MQL vs SQL: Where Marketing Hands Off to Sales

Two acronyms cause a surprising amount of arguments in growing companies: MQL and SQL. Marketing says it passed over great leads. Sales says they were not ready. Often, nobody ever wrote down what “ready” means.

Here is what the terms mean, and how to set them up so they actually help.

What is an MQL?

A marketing qualified lead is a contact who has shown enough interest and matches enough of your ideal customer profile to be worth passing to sales. They might have requested a quote, attended a webinar, downloaded a detailed guide or visited a pricing page several times.

The key point is that marketing decides, based on signals it can see.

What is an SQL?

A sales qualified lead is a contact that sales has spoken with, or assessed, and agrees is a real opportunity. They have a need you can solve, some authority or influence, a realistic budget and a timeline.

The key point is that sales decides, based on a conversation.

MQL vs SQL side by side

	MQL	SQL
Who decides	Marketing	Sales
Based on	Behaviour and profile fit	Conversation and confirmed need
Question answered	Is this person interested and a fit?	Is this a real opportunity?
Typical signals	Form fills, content engagement, page visits	Budget, timing, authority, clear problem
Next step	Handoff to sales	Proposal, demo, negotiation

Why the handoff goes wrong

• Different definitions. Marketing counts a download as qualified. Sales wants a budget and a timeline.
• No shared criteria. Each side applies its own judgement.
• Volume over quality. Marketing is measured on lead count, so it sends everything.
• Slow follow-up. Good MQLs go cold waiting for a reply. See speed to lead.
• No feedback loop. Sales never tells marketing why leads were rejected.

How to define an MQL

1. Describe your ideal customer. Industry, size, role, location, problem.
2. Pick the signals of intent. Requests for a quote or call, pricing page visits, repeated engagement.
3. Score or rule it. Some teams use a points system. Others use simple rules, such as “fits the profile and requested a quote.”
4. Write it down. Share it with both teams.

For small teams, keep it simple. A short list of clear rules beats a complicated scoring model nobody trusts.

How to define an SQL

A common approach is to confirm four things in a first conversation:

1. Need. A real problem you can solve.
2. Budget. A realistic range, or the ability to get one.
3. Authority. The person decides or influences the decision.
4. Timing. A reason to act within a sensible window.

Teams use names for this, such as BANT, but the labels matter less than agreeing on the questions.

How to set the handoff

Agree on the rules together

Marketing and sales should write definitions jointly. If one side imposes them, the other will ignore them.

Set a response promise

Agree how quickly sales will follow up on an MQL, and measure it. This is the part that most often breaks.

Give sales context

Pass along where the lead came from, what they viewed and what they asked for. A rep who knows what attracted the lead can start a better conversation.

Close the loop

When sales rejects a lead, record why: not a fit, no budget, wrong timing, never replied. Review the reasons monthly, and use them to improve targeting and offers.

Review the numbers together

Track the share of MQLs that become SQLs, the share of SQLs that become customers and how fast each step happens. If the first number is low, marketing may be sending the wrong people. If the second is low, look at the sales process.

Is the MQL stage always needed?

Not always. A small company with a simple sales process may not need a formal MQL stage at all. If every form fill goes to a salesperson who qualifies it, that works. As volume grows and specialisation increases, a clear MQL-to-SQL handoff becomes more useful.

What does research say about the handoff?

Handoffs fail on speed and clarity. The Harvard Business Review study The Short Life of Online Sales Leads found that the time a company took to respond to a web lead strongly affected whether it was qualified. If your MQL waits two days for sales to act, the label is meaningless.

Clarity comes from shared definitions. Gartner publishes research on sales and buying behaviour at The B2B Buying Journey: Key Stages and How to Optimize Them, and a recurring theme is that buyers now do much of their own research before talking to a seller. That is why an MQL should reflect real signals of research and fit, and an SQL should reflect a confirmed need and a conversation.

Finally, close the loop with your ad platforms. About offline conversion imports shows how to import the outcome of a lead into Google Ads so that campaigns can learn from SQLs and sales, not just form fills.

Put the definitions in writing and review them each quarter. For the numbers that sit above them, see cost per lead vs cost per closed deal, and for a full audit of the handoff, see why leads do not close. We also cover this in our sales pipeline service, or you can book a free diagnostic.

How this connects to your reporting

MQL and SQL counts are useful, but they are still steps on the way. The number that matters is closed deals and what they cost to win. See cost per lead vs cost per closed deal. And if leads are not closing, work through the marketing vs sales checklist.

For how we set this up in practice, see our pipeline and follow-up service.

A simple written definition

Write one sentence for each stage and put it where both teams can see it. For example: an MQL matches our ideal customer and took a meaningful action, and an SQL has confirmed a need, a budget and a timeline in a real conversation.

Review the definitions every quarter against the deals you actually closed, and change the criteria when the data shows that a signal you valued did not predict a sale at all.