An MQL is a marketing qualified lead: what it means, how teams set the criteria, and how to hand one to sales.

Definition

An MQL, short for marketing qualified lead, is a contact or account that the marketing team has qualified as ready for a sales conversation, because it fits the profile of companies that buy and has shown interest through its own actions.

The word that carries the meaning is "qualified." A lead becomes an MQL when it meets criteria that marketing and sales wrote down together: who the person is, where they work, and what they did. Until a lead meets those criteria, it stays a lead, however many emails it opens.

HubSpot's default lifecycle stages put it in one line: a marketing qualified lead is a contact or company that your marketing team has qualified as ready for the sales team. Adobe Marketo Engage describes it as a person whose behavior and characteristics meet your success criteria to be passed to sales.

So an MQL is a decision and a handoff at the same time. It records marketing's judgment that the lead is worth a salesperson's time, and it starts a clock on the sales side. What happens after that clock starts is what separates a useful MQL definition from a vanity number.

MQL meaning, in plain terms

In plain terms, MQL meaning comes down to this: marketing has looked at a lead and said "sales should talk to this one." The letters stand for marketing qualified lead. The qualification is done by marketing, usually through a scoring model, before any salesperson has spoken to the buyer.

The abbreviation shows up in three places. In conversation, it is a label for a promising lead. In a CRM or marketing automation platform, it is a value in a lifecycle stage field. In reports, it is a count, such as MQLs created this month by source.

Those three uses drift apart easily. A rep may call any warm inbound lead an MQL, while the CRM only applies the stage when a score crosses a threshold. When a team says "MQL," it helps to ask which of the three it means.

What is an MQL, and what it is not

What is an MQL in practice? It is a lead that passed two tests at once: it fits your ideal customer profile, and it engaged in a way that suggests interest now. Fit without engagement is a target account. Engagement without fit is an audience member.

An MQL is not

• Not any lead. A lead is any contact who converted or interacted beyond a subscription. Most leads never become MQLs, and that is the point of the stage.
• Not a buyer. An MQL has not confirmed need, budget, authority or timing. Those are confirmed in a sales conversation, after the handoff.
• Not a sales qualified lead. Sales qualifies an SQL. Marketing qualifies an MQL. The difference is who decided and on what evidence.
• Not a guarantee of revenue. It is a prediction from data. Some MQLs will be wrong, and the definition has to expect that.
• Not a marketing result on its own. MQL volume is easy to raise by lowering the bar, so a count without acceptance and outcomes says little.

Where a marketing qualified lead sits in the lifecycle

Most CRMs track a buyer's progress in a lifecycle stage field. The MQL is the stage where ownership starts to move from marketing to sales. HubSpot lists eight default stages in order, and the MQL is the third of them.

Two platform details matter for MQLs. HubSpot's default automatic updates only move the stage forward, and its tools cannot set an earlier stage until the value is cleared manually or by a workflow. That affects how you recycle a rejected MQL, covered below.

The stages can also be customized or extended with your own. Teams that add a sales accepted stage between MQL and SQL do it this way. Whatever you add, write the definition of each stage next to its name, so the field means the same thing in every report.

In Salesforce, the same journey is often split across two objects. A lead record carries a lead status while it is being qualified.

When a rep converts the lead, Salesforce creates an account, a contact and an opportunity from the lead's data. Teams often record the MQL in the lead status or a custom field before that conversion.

MQL criteria: fit plus engagement

Every working MQL definition combines two kinds of criteria. Fit describes who the lead is. Engagement describes what the lead did. The vendors name them differently, but the split is the same across the main platforms.

Fit criteria

Fit criteria come from your ideal customer profile and buyer personas. They answer whether this company could buy and whether this person could influence the purchase. Typical fit data is firmographic: industry, employee count, revenue band, region, and the technology the company already uses.

Person-level fit matters as much. A perfect company with the wrong contact still produces a wasted call. Job title, seniority and department show whether the person owns the problem, controls budget, or would only use the product.

Engagement criteria

Engagement criteria come from behavior data: pages viewed, content downloaded, events attended, emails clicked, forms submitted, replies sent. Not all engagement is equal. A pricing page visit or a demo request says more about buying intent than a blog view or an email open.

Recency matters too. A whitepaper downloaded last week is a signal. The same download from nine months ago is history. Scoring tools handle this with decay, which reduces the points from an action as it ages, so an old burst of activity cannot hold a lead above the line.

Negative criteria

A good MQL definition also says what removes points or blocks the stage entirely: competitors, students, job seekers, existing customers, countries you do not serve, and a free email domain where you expect a work address. Without exclusions, high engagement from the wrong people fills the MQL queue.

Setting the MQL threshold with sales

The threshold is the score or rule at which a lead becomes an MQL. It is the most important number in the definition, and it is a negotiation, not a calculation. Marketing knows the data; sales knows which conversations turned into deals.

Salesforce's Trailhead describes it plainly: marketing has traditionally decided which actions earn points and at what point level a lead goes to sales, and sales leaders should weigh in with what they know from working directly with customers. Marketo's glossary frames a scoring model as a way to judge lead quality and sales readiness.

A workable way to set the first threshold is to look backward. Pull the last set of leads that became opportunities, note their fit and the actions they took before the first meeting, and set the bar where most of them would have qualified. Then check how many non-buyers would also have crossed it.

• Write it down. The criteria, the threshold, the exclusions and the date the definition took effect, in one document both teams can find.
• Require both halves. A minimum fit score and a minimum engagement score, so neither can carry a lead over the line alone.
• Keep an override path. A named person can promote a lead that missed the score, with a reason logged, for cases the model cannot see.
• Set a review date. Revisit the threshold on a schedule, and when sales starts rejecting more MQLs than usual.

An MQL scoring table, written for this page

This example model was written for this page. It is a shape to copy, not a benchmark: the signals, points and threshold are illustrations, and your own closed deals should set the weights. It assumes a company selling sales software to B2B teams of 50 to 500 employees.

Score decay is built into some tools. HubSpot, for example, lets you set decay on engagement and combined score event groups at intervals of 1, 3, 6 or 12 months, reducing each event's points based on how long ago it happened. It also supports an overall score limit and limits per group.

Reading fit and engagement together

A single number hides the reason a lead scored well. A combined score built from fit and engagement separately keeps both visible. HubSpot's combined scores label records from A1 to C3: the letter is fit, where A is high fit, and the number is engagement, where 1 is high engagement.

The grid below was written for this page. It shows one way to turn those nine cells into an MQL rule and a next action. The labels follow HubSpot's convention; the actions are suggestions to adapt.

The C1 cell, a low-fit but highly engaged contact, deserves a look rather than a rule. Sometimes it is a researcher or an intern at a company that does fit, and the right move is to find the actual buyer at that account instead of discarding the interest.

MQL in marketing: what the marketing team owns

For a marketing team, the MQL in marketing reporting is the stage where its work becomes visible to sales. Marketing owns everything up to and including the decision: the audience, the content, the scoring model, the data quality, and the trigger that sets the stage.

• The definition document. Kept current, dated, and agreed with sales leadership.
• The scoring model. Fit and engagement rules, exclusions, decay, and the threshold.
• Data completeness. Enough firmographic and contact data for a rep to act, filled by forms, enrichment or research.
• Nurture before and after. Programs that move leads toward the threshold, and a path back for MQLs that sales returns. See B2B lead nurturing.
• Source tracking. Which campaign, channel and content produced each MQL, so quality can be compared by source.

Marketing should not own the verdict on whether an MQL was good. Sales gives that verdict by accepting, working and qualifying the lead. Marketing's job is to read the verdicts and change the model.

MQL sales handoff: what happens when a lead qualifies

The MQL sales handoff is the moment the record leaves marketing's queue and lands in a rep's. Most MQL problems are handoff problems: the lead went to the wrong person, arrived without context, or sat untouched while its interest cooled.

Routing

Routing decides who gets the MQL: by territory, account owner, segment or round robin. If the account already has an owner, the MQL should go to that owner, not to whoever is next in line. The rules are covered in lead routing.

The handoff note

An MQL should arrive with the reason it qualified: the actions, their dates, the score breakdown, and the fit data. A rep who knows the lead viewed pricing twice and downloaded a ramp checklist can open with something relevant. A rep who sees only a name has to guess.

The full process on the sales side, including what the receiving rep must confirm, is in sales handoff. For the MQL itself, the rule is short: no reason, no handoff.

The MQL SLA between marketing and sales

A service level agreement, or SLA, turns the MQL definition into commitments on both sides. Marketing commits to the definition and a volume target. Sales commits to response times, attempts and logging outcomes. Without the sales side, MQLs pile up unworked and nobody can tell whether the definition is good.

The SLA below was written for this page as an example. The numbers in braces are yours to set from your own sales cycle and team capacity, not figures from a study.

MQL definition: fit score of at least {{fitMin}} and engagement score of at least {{engMin}}, or a demo request. Exclusions: {{exclusions}}.

Marketing commits to: {{mqlTarget}} MQLs per month, each with a handoff note (actions, dates, score breakdown).

Sales commits to: accept or return each MQL within {{acceptWindow}}, make the first attempt within {{firstTouch}}, make {{attempts}} attempts over {{days}} days, and log one outcome from the closed list.

Outcomes: accepted, returned (reason), recycled (date), disqualified (reason), converted to SQL.

Review: monthly, owners {{marketingOwner}} and {{salesOwner}}.

HubSpot's documentation gives a concrete way to enforce one side: its lifecycle stage calculated properties record the date a record entered each stage, and a workflow can create a follow-up task for the owner when a record has sat in a stage longer than you allow.

MQL vs SAL vs SQL, briefly

Three stages sit close together and are often confused. The short version is below; the full comparison, with the boundary between them and the reject reasons, is on MQL vs SQL.

HubSpot's default stages go straight from MQL to SQL and keep the finer steps in the Lead Status property. Teams that want an explicit accepted step add it as a custom stage or as a lead status value.

Recycling and disqualifying MQLs

Every MQL ends one of three ways: it moves forward to SQL, it goes back to marketing to be nurtured again, or it is disqualified. The second and third paths are where most teams lose leads, because they were never designed.

The CRM has to support these paths. In HubSpot, the default Lead Status options include Unqualified, Attempted to Contact, Connected and Bad Timing, among others. Because automatic tools only move the lifecycle stage forward, recycling a lead to an earlier stage needs the value cleared first, manually or in a workflow.

Keep the return reasons to a short closed list, five to eight options, rather than free text. A closed list can be counted. Free text cannot. The counts are the most useful input the scoring model will ever get, and they also feed your view of lead quality by source.

MQL definitions for inbound vs outbound teams

The MQL was designed for inbound marketing: people find your content, raise their hand, and marketing decides when they are ready. Outbound teams use the term too, but the definition has to change, because in outbound the seller chose the prospect before the prospect showed any interest.

The cleanest approach keeps inbound and outbound in separate fields or sources. If an outbound reply is labeled an MQL, the MQL count stops meaning what marketing reports it means, and the comparison between channels breaks. Tag the source and let the stage follow the same rules.

For a wider view of the two motions, how to qualify sales leads covers the questions sales asks after the handoff, whichever way the lead arrived.

MQLs in account-based teams

Account-based teams sell to a named list of accounts, and their buying decisions involve several people. A person-level MQL fits that model badly: one engaged contact at a target account may matter less than five quiet ones showing interest together.

Many account-based teams therefore qualify the account as well as the person, a stage often called a marketing qualified account. The platforms support this. Adobe Marketo Engage's account scoring aggregates lead scores from multiple people into a score at the account level, and HubSpot can update lifecycle stages based on a record's associations.

• Account fit is fixed up front. The account is on the target list, so the fit question is mostly answered. Person fit still matters: is this the buyer, a user, or an influencer?
• Engagement is summed across people. Several roles at one account engaging in the same weeks is a stronger signal than one person engaging a lot.
• The handoff goes to the account owner. Not to a round robin, and with every engaged contact listed.
• Intent signals count. Third-party intent data showing research on your category can add to an account's score.

How the account stages fit into the wider funnel is covered in the ABM sales funnel. The person-level MQL can stay, as long as reports do not count five MQLs at one account as five separate opportunities.

MQLs from LinkedIn and cold outreach

Jeluvi's readers often work at the boundary: SDRs who book meetings from LinkedIn and email, founders who do their own outreach, and marketers who hand leads to a small sales team. In that world, the MQL definition has to cover signals that do not come from a website.

LinkedIn Lead Gen Forms

LinkedIn Lead Gen Forms pre-populate fields with a member's contact and professional details from their LinkedIn profile, which makes fit data more complete than a typical web form. LinkedIn also offers a Validate work email option that blocks the most commonly used free email domains in the work email field.

LinkedIn notes the trade-off: validating work email might improve lead quality, but form submission rates might drop. Hidden fields can carry campaign or ad set details into your CRM, which helps you compare MQL quality by campaign later. A form fill is still engagement, not proof of buying intent.

Signals from outreach

The common error is treating every outbound reply as an MQL to show pipeline. A reply is a conversation, positive or negative, and should be logged as one. Positive replies with a clear question are often closer to hot leads than to MQLs and deserve a same-day response.

Measuring MQL quality with your own CRM numbers

MQL quality is measured by what happens to MQLs after the handoff. The data already sits in your CRM if the stages and outcomes are logged. The measures below use only your own records, cut by source, campaign, segment and owner.

Count by cohort, not by calendar. An MQL created in March may become an opportunity in June. Comparing March MQLs with June opportunities mixes two groups. Follow each month's MQLs forward until enough time has passed for your typical sales cycle.

HubSpot's calculated properties support this kind of reporting: Date entered and Date exited each stage, plus latest and cumulative time in a stage on Professional and Enterprise subscriptions. Whatever the tool, record when each MQL entered the stage, or none of these measures can be built. The lead conversion rate guide covers the arithmetic.

MQL examples, written for this page

These examples were written for this page. The companies and people are invented to show how a definition behaves; none describe a real account.

The clean MQL

A VP of sales at a 120-person software company downloads the SDR ramp checklist, then views the pricing page twice in a week. Fit is high and engagement is recent and specific. The score crosses the threshold, the record routes to the account's SDR, and the note lists both actions with dates.

The false positive

A marketing coordinator at a small agency attends three webinars and reads a dozen posts. Engagement is high, fit is close to zero: wrong company type, no buying role. A model that weighted engagement alone would have made this an MQL and cost an SDR an hour. The minimum fit rule blocks it.

The one the model missed

A head of revenue at a target account replies to a nurture email asking about a CRM integration. The score falls short because the company is above the size band. The override path catches it: a marketer promotes the lead with a logged reason, and the next scoring review widens the band.

The outbound reply

An SDR's cold email gets the answer "interesting, but we are locked into a contract until spring." It is not an MQL and not a lost lead. The SDR logs the reply, sets a recycle date for the month before the renewal, and the contact enters a short nurture track.

The account signal

Three people at one target account, a sales director, a RevOps manager and an enablement lead, each view different pages over two weeks. None crosses the person threshold. The account score does, and the account owner gets all three names with a note about what each one read.

How to write your MQL definition in one session

A first MQL definition does not need a long project. It needs the right people in one room, your own data, and a decision written down. The steps below were written for this page.

1. Pull the last closed deals

   List recent won deals and the leads behind them. Note each contact's title, company size, industry, source, and the actions they took before the first meeting.

2. Agree on fit

   From that list and your ideal customer profile, agree on the company and person criteria that must be true. Write the exclusions at the same time.

3. Rank the actions

   Sort engagement actions by how often they preceded a deal. Direct requests and pricing views go at the top; opens and single blog visits at the bottom.

4. Set the threshold and both minimums

   Choose the score that most of your won deals would have reached, with a minimum for fit and for engagement. Note how many non-buyers would also cross it.

5. Write the SLA and outcomes

   Agree on response times, attempts, the closed list of return reasons, and what recycling means. Name an owner on each side.

6. Set the review date

   Put the first review on the calendar, and decide which accept rate change triggers an early review.

What an MQL means to each role

Common MQL mistakes

• Counting MQLs as the marketing result. Volume is easy to inflate by lowering the threshold. Report accept rates and outcomes alongside the count.
• Engagement without fit. A model that lets clicks alone cross the line fills the queue with students, job seekers and competitors.
• No handoff note. An MQL without the reason it qualified forces the rep to guess what to say.
• No return path. Rejected MQLs with nowhere to go are simply lost, and their reasons are never counted.
• Free-text rejection reasons. They cannot be counted, so they never change the model.
• No decay. A burst of activity from last year keeps a cold lead above the threshold.
• Labeling outbound replies as MQLs. It mixes two motions and makes channel comparison meaningless.
• Different definitions in different tools. If the CRM and the marketing platform disagree about what an MQL is, every funnel report is wrong.
• Never revisiting the definition. Markets, products and teams change, and a definition from two years ago describes a different company.

The first message to an MQL

When an MQL lands, the first message should reference the action that qualified it, ask one question, and stop. The template below was written for this page for an MQL that downloaded a resource. The placeholders are marked, and the note below it says when it backfires.

Subject: The {{resourceName}}, and one question

Hi {{firstName}},

You picked up the {{resourceName}} on {{dayDownloaded}}. One question so I can point you at the right part of it: are you {{situationA}}, or {{situationB}}?

Reply with a line and I will send the version that fits.

{{senderName}}

Frequently asked questions