A typical B2B win rate sits between 20% and 30%, but deal size explains more of the variation than anything else: 30–50% is common on small deals, while 10–20% is a good result on deals above €100,000. That makes industry averages close to useless as a target. The benchmark worth having is your own number segmented by rep, deal size, and lead source.
Win rate is one of the most-watched metrics in B2B sales, and one of the most misused. It compresses a complex process into a single number, which makes it easy to report and hard to interpret.
This article covers how to calculate win rate consistently, what ranges are typical at different deal sizes, and how to use the number to diagnose your sales process instead of reporting it every month without drawing conclusions.
How Do You Calculate Win Rate?
Win rate is the share of sales opportunities your team wins. The common formula is straightforward:
Won deals ÷ all closed deals (won + lost) × 100
The definition varies between organizations, though, and that matters more than most managers realize. Some teams count every deal that entered the pipeline, others only deals that reached a proposal. Neither is wrong, but the two numbers are not comparable.
Two things are worth locking down before the number is used for anything:
- Pick one method and stay with it. If you compare your number to an external benchmark, make sure the denominator matches. Otherwise you are comparing two different things.
- Clear out the dead pipeline. Deals that were never actively worked drag the win rate down artificially. Define when a deal counts as an opportunity and exclude the rest from the calculation.
This is the first place where data quality decides the outcome. If stage updates depend on a rep remembering to make them, the calculation reflects who kept records, not who won deals.
What Is a Good Win Rate in B2B Sales?
There is no universal good number. Win rate varies with deal complexity, sales cycle length, and how well the pipeline is qualified. The ranges below are indicative guideposts for judging the order of magnitude of your own number. They are not targets.
| Win rate | Interpretation | First action |
|---|---|---|
| Below 15% | Serious issue | Review the sales process stage by stage |
| 15–25% | Needs improvement | Improve qualification and lead quality |
| 25–35% | Typical level | Work on competitive differentiation |
| 35–50% | Good performance | Spread what works across the whole team |
| Above 50% | Excellent | Check whether the target group is too narrow |
Deal size explains most of the spread:
- Large deals (average value above €100K): typically 10–20%. Long cycles, several stakeholders, competitive evaluation.
- Mid-sized deals (€20–100K): 20–35%.
- Small and repeat deals (below €20K): 30–50%. Short cycle, fewer decision-makers.
- Inbound leads convert clearly more often than outbound, typically two to three times as often.
Use these to place your own number on a scale, not to set a goal. The only benchmark that tells you something about your team is your own historical data in the same segment.
Why Does Win Rate Alone Mislead You?
A single number hides what it is made of. Two misreadings show up in almost every team.
A high number is not automatically good. If your win rate is 60%, the first question isn't how to keep it there. It's whether you are entering too few competitive situations. Reps may be picking only the safe deals. That looks good in the report and caps growth.
A low number is not automatically bad. 15% looks alarming unless you sell large enterprise deals where a single win covers the losses several times over. Profitability decides, not the percentage.
So the right question isn't "what is our win rate". It's "what is it on the right opportunities, and how far apart are our best and weakest reps".
Which Metrics Should You Track Alongside Win Rate?
Win rate tells you the outcome, not the cause. Four metrics alongside it give you a picture you can act on.
| Metric | What it tells you | What it exposes |
|---|---|---|
| Pipeline coverage (pipeline value against target) | Whether there is enough raw material to hit the target | A thin pipeline or an unrealistic target |
| Average deal size | What kind of deals you actually win | The target group drifting toward smaller deals |
| Sales cycle length | How long the decision takes | An unmapped buying process or a missing decision-maker |
| Win rate by rep | Whether this is a process issue or a skills issue | Wide variance means the practice was never spread |
Pipeline coverage is worth looking at for quality, not just value. We covered separately how AI can identify stalled revenue before your team does, well before it shows up as a falling win rate.
What Causes a Low Win Rate?
Five causes come up far more often than the rest. They are not mutually exclusive, and usually at least three are present.
- Weak qualification. The team works deals that were never winnable. This is the single most common cause and the easiest to fix, because it also frees up rep time immediately.
- An unmapped buying process. Nobody knows who decides and on what grounds. The deal moves along nicely until it stops at an approval step no one knew about.
- Weak differentiation. The rep can't articulate why this option beats the alternative. It shows up most clearly in lost deals where the recorded reason is price.
- A mismatch between target group and pipeline. The pipeline holds customers who don't match the ideal customer profile. The number often improves simply by narrowing who you contact.
- Poor data quality. Stages are out of date, loss reasons go unrecorded, and the same field lives in two systems in different formats. This is typically a symptom of manual processes, whose hidden costs run well past the lost hours.
The fifth cause is what blocks fixing the other four. If loss reasons haven't been recorded consistently, causes two and three can't be verified, so they get discussed as opinions.
What questions would you ask your own sales data?
In this Sales Data Analysis Guide, we walk through 10 concrete questions every sales manager should be able to ask their sales data and what kind of answers to expect.
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How Do You Use Win Rate as a Diagnostic Tool?
One number reported monthly steers nothing. Four moves make it useful:
- Segment it. Calculate it separately by rep, deal size, lead source, and industry. The headline number is an average that hides exactly the information you need.
- Look at it stage by stage. Find out where deals die. A deal lost at proposal points to a different problem than one that goes quiet after the first meeting.
- Compare internally. The gap between your best rep and the team average is more useful than the gap to an industry figure.
- Categorize loss reasons systematically. Fixed reason codes and a regular review, not a free-text field nobody reads.
The practical obstacle is usually effort. When a segmented view is compiled by hand into a spreadsheet, it gets done once a quarter and then forgotten. This is where automation pays off most: compiling the report is repetitive, rule-based, error-prone work, which is exactly the kind worth automating first. Once the same view refreshes itself weekly, win rate becomes a steering signal rather than a line in a monthly report.
The same applies to forecasting. Stage probability should be calculated from historical win rates rather than estimated deal by deal, something we covered in why sales forecasts fail.
Where Should You Start?
If you recognize your own team in this, these steps move things forward without a separate project:
- Lock down the calculation method and recalculate the number for the last 12 months.
- Segment it by deal size and lead source. Find the segment dragging the average down.
- Review the last 20 lost deals and categorize the reasons.
- Count how many hours a month go into compiling sales reports, and whose hours they are.
- Automate one manual compiling step and measure the time saved.
If the list won't fall into order on its own, a fixed-price Automation Assessment walks through your sales processes and tells you which targets to automate first and what they're worth in euros. Implementations are built on Microsoft Azure, and we're a Claude Partner Network member, so where data is processed and who can access it is known from the start.
The difference between a 22% and a 35% win rate is rarely talent. It's how strictly you qualify, how reliable the data is, and how quickly a deviation gets noticed.
Frequently Asked Questions
What is a good win rate in B2B sales?
The typical level is 20–30%, but deal size matters more than industry. On deals below €20,000, 30–50% is common; between €20,000 and €100,000 the usual range is 20–35%; and above €100,000, 10–20% is a good result. Below 15% usually points to a qualification problem regardless of segment.
How do you calculate win rate?
Divide won deals by all closed deals, meaning won plus lost, and multiply by 100. Some teams include every deal that entered the pipeline, others only those that reached a proposal. Either method works as long as you stay with it and exclude deals that were never actively worked.
Is a high win rate always a good thing?
No. A figure above 50% can mean the team only enters competitive situations it knows it will win. That looks good in the report but caps growth, because too few opportunities enter the pipeline. Check average deal size and pipeline coverage against target at the same time.
Why is our win rate low?
The most common causes are weak qualification, an unmapped buying process, weak competitive differentiation, a mismatch between the target group and the pipeline, and poor data quality. The last one is the hardest, because it prevents verifying the others: if loss reasons aren't recorded consistently, causes get discussed as opinions rather than observations.
Want to remove manual work from your sales process?
The Automation Assessment identifies which repetitive sales steps to automate first and how much time it frees up for actual selling.
Book an Automation AssessmentEmpirica helps growth companies remove operational bottlenecks with automation and operational AI. The Automation Assessment is a fixed-price way to find out where to start.
