Sales Productivity
Each unqualified lead wastes 15-30 minutes of sales time. A 10% improvement in MQL-to-SQL can reclaim 5+ hours per rep per week.
Step 1: Lead Volume
Lead Volume
505,000
51,000
MQL-to-SQL Rate
20.0%
Below 25% benchmark
Pipeline Leakage
$10,000.00
400 MQLs lost monthly
Revenue per MQL
$25.00
Expected value per lead
Closed Deals
2.5
From 100 SQLs
Total Revenue
$12,500.00
Monthly revenue from funnel
Cost per SQL
$150.00
CPL: $20.00
Industry Benchmark Comparison
Below BenchmarkYour MQL-to-SQL Rate20.0%
Bottom: 12%Median: 25%Top: 40%
Related Calculators
Calculator Knowledge Base and Scientific Documentation
Quick Reference
Quick Reference
MQL-to-SQL conversion rate measures how efficiently your funnel qualifies leads. The 2026 B2B benchmark is 13-27%, varying by industry and lead source. Low MQL-to-SQL rates often indicate misaligned scoring criteria, poor lead quality, or sales/marketing disconnect rather than sales team performance issues.
The Scientific Model
The Scientific Model
MQL-to-SQL Conversion Formula
Formula
Divides the number of Sales Qualified Leads accepted by sales by the total Marketing Qualified Leads generated in the same period.
Why this approach: This ratio reveals funnel efficiency and lead quality. A rising rate with stable volume indicates improving targeting; falling rates signal potential lead quality issues.
People Also Ask
People Also Ask
- What is a good MQL-to-SQL conversion rate in 2026?
- For B2B SaaS, target 20-30%. Enterprise deals see 15-25% (longer qualification), while SMB/self-serve sees 25-40%. Rates below 15% typically indicate scoring misalignment or poor lead quality from top-of-funnel sources.
- How do I improve MQL-to-SQL conversion?
- Focus on: 1) Tighten MQL criteria using intent signals, 2) Implement lead scoring based on closed-won analysis, 3) Ensure sales/marketing alignment on qualification criteria, 4) Track conversion by lead source to identify quality channels, 5) Reduce time-to-contact for hot leads.
- What causes low MQL-to-SQL rates?
- Common causes: overly broad MQL criteria, gated content attracting researchers not buyers, poor lead source quality, misalignment on ideal customer profile, inadequate lead enrichment, and slow sales follow-up causing lead decay.
- How does MQL-to-SQL rate vary by lead source?
- Typical rates by source: Demo requests 40-60%, Content downloads 10-20%, Webinar attendees 15-25%, Trade show leads 20-35%, Outbound 5-15%, Paid social 8-15%. Prioritize sources with both high volume AND high conversion.
Contextual ROI: The Intangibles
Contextual ROI: The Intangibles
MQL-to-SQL efficiency impacts downstream metrics and overall marketing ROI.
Pipeline Predictability
Consistent conversion rates enable accurate revenue forecasting. High variance signals process or data quality issues.
Marketing ROI
Higher conversion means lower effective CPL. A 25% vs 15% conversion rate makes your $200 CPL effectively $133 in SQL terms.
Speed to Revenue
Better qualified MQLs convert faster. Intent-qualified leads typically close 40% faster than behavioral-only scored leads.
Assumptions & Limitations
Assumptions & Limitations
Key Assumptions
- *MQL and SQL definitions are consistent throughout measurement period
- *Sales team has capacity to work all MQLs passed
- *Lead source attribution is accurate for channel analysis
- *Time period is sufficient to capture full conversion cycle
Limitations
- !Seasonality and market conditions affect baseline rates
- !New product launches or ICP changes reset historical benchmarks
- !Multi-touch attribution complicates source-level analysis
Aggregated from 2026 industry-standard B2B performance research