tell you whether the plan itself makes sense.

If you need the basic OTE formula and terminology first, our on-target earnings guide covers that. This article is about what I would look at when using market data to set or review an actual OTE structure.

Start by making sure you are benchmarking the same job

This is probably the most obvious point, but it is also one of the easiest places to get misleading data.

“Account Executive” can describe very different roles.

An AE selling €5,000 contracts to small businesses with a short sales cycle does not necessarily have the same job as an enterprise seller managing a handful of seven-figure opportunities. They may share the same title, but the deal size, sales cycle, quota, support model and level of individual responsibility can be completely different.

The same applies to Customer Success. A CSM responsible mainly for onboarding and adoption is different from a commercially accountable CSM who owns renewals and expansion.

Before comparing OTE, I would therefore try to match the actual role rather than the title alone. Geography and seniority matter, but so do customer segment, commercial responsibility, sales motion and the size of the target being carried.

This is also why I am sceptical of compensation tables that give one “normal OTE” for a job title without much context.

They look precise, but the role underneath the number may be quite different from yours.

Separate base salary, variable pay and total OTE

Suppose two compensation surveys both show an OTE around €120,000 for a particular sales role.

That still leaves an important question: how much of the €120,000 is fixed?

One company might structure it as:

Another might use:

The headline OTE is identical, but these are clearly not identical offers.

The employee in the second plan has a larger proportion of compensation dependent on performance. That changes the earnings risk for the employee and the incentive economics for the company.

When looking at benchmark data, I would therefore avoid comparing OTE alone. Look at the base salary, target variable amount and resulting pay mix separately.

Our OTE-by-role guide goes deeper into why the appropriate pay mix can differ substantially depending on what the role actually controls.

Market pay and pay mix answer different questions

This distinction is useful.

A market benchmark can help answer:

What are comparable companies paying for this kind of employee?

Pay mix answers something different:

How much of that compensation should depend on performance?

Those two decisions are related, but they should not be collapsed into one.

Imagine market data suggests that €120,000 is a competitive OTE for the role. You still need to decide whether your own plan should use 70/30, 60/40, 50/50 or another structure.

I would make that decision based on the role rather than simply copying the median pay mix from a survey.

How directly can the employee influence the result? How accurately can it be measured? How long is the performance cycle? How much earnings variability do you actually want the employee to carry?

A benchmark can show you what other companies have chosen.

It cannot tell you whether those companies have designed a good plan for your business.

Then check whether the quota and OTE reconcile

This is where benchmarking starts to become more operational.

Suppose the market data leads you to an OTE of €120,000, consisting of €70,000 base salary and €50,000 target variable pay.

You then give the salesperson a €1 million eligible quota.

For a simple flat commission plan, the implied rate at target would be:

€50,000 ÷ €1,000,000 = 5%

That gives you something useful to test.

Does the company actually want to pay approximately €50,000 when that employee produces €1 million of eligible performance? Does the quota reflect a credible level of output for the territory and sales motion? Does the resulting incentive cost work with the economics of the business?

If the answer is no, changing the OTE benchmark alone does not solve the problem.

This is why I would always look at OTE and quota together rather than treating one as a compensation decision and the other as a sales-planning decision.

A competitive OTE can still sit on top of an unrealistic quota

This is one of the areas where a benchmark can give a false sense of comfort.

Imagine you have matched the market median perfectly. Base salary looks competitive, variable pay looks competitive and the total OTE is exactly where you wanted it.

Then almost nobody reaches the performance level required to earn the target variable amount.

On paper, you have a competitive OTE.

In practice, employees may value the offer very differently because the target earnings do not feel achievable.

The opposite can happen too. If targets are consistently too easy, the stated OTE may no longer represent the economics the company intended when the plan was designed.

I would therefore look at historical attainment, territory potential, capacity and the sales motion alongside the external compensation data.

There is no universal percentage of employees who should hit quota. A benchmark showing what other companies report can be useful context, but it does not replace understanding whether your own target is credible.

Geography matters, but be precise about what you are comparing

Compensation varies by market, so geographical data is useful. It can also become messy quite quickly, particularly for companies hiring remotely across several countries.

If the benchmark is based on San Francisco compensation and you are hiring in Oslo, you need to know that before treating the number as relevant. The same applies if a data set mixes national, regional and remote compensation models.

Currency creates another layer.

Comparing a USD OTE with a EUR or NOK compensation package is not only a matter of converting currencies. The underlying labour market, benefits, employment costs and compensation practices may also be different.

I would use geographically relevant data where possible and document which market the benchmark represents. If you deliberately pay against another market, that is fine too, but it should be an intentional compensation decision rather than the accidental result of using whichever benchmark was easiest to find.

Company stage is useful context, but not a rule

You will often see statements suggesting that startups should use one pay mix while larger companies should use another.

I would be careful with that.

Company stage can influence compensation because cash availability, equity, role scope, risk and commercial maturity may all differ. But saying that “startups use more variable pay” or “mature companies use more fixed compensation” turns a possible pattern into a rule.

The actual job still matters more.

A highly transactional sales role at a large company may have a substantial variable component. A senior strategic role in a startup may reasonably have much more fixed compensation.

Use company stage as one dimension when selecting comparable companies, but do not let the funding stage design the compensation plan for you.

Benchmark the role, then test the economics of your own business

This is the part I think gets missed most often.

External compensation data tells you about the labour market.

Your own unit economics tell you what the compensation plan means for the company.

Assume again that you settle on:

Base salary: €70,000
Target variable pay: €50,000
OTE: €120,000
Quota: €1,000,000

At target, the company expects to pay €50,000 of variable compensation.

But what happens at 120% attainment? What happens at 150% if accelerators apply? What happens if one unusually large deal takes a salesperson far beyond quota?

Those are not questions a salary survey will answer for you.

The OTE calculator can be used to model the plan at different attainment levels once you have a tentative OTE, quota and pay mix.

This is where the external benchmark and the internal plan design start to come together.

Do not benchmark the headline number and ignore the payout curve

Two companies can offer the same base salary, variable pay and quota and still have different compensation economics.

Consider two employees with a €50,000 target variable amount.

In Plan A, payout grows linearly with attainment.

In Plan B, an accelerator begins at 100% and increases the rate substantially on performance above target.

At exactly 100%, the two plans look identical.

At 130%, they may be very different.

That is why a useful compensation comparison needs to look beyond OTE and pay mix. Thresholds, accelerators, tiers and caps can materially change what employees are likely to earn and what the company is likely to spend.

If you are comparing your plan with the market, try to understand the mechanics underneath the headline compensation where that data is available.

If it is not available, do not assume that two identical OTE numbers represent identical plans.

Benchmark data also gets old

Compensation data has a shelf life.

That sounds obvious, but it matters because salary reports tend to remain online for a long time.

If you are using external benchmarks, check when the data was collected rather than only when the webpage was updated. A report published this year may still contain compensation observations from an earlier period.

I would also look at the sample behind the number.

How many observations are there? Which countries are included? Which company sizes? Are the figures self-reported, employer-reported or based on actual payroll data? Does the data distinguish between roles properly?

A large-looking data set is not automatically relevant if the underlying companies or roles are very different from yours.

This does not mean benchmarking needs to become an academic research project. It just means understanding what the number actually represents before using it to make a compensation decision.

Use more than one source when the decision matters

If the difference between two possible OTE levels is small, one credible benchmark may be enough to give you direction.

For a material compensation decision, I would normally want more than one reference point.

You might have a specialist compensation survey, recruitment data, information from your own recent hiring processes and actual compensation from comparable roles already inside the company.

If they point in roughly the same direction, you probably have a reasonable market range.

If they disagree substantially, that is useful information too. It may indicate that the role definition is too broad, the geography differs, or one source is measuring a different type of company.

The goal is not to produce a mathematically perfect “market OTE.” I am not convinced such a number really exists.

The goal is to get enough evidence to make an informed compensation decision.

Internal consistency matters as much as external competitiveness

There is another comparison that should happen before the plan is finalised.

Look inside the company.

If a new employee is hired at an OTE that is materially different from people doing essentially the same job, there may be a good reason. Geography, experience, scope or performance expectations could explain it.

But the difference should at least be understood.

The same applies across related roles.

If an SDR can earn almost as much variable compensation as the AE who owns the final commercial outcome, perhaps that is intentional. If a CSM carries a significant retention target but has almost no variable opportunity attached to it, perhaps that is intentional too.

Benchmarking the external market without looking at internal role relationships can create a collection of individually reasonable offers that do not make much sense together.

External competitiveness and internal consistency are separate checks.

You usually need both.

Be careful when using benchmarks in employee communication

I would also be cautious about presenting market data as proof that a compensation plan is fair.

You can reasonably say that external benchmarks were one input into the compensation review.

That is different from saying that a particular OTE or pay mix is “the market rate.”

Markets rarely produce one exact answer.

There is usually a range, and that range depends on role definition, geography, experience, company characteristics and the data source being used.

This matters because employees will often treat a benchmark presented by the company as a much more precise statement than it actually is.

If you use market data in the conversation, explain what was benchmarked and what was not.

Our guide to communicating OTE to employees covers the rest of the plan information employees should understand, including targets, payout mechanics, timing and changes.

A simple way to review an OTE benchmark

I would separate the exercise into a few questions rather than looking for one magic number.

If those answers make sense together, the OTE is probably much more useful than a number copied directly from a compensation report.

If they do not, being exactly at the market median will not fix the plan.

Frequently asked questions about OTE benchmarking