MQL (Marketing Qualified Lead)

MQL stands for marketing qualified lead. The term comes from companies where two teams split the work: marketing attracts contacts with content, webinars or ads, then sales turns them into customers. The MQL is the handoff point between the two.

An MQL meets two conditions. It looks like your ideal customer (right industry, right role, right company size) and it did something that signals interest: downloaded a white paper, came back to your pricing page three times, signed up for a webinar. That interest is often in a topic, not yet in your offer.

That is the concept's limit. An MQL has not asked for anything, it has consumed content. Treating it like a buyer ready to sign is the surest way to put it off.

Most teams use a score. Each profile criterion and each action earns points: a leadership title, a company with more than 50 employees, an email opened, a visit to the demo page. Above an agreed threshold, the lead becomes an MQL and lands with a salesperson.

The threshold is only worth something if you check it. Take the customers you signed in the last six months and look at what they did beforehand. If half your MQLs never pick up the phone, your score rewards curiosity, not intent. Lower the weight of easy actions, such as opening an email, and raise the weight of costly ones, such as asking for pricing.

Small teams often skip scoring entirely. They set a simple rule: a lead that fits the target and takes one specific action goes to sales the same day. It is less precise, and often more effective than a scoring model nobody maintains.

The SQL, or sales qualified lead, is the next stage. A salesperson has spoken to the lead, or read their reply, and confirmed there is a real topic: a need, a timeline, someone who decides. An MQL rests on indirect signals, an SQL on a conversation.

The handoff between the two is the classic friction point between marketing and sales. Marketing complains that sales never calls leads back, sales complains that marketing sends tire kickers. A written agreement on the MQL definition, the response time and the rejection reasons settles most of these fights.

In a small business where one person runs both marketing and sales, the distinction matters less. Keep the underlying idea: you do not call someone who read one article as if they had asked for a quote. The first contact should start from what they looked at and check whether a real need sits behind it.

The rate that matters is MQL to SQL conversion: out of a hundred MQLs handed over, how many become real opportunities after a conversation? Track it by source, because an MQL from a webinar and an MQL from a free template download are not worth the same.

Measure response time too, since a lead who just showed interest cools down quickly. And have salespeople log a reason for every rejection: off target, no need, unreachable. Those reasons tell you exactly what to fix in your scoring.

Avoid setting a target on MQL volume. As soon as a team is judged on that number, the threshold quietly drops and sales drowns in contacts who will never buy.

In outbound LinkedIn prospecting the idea changes shape. Nobody downloaded your white paper: you go and find the person. The closest thing to an MQL is checking that a profile fits your target before inviting it, plus the signs of interest LinkedIn shows you: a visit to your profile, a reaction to your post, an invitation you received.

Those signs are worth a lot. In MimikFlow's 2026 LinkedIn Prospecting Observatory (published in French), profile visitors who were contacted ended up in an observed meeting 22.4% of the time, against 4.6% for strangers found through search. A profile visitor who fits your target behaves like an excellent MQL.

MimikFlow turns your profile visitors and the people who react to your posts into prospects after checking them against your target. The first message takes into account how the person arrived, and the AI that answers prospects takes over as soon as they reply, all the way to the meeting.