Marketing Qualified Lead (MQL)
A Marketing Qualified Lead (MQL) is a lead that has shown enough interest and fit — through behaviors like downloading content, attending a webinar or repeated site visits — that marketing deems it worth passing to sales for follow-up. It is more engaged than a raw lead but not yet vetted by a salesperson.
How it works
Leads become MQLs when they cross an agreed lead-score threshold or trigger a qualifying action. The MQL is then routed to an SDR or sales rep who attempts contact and further qualification, either accepting it or sending it back.
Why it matters
The MQL is the handoff point between marketing and sales, so a shared, precise definition prevents the classic friction where marketing claims to deliver leads that sales dismisses as junk. Tracking MQL-to-SQL conversion reveals whether the definition is calibrated.
What does a written MQL definition look like?
It fits on one line and can be checked by anyone. For example: "A contact at a company with 20 to 500 employees in our target industries, with a work email, who has requested a demo, or has visited the pricing page twice and downloaded a guide within 30 days." Each condition is observable, and the time window stops old activity from counting.
Write the exclusions into the same definition: job seekers, students, vendors, competitors and existing customers usually should not become MQLs. The SQL vs MQL article shows how the MQL definition hands off to the sales one.
Why do MQL volumes often look better than the pipeline they create?
Because MQL counts are easy to raise without raising quality. Lowering the score threshold, gating more content behind forms, or counting a webinar registration as qualifying will all lift the MQL number in the next monthly report. If sales accepts the same absolute number of leads as before, the extra MQLs were noise.
Track MQL to SQL conversion alongside the count. Say marketing delivers 200 MQLs in March and 300 in April, and sales accepts 40 in each month: conversion fell from 20% to 13% and pipeline stayed flat. That is the number to discuss, not the 50% rise in MQLs.
A second check is sales acceptance time. If accepted MQLs sit untouched for days, the problem may be capacity rather than quality, and adding more MQLs will make it worse. Report accepted, rejected and untouched as three separate numbers so each problem is visible on its own.
How fast should sales follow up on an MQL?
Quickly, measured in minutes or hours rather than days, especially for hand-raisers such as demo requests. A person who filled a form is most likely to answer while they are still thinking about the problem. Set a response-time expectation in the handoff agreement (for example, first contact within one business hour for demo requests, one business day for content leads) and report against it.
Routing rules decide who owns the lead the moment it arrives, so nobody has to assign it by hand. Lead routing best practices covers round-robin, territory and account-based routing.
Measure the median, not the average. A handful of leads handled in two minutes can hide a long tail that waited three days.
What should happen to MQLs sales rejects?
They should go back to nurture with a reason, and the reasons should be reviewed monthly. "Wrong company size" points at the fit rules; "no project this year" points at timing and suggests a longer nurture. A rejected MQL with no reason teaches marketing nothing. Autocloz keeps the engagement history on the contact's timeline, so a rep rejecting a lead can see what it did first, and the deals CRM records the outcome once a lead becomes an opportunity.
How Autocloz handles it
Autocloz keeps the full engagement history that qualifies an MQL — opens, clicks, replies, calls, meetings — on one contact timeline, so the marketing-to-sales handoff carries the context the rep needs to act.
Free tools for this
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FAQ
What is the difference between an MQL and an SQL?
An MQL is qualified by marketing based on engagement and fit signals; an SQL is a lead a salesperson has vetted and accepted as a genuine opportunity worth pursuing. The MQL-to-SQL step is where sales validates marketing's judgment.
What makes a good MQL definition?
One that sales and marketing agree on and that correlates with real conversion. It usually combines fit criteria (matches the ICP) with a meaningful action or score threshold, and is revisited as MQL-to-SQL conversion data comes in.
Related terms
Lead enrichment is the process of automatically adding missing data to a lead or company record — job title, company size, industry, verified email, phone, LinkedIn, technographics — from third-party data sources, so reps can segment, personalize and prioritize without manual research.
An Ideal Customer Profile (ICP) is a description of the company that gets the most value from your product and is easiest to win and retain — defined by firmographics like industry, company size, revenue, geography and technology stack. It targets accounts (the company), distinct from a buyer persona, which describes the individual within the account.
Lead scoring is the practice of assigning a numeric value to each lead based on how well they fit your ideal customer profile (demographic/firmographic fit) and how engaged they are (behavioral signals like email opens, site visits, demo requests). The score ranks leads so sales works the hottest ones first.
Intent data is behavioral information that signals a company or person is actively researching a product, category or solution — indicating they may be in-market to buy. It includes first-party signals (activity on your own site and content) and third-party signals (content consumption and search behavior across a data provider's network of sites).