MQL vs SQL: Fix the Handoff That Decides Whether Good Leads Get Worked

Marketing qualified lead

An MQL is a lead that marketing believes is worth a sales conversation. The judgement is made from observable behaviour and fit: the right kind of company, the right role, and some action that suggests intent, such as a pricing enquiry, a demo request or repeated visits. No human from your company has spoken to them yet.

Sales qualified lead

An SQL is a lead a salesperson has engaged with and confirmed is worth pursuing. The judgement is made from conversation: there is a real need, a plausible budget, someone who can decide, and a timeframe. The evidence is what the buyer said, not what they clicked.

Most teams inherit the definitions from a template rather than writing their own, so nobody can say precisely what makes an MQL an MQL at their company. Marketing then reports a volume that sales does not recognise, and both sides argue about lead quality with no shared evidence.

A second problem is a missing middle. Between marketing passing a lead and sales qualifying it sits an acceptance step, where a rep confirms the lead is worth an attempt. Without it, leads nobody contacted are counted as sales rejections.

Who owns the definition

MQL criteria should be agreed jointly and owned by marketing; SQL criteria agreed jointly and owned by sales. Definitions written by one side alone cause most lead-quality disputes.

What evidence is required

An MQL rests on inferred signals. An SQL rests on stated facts from a conversation. That is why no scoring model, however good, can create an SQL on its own.

What happens next, and how fast

An MQL needs a contact attempt within an agreed window, because intent decays quickly. An SQL needs a next step with a date. Both need a written rule for what happens when it does not.

The naming clash

SQL in sales means sales qualified lead. SQL in software means the database query language. If your team includes engineers, spell it out once in any document.

You need it when marketing and sales are separate functions with separate targets, because the label is the contract between them. It is how you agree what marketing is accountable for delivering and what sales is accountable for working. You do not need it when the founder or a small team does both jobs; there the useful distinction is simply between a lead and a qualified opportunity. Adding stage labels that nobody uses creates reporting overhead without improving a single conversation.

Lead status is a field you define, so MQL, accepted and SQL can be modelled as statuses with entry rules attached rather than as folklore. AI lead scoring ranks incoming leads by the signals on the record, which makes the MQL decision consistent rather than dependent on who reviewed the list that morning.

Routing rules assign each qualified lead to an owner immediately, and speed-to- first-contact reporting shows the gap between arrival and first attempt. Leads with no logged activity escalate automatically, which closes the acceptance gap most handoff arguments come from. Scoring, routing and escalation are available on paid plans.

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