# Belkins finds 38% of B2B teams don't measure MQL-to-SQL conversion By MarketScale Newsroom · Published 2026-11-01 · Marketing Tech on MarketScale Canonical: https://www.marketscale.com/industries/marketing-tech/belkins-finds-38-of-b2b-teams-dont-measure-mql-to-sql-conversion > Belkins' 2026 survey puts the typical B2B win rate at 21–30%. It also found 38% of teams don't measure the handoff from MQL to SQL. ## Key points - A benchmark only helps at a stage someone measures, and 38% of B2B teams don't track the handoff from marketing-qualified to sales-qualified lead. - Untracked mid-funnel tracking is higher at small firms: 44% of 1–10 employee companies skip MQL-to-SQL, vs 10% at 201+. Belkins' 2026 B2B conversion benchmarks start with a steady number. The median win rate is 21, 30%, so the typical team closes roughly one opportunity in four. Belkins says that holds for small and large companies alike, and across industries and sales cycle lengths. For the survey, updated October 29, 2026, Belkins asked B2B teams for their conversion rates at four stages, from lead to closed-won deal, and also asked about company size and go-to-market motion. The rest of the report is messier, and that makes it more useful. For the first three stages, the median lands in the 11, 20% band. Those stages are raw lead to marketing-qualified lead (MQL), then MQL to sales-qualified lead (SQL), then SQL to opportunity. Lead-to-MQL barely makes it into that band. Nearly half of teams, 49.5%, turn fewer than 11% of their leads into MQLs, which leaves the median team at the very bottom of the range. Then comes the finding that matters most to anyone running revenue operations. A large share of teams can't benchmark the middle of their funnel because they don't measure it. A benchmark only helps at a stage someone measures, and 38% of B2B teams in Belkins' survey don't measure the handoff from marketing-qualified to sales-qualified lead. For a sales VP trying to work out whether the pipeline problem is lead volume, qualification or closing, that blank is the first thing to fix. ## The stages teams stop counting Belkins reports each benchmark only among teams that track the stage, and respondents chose ranges rather than exact figures, so the medians are ranges too. In the survey, 37% of teams don't track lead to MQL, 38% don't track MQL to SQL, 39% don't track SQL to opportunity and 30% don't track win rate. Block Field The spread tells you as much as the midpoint does. At MQL to SQL and at SQL to opportunity, the middle half of teams runs from the 5, 10% band up to the 36, 50% band. Lead to MQL runs from 5, 10% to 21, 35%, and win rate from 10, 20% to 41, 50%. A team in the 5, 10% band and a team in the 36, 50% band both count as normal at the mid-funnel stages, so the median alone can't tell a leader whether their own rate is a problem. Block Field The tracking gap closes sharply at larger companies. Among companies with 1, 10 employees, 44% leave MQL-to-SQL conversion untracked. At companies with 201 or more employees, that drops to 10%. The survey doesn't give a cause, but the pattern suggests mid-funnel measurement tends to arrive once there are enough people, and enough handoffs between marketing and sales, to make an unmeasured stage expensive. An older argument points the same way. In a 2019 MarketingTech column, Andy McCaul suggested that marketers who don't use their CRM data correctly miss the best insights and end up seeing the system as more time-consuming than helpful. He added that the sheer volume of information can put them off. His point was about CRM adoption in general, but it describes the habit that leaves a funnel stage blank. Tracking a stage is also not the same as tracking it well. HubSpot's glossary on sales conversion rate lists duplicate records, pipeline stages that aren't updated, and qualification criteria that differ from rep to rep as common ways conversion figures get skewed. The arithmetic itself is simple: conversions divided by prospects, times 100, so 50 customers from 200 leads is a 25% rate. Every one of those data problems quietly changes the count above or below the line. HubSpot adds that market conditions and seasonal swings can push conversion rates up or down without any real change in sales performance. So a quarter of consistently recorded data is likely to say more than one unusually good or bad month. HubSpot also says conversion rate directly influences customer acquisition cost by determining how many prospects a team needs to engage before securing a sale. For a small company deciding where to start, a consistently updated MQL-to-SQL field may be worth more than any outside benchmark. ## Lead scoring, outbound and the right yardstick Two business choices change the picture in Belkins' data. One tends to come with a complete view of the funnel. The other changes what a normal conversion rate looks like. Block Field Teams that score leads are more than twice as likely to measure their whole funnel. Read that carefully: it links scoring to measurement, and on its own it doesn't show that scoring lifts conversion. The likely explanation is that a scoring model makes a team define what counts as qualified, and once that definition exists in the CRM, counting what passes it is easy. MarketJoy's stage definitions show what that line can look like. An MQL fits the ideal customer profile and shows engagement. An SQL has been vetted for budget, authority, need and timeline, the BANT framework. HubSpot describes lead scoring and pipeline reporting working together to show which lead sources produce the highest-converting prospects. That makes scoring a way to choose channels as well as to qualify leads. The outbound gap matters for anyone comparing their numbers against a blended median. Belkins says it holds for companies of every size, so it isn't just small outbound shops pulling the figure down. For a team built mainly on outbound prospecting, a low MQL-to-SQL rate against the overall 11, 20% median may describe the sales model more than it shows a qualification failure. MarketJoy lists lead quality, cold versus intent-driven, among the main factors in whether a lead progresses, which fits that reading. The fairer comparison is with other outbound teams. ## A second benchmark, a very different win rate MarketJoy, a B2B lead generation firm, published its own pipeline benchmarks on May 6, 2026. They draw on data labeled 2024, 2025 from what the firm describes as thousands of sales pipelines across industries. By its average ranges, 20, 25% of leads become MQLs and 12, 18% of MQLs advance to SQL. The SQL-to-opportunity stage averages 10, 12%, and closed-won deals land at 6, 9%. Its single-figure benchmarks for each stage are below. Block Field The middle stages broadly overlap with Belkins' medians. MarketJoy's lead-to-MQL figure sits above Belkins' median, which lands at the bottom of the 11, 20% band, but it still falls within the middle half of Belkins' teams. The close does not overlap. Compared with Belkins' 21, 30% median win rate, MarketJoy's 6, 9% falls below the 10, 20% band that marks the bottom of Belkins' middle half. Belkins notes that teams define each stage differently, so differences in definitions could account for part of the gap. Belkins raises the same problem in its own report. One company might count a whitepaper download as an MQL, while another saves the label for a contact who has already agreed to a call. Depending on where those lines fall, two teams with identical pipelines can report very different conversion rates. The two sources also disagree about what moves the numbers. MarketJoy treats sales cycle length as a factor, contrasting enterprise cycles of 6, 18 months with 2, 6 months for SMB. It also says SaaS and cybersecurity often convert at lower rates because their markets are competitive, while healthcare and manufacturing are steadier. Belkins found the median win rate holds across industries and cycle lengths. For a leader in a long-cycle or crowded market, that means the choice of benchmark could matter as much as the rate being compared. ## Where the funnel leaks MarketJoy's reading is that the biggest drop-off usually comes at MQL to SQL, where marketing passes along leads that aren't ready for sales. Belkins' respondents aren't so sure. Among teams that named a leaking stage, answers were spread almost evenly across all four, and 29% of teams couldn't say where their funnel leaks. MarketJoy's suggested fixes come from a lead generation provider's playbook. It says AI-driven intent data combined with human qualification consistently improves its rates, and that contacting leads within 24 hours increases conversion by 5x. A team can only check claims like these against its own funnel at a stage it actually measures. MarketJoy also names misaligned ideal customer profile definitions between sales and marketing as a drag on conversion. That ties the measurement gap to the definition problem Belkins describes. With 38% of teams not tracking MQL to SQL and 29% unable to name a leak, the CMO and sales leader who have to agree on where next quarter's budget goes should start with their own MQL-to-SQL rate, measured the same way every month. ## Sources - [B2B Sales Pipeline Conversion Rates](https://belkins.io/blog/sales-pipeline-conversion) - [B2B Sales Pipeline Conversion Rates – Benchmarks and MarketJoy Data](https://marketjoy.com/b2b-sales-pipeline-conversion-rates-marketjoy-data/) - [Sales Conversion Rate – Definition, FAQs & How HubSpot Helps](https://www.hubspot.com/glossary/sales-conversion-rate) Tags: Belkins, MarketJoy, HubSpot, B2B sales, sales pipeline, conversion rate benchmarks, revenue operations, lead scoring, MQL to SQL, win rate, sales and marketing alignment, CRM --- Source: MarketScale, https://www.marketscale.com/industries/marketing-tech/belkins-finds-38-of-b2b-teams-dont-measure-mql-to-sql-conversion. Published for AI indexing and citation; cite the canonical URL. 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