MQL (Marketing Qualified Lead)

An MQL (Marketing Qualified Lead) is a contact who, based on their behavior and information, has reached a level of interest that Marketing considers enough to move forward: typically closer to buying than an ordinary lead, but not yet validated by a sales rep. They are identified by combining engagement signals (opening emails, downloading a resource, visiting the pricing page) with profile fit data (role, industry, company size).

Qualification usually relies on a lead scoring model that adds points for each action and, once a threshold is crossed, flags the contact as an MQL. It is one of the central stages of the conversion funnel and part of what Revio automates in the marketing layer: capturing the contact, scoring it and handing it off in an orderly way once it becomes relevant.

The key point is that an MQL measures intent, not a purchase: it defines which contacts deserve more intensive follow-up (nurturing) or handoff to Sales, and keeps the sales team from wasting time on cold or off-target leads.

How an MQL is defined in practice

There is no universal definition of an MQL: each company sets its own threshold based on its sales cycle and its ideal customer profile (ICP). The usual approach is to build a score that combines two dimensions. On one side is fit: how closely the contact resembles the ideal customer (decision-making role, target industry, country, revenue). On the other is behavior (engagement): how active they are (visited the website three times this week, downloaded a case study, opened the pricing page). A contact with good fit but little activity usually stays in nurturing; one with high activity and good fit becomes an MQL and triggers the next action.

The signals that carry the most weight in B2B tend to be: downloading a bottom-of-funnel resource (a comparison, a demo, an ROI calculator), repeat visits to product or pricing pages, opening and clicking through an email sequence, or filling out a form with company data consistent with the target. By contrast, a single download of a generic ebook is rarely enough to qualify.

Why it matters

The MQL is the common language between Marketing and Sales. Without an agreed criterion, Marketing delivers leads that Sales considers noise, and Sales ignores contacts who actually were ready. Defining the MQL with numbers (which actions, what threshold, what minimum data) aligns both teams and makes it possible to measure the funnel end to end: how many visitors become leads, how many leads become MQLs, how many MQLs become SQLs, and how many end in a sale. That map is what makes demand generation predictable.

MQL vs SQL

The natural counterpart of the MQL is the SQL (Sales Qualified Lead). The difference lies in who validates it, with what criteria and at what stage of the funnel:

In short: the MQL is a promise from Marketing (this contact looks ready) and the SQL is confirmation from Sales (I talked to them and they really want to buy). An MQL can be accepted as an SQL, sent back to nurturing if it is not mature yet, or discarded if it does not qualify. Frameworks such as BANT or MEDDIC are often used to make that jump from MQL to SQL with sound criteria.

A concrete example (Argentina)

A B2B software company in Buenos Aires defines as an MQL any contact who: holds a management or executive role, works at a company with more than 50 employees, and has accumulated 40 scoring points (for example: 15 for downloading the product comparison, 10 for two visits to the pricing page and 15 for opening three emails in the sequence). When an operations manager at a distributor crosses that threshold, the system flags them as an MQL and alerts the SDR, who reaches out to confirm whether there is a real project. If the conversation confirms budget and a timeline, the lead becomes an SQL.

Common mistakes

• Confusing an MQL with a customer: an MQL is intent, not a purchase. Treating it as a closed sale inflates expectations and throws off the forecast.
• A threshold that is too low: if any download turns a contact into an MQL, Sales receives noise and stops trusting lead quality.
• Not reviewing the model: scoring goes stale. If the MQL-to-SQL rate drops, you need to recalibrate the points and the fit criteria.
• Forgetting fit: scoring on activity alone ends up qualifying curious browsers or students who will never buy.

When it is well defined, the MQL stops being a vanity metric (we generated 500 MQLs) and becomes a tool for alignment and prediction between marketing and sales.