MQL vs SQL: what separates them, and the handoff where most leads are lost
An MQL has shown interest. An SQL has asked for a conversation. The definitions, a side-by-side table, and the handoff rules that stop good leads going cold.
An MQL, or marketing qualified lead, is someone whose behaviour suggests interest: they downloaded a guide, came to a webinar, or visited your pricing page. An SQL, or sales qualified lead, is someone a salesperson has confirmed has a real need and is willing to talk about it. The difference between them isn't really a score. It's whether the person has asked for, or agreed to, a conversation.
MQL vs SQL comes down to who decided, and on what evidence. Marketing marks a lead as an MQL based on fit and activity, like downloads and page visits. A lead becomes an SQL when sales confirms a real need, someone who can buy, and a reason to talk now, usually in a first conversation. How quickly sales follows up often matters as much as how the lead was scored.
MQL, SAL and SQL definitions
Marketing qualified lead (MQL). A lead that meets criteria marketing has set, usually a mix of fit (the right industry, company size or job title) and activity (content downloaded, emails clicked, pages visited). An MQL hasn't necessarily asked to talk to anyone.
Sales accepted lead (SAL). An MQL that sales has reviewed and agreed to work. Not every company uses this stage, but it's useful because it makes sales say yes or no to each MQL, rather than quietly ignoring the ones they don't like.
Sales qualified lead (SQL). A lead sales has confirmed is worth pursuing as a deal: there's a need the product meets, someone who can buy, and a reason to act before long. A checklist like BANT is the usual tool for this step.
Opportunity. An SQL that has entered the pipeline with a value and a stage attached.
MQL vs SQL: what's the difference?
Marketing decides a lead is an MQL, usually with a scoring rule. Sales decides it is an SQL, usually in a conversation.
| MQL | SQL | |
|---|---|---|
| Who decides | Marketing, usually by a scoring rule | Sales, usually in a conversation |
| Based on | Fit plus activity | Confirmed need, buyer and timing |
| What the lead has done | Consumed something | Asked for, or agreed to, a conversation |
| Typical next step | Nurture, or pass to sales for review | Discovery call or proposal |
| What goes wrong | Scoring rewards curiosity | Sales qualifies loosely to hit a target |
Why lead scores confuse interest with intent
Most MQL definitions are points systems: ten points for a download, five for each email click, twenty for a visit to the pricing page. Pass the threshold and the lead goes to sales.
The trouble is that points measure how much someone has read, and reading is what students, competitors, job applicants and the merely curious do all day. The person who downloaded four guides and came to a webinar might be researching a blog post. The person who visited once and replied with a question about your integration has asked exactly what a buyer asks.
The fix isn't a cleverer scoring model. Treat a hand-raise (a demo request, a reply asking about price, a "can we talk?") as an SQL candidate straight away, whatever its score. Treat accumulated activity as a reason for marketing to keep nurturing, not as a reason for sales to call.
Why the MQL to SQL handoff matters so much
Scoring gets most of the attention, but a lot of leads are lost in the gap between marketing and sales, simply to delay.
A widely cited Harvard Business Review study, The Short Life of Online Sales Leads, found that firms that tried to contact a lead within an hour were nearly seven times as likely to qualify it as firms that waited even an hour longer, and more than sixty times as likely as firms that waited a day or more. The research is from 2011 and the exact figures will have shifted, but the direction is hard to argue with.
So the handoff needs three things, written down and agreed by both teams:
- A shared definition. Exactly what makes an MQL, and what makes an SQL, in writing, with a real example of each.
- A response time. How quickly sales contacts a new MQL or hand-raise. Hours, not days, and for a hand-raise, as close to immediately as you can manage.
- A way back. What happens to leads sales turns down. They should go back to marketing with a reason attached, not vanish. Those reasons are some of the most useful feedback marketing will ever get on its scoring.
An example week of leads
Here's an illustration, with made-up numbers, of how the two approaches differ. Take a small accounting software company that gets 60 new leads in a week.
- The scoring model marks 14 as MQLs: people who downloaded a couple of guides and clicked a few emails.
- Sales reviews them the next morning, accepts 9 and rejects 5: two students, a competitor, a consultant researching a report, and a company far too small to buy.
- First calls with the 9 produce 3 SQLs.
- Meanwhile, 4 leads that never came near the threshold replied to a nurture email with a question: "does this handle multi-currency?", "can it import from our current tool?". Under the scoring model they stay in nurture. Treated as hand-raises and called the same day, 3 of them become SQLs.
In that example, half the week's SQLs came from outside the MQL queue. The scoring model wasn't wrong about the 14; it simply never looked at the 4, because they hadn't read enough to earn a call.
What is MQL to SQL conversion rate?
MQL to SQL conversion rate is the share of MQLs that sales qualifies as SQLs over a period:
SQLs created from MQLs ÷ MQLs created, over the same period
Benchmarks for it get quoted a lot and mean very little, because every company defines an MQL differently. A company whose MQL requires a demo request will convert far more of them than one whose MQL is "downloaded anything". Compare your rate with your own previous quarters rather than with someone else's industry average.
What usually improves it: responding faster, treating hand-raises as their own category, and tightening the fit criteria so obviously wrong companies never become MQLs in the first place.
Where outbound fits
Outbound prospects never go through the MQL stage, because nobody downloaded anything. They go from a researched account straight to a conversation, and the qualifying happens before the first message rather than after a form fill.
That's why outbound needs its own kind of qualification, based on what you can see from outside the company: fit, whether they already solve the problem, and a dated reason to get in touch. That process is covered here. If you track both in one CRM, keep the source on every record. Outbound and inbound SQLs tend to convert at very different rates, and mixing them hides the difference.
When MQL and SQL are the wrong model
If you have no marketing team, you probably don't need a scoring model at all. A founder with fifty leads a month needs two lists: people who asked to talk, and everyone else. Talk to the first list the same day.
If your product is bought without a conversation, the model doesn't really apply either. Self-serve products qualify by usage rather than calls, and the nearest thing to an SQL is an account whose usage suggests it should talk to someone about a bigger plan.
And if marketing and sales argue about every lead, that's a definition problem that no scoring will fix. Sit down together with ten real leads from last month and label each one. The definitions that come out of that hour are worth more than any benchmark.