What is MQL and SQL?
An MQL is a lead marketing considers ready to hand over. An SQL is one sales has looked at and accepted as a real opportunity. The gap between those two numbers is where most B2B and high-ticket teams lose money, and it is almost always a definition problem rather than a lead quality problem.
Marketing Qualified Lead and Sales Qualified Lead
How MQL and SQL actually work
Marketing sets the criteria for an MQL: a form fill plus a matching job title, a budget range selected, a pricing page visit, a demo request. Anyone clearing the bar goes across.
Sales then applies its own filter, asking whether the budget is real, whether this person decides anything and whether there is a timeline worth working, and the leads surviving that become SQLs and enter the pipeline. What happens to the rejections is the entire story. In a healthy setup they come back with a reason code somebody reads. In most setups they vanish.
Where teams get MQL and SQL wrong
Letting the two teams define the terms separately. Marketing counts a whitepaper download as an MQL, sales considers a whitepaper download to be nothing whatsoever, and the quarterly review gets spent disputing a number instead of repairing the process that produced it.
Second, compensating marketing on MQL volume. Whatever you measure goes up, so you will get more MQLs and they will be worse. Tie the target to SQLs or pipeline value.
Third, ignoring the conversion rate between them. One in fifteen means your criteria are wrong, and no amount of extra lead volume repairs that. Tighten the definition, accept fewer, watch closed revenue climb.
What good looks like in India
For a Gurugram developer, an MQL might be someone submitting a form with a budget above a threshold and a location preference matching an active project, while the SQL is the one who took the call and agreed to a site visit. Site visits are the real currency, so the definition should point there.
Two habits separate the teams that get this right: reason codes on every rejection reviewed monthly by both sides together, and a single CRM both teams work inside so nobody is comparing spreadsheets.
Related terms: Multi-Touch Attribution · NAP Consistency · Noindex.
Where this shows up in the work: MarTech & Automation · Full glossary.
MQL and SQL — questions, answered.
It varies too much across industries and lead sources to benchmark usefully. Judge it internally instead. A very low rate means your MQL bar is too loose, while a near-total rate means it is too tight and viable leads are being discarded.
Both teams together, in one session, written down. Marketing proposes criteria it can observe, sales validates them against what actually closes, and the definition gets revisited each quarter as the product and the market shift underneath it.
Last updated 2026-08-08
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