MQL vs SQL: the difference
An MQL (marketing qualified lead) is someone marketing judges more likely than average to buy, usually from fit plus engagement such as downloads or visits. An SQL (sales qualified lead) is someone sales has checked and accepted as worth active pursuit, typically with a confirmed need, authority and timing.
Side by side
| MQL | SQL | |
|---|---|---|
| Who decides | Marketing, often automatically | Sales, after review or a conversation |
| Based on | Fit plus engagement | Confirmed need, authority, budget or timing |
| Typical trigger | Lead score crosses a threshold | A discovery call or a qualifying reply |
| What happens next | Handed to sales | Becomes an opportunity, or is rejected |
| Typical volume | Larger | Smaller |
The order is usually lead, then MQL, then SQL, then opportunity, then customer. Some teams add an SAL (sales accepted lead) between MQL and SQL: sales agrees to work the lead before it is fully qualified.
A worked example
A design agency founder downloads your guide to client onboarding, then visits your pricing page twice in a week. Your lead scoring model gives 25 points for fit (agency, founder) and 30 for engagement. She crosses the 50-point MQL line and is passed to sales.
The salesperson emails her. She replies that they are moving off spreadsheets this quarter, she signs off on tools, and the budget is approved. That reply turns her into a sales qualified lead. If she had replied "just researching for a blog post", sales would reject the MQL and send her back to marketing.
How to measure the handoff
MQL to SQL conversion rate = SQLs created from MQLs in a period / MQLs created in that period x 100
Say marketing hands over 200 MQLs in a quarter, sales accepts 60 and qualifies 30 as SQLs. That is 15% MQL to SQL. Watch three numbers together: the rate, the time from MQL to first sales contact, and the share of MQLs sales never touched. A low conversion rate with many untouched leads is a sales follow-up problem, not a lead quality problem.
Common mistakes
- Engagement-only MQLs. Students and competitors download guides too. Without fit, an MQL means little.
- No agreed definition. If marketing and sales define each stage differently, every report starts an argument.
- Slow handoff. Interest fades fast. An MQL that waits a week for a first touch converts worse.
- Counting MQLs as success. Marketing targets based on MQL volume encourage loose criteria.
Related terms
Both stages are measured against your ideal customer profile. For outbound teams, timing often comes from buying signals rather than content downloads.
How this maps to Sluice
Sluice is an outbound tool, so it skips the MQL stage. Each person gets a fit score from 0 to 100 against your profile, a judgement on whether they are worth approaching, and a read on whether they have the problem now; 70 is the line for worth contacting. A reply then decides whether they become an SQL in your CRM. With HubSpot connected, each lead arrives with a note saying why it is a lead.
Questions people ask
- What is a good MQL to SQL conversion rate?
- It varies too much by market and definition for a universal benchmark. Track your own rate over time; a sudden drop usually means marketing loosened its criteria or sales tightened theirs.
- What comes after an SQL?
- Usually an opportunity: a deal record with a value and a stage in the CRM, created once a sales conversation is under way.
- Do outbound leads go through the MQL stage?
- Often not. A prospect found by sales through outbound has not engaged with marketing, so many teams qualify them straight to SQL after a first conversation.
Try it on your own market
Sluice quotes the worst-case price before anything runs and charges only for lookups that found something, so finding out costs close to nothing.
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