MQL to SQL conversion rate benchmarks and B2B funnel conversion rates by stage

A good marketing qualified lead (MQL) to sales qualified lead (SQL) conversion rate for B2B software as a service (SaaS) is about 38%, per First Page Sage's 2026 funnel benchmarks report. By lead source, the same firm's data puts it between 26% and 51%. A widely quoted 13% average counts an SQL only once a meeting is met or booked, so match your SQL definition before you compare.

What is a good MQL to SQL conversion rate

For B2B software as a service (SaaS), a defensible answer is 38%. First Page Sage's 2026 funnel benchmarks report gives B2B SaaS a 38% rate from marketing qualified lead (MQL) to sales qualified lead (SQL). By lead source, the same firm's B2B SaaS data puts MQL to SQL between 26% and 51%.

B2B SaaS MQL to SQL conversion ranges from 26% for PPC to 51% for SEO, depending on the channel that sourced the lead.

B2B SaaS MQL to SQL conversion rate by channel

Item	Value
SEO	51%
Email	46%
Webinar	39%
LinkedIn	30%
PPC	26%

Source: Understory, citing First Page Sage B2B SaaS funnel benchmarks, 2026

Neither number means much without its sample and its definitions, so here they are side by side.

The 38% report defines an MQL as a lead that has "expressed a clear buying interest and can afford the product or service." The channel report uses a fit test instead, and it carries a caveat: it assumes "a high level of competence" from whoever runs each channel.

Why 13% sits so far below

The widely quoted 13% counts an SQL only once a lead has "met or booked a meeting." That is a later gate than the 38% report uses. The same analysis notes that the 13% figure "averages all MQL sources under a strict definition across all funnel maturities," while the channel table reports per-source rates for competent teams.

In our reading, most of the gap comes from where each report draws the SQL line, not from teams performing differently.

The spread also shows up inside your own funnel. Within B2B SaaS, SEO-sourced MQLs convert to SQL at 51% and PPC at 26%. We think a blended rate will drift whenever the channel mix shifts, so segment by source before you compare.

Worked example

Take 1,000 MQLs as an example input, not a benchmark. At 38% that is 380 SQLs, and at 13% it is 130 SQLs. That is a gap of 250 SQLs from the same 1,000 MQLs.

Before you pick a target, write down your own MQL rule and your SQL gate, and check whether a booked meeting is required. Then choose the row in the table above that matches. If your rule rests on a score, line up your lead scoring thresholds with that definition first.

B2B funnel conversion rates for each stage, with the source's definition

The table below lines up one figure per stage for B2B software as a service (SaaS) companies, with the sample behind it and the definition the source used. Read the definition column before the number. Two reports can agree on a percentage and still be counting different events.

| MQL to sales qualified lead (SQL) | Covered in the previous section | Same report | Covered in the previous section | | SQL to opportunity | 42% | Same report | An SQL with a proposal or contract in hand, actively considering a purchase | | Opportunity to won | 37% | Same report | Signed contract, first payment not yet made |

The visitor row is a page-level rate. In the landing page study, conversion is measured per page and measurements are typically taken by quarter. It is not a sitewide visitor to lead rate, and it covers only SEO (search engine optimization) clients.

Where "opportunity" changes meaning

The 2026 funnel report defines an opportunity as an SQL who has a proposal or contract in hand. The channel report defines it as an MQL with contract in hand. In the second report, a deal can count as an opportunity without passing through the SQL stage. In the first, it must pass through SQL. We think that makes their SQL to opportunity rates unsafe to compare side by side.

"Won" also needs checking. In the 2026 report a closed won deal has signed a contract but has not yet made their first payment. If your customer relationship management (CRM) system marks a deal won at first invoice paid, your rate should run lower than this one.

The lead definitions differ too. The channel report counts any non-spam website visitor who has submitted contact information, so email replies and other non-web contacts fall outside it.

Worked example for the bottom of the funnel

Take 1000 SQLs as an example input, not a benchmark. At 42% SQL to opportunity, 1000 SQLs give 420 opportunities. At 37% opportunity to won, 420 opportunities give about 155 signed contracts.

Before you plan against those rates, map your own stage gates to the definitions in the table. If your opportunity stage starts at first discovery call rather than proposal, your SQL to opportunity rate will sit above the benchmark and your opportunity to won rate below it. Neither difference means the team is performing better or worse. Run your own numbers backward from a bookings target in the reverse funnel calculator, and write the stage definitions into your service level agreement so marketing and sales count the same events.

Visitor to lead conversion rates on B2B landing pages

In our reading, the top of the funnel is where published benchmarks disagree the most, because each study counts a different kind of page and a different kind of traffic. Before you compare your own rate, check what sits in the denominator.

Conversion rates across six B2B landing page types range from 3.5% for customer type pages down to 1.1% for location pages.

B2B landing page conversion rate by page type

Item	Value
Customer type	3.5%
Application	3.1%
Product	2.9%
Service	2.7%
Industry	1.8%
Location	1.1%

Source: First Page Sage, B2B Landing Page Conversion Rates: 2026 Report

Unbounce's SaaS benchmark puts the median at 3.8%, set against an all-industry median of 6.6%. In its words, the median conversion rate for SaaS landing pages is 3.8%,.

First Page Sage's B2B landing page study, the SEO-only dataset described earlier, splits rates by page type instead. Customer type pages convert at 3.5%, application pages at 3.1%, product pages at 2.9% and service pages at 2.7%. Industry pages sit at 1.8% and location pages at 1.1%. In our reading, the spread tracks how closely a page matches one buyer profile.

A worked example

Take 1000 monthly visitors as an example input, not a benchmark. At the customer type rate, 1000 visitors × 3.5% = 35 leads. At the product page rate, 1000 visitors × 2.9% = 29 leads. Run the same split on your own analytics by page template, then feed the lead counts into the reverse funnel calculator to see what each page type contributes to pipeline.

Form length moves this stage too. UXCam reports that a form with one input field had a 3.20% conversion rate, while a form with 5 input fields had a much lower 0.81% conversion rate. We would test field count before rewriting a page that already matches its audience.

Product-led and sales-led models

The go-to-market model changes what a conversion even is. In a product-led motion the visitor starts a trial, and UXCam notes that PLG funnels often show higher top-of-funnel conversion rates because users experience the product before committing. It gives an average visitor to trial rate of 9% for product-led growth.

Sales-led funnels ask for a form or a demo, so the top-stage rate runs lower. UXCam's guidance: expect lower sign-up rates but focus on improving lead-to-close performance. Compare a trial rate only to trial rates, and a form fill rate only to form fill rates. Mixing them makes a sales-led site look weak and a product-led site look strong for reasons that have nothing to do with the page. The stages that follow, from lead onward, then carry the qualification work that the trial skipped.

Lead to opportunity and SQL to opportunity conversion rates

Stage rates only become a planning number once you chain them. A lead to opportunity rate is the product of every step between the two, so a small gap at one stage carries all the way down.

Worked example: chaining three stage rates

Take the Cybersecurity row of the 2026 funnel report, which gives 24% lead to MQL, 40% MQL to SQL and 43% SQL to opportunity. Start with 1,000 leads as an example input, not a benchmark.

1. 1,000 leads × 24% = 240 MQLs.
2. 240 MQLs × 40% = 96 SQLs.
3. 96 SQLs × 43% = 41.28 opportunities, call it 41.

41 opportunities from 1,000 leads is a 4.1% lead to opportunity rate. The shortcut is the same: 24% × 40% = 9.6%, and 9.6% × 43% = 4.128%. Run it backward from a target in the reverse funnel calculator, then check the result against your pipeline coverage need.

SQL to opportunity by industry, channel, size and model

In our reading, the SQL to opportunity step moves more by segment than the averages suggest.

Industry rows come from the 2026 funnel report (eCommerce, IT & Managed Services, Real Estate). Channel rows come from the B2B SaaS channel report, with the same competence assumption noted earlier. Size and model rows come from CausalFunnel's 2026 guide (size, model).

The SMB figure of 42% and the enterprise figure of 36% sit 6 points apart. In our reading, longer buying committees at large accounts account for part of that gap, but no source here measures the cause.

Why definitions move the number

The two First Page Sage reports define an opportunity differently, as covered earlier: one counts it from the SQL, the other from the MQL. A rate built on a looser entry stage will not match one built on a stricter stage, even inside the same industry. Before you compare your rate to any row above, write down your own SQL and opportunity entry criteria and match them to the source's. If they differ, treat the benchmark as a range check, not a target.

How conversion rates differ by channel

Channel shapes every stage of the funnel, not only the marketing qualified lead (MQL) to sales qualified lead (SQL) step covered earlier. The other stage rates by channel come from First Page Sage's B2B software as a service (SaaS) funnel benchmarks, last updated in June 2025:

The order changes by stage. Webinar leads close at 40% against 32% for email, even though email does better at the SQL to opportunity step. First Page Sage also states that its figures carry an assumption of a high level of competence from whoever runs the channel. Treat them as figures for a channel that is run well, not as a floor.

Email at the landing page

The gap starts before a lead exists. In Unbounce's SaaS data, email traffic converts over 4x better than any other traffic source. Email posted a median landing page conversion rate of 16.9%, against 4.1% for paid search, 2.9% for paid social and 0.3% for display.

In our reading, much of this gap comes from who the visitors are. An email click comes from someone already on your list. A display click usually does not.

Why mix alone moves the blended rate

A blended rate is a weighted average, so it moves when the weights move. Understory's analysis puts it this way: a quarter where paid volume grows faster than organic will show a "declining" conversion rate even if every channel improved.

Here is a worked example using the SQL to opportunity rates above, with 1000 as a round example input, not a benchmark:

• Quarter one: 1000 SEO SQLs at 49% produce 490 opportunities.
• Quarter two adds 1000 PPC SQLs at 38%, which produce 380 opportunities.
• Quarter two has 490 plus 380, or 870 opportunities, from 1000 plus 1000, or 2000 SQLs.

Neither channel got worse.

What to do with it

1. Report each stage rate by source channel before you report the blended figure.
2. Hold each channel to its own row of the table, not to the overall average.
3. When the blended rate moves, check whether the channel shares changed before you change the lead scoring model.
4. Run the mix you plan for next quarter through the reverse funnel calculator, one channel at a time.

If your source data is unreliable, the self-reported attribution playbook shows one way to capture channel at the form.

What is a good opportunity win rate

Check your own CRM before you compare. If your closed-won stage waits for a booked invoice or first payment, your rate will run lower than this benchmark for reasons that have nothing to do with sales performance.

Opportunity to closed-won by industry

Figures from First Page Sage's 2026 report, drawn from client data gathered from 2017 through 2025.

Here is a worked example with a round input, not a benchmark. A B2B SaaS team with 1000 opportunities at the 37% B2B SaaS rate would close 370 deals. The same 1000 opportunities at the 59% software development rate would close 590. The difference between 590 and 370 is 220 deals. That gap is why the industry row you pick changes the pipeline coverage you plan for. Run your own numbers backward from the bookings target with the reverse funnel calculator.

What moves win rates inside a deal

Industry averages set the range. Deal behavior shifts where you land within it. Two findings bear on win rate:

• Decision-maker timing. The report states that "Early decision-maker involvement boosts win rates by 55%."
• Slippage. It also states that "Delayed deals reduce win rates by 113%."

A deal that pulls in an executive early may simply be a stronger deal to begin with.

They still give you two fields to track on every opportunity:

1. The date a named decision-maker first joined a meeting, measured against the opportunity creation date.
2. The count of times the close date was pushed.

Report win rate split by both fields each quarter. If slipped deals close well below your unslipped deals, treat a pushed close date as a forecast risk signal and weight it in pipeline velocity reviews instead of carrying those deals at full value.

How to benchmark your funnel fairly

A benchmark only helps if your number and the published number count the same things. The stage formula is simple: Klipfolio gives it as (Number of SQLs / Number of MQLs) x 100. The division is rarely the problem. The problem is what each side of it contains. This is the four-step method we use before we compare any rate to an outside figure.

Step 1: Match the stage definitions

Write down what your team counts at each stage, then write down what the benchmark source counts. Klipfolio advises that a written sales and marketing agreement should include firmographic criteria, behavioral signals and disqualifying factors. Use those three fields for both sides. If your marketing qualified lead (MQL) triggers on a single download and the source's MQL requires fit plus intent, the two rates do not describe the same funnel.

For a concrete MQL line, UXCam's example standard is "From a company with 50+ employees AND visited the pricing page." Put your own sales qualified lead (SQL) and handoff terms into a service level agreement so the definitions stay fixed between benchmark checks.

Step 2: Measure by cohort, not by calendar month

Klipfolio defines the rate as the share of MQLs accepted as SQLs in a given period. A calendar month mixes leads. Many of this month's SQLs came from earlier months' MQLs.

Take 1000 MQLs created in March as an example, not a benchmark. Leave the cohort open until the window closes.

Step 3: Split by channel and model

Porter lists channel as one segment for this metric, so you can see which marketing channels are most effective in converting MQLs to SQLs. Split by sales-led, product-led or demo-led motion as well. As the channel section showed, a shift in mix can move the blended rate even when no single channel changed.

Step 4: Compare, then sanity-check

Compare each cohort and segment to the matching published figure. Then check the result. UXCam warns that a lead to MQL rate above 50% may mean your definition of MQL is too loose. We treat a rate well above any benchmark as a cue to recheck Step 1 before celebrating. After that, Porter suggests focusing on your own rate over time rather than holding strictly to outside benchmarks. Feed the cohort rates into the reverse funnel calculator to set volume targets.

A worked example, from a pipeline target back to leads

This example runs the funnel in both directions with one set of benchmark rates, then swaps in your own. The 1,000 leads below are a round example input, not a benchmark.

Step 1: run the benchmark chain forward

First Page Sage's B2B SaaS row gives 39% lead to MQL (marketing qualified lead), 38% MQL to SQL (sales qualified lead) and 42% SQL to opportunity. The same report defines an opportunity as an SQL who has a proposal or contract in hand. Check that your CRM stage means the same thing before you borrow the rate.

• 1,000 leads at 39% gives 390 MQLs.
• 390 MQLs at 38% gives 148.2 SQLs.
• 148.2 SQLs at 42% gives 62.244 opportunities.

Step 2: turn it into a reverse ratio

If 1,000 leads produce 62.244 opportunities, each opportunity needs about 16.07 leads (1,000 divided by 62.244).

Now work back from the target:

1. Divide the new annual recurring revenue (ARR) target by your average contract value. That gives closed deals needed.
2. Divide closed deals by your win rate. For the benchmark, use the B2B SaaS figure from the win rate section. That gives opportunities needed.
3. Multiply opportunities needed by 16.07. That gives leads needed.

Then check the opportunity count against your pipeline coverage target.

Step 3: replace benchmarks with your cohort rates

Take leads created in one past period and follow that cohort to each stage. Do not divide this quarter's SQLs by this quarter's MQLs. Then fill in the template.

Rerun Step 2 with your own rates. If your chain needs more leads than the plan funds, that gap is the planning problem. The benchmark lead count is only a reference point.

Step 4: check the plan against CAC

Benchmarkit gives the new customer acquisition cost (CAC) ratio formula as Total Sales & Marketing expenses / New Customer ARR. It reported that the ratio increased by 14% in 2024 to a median of $2.00 of sales and marketing expense per $1.00 of new customer ARR. The 4th quartile of companies are spending $2.82.

As an example, $1,000 of new customer ARR at the $2.00 median carries $2,000 of sales and marketing expense. At $2.82 the same $1,000 carries $2,820.

Divide your planned sales and marketing budget by the ARR target. If the result sits well above the median, we think the lead count from Step 3 is not the problem to solve first. Look at contract value and cost per lead first. See CAC payback benchmarks for the payback view.

What this page does not cover

This page does not cover how to build a lead scoring model, how to set up attribution, or CAC payback in detail beyond the ratio check in the worked example. It reports published benchmarks with their samples and definitions and does not measure why rates differ between segments. It is not financial or legal advice.

Related on Take the Market: Demand generation calculator: reverse funnel math from a revenue target to visitors, leads, MQLs, SQLs and opportuniti; CAC payback benchmarks for B2B: by ACV band, motion and growth rate; Pipeline coverage calculator: the ratio you need from your own win rate and slippage.