---
title: "B2B Sales Pipeline Statistics & Benchmarks [Belkins' Study]"
description: "Only 30% of B2B teams track all 12 core pipeline metrics. See 2026 sales pipeline statistics and benchmarks on conversion, forecasting, why deals are lost, and more."
url: "https://belkins.io/blog/sales-pipeline-statistics"
---

[Blog](https://belkins.io/blog)

[Sales](https://belkins.io/blog/category/sales)

# B2B sales pipeline statistics and benchmarks: What revenue teams told us in 2026

Author

Iryna Yelisova

Iryna is a Content Manager at Belkins with over 10 years in content writing and strategy.

Reviewed by

Margaret Lee

Margot, a CMO of Belkins, is a seasoned professional with over 14 years of experience and a remarkable track record of managing marketing teams.

Published: 2026-10-29

Reading time: 18 min

Scroll through LinkedIn on any given week, and B2B sales looks like a streak of record quarters and pipelines that quadrupled since spring. Ask the same people anonymously how their pipeline is doing, and the tone changes.

We wanted to see what B2B pipelines actually look like from the inside. So, in 2026, we surveyed 164 B2B professionals about how they track, qualify, forecast, and lose deals. We also asked about 12 core pipeline metrics, along with how teams qualify opportunities, how often they review the pipeline, and what they plan to fix next:

- **Sales funnel conversion:** lead-to-MQL conversion, MQL-to-SQL conversion, SQL-to-opportunity conversion, and close rate (opportunity to closed-won).
- **Pipeline health and velocity:** pipeline coverage ratio, share of stale pipeline, close-date slips, meeting no-show rate, and the stage with the sharpest drop-off.
- **Forecasting and lead quality:** forecast accuracy, SQL quality trend, and the share of losses that end in no decision.

The most important finding is not a conversion rate. It is that **most B2B teams can see only part of their own pipeline**. Only 30% of respondents track all 12 sales pipeline metrics we asked about, and the gap is concentrated in companies with 200 or fewer employees. The teams that do measure report forecasts that often miss, close dates that slip more than once, and deals lost to buyer inaction far more often than to competitors. Yet when asked what they will prioritize over the next 12 months, most teams choose getting more leads over seeing the pipeline they already have more clearly.

📌 **A quick tip:** Read the percentages in this report as directional signals from a focused sample. Respondents answered in ranges rather than exact figures, and most pipeline metrics are reported only among the teams that track them, so each figure has its own base. Where a finding rests on a small group, we say so.

## Key B2B sales pipeline statistics for 2026

- **Only 30% of B2B teams track all 12 core pipeline metrics**, and 10% track none of them.
- 52% of B2B teams don’t calculate pipeline coverage, making it the most common blind spot among the 12 metrics.
- **51% of B2B teams are dissatisfied with their lead volume**, compared with **38% dissatisfied with lead quality**, and 60% name lead volume as a top-three priority for the next 12 months.
- **43% of small companies (1–10 employees) and 36% of mid-size companies (11–200 employees) leave half or more of the 12 metrics untracked**, compared with 10% of large companies (201+ employees).
- **86% of large companies qualify deals with a formal framework**, compared with 47% of small and mid-size companies.
- Among teams that track them, the median **MQL-to-sales-qualified lead (SQL) and SQL-to-opportunity conversion rates are both 11–20%**, and the median **opportunity-to-closed-won rate is 21–30%**.
- 38% of teams that measure forecast accuracy usually **miss their forecast by more than 25%**.
- Only 9% of B2B teams name a competitor as **the most common reason they lose deals**.

## Who we talked to about their sales pipeline health

Before the findings, a word on who answered, because it shapes how we recommend reading every number below.

We collected 164 usable responses through an open survey on the Belkins website and through direct outreach to peers, Belkins clients, and former clients. Fifty-four percent of respondents work at companies with 10 or fewer employees. Large companies (201+ employees) are the smallest of the three size groups we use in this report. Nearly half of respondents (46%) describe their go-to-market motion as outbound-led.

![Survey respondents by company size and go-to-market motion](https://belkins.directus.app/assets/ae131239-18b4-4f1f-8a0f-746a4cd1e4ab.png?width=2730&height=1250)

We flag this upfront for two reasons. First, small companies often run a simpler sales process. A founder-led team with no separate marketing function may have no MQL stage at all, so “we don’t track it” can mean the stage doesn’t exist rather than that it goes unmeasured. Second, an outbound-heavy sample matters for any question about which channels work best. We point out each place where these skews are likely to change the reading.

Throughout the report, we compare three company-size groups: **small** (1–10 employees), **mid-size** (11–200 employees) and **large** (201+ employees). We only report a size difference when it is large enough that chance alone is unlikely to explain it, and each group has at least 20 respondents behind it. When a difference falls short of that bar but is still worth knowing, we label it as directional.

## Most B2B teams can see only part of their sales pipeline

Full pipeline visibility is the exception. Only 30% of respondents track all 12 metrics we asked about, 10% track none of them, and the typical respondent leaves three of the 12 untracked.

The most common blind spot is also one of the most consequential. **52% of teams don’t calculate the pipeline coverage metric**, the ratio of open pipeline value to the revenue target it is meant to deliver. Coverage is the basic input for deciding whether the pipeline can carry next quarter’s number. Forecast accuracy comes next, untracked by 40% of teams. The middle of the funnel follows closely: between 37% and 39% of teams don’t track lead-to-MQL conversion, MQL-to-SQL conversion, SQL-to-opportunity conversion, or the trend in SQL quality. Even the most widely tracked metric in the survey, the share of losses that end in “no decision,” goes unmeasured by 23% of teams.

![Share of B2B teams not tracking each of 12 sales pipeline metrics](https://belkins.directus.app/assets/1ec883e6-82fb-4925-a532-c6f61a9a5b6c.png?width=2048&height=1415)

This matters for a simple reason: a team can’t fix a leak it can’t locate. Without coverage, pipeline targets are set by feel. Without forecast accuracy, you can’t know whether the forecast is a plan or a guess. It also matters for this report. Every benchmark in the sections that follow comes from the teams that do measure, making them a more structured subset of the market than the sample as a whole.

The gap is not spread evenly. It sits almost entirely in companies with 200 or fewer employees. Here’s how many companies at each size actually track each metric:

| **Metric tracked** | **% of small companies (1–10 employees) tracking the metric** | **% of mid-size companies (11–200 employees) tracking the metric** | **% of large companies (201+ employees) tracking the metric** |
| --- | --- | --- | --- |
| **Track 7 or more of the 12 metrics** | **57%** | **64%** | **90%** |
| Pipeline coverage | 41% | 45% | 72% |
| Forecast accuracy | 52% | 57% | 86% |
| SQL quality trend | 50% | 64% | 90% |
| MQL-to-SQL conversion | 56% | 57% | 90% |
| Close-date slips | 57% | 66% | 90% |
| SQL-to-opportunity conversion | 55% | 60% | 83% |
| Lead-to-MQL conversion | 59% | 62% | 76% |
| Stale pipeline share | 60% | 62% | 79% |
| Win rate | 65% | 70% | 83% |
| Stage with the sharpest drop-off | 68% | 68% | 83% |
| Meeting no-show rate | 69% | 72% | 83% |
| No-decision share of losses | 77% | 70% | 90% |

![Share of companies tracking each pipeline metric by company size](https://belkins.directus.app/assets/fa0048ff-2854-4aa1-8f93-1ca90269f5a8.png?width=1800&height=1548)

The climb isn’t gradual. Mid-size companies look far more like small companies than like large ones. The real change happens past 200 employees, where the share of teams leaving half their metrics untracked drops from roughly four in ten to one in ten.

Our data doesn’t explain why the change happens at that point. One plausible reading is that companies past 200 employees are more likely to have someone whose job includes pipeline reporting, such as a revenue operations (RevOps) function or a dedicated sales operations role. Since we didn’t ask about team structure, we can’t confirm it.

Two cautions apply. First, the headline figures are pulled up by the large share of very small companies in the sample. A reader at a 500-person company should look at the large-company column, not the overall average. Second, “not tracked” can mean a stage doesn’t exist. A five-person company that sells through referrals may have no MQL stage to measure, and the survey can’t tell those teams apart from teams that have the stage and ignore it.

One related pattern points in an interesting direction. **Teams that track all 12 metrics are far more likely to be satisfied with their lead quality (45%)** than teams that leave six or more untracked (12%). Dissatisfaction runs the other way: 20% in large versus 59% in small. This is rather an association than evidence that measurement improves lead quality. Better-resourced companies may both measure more and attract better leads, and the same person rated both, which can strengthen the link on its own.

**Practical takeaway:** before comparing your pipeline against anyone else’s benchmarks, check which of these 12 metrics your team can actually produce today. For mid-size companies in particular, the gap to large companies is wider than the gap to the smallest teams in the market.

## Nearly half of B2B teams qualify deals without a formal framework

The visibility gap has a companion in how teams decide which deals are real. **46% of respondents use no formal deal-qualification framework.**

Among the teams that do use one, homegrown systems lead. Custom or internal frameworks account for 29% of all respondents, ahead of BANT (budget, authority, need, timeline) at 14% and MEDDIC or MEDDPICC at 9%. Another 2% use something else.

Company size follows the same pattern as pipeline visibility:

|  | **Small (1–10)** | **Mid-size (11–200)** | **Large (201+)** |
| --- | --- | --- | --- |
| Use a formal qualification framework | 47% | 47% | 86% |

Again, the change comes after 200 employees, and mid-size companies look exactly like the smallest ones.

The other two operating habits we asked about don’t follow company size. A third of teams (34%) have no formal pipeline review cadence. Among those that do, weekly reviews are the most common (30%), followed by ad hoc reviews (16%), biweekly (10%), and monthly (9%). Fewer than half of teams (43%) score leads (15% automated, 15% manual, 13% a hybrid of both); another 40% plan to start, and 17% have no plans to. Review cadence and lead scoring don’t differ meaningfully between small, mid-size, and large companies.

![Lead scoring models used by B2B companies](https://belkins.directus.app/assets/0572919a-b04f-49b0-9d9d-4e943c93c02c.png?width=1350&height=512)

These practices tend to appear together. Teams that use a framework are more likely to score leads, review the pipeline on a schedule, and track more metrics. In practice, most teams either have a pipeline operating system or they don’t.

These approaches also align with how teams feel about lead quality. **45% of teams that score leads are satisfied with lead quality, compared with 14% of teams that don’t.** The gap held within each company-size group we checked, so company size alone doesn’t explain it. Teams with a formal framework show a similar pattern: 39% are satisfied with lead quality, compared with 14% of teams without one.

That is a meaningful signal, but not proof that scoring or frameworks improve lead quality. Teams with more resources may both adopt these practices and attract better leads. A team that has defined what a good lead looks like may also judge its leads more confidently. In this survey, we can’t separate these explanations.

**Practical takeaway:** if your team has no shared definition of a qualified deal, conversion rates between stages mean different things for different reps. A formal framework, even a simple custom one, is the precondition for any stage benchmark to be useful.

## Teams are more frustrated by lead volume than by lead quality

Conventional B2B sales advice is “quality over quantity,” which is the cornerstone of [our lead gen approach at Belkins](https://belkins.io/belkins-approach). However, teams in this survey are telling a different story. **51% are dissatisfied with the volume of leads they get, compared with 38% dissatisfied with lead quality.**

|  | **Dissatisfied** | **Neutral** | **Satisfied** |
| --- | --- | --- | --- |
| Lead volume | 51% | 26% | 23% |
| Lead quality | 38% | 34% | 27% |

![B2B teams’ satisfaction with lead volume and lead quality](https://belkins.directus.app/assets/e6ac306e-38f8-4d90-9314-417abd09e0ab.png?width=1350&height=636)

The gap holds when we compare each respondent’s two answers side by side: individual teams consistently rate their volume worse than their quality, and the difference is too large to put down to chance. Priorities point the same way. **Improving lead volume is the most common top-three priority** for the next 12 months (60%), ahead of improving lead quality (44%). Among teams dissatisfied with volume, 71% put it in their top three.

There is a nuance worth keeping. The two ratings move together strongly: teams frustrated with one tend to be frustrated with the other. Answers partially capture a general attitude about lead generation, not two separate verdicts. Even so, volume comes out worse in that comparison.

The alternative reading is that volume frustration can partly mask a quality problem. A team that disqualifies most of what comes in will experience the result as “not enough leads,” even when the underlying issue is fit. The survey can’t distinguish the two, so we read this finding as teams feel short of workable pipeline rather than “quality no longer matters.”

Volume frustration is close to universal across company sizes:

|  | **Small (1–10)** | **Mid-size (11–200)** | **Large (201+)** |
| --- | --- | --- | --- |
| Dissatisfied with lead volume | 52% | 51% | 45% |
| Dissatisfied with lead quality | 47% | 32% | 24% |

💡 **Note:** The volume figures don’t differ meaningfully by size. The quality figures fall as companies get larger, but that difference sits just short of the threshold we use for a reliable result, so treat it as directional.

What the numbers do show clearly is that company size doesn’t relieve volume pressure: roughly half of teams at every size want more leads than they get. If the quality difference holds up in a larger sample, it would suggest that bigger companies ease the quality problem without easing the volume one.

**Practical takeaway:** if lead volume is your biggest frustration, you are in the majority. Before adding volume, though, check what share of current leads your team disqualifies and why. If that share is high, the fix may sit in targeting rather than in more outreach.

## A quarter of teams can’t say where their best SQLs come from

If volume is the top priority, the next question is where to find more of it. For a quarter of teams, the honest answer is “We don’t know”: **25% can’t identify which channel produces their best SQLs.** The attribution gap follows company size:

|  | **Small (1–10)** | **Mid-size (11–200)** | **Large (201+)** |
| --- | --- | --- | --- |
| Can’t attribute their best SQLs to a channel | 33% | 19% | 10% |
| Name inbound/SEO among their top SQL channels | 12% | 30% | 38% |

Unlike the visibility and framework gaps, this one narrows step by step as companies grow, rather than changing only past 200 employees.

Among teams that can attribute, one pattern dominates: **teams credit the channel they already run.** 46% of outbound-led teams name outbound as a top source of their best SQLs, compared with 4% of inbound-led teams. Inbound-led teams name inbound or SEO 48% of the time, compared with 13% of outbound-led teams. Hybrid teams split almost evenly between the two (32% and 29%).

![Channels that deliver the best SQLs by go-to-market motion](https://belkins.directus.app/assets/8f967f34-2f56-4ba2-bc67-8d2e13018f71.png?width=1350&height=795)

This is why we don’t publish a ranking of the “best” channels for SQLs. It would be convenient for a company that runs outbound programs for its clients to report that outbound comes out on top, and in raw counts it does. But respondents tend to credit the channel they invest in, and our sample leans outbound, with 46% of respondents running outbound-led motions. A raw ranking would mostly reflect who answered the survey.

The same caution applies to the size pattern. Larger companies naming inbound more often may reflect where they invest, not which channel performs better for them.

> “It’s not surprising that enterprise-sized companies rely more on inbound tactics. Smaller companies, on the contrary, are likely to have outbound-centered sales, which is ideal for rapid growth and finding their product-market fit.”
> 
> — Margaret Lee, CMO at Belkins

**Practical takeaway:** perceived channel performance tracks investment, not outcomes. If you are deciding where to put next year’s pipeline budget, measure SQL-to-opportunity conversion and win rate by source in your CRM (customer relationship management system) rather than relying on your team’s sense of which channel works.

## Sales pipeline conversion rates: the typical team converts 11–20% at each mid-funnel stage

For teams that do track conversion, the middle of the funnel converts at similar rates. The median team converts 11–20% of MQLs to SQLs and 11–20% of SQLs to opportunities, and wins 21–30% of its opportunities. At the top of the funnel, about half of tracking teams (49%) convert fewer than 11% of leads to MQLs.

A median is the middle answer: half of teams report a higher rate and half a lower one. It is a more honest summary than an average here, because respondents chose from ranges rather than typing exact figures.

| **Stage** | **Teams tracking** | **Median B2B sales funnel conversion rates** |
| --- | --- | --- |
| Lead-to-MQL | 103 | 11–20% (low end of the range) |
| MQL-to-SQL | 102 | 11–20% |
| SQL-to-Opportunity | 100 | 11–20% |
| Opportunity-to-Won (win rate) | 114 | 21–30% |

![Median B2B sales pipeline conversion rates by stage](https://belkins.directus.app/assets/61eb29a8-2dc8-4490-af87-cc3465914448.png?width=1350&height=1016)

The medians are only half the story. The spread between teams is wide at every stage. No single answer range holds more than about a quarter of responses, and the middle half of teams spans three to four ranges. At the MQL-to-SQL stage, for example, the middle half of teams runs from 5–10% to 36–50%.

There is no universal good conversion rate in B2B sales. A rate that is healthy for one team can signal a problem for another, depending on how each defines a lead, what it sells, and how it sources pipeline. A benchmark is useful only when it comes from teams like yours.

We also don’t multiply these stage rates into an end-to-end figure such as “X of every 1,000 leads become customers.” Multiplying ranges compounds their uncertainty, and teams define “lead” too differently for the result to mean much.

📚 **Relevant reading:** Our [sales pipeline conversion data study](https://belkins.io/blog/sales-pipeline-conversion) covers full distributions, industry, size, and go-to-market differences, as well as how we checked the data.

## Pipelines are holding steady, but stalled deals and slipping close dates are common

The next question is how healthy the pipeline is overall and how fast deals move through it. The overall picture is rather mixed:

- **38% of teams say their pipeline is healthier than a year ago**
- **34% say it is about the same**
- **29% say it is worse**
- Only 11% describe it as significantly better and 10% as significantly worse.

This picture barely changes with company size. 36% of small and mid-size teams and 45% of large teams report improvement, a difference too small to be meaningful at these group sizes.

The trend in SQL quality looks similar among the 100 teams that track it:

- **41% say it improved**
- **36% say it stayed the same**
- **23% say it declined.**

The two trends move closely together, likely reflecting one person’s overall impression of the pipeline as much as two separate measurements.

Beneath that steady surface, the teams that measure pipeline health and speed report recurring friction.

**2-3x pipeline coverage is the most common ratio across B2B teams.** Among the 78 teams that calculate this pipeline metric, 2–3x coverage is the most common (35%), followed by 3–4x (27%) and over 4x (14%). A quarter (24%) run under 2x, leaving little room for the deals that will slip or die before the end of the period. Keep in mind that more than half of all respondents don’t calculate coverage at all.

**Sales close dates slip more than once.** Among the 107 teams that track it, 64% say a deal’s close date typically slips two or more times before the deal closes or is lost. 49% report two to three slips, 15% report four or more, and only 21% say slips are rare.

**A meaningful share of pipeline is stale.** Among the 105 teams that track it, 29% say more than 40% of their open pipeline has had no engagement in the last 30 days. A quarter (25%) keep stale pipeline under 10%.

**No-shows are high for a large minority.** Among the 119 teams that track it, 37% keep meeting no-shows under 10%, but 39% see more than **one in five scheduled meetings end in a no-show**.

![Pipeline coverage, close-date slips, stale pipeline and no-show rates among B2B teams](https://belkins.directus.app/assets/42de8126-e4b7-4d1c-a32e-6ee77b6a9c7b.png?width=1350&height=1221)

These four signals appear to travel together. Teams with more stale pipeline also tend to report more no-shows, more close-date slips, and a larger share of no-decision losses, and the relationships are moderate rather than weak. It is a plausible picture of deals losing momentum at several points at once.

Two more cautions. Teams that track these metrics are a more structured subset of the sample, so the wider market may look different in either direction. And a 30-day definition of “stale” is strict for companies with long sales cycles, which may inflate their stale share.

**How long is the B2B sales cycle length ?** The typical team in our survey closes deals in 30–60 days, from first contact to signed contract. That figure reflects the many small companies in the sample: for 40% of all respondents, the sales cycle length is under a month, and only 8% take longer than six months to close a deal.

Company size and deal size change the picture. **Half of small companies (52%) close in under 30 days**, while the typical **large company takes 61–90 days**, and nearly half of large companies (49%) take longer than three months. Contract value follows the same pattern: **teams with an average contract under $10K typically close within a month**, while **teams selling contracts of $50K or more typically need two to three months**.

**Practical takeaway:** a pipeline that looks stable year over year can still carry a large share of stalled deals. If your team tracks only one velocity metric, close-date slips are a good place to start: two or more slips per deal is the typical experience.

## Sales forecasts miss by more than 25% for over a third of the teams that measure them

Slipping close dates and stalled deals end up in one place: the forecast. **Among the 98 teams that measure forecast accuracy, 38% are usually off by more than 25%.** Only 30% typically land within 10% of actual results.

| **Typical forecast accuracy** | **Share of teams that measure it** |
| --- | --- |
| Within 10% of actual | 30% |
| 10–25% off | 33% |
| 25–50% off | 23% |
| More than 50% off | 14% |

Another 40% of respondents don’t measure forecast accuracy at all. Accuracy among measuring teams didn’t differ meaningfully by company size.

The practice most closely linked to forecast accuracy is how often teams review the pipeline:

| **Pipeline review cadence** | **Teams** | **Usually within 10%** | **Usually more than 25% off** |
| --- | --- | --- | --- |
| Weekly or biweekly | 54 | 43% | 24% |
| Monthly or ad hoc | 33 | 12% | 55% |
| No formal cadence | 11 | 18% | 55% |

Teams that review weekly or biweekly are more than three times as likely as monthly or ad hoc reviewers to forecast within 10%. That difference is too large to be explained by chance in a sample of this size.

![Sales forecast accuracy by pipeline review cadence](https://belkins.directus.app/assets/cd9dbf0d-4170-4712-8e2c-d7ca1b18d80d.png?width=1350&height=825)

This is an association: teams with strong forecasting discipline may simply do both. Accuracy is self-reported, the no-cadence group has only 11 teams, and the first answer option on both questions may attract the same respondents.

One result here is genuinely surprising. Having a formal qualification framework shows no link to forecast accuracy at all. Teams with and without one forecast about equally well. In this sample, forecast accuracy lines up with how often teams review the pipeline, not with how they qualify deals.

Forecasting also ranks last among the priorities teams named for the next 12 months: only 8% put improving forecast accuracy in their top three.

**Practical takeaway:** if your forecast misses by more than 25%, you are not unusual, but you are on the wrong side of a wide spread. A regular weekly or biweekly pipeline review is the practice most clearly associated with accurate forecasts in this data, and it costs far less than a new tool.

## B2B deals are lost to buyer inaction far more often than to competitors

When deals are lost, competitors are rarely the main reason. **Only 9% of teams name losing to a competitor as their most common loss reason**, which puts it fifth on the list.

| **Most common reason deals are lost** | **Share of teams** |
| --- | --- |
| No decision, prospect goes dark | 27% |
| Price or budget | 21% |
| Timing, not ready to buy | 21% |
| Lack of budget authority or wrong stakeholder | 11% |
| Lost to a competitor | 9% |
| Product or feature fit | 5% |
| Other | 5% |

> *“When a prospect goes dark, sellers log it as no decision. But silence doesn’t always mean the buyer stopped. Often they moved forward with someone else and never closed the loop. That’s why ‘no decision’ is less a loss reason than a blind spot: most teams can’t say for certain why these deals ended.”*
> 
> — Margaret Lee, CMO at Belkins

“No decision” leads, but only narrowly. The gap between no decision (27%) and price (21%) is small enough that chance could explain it, so the accurate reading is a near three-way tie between no decision, price, and timing, not a single dominant cause.

![Most common reasons B2B companies lose deals](https://belkins.directus.app/assets/f4a59ada-d930-49cd-b519-a8cf42bab751.png?width=1350&height=758)

The pattern appears clearer when we group the two answers that describe the same underlying problem. “No decision” and “not ready to buy” both describe a buyer who doesn’t act. Together, they are the most common loss reason for **48% of teams**. That grouping is ours, not the respondents’, but the two options overlap closely in meaning.

The scale of no-decision losses confirms the point. Among the 127 teams that track it, **53% say 26% or more of their lost deals end in no decision**, and 28% say more than 40% do. These patterns don’t differ meaningfully by company size.

Why does this matter? A team that believes it mainly loses to competitors invests in competitive positioning, battle cards, and pricing. A team that mainly loses to inaction needs something else: a stronger business case, earlier access to the people who control budget, and a clear reason for the buyer to act now. One in nine teams (11%) already names lack of budget authority or the wrong stakeholder as its main loss reason, a related symptom.

**Practical takeaway:** most lost deals don’t go to a competitor. They stall. If you aren’t yet tracking no-decision losses separately, start there, and review whether your qualification process confirms a reason to buy now, not just a fit.

## Large companies measure far more, but their pipelines don’t look healthier

Large companies are much better instrumented than everyone else. They track more metrics, they are almost twice as likely to use a formal qualification framework, and they are far more likely to know where their best SQLs come from.

Yet on the outcomes we can compare, their pipelines look much the same as smaller companies’:

- **Pipeline health trend:** 45% of large companies report year-over-year improvement, compared with 36% of small and mid-size companies, a difference too small to be meaningful at these group sizes.
- **Forecast accuracy:** among teams that measure it, accuracy doesn’t differ meaningfully by company size.
- **Loss reasons:** the mix of reasons deals are lost doesn’t differ meaningfully by company size.

![Pipeline metrics tracked vs pipeline health improvement by company size](https://belkins.directus.app/assets/439c8322-13da-4fc4-810a-e01522c2d520.png?width=1350&height=783)

We present this as an observation, not an explanation, because the data can’t tell us why it happens. Several explanations are possible:

1. Large companies may sell larger, more complex deals that are harder to forecast and easier to lose to inaction, so their better measurement offsets a harder sales motion.
2. Measurement may also expose problems that less-instrumented teams never see, making large companies’ self-assessments more critical.
3. The large-company group is the smallest in the sample, so modest real differences in outcomes could go undetected.

**Practical takeaway:** measurement is a diagnostic. It tells a team where the pipeline leaks. Whether the leak gets fixed depends on what the team does with that information.

## Teams place the problem at the top of the funnel, rarely in their own process

We closed the survey by asking teams to name their single biggest pipeline bottleneck and their priorities for the next 12 months. The answers show where teams believe the problem sits, which is not always where the data elsewhere in this report points.

First, very few teams are content. **Only 6% (10 of 164) say their pipeline conversion is healthy.** Even that small group isn’t uniformly positive: two of the 10 also report that pipeline health has worsened over the past year.

| **Biggest pipeline bottleneck** | **Share of teams** |
| --- | --- |
| Lead quality | 40% |
| Pricing concerns | 13% |
| People or skills gaps | 10% |
| Need to educate prospects on the value proposition | 9% |
| Data quality issues | 8% |
| None; our conversion is healthy | 6% |
| Process or handoff issues between teams | 5% |
| Tooling or systems limitations | 4% |
| Other (write-in) | 5% |

The 40% figure for lead quality needs a warning label, and we don’t read it as “lead quality is the #1 pipeline bottleneck.” The question offered no option for lead volume, and four respondents wrote one in. Choosing lead quality here also bears no relationship to how satisfied a team is with its lead quality: 38% of teams satisfied with lead quality still picked it. The defensible reading is broader: **lead-related problems, quality and volume together, dominate how teams see their bottleneck.**

What stands out is how rarely teams look inward. Data quality, handoffs between teams, and tooling together account for 17% of answers. That sits uneasily with the visibility gap at the start of this report. A team that doesn’t track coverage, mid-funnel conversion, or forecast accuracy has limited means to know whether its own process is the problem.

The same gap shows up when teams try to locate the leak. Asked which stage has the sharpest drop-off, 29% of all respondents say they don’t track it well enough to know. Among those who answered, no single stage dominates.

Priorities follow the same direction as the bottlenecks:

| **Top three priorities for the next 12 months** | **Share of teams** |
| --- | --- |
| Improving lead volume | 60% |
| Improving lead quality | 44% |
| Improving qualification process or criteria | 18% |
| Improving marketing-to-sales handoff | 15% |
| Investing in AI or automation for pipeline management | 15% |
| Improving sales training or enablement | 12% |
| Decreasing no-shows | 11% |
| Cleaning up CRM data | 9% |
| Improving forecasting accuracy | 8% |

![B2B companies’ top pipeline priorities for next year](https://belkins.directus.app/assets/047e83b8-1c3a-47af-b732-416f529c13ba.png?width=1350&height=800)

The operational fixes rank lowest: AI and automation for pipeline management (15%), CRM data cleanup (9%), and forecasting accuracy (8%). That ordering contrasts with what the measuring teams report elsewhere in this study: forecasts that miss by more than 25% for over a third of them, close dates that slip more than once, and a large share of losses that end in no decision.

**📚Relevant reading:** [How B2B sales teams actually use AI for lead generation](https://belkins.io/blog/ai-for-lead-generation-study)

In short, teams plan to fill the pipeline rather than see it more clearly. That is a reasonable choice for a team that is genuinely short of opportunities. For a team that can’t yet say where its opportunities are lost, more volume may simply mean more deals leaking at the same points.

**Practical takeaway:** before committing next year’s budget to more top-of-funnel volume, confirm that you can see what happens to the pipeline you already have. If you can’t name the stage where deals stall, that is the first problem to solve.

## What these stats say about B2B sales pipelines in 2026

Step back from the individual findings, and a consistent picture emerges. Most B2B teams are running their pipeline with partial information. They feel the pressure most at the top of the funnel and plan to spend the next year there. Meanwhile, the teams that do measure the whole pipeline report losses spread through the middle and bottom: deals that stall, close dates that slip, or buyers who never decide.

Those two views don’t contradict each other. They describe the same pipelines seen with and without measurement. A team that can see only the top of its funnel will reasonably conclude that the problem sits there.

Some conclusions are strong enough to state with confidence:

- **The visibility gap is real and concentrated below 200 employees.** Only 30% of teams track all 12 metrics, and mid-size companies look far more like small companies than like large ones.
- **Teams feel volume pressure more than quality pressure,** and that pressure is roughly the same at every company size.
- **Competitors are rarely the main reason deals are lost.** Buyer inaction outweighs every other cause.
- **There is no universal “good” conversion rate.** The spread between teams at every stage is too wide for a single benchmark to serve everyone.

Other patterns are worth watching but too thin to build a strategy on: the link between weekly pipeline reviews and accurate forecasts, the link between lead scoring and satisfaction with lead quality, the tendency of stalled deals, no-shows, and slipped close dates to appear together, and the possibility that lead-quality frustration falls as companies grow.

This research also leaves open questions that a single survey can’t answer. Why does visibility change so sharply past 200 employees? Does measuring more improve pipeline outcomes over time, or do better-run companies simply measure more? Which channels produce the best opportunities when performance is taken from CRM records rather than from perception? And how do these patterns look in a larger sample of enterprise companies and by industry? Tracking the same teams over time, using CRM data rather than recall, would answer several of these questions.

For revenue leaders, the most practical conclusion is also the least glamorous. Before investing in more pipeline, invest in seeing the pipeline you have: know your coverage, track conversion at every stage, review regularly, and record why deals are lost. The teams that know where their pipelines leak from data rather than instinct can fix it more efficiently.

## Methodology

This report is based on the Belkins “Sales pipeline statistics & health” survey, conducted between July 31 and October 6, 2026. We recruited respondents through an open survey on the Belkins website and through direct outreach to peers, partners, Belkins clients, and former clients.

**Sample.** 54% of respondents work at companies with 1–10 employees. We group company size into three tiers: small (1–10 employees, n=88), mid-size (11–200 employees, n=47), and large (201+ employees, n=29). 46% of respondents describe their go-to-market motion as outbound-led. Because the sample includes Belkins clients and former clients and leans toward small and outbound-led companies, it should not be read as representative of all B2B companies.

**Data type.** All answers are anonymous and self-reported, and pipeline metrics were collected as ranges rather than exact figures. Most pipeline metrics are reported only among respondents who track that metric, so each has its own base, shown as n. Conversion benchmarks are reported as median ranges, because averaging range answers doesn’t produce real percentages.

**Comparisons.** We report a difference between groups only when it is large enough that chance alone is unlikely to explain it, and each group has at least 20 respondents. Differences that fall just short of that bar are labeled as directional. All findings are descriptive and observational, and we make no causal claims. Results by industry are not reported because no industry had enough respondents to support reliable conclusions.

Author

Iryna Yelisova

Content Manager at Belkins

Iryna is a Content Manager at Belkins with over 10 years in content writing and strategy. She built her expertise in marketing content and editorial leadership across e-commerce and B2B services before joining Belkins as a writer and editor. Today, she leads Belkins’ content strategy end-to-end.

Expert

Margaret Lee

CMO at Belkins

Margaret is a seasoned professional with over 14 years of experience and a remarkable track record of managing marketing teams in both B2B and B2C. With expertise in strategy development, analytics-driven decision-making, and team management, she brings invaluable skills to drive growth and success.

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