What is a marketing qualified lead? Criteria, scoring and handoff

What is a marketing qualified lead?

A marketing qualified lead (MQL) is a prospective customer that marketing has judged worth a sales follow-up: the lead fits your target market and has shown buying signals, such as visiting the pricing page or requesting a demo. The judgment rests on evidence, not on a sales conversation — that is what separates an MQL from a sales qualified lead.

MQL is one stage in the lead lifecycle. A raw lead (someone who downloaded an ebook or left an email) becomes an MQL when fit and engagement cross an agreed bar; it becomes an SQL when sales accepts it after direct contact, and an opportunity when a real deal with budget and timeline emerges. HubSpot's lifecycle-stage documentation describes the same progression from lead to MQL to SQL to opportunity. The stage matters because it tells you who owns the lead next: marketing nurtures it, sales works it.

How leads qualify: fit + behavioral signals

Lead qualification is the process of deciding which leads deserve attention. Most MQL definitions combine two dimensions, and a lead needs both:

• Fit — does this lead look like your target customer? Company size, industry, job role and seniority, region, and sometimes budget or the tools already in use. Fit data usually comes from the form itself or from enrichment in the CRM.
• Behavior — has this lead acted like a buyer? Pricing-page views, demo or contact-form submissions, webinar attendance, email clicks, repeat visits to high-intent pages. These are the signals your analytics records.

Either dimension alone misleads. A perfect-fit company that never engages is a name in a database, not a lead to call today; a student who reads every blog post is engaged but will never buy. If the buying signal comes from product usage rather than marketing content — say, a team that hit a usage limit — the lead is usually classified as a product qualified lead instead.

The common mechanism for combining the two is lead scoring: points for fit plus points for behavior, compared with a threshold. HubSpot's lead scoring tool, for example, keeps engagement and fit as separate scores.

A fit-and-behavior scoring example

Sample data: a B2B software team sets the MQL threshold at 70 points. Fit can contribute up to 40 points and behavior up to 60. Two leads from the same campaign:

Lead A: 15 + 10 + 15 = 40 fit, 15 + 25 + 10 + 10 = 60 behavior, 100 total — above 70, so it becomes an MQL and goes to sales. Lead B: 10 + 10 + 5 = 25 fit, 15 + 5 = 20 behavior, 45 total — below 70, so it stays in nurture until new behavior adds points.

The weights are illustrative; every team sets its own. Two rules make the sheet useful: write the weights and threshold down before you see results, and re-score leads when new signals arrive instead of scoring once at signup. The scoring itself runs in your CRM or marketing automation tool.

From MQL to SQL to opportunity

An MQL is marketing's judgment that a lead is worth contacting; an SQL is sales' confirmation, after direct contact, that the lead has a real need and a plausible path to buying — the full comparison lives in MQL vs SQL. The handoff between the two is where most MQL programs leak, so make it concrete:

• Fields that travel with the lead. Contact details, company and role (the fit data), source and UTM parameters, the behavior trail and score, and what the lead actually asked for. A rep should not have to re-ask what the form already captured.
• An SLA both teams sign. For example: sales makes first contact within one business day of the handoff and logs the outcome. Speed matters because buying signals decay; a demo request answered a week late is a cold lead.
• Sales confirms the SQL. The rep accepts or rejects each MQL after a discovery conversation and records a reason for rejection. Accepted leads that show budget, authority, need and timeline — the BANT criteria Salesforce describes — move on to opportunity.

Rejected MQLs are not waste: their rejection reasons are the feedback that keeps the scoring model honest.

MQL-to-SQL conversion rate

The rate that tells you whether your MQL definition matches sales' reality:

MQL-to-SQL conversion rate = SQLs ÷ MQLs × 100%

Sample data: in one month marketing hands over 240 MQLs; sales accepts 60 of them as SQLs. The rate is 60 ÷ 240 = 25%.

Count the same cohort: take the MQLs created in a period and follow them until sales decides, rather than dividing this month's SQLs by this month's MQLs, which mixes cohorts when your sales cycle is longer than a month. Then track the rate over time against your own baseline — there is no universal benchmark we can cite with a primary source, and your definition changes the number more than any industry average would. For the rest of the funnel math, see lead conversion rate.

Common problems

MQL volume looks good, but nothing closes. Work backwards down the funnel before blaming sales. If MQLs are many but the MQL-to-SQL rate is low, the threshold is too loose — often because behavior-only scoring counts ebook downloads as buying intent. If SQLs are accepted but opportunities and deals do not follow, the fit criteria are wrong (you are qualifying the wrong companies) or the handoff is too slow. Lead tracking end to end, from first touch to closed deal, is what makes this diagnosis possible.

Marketing and sales definitions have drifted apart. The symptom is reps complaining that the leads are junk while marketing reports a record month. Fix it with a written definition both teams sign: the fit requirements, the scored behaviors and weights, the threshold, the handoff fields and the SLA. Then review rejected MQLs together once a month and adjust the weights against the rejection reasons. A definition nobody revisits slowly stops describing real buyers.

How to track MQL signals in TapCub

TapCub does not score leads and is not a CRM; the scoring sheet above runs in your CRM or marketing automation tool. TapCub supplies the behavior half of that sheet, with the source attached.

• Turn the behavior rows into events and goals. Track a demo form, signup or webinar registration as an event, then mark the ones that matter as conversions in Web analytics › Goals. In the example, "Viewed pricing page" and "Submitted demo form" are exactly this kind of goal. See track form submissions.
• Keep the campaign attached. Web analytics › Sources shows which channel, source and UTM campaign brought the visitors who completed those goals, so you can compare MQL-forming behavior by campaign rather than lead counts alone.
• Count people, not bots. Bot and crawler visits are counted separately from people, so automated traffic does not inflate the visitor numbers behind these signals.
• Give sales the trail. Identify signed-in or form-submitting users with tapcub.login on the analytics script; they then appear in All-platform analytics › Users › People, where the profile shows a day-by-day timeline of what they did, the context a rep wants before the first call. If chat is one of your lead sources, see live chat lead generation.

Sources

• Salesforce, MQL vs. SQL.
• HubSpot, Use contact and company lifecycle stages.
• HubSpot, Overview of the lead scoring tool.