How to distinguish an MQL from an SQL
MQL and SQL mark two gates in the funnel. The MQL bar is set by marketing on fit plus behavior; the SQL bar is set by sales after human qualification. The friction between them is where most leads die, so the definitions and handoff must be airtight.
Short answer
An MQL (marketing qualified lead) has shown enough fit and engagement to warrant sales attention; an SQL (sales qualified lead) has been vetted by a rep and confirmed to have real need, authority, and timing. Define both with explicit, agreed criteria and a clean handoff so no lead falls through the gap.
Step by step
Define the MQL bar
Set the fit and engagement score that says a lead is worth a rep's time: right ICP attributes plus meaningful buying behavior.
Define the SQL bar
Set what a rep must confirm to accept a lead as sales-qualified: a real need, a path to authority, and viable timing.
- MQL: fit + engagement threshold met (marketing owns)
- SQL: need, authority, timing confirmed by a rep (sales owns)
Write an SLA for the handoff
Agree how fast sales works an MQL and what feedback goes back if it is rejected. The loop keeps marketing's scoring honest.
Track acceptance and conversion
Measure MQL-to-SQL acceptance rate and SQL-to-opportunity rate. Low acceptance means the MQL bar is too loose or the definitions disagree.
Recalibrate together
Review the criteria jointly each quarter using conversion data so marketing and sales share one definition, not two.
How Ardovo helps
Ardovo scores fit and engagement for the MQL gate and captures the rep's SQL confirmation in structured fields, then reports acceptance and conversion across the handoff so both teams see exactly where leads leak.
Frequently asked questions
What is the difference between an MQL and an SQL?
An MQL is a lead marketing deems ready for sales based on fit and engagement. An SQL is a lead a rep has vetted and confirmed has genuine need, authority, and timing. MQL is a scoring gate, SQL is a human qualification gate.
Who owns the MQL and SQL definitions?
Marketing owns the MQL criteria, sales owns the SQL criteria, but both should be agreed jointly with an SLA and a feedback loop. Misaligned definitions are the top cause of the classic sales-marketing handoff friction.
What is a good MQL to SQL conversion rate?
It varies widely by industry and lead source, but a low acceptance rate signals your MQL bar is too loose or the two teams disagree on quality. Track your own baseline and improve it rather than chasing a universal benchmark.