Glossary
What is an MQL?
A lead that marketing judges ready for sales follow-up, based on fit and engagement, before sales has qualified it.
Definition
An MQL (marketing qualified lead) is a lead that marketing has judged ready for sales follow-up, because the person fits the target customer profile and has engaged enough to suggest interest. It's a handoff point, not a sale: sales still has to confirm a real need and timing before the lead becomes an SQL.
How a lead becomes an MQL
Most teams use a lead score that combines two kinds of points:
- Fit. Job title, company size, industry, location. Does this look like a buyer?
- Engagement. Pricing page visits, webinar attendance, email clicks, repeat visits. Is this person interested?
When a lead crosses an agreed threshold, it's marked an MQL and routed to sales. High-intent actions, like a demo request, often skip scoring and go straight to sales. Many models also let points fade over time, so a burst of activity last year doesn't keep someone above the line.
An example
Say a project management tool scores leads out of 100, with the MQL line at 60. A marketing director at a 200-person agency earns 30 fit points. She downloads a template (+10), attends a webinar (+15) and views pricing twice (+10). At 65 she becomes an MQL, and an SDR gets a task to follow up within a day. A student who downloads ten templates scores high on engagement but near zero on fit, so they never cross the line.
MQL vs SQL
An MQL is marketing's judgment, based on data. An SQL is sales' judgment, usually after a conversation, that there's a real opportunity. Tracking how many MQLs become SQLs shows whether marketing's definition matches what sales finds. If sales rejects most MQLs, the scoring is off. If sales accepts nearly all of them, the bar may be too high and good leads are waiting in nurture.
Common mistakes
- Setting the threshold so low that sales stops trusting MQLs.
- Scoring engagement but ignoring fit.
- Letting MQLs sit. Interest fades quickly, so agree on a follow-up time.
- Defining MQLs without sales in the room. Sales works these leads, so they should help set the threshold.
How Siela AI uses it
Siela AI scores every lead it finds hot, warm or cold with a one-line reason, so you can see why a lead is worth following up.
Questions
What is the difference between an MQL and an SQL?
An MQL is a lead marketing considers ready for sales, based on fit and engagement data like page visits or downloads. An SQL is a lead sales has confirmed, usually through a conversation, as a real opportunity with need and timing. The MQL stage comes first.
What actions make someone an MQL?
It depends on the company's scoring model. Common qualifying actions include visiting the pricing page, attending a webinar, starting a free trial, downloading a buying guide or clicking several emails, combined with a job title and company that fit the target profile.
Are MQLs still useful?
They can be, if sales and marketing agree on the definition and track what happens next. Critics say MQL counts reward volume over quality, so some teams focus on opportunities or pipeline instead. The useful part is a shared, written rule for when sales should act.
Less jargon, more conversations.
Siela AI finds people with a fresh reason to talk, reaches them on LinkedIn, X and email from your own accounts, and keeps your social going.