MQL to SQL Conversion Rate Calculator

What is a good MQL to SQL conversion rate?

A healthy B2B SaaS funnel converts roughly 45% of MQLs into SQLs. That is the median across public compiled benchmark data, with the top quartile pulling 58% and the bottom quartile hovering near 32%. Anything above 58% is strong; below 32% almost always signals one of two problems — either the MQL bar has drifted down and marketing is pushing through leads that do not match ICP, or sales has quietly stopped honoring the sales-accepted-lead SLA.

Before accepting an industry average, look at which cohort is being measured. Rates reported on inbound-only self-serve SaaS funnels (think PLG tools like Notion, Linear, or Figma) tend to be inflated because self-selection filters out unserious leads upstream. Outbound-heavy sales motions and enterprise-only funnels usually run 8–15 percentage points lower. The per-industry preset in the calculator loads the correct distribution so you are comparing apples to apples.

MQL to SQL conversion rate by industry

Industry is the single biggest driver of MQL-to-SQL variance — larger than ACV, larger than marketing budget, larger than sales team size. Buyers in different verticals arrive with wildly different intent, and that flows directly into sales acceptance rates:

Manufacturing and cybersecurity post the highest MQL-to-SQL rates because their MQL definition is already tight at the top of the funnel — CISOs and plant directors do not fill out marketing forms casually. MarTech runs lowest because self-serve trials and lead magnets produce a flood of curiosity-level MQLs; filtering them is sales' job, and many of them fail qualification.

How to calculate MQL to SQL conversion

The calculation is straightforward; the trap is in the definitions:

Use the same time window. If an MQL created in January becomes an SQL in March, that is still a January-cohort conversion — not a March-cohort one. Measuring SQL/MQL in the same calendar bucket confuses cohort timing and inflates or deflates the ratio depending on seasonality. Most RevOps teams use a 60-day lookback aligned to the MQL date.

Anchor SQL to sales-accepted, not opportunity-created. SQL and opportunity are different stages. A sales-accepted lead is the moment a rep formally takes the MQL into their queue with notes and next-step commitment. Conflating the two makes your conversion rate look lower than it is and hides whether the problem is at acceptance or at discovery. The 5-stage funnel in the tool keeps these separate on purpose.

Where B2B funnels leak revenue most often

Compound funnel math means a single stage well below p25 can cost more annual revenue than three other stages slightly below median combined. The tool annotates each stage drop with a dollar number equal to the revenue recoverable if that one stage hit industry p75 — holding all other stages constant.

For a SaaS B2B company with 80,000 monthly visitors, a $25,000 ACV, and rates sitting at the industry p25, the weakest stage alone is usually worth $800K–$1.5M in annual recoverable revenue. That is why the calculator sorts all five stages by leak dollars and pulses the single biggest one red — fix that stage first, then re-benchmark. Spreading attention across all five stages is how marketing-versus-sales meetings turn into two hours of nobody fixing anything.

• Lead→MQL leaks usually come from a missing or badly-calibrated lead scoring model. If a third of leads are being discarded without ever being touched, tighten the firmographic filter and retest.
• MQL→SQL leaks are the SLA conversation: speed-to-lead ≥5 minutes, missing qualification notes, no round-robin routing.
• SQL→Opportunity leaks point at discovery call quality — BANT or MEDDPICC gaps, rushed first calls, weak pain identification.
• Opp→Close leaks are usually pricing, ICP, or late-stage negotiation craft — the cheapest stage to improve because sample sizes are small.

Marketing vs sales: who owns each stage?

Ownership drives which team gets paged when a stage is broken. The calculator uses the industry-standard allocation:

• Marketing owns Visitor→Lead and Lead→MQL. If either stage is below p50, the fix starts in the marketing org.
• Shared ownership at MQL→SQL — this is the classic handoff. The tool splits MQL→SQL leak 50/50 for blame attribution, because the stage depends equally on MQL quality (marketing) and sales speed + acceptance discipline (sales).
• Sales owns SQL→Opportunity and Opportunity→Closed-Won. Below-p50 on either of those stages is an SDR / AE coaching problem, not a marketing problem.

The blame pie chart sums the weighted leak across the five stages and presents a single number — “76% sales, 24% marketing” or vice versa — which is the number CMO-CRO meetings usually spend 40 minutes arguing about. Having it on the screen in advance moves the conversation from “whose fault is this” to “which stage do we fix first.”

SLA between marketing and sales: what's normal?

The common sales-accepted-lead SLA, originating in SiriusDecisions B2B demand waterfall guidance and widely adopted across B2B SaaS, is 70% of MQLs accepted within 24 hours. Teams that enforce it see MQL→SQL rates 10–15 percentage points higher than teams that do not — the speed matters more than the acceptance discipline alone, because a lead that sits for 48 hours has already cooled off.

The tool flags an SLA violation whenever your MQL→SQL rate is 15 percentage points or more below the industry median. That threshold is chosen because a 15-point gap cannot be explained by MQL quality variance alone; it implies sales is actively declining to accept MQLs, either because they have lost faith in marketing's filter or because they prefer running their own outbound pipeline. The fix is rarely a lead-scoring tweak — it is a 90-minute marketing ↔ sales meeting with the funnel on screen and a signed SLA by the end of it.

The 5-minute speed-to-lead target (a smaller sub-SLA that rides under the 24-hour umbrella) matters separately because the odds of qualifying a lead drop roughly 10× once the first 5 minutes pass — a finding popularized by the Oldroyd / McDonald “Lead Response Management” study (reported in Harvard Business Review, 2011) and repeatedly replicated by inside-sales practitioners since. Round-robin routing with auto-assignment handles this at zero marginal cost.

How this calculator works

Six engines drive the numbers on screen:

• Funnel propagation: multiplicative 5-stage model (traffic × leadRate × mqlRate × sqlRate × oppRate × closeRate × ACV × 12) for annual revenue.
• Leak-dollar engine: per stage, (volume dropped × industry p75 rate × downstream stages × ACV × 12) = revenue recoverable if that one stage hit top-quartile. The largest becomes the weakest-stage callout.
• Percentile positioning: piecewise-linear rank function across each industry's p25 / p50 / p75 / p90 stage distribution, so the “you are at the 42nd percentile” number is legitimately positioned inside the actual distribution shape.
• 6-dimension report card: weighted average of Top-Funnel Volume (15%), MQL Quality (20%), SQL Acceptance (25%), Opp Conversion (15%), Close Rate (15%), Velocity (10%). Composite score mapped to letter grades — A (≥85), B (70–84), C (55–69), D (40–54), F (below 40) — with +/- modifiers on 5-point sub-bands.
• Blame engine: marketing = Visitor→Lead + Lead→MQL + 50% of MQL→SQL leak; sales = 50% of MQL→SQL + SQL→Opp + Opp→Close leak. Shown as a pie chart with dollar values.
• Reverse calculator: three modes — “fix weakest stage” (solves ARR delta if weakest stage hits p75), “target ARR” (distributes lifts across sub-p75 stages by gap size until target met), “match top-quartile” (per-stage single-lift solve).

All computation runs client-side in JavaScript. No data is sent to a server. Benchmarks are compiled from publicly available B2B conversion rate reports (First Page Sage annual B2B conversion benchmarks, HubSpot State of Marketing, and widely cited RevOps practitioner data) and curve-fit to p25 / p50 / p75 / p90 distributions per industry.

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