MQL · SQL
Marketing/Sales Qualified Lead
What is an MQL?
- MQL = the “interested lead” as marketing sees it
- SQL = a lead Sales has accepted as worth contacting
- A shared language for lead maturity
Adopting MQL and SQL turns the habit of passing lead lists over in Excel into a 'conditional handoff.' Marketing is judged not by how many leads it generates but by how many Sales accepts, and Sales has to record a rejection reason instead of quietly dropping leads it doesn't like. In other words, what these terms really define is not a classification scheme but a accountability boundary between two teams, and the moment that boundary exists, you can see for the first time where leads are leaking.
In practice, teams often add one more stage between MQL and SQL: SAL (Sales Accepted Lead). Separating “sales agreed to take it” from “sales finished qualifying it” makes it possible to tell whether the problem is lead quality or sales response speed. It is more realistic to set the scoring threshold by working backward from the sales team’s capacity than from the score itself. If five reps each handle 10 leads a week, monthly MQLs top out around 200, and anything beyond that simply goes untouched. Tracing recent won deals in reverse to extract shared attributes and behaviors and building them into the criteria gives you evidence instead of gut feeling. PQL, which qualifies leads from usage data of people who have tried the product for free, is a separate axis and is best kept distinct.
The most common mistake is hard-coding the MQL count as a marketing KPI. If you lower the threshold to hit the number, competitors’ employees, job seekers, and people collecting material for benchmarking all get promoted to MQL, and within weeks sales stops trusting the leads altogether. Conversely, if sales doesn’t record rejection reasons, marketing never knows what to fix. Dropping a lead rejected because “the budget is next year” is also a loss. A lead that isn’t ready now may become a customer in six months, so return it to the nurturing pool and define re-entry conditions at the same time; only then do the criteria actually work.
John KimInstructor John Kim, author of "B2B Marketing Design" and CEO of Nanum Consulting
Jongsung EunExpert in AI, marketing, business models, and management strategy| Category | MQL (Marketing Qualified Lead) | SQL (Sales Qualified Lead) |
|---|---|---|
| Qualified by | Marketing | Sales |
| Criteria | Interest and behavior score | Purchase intent · budget · timing |
| Step | Nurturing target | Sales follow-up target |
As inbound marketing and marketing automation spread (late 2000s), these became the standard terms for the criteria governing lead handoff from marketing to sales. The roots of lead qualification lie in frameworks such as BANT, believed to have originated at IBM.
Consider a hypothetical case. Company A is a B2B SaaS firm selling predictive maintenance for equipment (40 employees, about KRW 6 billion (≈ US$4,489,000) in annual revenue). Marketing generated 320 MQLs a month through white papers and webinars and passed them to sales, but for three months sales had given up, saying “nobody picks up the phone.” MQLs kept piling up while the quarterly pipeline was actually shrinking.
- The marketing lead and the head of sales traced back 37 deals won over the past six months and found a common pattern: 100+ machines, manager level or above, and two or more views of the pricing page.
- When the head of sales said, “People who only downloaded a white paper won’t pick up even if we call,” they reset the threshold based on the six sales reps’ weekly capacity of 60 leads and cut monthly MQLs from 320 to 95.
- They added an SAL stage between MQL and SQL and changed the rules so that sales accepts or rejects within 24 hours and logs one of five reasons in the CRM when rejecting.
- In a 30-minute lead review every Friday, the team found that the top rejection reason, at 38%, was “budget timing 6+ months out.” Rather than dropping this segment, they moved it back into a bimonthly case-study newsletter track.
- Four months later, the MQL-to-SQL conversion rate rose from 8% to 24%, average first-response time by sales fell from 52 hours to 5 hours, and quarterly pipeline value grew 1.7x.