Is your sales pipeline converting well? B2B conversion rate benchmarks for 2026

If you run a B2B sales or marketing team, you’ve probably wondered whether your pipeline converts as well as it should. For reference, we asked B2B teams about their conversion rates at four pipeline stages, from lead to closed-won deal, and used their responses to establish benchmarks for each stage.

Teams also told us about their business, including company size and go-to-market motion, so you can see which of those factors change the numbers and which leave them as they are.

One note before the numbers: teams define each stage differently. An MQL at one company is someone who downloaded a whitepaper, and at another it’s a contact who has already agreed to a call, so keep your own definitions in mind as you compare. For the wider picture of pipeline health, forecasting, and loss reasons, see our companion report on B2B sales pipeline statistics & benchmarks.

Key B2B sales funnel conversion rates and insights for 2026

• The median B2B win rate (close rate) is 21–30%. The typical team closes roughly 1 in 4 opportunities, and that holds for small and large companies alike, across industries and sales cycle lengths.
• The median conversion rate is 11–20% at each of the first 3 stages: lead to marketing-qualified lead (MQL), MQL to sales-qualified lead (SQL), and SQL to opportunity. At the lead-to-MQL stage, the median sits at the low end of that range: about half of teams (49.5%) convert fewer than 11% of their leads.
• More than a third of teams don’t measure the middle of their funnel. 38% of companies don’t track MQL-to-SQL conversion, and 39% don’t track SQL-to-opportunity.
• The tracking gap closes sharply at 201+ employees. 44% of companies with 1–10 employees leave MQL-to-SQL conversion untracked, compared with 10% of companies with 201+ employees.
• Outbound-led teams report the lowest MQL-to-SQL rates. 54% of them convert fewer than 10% of MQLs, compared with 27% of teams using other sales approaches, and the gap holds for companies of every size.
• Teams that score leads are more than twice as likely to track their whole funnel. 79% of teams with a lead scoring model track all 4 stages, compared with 33% of teams without one.
• 29% of teams can’t say where their funnel leaks. Teams that do name a stage spread their answers almost evenly across all 4.

Sales pipeline conversion rates by stage

In the table below, each row includes only the teams that track that stage. Teams picked a range, for example 11–20%, instead of an exact number, so the benchmarks are ranges too.

The typical rate is the median: if you lined all teams up from lowest to highest, it’s the answer of the team in the middle. The middle half of teams leaves out the top and bottom quarters, so it shows where most teams land without the extremes.

Lead-to-MQL sits at the very edge of its range. 49.5% of teams convert fewer than 11% of their leads, so the typical team lands just inside 11–20%, at the low end.

Further down the funnel, the numbers line up. The average team converts 11–20% of MQLs into SQLs and 11–20% of SQLs into real opportunities, then wins 21–30% of those opportunities.

Takeaway: If your mid-funnel stages convert at 11–20% and you win 21–30% of opportunities, you’re right in the middle of the pack. Whether that’s good depends on which teams you compare yourself with, and the next numbers show how far apart teams really are.

Why there’s no universally good B2B conversion rate

The typical rates hide how far apart teams really are. At every stage, answers spread across all 6 ranges, from under 5% to over 50%, and even the least popular range was picked by about 1 in 10 teams.

The tables below show, stage by stage, what share of teams falls into each range. For example, 24% of teams convert fewer than 5% of their leads into MQLs, while 12% convert more than half. Win rate uses slightly different ranges, starting at under 10%.

A quarter of teams win fewer than 1 in 10 opportunities, and another 16% win more than half. Both groups answered the same question, which suggests they run very different sales models.

The same pattern holds higher in the funnel. Close to a quarter of teams convert fewer than 5% of MQLs into SQLs, while 12% convert more than half. Teams at these 2 extremes are unlikely to define an MQL the same way: one may count anyone who downloaded a guide, while the other may wait until a contact has agreed to a call.

How should you compare your conversion rates with a benchmark?

A single industry-wide figure can mislead in either direction. For example, a team converting 8% of MQLs may be underperforming, or it may simply apply a looser MQL definition than its peers. Likewise, a team converting 40% may be excellent, or it may only label a lead an MQL once a meeting is booked.

Before comparing your rate with any benchmark, including this one, write down what moves a record from one stage to the next in your customer relationship management (CRM) system. Part of the gap between your rate and a benchmark may come down to how you define a stage rather than how well you sell.

Takeaway: A conversion rate means little without its stage definition and its peer group. It also assumes the stage is measured at all, and many teams never get that far.

Which sales funnel stages do B2B teams track, and what impacts it?

Every benchmark above comes from teams that track conversion, and many teams don’t. 38% don’t track MQL-to-SQL conversion, and 39% don’t track SQL-to-opportunity. Only 53% track all 4 stages, while 23% track none of them.

Company size: the mid-funnel tracking gap closes at 201+ employees

Company size matters most in the middle of the funnel. Small companies (1–10 employees) and mid-size companies (11–200) leave these stages untracked at similar rates, while large companies (201+) almost always track them.

Tracking jumps once companies pass 200 employees, and mid-size teams look much more like small teams than like large ones. One possible explanation, which the survey can’t confirm, is that larger companies are more likely to run marketing and sales as separate teams, and managers have to report on the handoff between them.

At the 2 ends of the funnel, size made less difference: the gaps in tracking lead-to-MQL conversion and win rate were too small to rule out coincidence.

Teams with lead scoring are more than twice as likely to measure every funnel stage

In this report, a team claims it tracks a stage when it can report its conversion rate for that stage. Without that number, a team can’t see where deals stall or compare itself with the benchmarks above. That’s why it matters which teams can measure their whole funnel and which can’t.

The clearest dividing line in the survey is lead scoring: a set of features that scores each lead and marks it as an MQL once it qualifies. 79% of teams with a lead scoring model can report conversion at all 4 stages, compared with 33% of teams without one. Two related practices show the same pattern with smaller gaps:

A lead scoring model only works if the team has agreed in writing on what turns a lead into an MQL, and a qualification framework does the same for SQLs. Once those rules are set up in the customer relationship management (CRM) system, sales can calculate conversions between stages. Teams without clear rules may have no fixed line between stages, leaving nothing to measure.

As the previous section showed, large companies track their funnels far more consistently, and they could simply be the teams that also use lead scoring. But the data says otherwise. Among small companies alone, 78% of teams with lead scoring track all 4 stages, compared with 29% of teams without it. The type of scoring made little difference too: manual, automated, and hybrid scoring all show similar tracking rates.

Go-to-market motion and industry made no clear difference. Outbound-led teams tracked conversion about as often as all other motions combined, and the largest industries in the sample looked the same.

Does lead scoring cause teams to track conversion?

Part of the link is built in. A scoring model needs a cut-off score, and that cut-off makes an MQL something you can count, so once the stage exists in the CRM, you can easily report conversion into and out of it.

The survey can’t tell which comes first. Teams may track more because they adopted scoring, or adopt scoring because they already track closely, and something else, such as having a dedicated revenue operations (RevOps) owner, could drive both. The survey didn’t ask about that role.

Takeaway: If you can’t report MQL-to-SQL conversion today, the first step is usually a written MQL definition your CRM can apply, and lead scoring is one common way teams get there.

Do larger companies have higher sales pipeline conversion rates?

If larger companies measure more, it’s fair to ask whether they also convert better. Among teams that track each pipeline stage, they often don’t: small, mid-size, and large companies reported similar mid-funnel rates and similar win rates. The one possible exception sits at the very top of the funnel.

Research note: Small groups can differ just by luck. So we only call a gap between groups real if it passes three checks:

• It’s big enough that luck is unlikely.
• At least 20 teams in each group answered the question.
• It’s still there after we leave out answers that looked rushed.

Several explanations for this gap exist. Large companies in this sample sell bigger deals and rely less on outbound, and both factors affect how a lead gets counted. A large company may also apply a stricter filter before a contact enters the funnel, which would lift its conversion rate without changing the number of real buyers.

Conversion from MQL to closed-won shows no real size difference

For the 3 lower stages, the differences between company sizes were too small to rely on, so we don’t publish separate numbers for them.

This result runs against intuition. Large companies track their funnel far more consistently and use formal qualification frameworks far more often, yet they report conversion rates and win rates in the same range as a 5-person team. Better measurement and more formal process did not show up as better conversion in this data, and the survey can’t explain why. Larger teams may set stricter stage definitions that hide real gains, or measurement may simply not be what limits conversion.

Takeaway: Small teams shouldn’t assume that enterprise peers convert better at every stage. The clearest size difference in this survey appeared at lead-to-MQL, and even that one is only an early signal.

Outbound-led teams have lower MQL-to-SQL conversion rates, then close at the same rate as everyone else

How a team sells made the clearest difference in the study. Teams that describe themselves as outbound-led, meaning their pipeline comes mainly from cold email and calling run by sales development reps (SDRs), reported much lower MQL-to-SQL conversion than teams using any other approach. After that stage, the gap disappeared.

“All other approaches” combines 5 groups, each too small to benchmark on its own:

• Hybrid or allbound teams, which run a structured mix of inbound and outbound
• Inbound-led teams
• Product-led teams
• Account-based teams
• Event-led teams

Is the outbound gap really about company size?

Large companies in this survey are less likely to be outbound-led (34%, against 49% of small and mid-size companies), so size could explain the gap. It doesn’t: the gap held among small companies and among companies with 11 or more employees, and it survived our recheck. That makes it the strongest finding in this report.

Why do outbound teams convert fewer MQLs into SQLs?

The most likely reason is how outbound teams define an MQL. Outbound-led teams often have no marketing stage in the traditional sense, so an MQL can mean anything from an opened email to a positive reply, and the SQL label usually comes only after a qualifying conversation. That makes the MQL pool wide and the step to SQL steep, while an inbound team usually applies the MQL label after a prospect has already asked for contact, for example, by filling out a demo form.

The later stages support that reading. Once outbound-sourced deals reach SQL, they become opportunities and close at the same rates as deals from every other approach. If outbound leads were simply weaker, you’d expect the gap to widen further down the funnel, but it doesn’t in this data.

This reading comes with 2 cautions. The survey didn’t ask how each team defines an MQL, so this explanation is our best reading rather than something we measured. Part of the sample also came from Belkins’ own network of clients and former clients, which is why outbound-led teams make up 46% of respondents, likely more than they would in a random sample of B2B companies.

Takeaway: If you run outbound, compare your MQL-to-SQL rate with other outbound teams rather than with an inbound benchmark. A 5–10% rate is the median for outbound-led teams here, and once leads pass qualification, outbound and inbound pipelines look alike.

Do sales funnel conversion rates differ by industry, deal size or sales cycle?

Many benchmark reports compare conversion by industry first, yet in this survey industry made no clear difference at any stage. Deal size showed a pattern that faded on a closer look, and sales cycle length showed none.

The numbers in brackets count all teams in each group.

Sales funnel conversion rates by industry: SaaS, marketing and IT services

Readers often compare themselves with their own industry first, so here are the typical rates for the 3 largest industries in the survey. Treat them as a rough guide: the differences between these industries are too small to rely on, and 2 of the 3 groups have fewer than the 20 teams per stage we use elsewhere in this report.

The only visible gap is IT services’ typical MQL-to-SQL rate of 5–10%, against 11–20% for the other 2 industries. With only 12 IT services teams behind it, that gap could easily be a coincidence, so treat it as something to recheck in a larger survey.

The other 75 teams come from more than 25 industries, too few per industry to compare. So this survey can’t show industry differences, but that doesn’t mean they don’t exist.

Conversion rates by deal size: a pattern that faded on a closer look

Teams selling contracts worth $50K or more a year reported higher conversion at the 3 stages before closed-won. Once rushed answers were removed, those gaps became too small to rely on, and win rate showed no difference at all.

Deal size also overlaps with company size: 20 of the 29 large companies in the survey sell contracts worth $50K or more, compared with 13 of 88 small companies. Because we can’t tell the 2 apart, we treat deal size as an early signal and don’t publish separate numbers for it.

Conversion rates by sales cycle length: no pattern

Teams with sales cycles under 30 days and teams with cycles of 3 to 6 months reported conversion in the same ranges. Groups with cycles longer than 6 months were too small, with fewer than 10 respondents each, to assess on their own.

Takeaway: In this survey, how a team sells mattered more for mid-funnel conversion than what it sells or how long its deals take. Industry benchmarks are only as useful as the stage definitions behind them.

B2B win rate benchmarks: wide variation between teams, little between company types

Win rate barely changed from one group of teams to another. The typical team wins 21–30% of its opportunities, and none of these factors moved that number in a way we could rely on:

• Company size
• Go-to-market motion
• Industry
• Deal size
• Sales cycle length
• Lead scoring, qualification frameworks, or pipeline review cadence

That doesn’t make win rates similar. A quarter of teams that track win rate close fewer than 10% of opportunities, while 16% close more than half. The variation is real, but it shows up inside every group rather than between groups, so a SaaS company or an IT services firm, a 5-person team or a 500-person one, can land at either end.

In practice, win rate likely depends on factors this survey didn’t measure. Plausible candidates include:

• How strictly a team defines an opportunity
• How well it works with a buying committee
• How often its deals stall without a decision

Our companion report on B2B sales pipeline statistics covers that last point in detail.

Takeaway: Comparing your win rate with similar companies tells you little. Comparing it with your own history, and with how you define an opportunity, tells you more.

Where does the sales funnel leak? Nearly half of teams can’t name one stage

When we asked where their funnel sees the sharpest drop-off, 29% of respondents said they don’t track well enough to know, and another 16% said they don’t see one clear bottleneck. Together, 46% couldn’t point to a single stage. The 54% who did name a stage spread their answers almost evenly across the funnel.

Opportunity-to-closed-won came out on top, but its lead is small enough to be a coincidence. Neither company size nor go-to-market motion changed the answers, and industry made no difference either.

Why perceived funnel leaks don’t match measured conversion rates

The data holds a tension worth noticing. By raw numbers, the top of the funnel is where the most leads disappear: half of teams convert under 11% of leads to MQL, while the typical win rate is 21–30%. Yet respondents named the bottom stage as their sharpest drop-off slightly more often than the top.

That suggests teams judge a leak against what they expect from a stage rather than by its raw rate. Losing 9 in 10 raw leads may register as normal, while losing a late-stage deal after weeks of work registers as a failure. Both readings are reasonable, but they lead to different fixes. Some answers also rest on judgment rather than data.

Takeaway: If your own numbers can’t tell you which stage leaks most, measuring stage-to-stage conversion consistently for a single quarter is a sensible first step. After that, each stage is best compared with its own benchmark rather than with the other stages.

Does lead scoring improve conversion rates?

Teams that use the practices from the tracking section are far more likely to measure their whole funnel. The natural next question is whether they also convert better, and in this data they don’t, at least not measurably.

Teams with and without a lead scoring model reported conversion in the same ranges at every stage. So did teams with and without a formal framework such as BANT (budget, authority, need, timeline), MEDDIC (metrics, economic buyer, decision criteria, decision process, identify pain, champion) or a custom internal model. How often a team reviews its pipeline made no clear difference either. A few comparisons looked different at first but didn’t hold up on a recheck, which is what you’d expect from coincidence when you compare many groups.

Why lead scoring may not show up in conversion rates

A practice that doesn’t show up in conversion rates isn’t necessarily useless. The data fits 3 explanations, and the survey can’t choose between them:

1. Stricter definitions offset real gains. A team that adopts lead scoring may raise the bar for what counts as an MQL, so its MQL-to-SQL rate could stay flat even as lead quality improves, because the number of MQLs it counts changed too.
2. Adopting a framework is different from using it well. The survey asked whether a team uses a framework and didn’t measure how well, so a framework applied loosely and one applied rigorously both count as "yes."
3. The effect is real but small. With about 100 teams per stage split into 2 groups, only large differences show up clearly, so a modest improvement could go unnoticed.

It’s also unclear which comes first. Teams with weak conversion may adopt scoring and frameworks precisely to fix it, which would hide any benefit in a one-time survey, and only tracking the same teams over time could tell the 2 apart.

Takeaway: Lead scoring and qualification frameworks are worth adopting to make your funnel measurable and your stage definitions consistent. This data doesn’t support expecting them to raise your reported conversion rates on their own.

What these B2B conversion metrics mean for revenue leaders

Taken together, the findings point to one idea: in B2B pipelines, a conversion rate describes a team’s definitions and operating habits as much as its sales performance. The data shows 3 patterns that support that reading:

1. The spread is wide at every stage. The factors most benchmark reports slice by, such as industry and deal size, explained little of it.
2. The clearest gap between groups sits where definitions vary most. Outbound-led teams’ lower MQL-to-SQL rate appears exactly at that stage and disappears once a deal is qualified.
3. Practices changed measurement more than results. Lead scoring changed whether teams track their funnel far more than the rates they reported.

For revenue leaders, the implication is practical. Before asking whether your conversion is good, it’s worth ensuring each stage has a written entry rule your whole team applies the same way, and then comparing yourself with teams that sell the way you do. A benchmark from a different motion, or from a team with a looser MQL definition, is likely to mislead in one direction or the other.

Methodology

Sample and fieldwork. Belkins ran the survey from July 31 to October 6, 2026, and collected 164 unique responses.

Recruitment. Respondents came from an open survey on belkins.io and from direct outreach to Belkins’ peers, clients, and former clients. The sample isn’t random, and it over-represents small companies and outbound-led teams relative to the wider B2B market.

Who responded:

What we asked. Respondents reported conversion at 4 stages: lead → MQL → SQL → opportunity → closed-won. Each question offered 6 ranges and a "we don’t track this" option. The first 3 stages used ranges from under 5% to over 50%, while win rate used ranges from under 10% to over 50%. All figures are self-reported.

How we analyzed it. Each conversion figure includes only the teams that track that stage. Because answers are ranges, we report typical rates (medians) and the range for the middle half of teams instead of averages, and we used standard statistical tests to compare groups.

We call a difference between groups real when it meets 3 conditions:

• It passes a standard statistical test at the usual 5% level.
• Each group has at least 20 teams reporting that stage.
• It holds after the recheck described below.

A difference that shows up in the full data but fades after the recheck is labeled an early signal. Differences that meet neither standard are reported as no clear difference, without separate numbers per group. The exception is industry: because readers often compare themselves with their own industry, we publish typical rates for the 3 largest industries, with a caveat.

Limitations. Respondents defined lead, MQL, SQL, and opportunity in their own terms, and the survey didn’t ask for those definitions. Answers are ranges rather than exact rates, and the data is a single snapshot, so it shows which things go together rather than what causes what. Results describe this sample and may not apply to all B2B companies.