What is an MQL?
A marketing qualified lead (MQL) is a contact who has demonstrated enough interest and fit to justify a one-to-one conversation, but hasn’t yet been accepted by sales. MQL is a marketing-owned definition based on a combination of fit (firmographic match to ideal customer profile) and intent (specific behaviour signals such as demo request, pricing page visit or content download above a threshold).
What is an SQL?
A sales qualified lead (SQL) is an MQL that a sales rep has accepted as worth pursuing after their own qualification check. The shift from MQL to SQL is the formal handoff: marketing has done its job sourcing and qualifying; sales now owns the relationship and the timeline.
MQL vs SQL: what is the actual difference?
The difference between an MQL and an SQL is ownership and acceptance. An MQL is a marketing assertion: this contact looks ready. An SQL is a sales acceptance: we agree, we’ll work it. Without an explicit acceptance step you don’t have SQLs, you have unqualified leads sitting in a sales rep's inbox.
| Dimension | MQL | SQL |
|---|---|---|
| Owned by | Marketing | Sales |
| Defined by | Fit + intent score thresholds | Sales rep acceptance after first contact |
| Triggered by | Behaviour or score change | Manual or workflow on rep disposition |
| Typical conversion rate | 10 to 25% of MQLs become SQLs | Varies by segment |
| SLA | Routed to a rep within minutes | Worked within agreed cadence |
| Reporting impact | Source and channel attribution | Pipeline created and velocity |
How to define MQL and SQL criteria for your business
Define MQL and SQL criteria by combining fit (does this contact match your ICP?) and intent (have they done something that signals readiness?). Keep the criteria short, written down, and reviewed quarterly with both marketing and sales in the room.
- List the firmographic fit criteria your ideal customer profile must satisfy (industry, size, geography, tech stack).
- List the behavioural intent signals that warrant a one-to-one conversation (demo request, repeat pricing visits, contact form, specific content thresholds).
- Set the MQL threshold using a simple scoring model. Resist the temptation to build a complex one until the simple one is in use.
- Agree the SQL acceptance rule with sales: timeframe to respond, what disqualifies, what triggers a return to nurture.
- Document both definitions in a one-page lead handoff agreement signed by the heads of marketing and sales.
- Review the criteria quarterly using actual conversion data, not opinion.
How to set up MQL and SQL in HubSpot
In HubSpot, model MQL and SQL using the lifecycle stage property combined with lead status. Use the native lead scoring app for intent and fit, route MQLs to reps automatically with a workflow, and let reps progress to SQL using a clear lead-status disposition such as Accepted, Disqualified or Recycle.
Avoid using lifecycle stage backwards transitions. Instead, use a Recycle status that triggers a nurture workflow and returns the contact to marketing-owned cadence until the next qualifying event.
Common mistakes when defining MQL and SQL
The most common MQL and SQL mistakes are: defining MQLs on intent only without fit, using too many scoring criteria, treating an SQL as anything a rep has touched, and never reviewing definitions against actual conversion rates. Each leads to the same outcome: a marketing team chasing volume and a sales team ignoring leads.
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