MQL (Marketing Qualified Lead): Definition and Meaning

What does MQL stand for?

MQL stands for Marketing Qualified Lead. It’s one half of the most important (and most argued-about) handoff in B2B business: the point where marketing says “this lead is ready for you” and passes it to sales.

The concept is straightforward. Not every lead is worth a salesperson’s time. Someone who accidentally clicked an ad isn’t the same as someone who downloaded your pricing comparison guide, attended your webinar, and works at a company that matches your ICP. Lead qualification exists to make that distinction, and the MQL is where marketing draws the line.

What is a marketing qualified lead?

An MQL is a lead that has crossed a threshold, typically defined by a lead scoring model in your marketing automation platform (MAP), that indicates enough engagement or fit to warrant sales attention.

That threshold is made up of two types of criteria:

Demographic / firmographic fit. Does this person match your ideal customer profile? Right industry, company size, job title, geography? These are sometimes called “explicit” scoring factors because the lead provides them directly (form fills, LinkedIn data enrichment, etc.).

Behavioral engagement. Has this person taken actions that suggest genuine interest? Page visits, content downloads, email opens, webinar attendance, pricing page views? These are “implicit” scoring factors because you’re inferring intent from behavior.

Most lead scoring models assign point values to both categories. A Director of Marketing at a mid-market SaaS company (good fit) who downloaded three ebooks and visited the pricing page twice (high engagement) would score differently than a student who downloaded one ebook for a research paper.

When a lead’s score crosses the agreed-upon threshold, the MAP tags them as an MQL and routes them to sales, either directly to a rep or into an SDR queue for further qualification.

MQL vs. SQL: the handoff

The MQL is marketing’s stamp of approval. The SQL (Sales Qualified Lead) is sales’ stamp of approval. The gap between them is where most marketing-sales friction lives.

Here’s how the pipeline typically flows:

1. Lead. Someone fills out a form, downloads content, or otherwise enters your database.
2. MQL. Lead scoring model flags them as qualified based on fit + engagement.
3. SAL (Sales Accepted Lead). Sales acknowledges receipt and agrees to follow up. (Some orgs skip this stage.)
4. SQL. After a discovery call or initial outreach, sales confirms this is a real opportunity.
5. Opportunity. Lead enters the sales pipeline with a deal attached.

The MQL-to-SQL conversion rate is one of the most watched metrics in B2B marketing ops.¹

If sales is rejecting most MQLs, either the scoring model is wrong, the ICP definition is off, or marketing is chasing volume over quality. If sales is accepting most MQLs but not closing them, the problem is downstream. Either way, the MQL-to-SQL handoff is where marketing and sales alignment gets tested.

The “MQLs are dead” debate

Every 18 months or so, someone publishes a post declaring that MQLs are dead. The argument usually goes something like this: lead scoring is broken, content downloads don’t indicate intent, sales ignores MQLs anyway, and we should all switch to account-based marketing (ABM) or intent data or some other paradigm.

There’s a kernel of truth here. A lot of MQL programs are broken. The most common failure modes:

• Scoring content downloads as high-intent. Someone who downloaded your “State of the Industry” report might be a journalist, a competitor, or a student. A download is not a buying signal by itself.
• No feedback loop. Marketing generates MQLs, throws them over the wall, and never hears back about what happened. Without sales feedback flowing back into the scoring model, the model never improves.
• Vanity MQL targets. When marketing is measured on MQL volume, they optimize for volume. This leads to lower thresholds, weaker leads, and a sales team that learns to ignore MQLs entirely.
• Static models. The scoring model was set up three years ago and never updated. The market changed, the product changed, the buyer changed, but the model didn’t.

But declaring MQLs “dead” because many implementations are bad is like declaring email marketing dead because most emails are spam. The concept, qualifying leads before sending them to sales so salespeople spend time on real opportunities, is sound. The execution is where things go wrong.

What’s actually changing is that the inputs to lead scoring are getting more sophisticated. Instead of relying solely on first-party engagement (did they visit our website?), modern scoring models can incorporate third-party intent data (are they researching our category on review sites?), technographic data (do they use tools that integrate with ours?), and buying committee signals (are multiple people from the same account engaging?).

The MQL isn’t dead. It’s evolving.

How marketing ops operationalizes MQLs

For marketing operations teams, the MQL isn’t just a concept. It’s a specific technical implementation in your marketing automation platform. Here’s what that looks like in practice:

Lead scoring setup. Build and maintain the scoring model in your MAP (HubSpot, Marketo, Pardot, etc.). This means defining point values for demographic attributes and behavioral actions, setting the MQL threshold, and configuring decay rules so that stale engagement doesn’t keep someone artificially inflated.

Lifecycle stage management. Configure the lead lifecycle stages in your CRM so that MQL is a distinct, trackable stage. This is how you measure conversion rates between stages and identify bottlenecks.

Routing rules. When a lead hits MQL status, what happens? Does it get assigned to a specific rep based on territory? Does it go into a round-robin SDR queue? Does it trigger a Slack notification? Marketing ops owns this automation.

SLA monitoring. Most marketing-sales SLAs include a response time requirement for MQLs (e.g., “sales must follow up within 4 hours”).² Marketing ops builds the reporting that tracks SLA compliance.

Feedback loops. The most important, and most often missing, piece. Sales needs a way to flag MQLs as “good lead, bad timing,” “bad fit,” or “not actually interested.” That feedback should flow back into scoring model refinements.

What makes a good MQL definition for a lean team?

If you’re at a smaller company without a dedicated revenue operations team, keep your MQL definition simple:

1. Pick 3-5 firmographic must-haves. Company size, industry, and geography are the big ones. If someone doesn’t fit these, they’re not an MQL regardless of engagement.
2. Identify 2-3 high-intent behaviors. Pricing page visits, demo requests, and bottom-of-funnel content downloads are the strongest signals. Weight these heavily.
3. Set a threshold that sales agrees to. Literally sit down with your sales lead and agree: “If a lead meets these criteria, you’ll follow up within X hours.” If they won’t agree, your criteria aren’t strong enough.
4. Review monthly. Look at which MQLs converted to SQLs and which didn’t. Adjust the model based on what you learn.

The goal isn’t a perfect model. It’s a shared definition that both teams trust enough to act on.

Frequently Asked Questions

What does MQL stand for?

MQL stands for Marketing Qualified Lead. It refers to a lead that has met a predefined set of engagement or fit criteria established by the marketing team, signaling that the lead is more likely to become a customer than a raw, unqualified lead.

What is the difference between an MQL and an SQL?

An MQL (Marketing Qualified Lead) is qualified by the marketing team based on engagement signals and fit criteria. An SQL (Sales Qualified Lead) has been reviewed and accepted by the sales team as a genuine opportunity worth pursuing. The MQL-to-SQL handoff is one of the most important (and most contentious) processes in B2B revenue operations.

Are MQLs dead?

The 'MQLs are dead' take resurfaces every year or two, usually from companies selling intent data or ABM platforms. MQLs are not dead, but poorly defined MQLs should be. The metric works when marketing and sales agree on the definition, the lead scoring model reflects real buying signals, and the handoff process includes feedback loops. What's dying is the lazy version: scoring leads solely on content downloads and throwing them over the wall to sales.