MQL vs SQLand the handoff between them
Short answer
An MQLis a contact marketing believes is worth a salesperson’s next hour. An SQL is a contact a named salesperson has qualified in a discovery conversation and agreed to forecast, which is a later and harder test than simply accepting the handoff. The definitions are the easy part, and almost every team already has them written down somewhere. What breaks is the handoff, because the two teams never agreed in writing who counts as in-ICP, what action promotes a lead, what sales must do within how many hours, and what happens to a lead sales rejects. Agree those four in writing, sign them, and the argument about lead quality mostly stops.
Written by WeeGrow. The worked example further down is arithmetic chosen to show the shape of the problem, not an industry benchmark. Where no trustworthy benchmark exists, this page says so instead of estimating one.
At a glance
- Set by
- MQL: MarketingSQL: Sales only
- Triggered by
- MQL: An observable actionSQL: A completed discovery
- Answers
- MQL: Worth an hour?SQL: Worth forecasting?
- Clock starts
- MQL: On promotionSQL: On qualification, not acceptance
The four fields to agree in writing
- 01Who counts as in-ICP
- 02What action promotes a lead
- 03What sales must do, and within how many hours
- 04What happens to a rejected lead
Section 02 turns these into a one page contract both teams sign.
Marketing qualified lead vs sales qualified lead
Both terms describe the same contact at two different moments, which is why swapping definitions back and forth helps nobody. The useful question is not what the letters stand for. It is who is allowed to change a lead’s status, on what evidence, and what the other team owes them the moment it changes.
What is an MQL?
A marketing qualified lead is a contact who matches your ideal customer profile and has done something observable that suggests a sales conversation is worth having: replied to an outbound message with a real question, asked for pricing, booked a slot, or come back to a high intent page more than once. The load bearing word is observable. If your MQL rule contains the phrase “shows interest”, it is not a rule, it is a mood, and it will give a different answer every week depending on who is looking at the dashboard and what their target is.
What is an SQL?
A sales qualified lead is an MQL that a named salesperson has accepted, taken through a short discovery conversation that confirms the problem, the authority and some sense of timing, and then agreed to forecast. Acceptance is the start of that process rather than the end of it: in the worked example below, the loose column has sales accepting 72 leads and only 32 of them reaching SQL. The status change belongs to sales. That single rule ends the most common version of this fight, which is marketing marking its own homework by promoting contacts straight into a pipeline stage sales never agreed to.
Some teams insert a sales accepted lead, or SAL, between the two, purely to record the moment sales takes the lead off marketing’s hands. It is worth having even if you never report it to anyone, because it is the only place in the funnel where a rejection reason can be captured cleanly.
| Dimension | MQL | SQL |
|---|---|---|
| Who owns the status | Marketing. | Sales, and only sales. |
| What it claims | This contact fits our profile and has done something that suggests a conversation is worth having. | A named salesperson has spoken to this contact, confirmed there is a deal here, and agreed to forecast it. |
| The trigger | A named, observable action: a reply asking about price, scope or timing, a quote request, a booked slot, a repeated high intent visit. | An accepted handoff followed by a short discovery conversation confirming problem, authority and some sense of timing. Acceptance on its own is not enough. |
| The question it answers | Is this worth a salesperson's next hour? | Is this a deal we are prepared to forecast? |
| Who acts next, and when | Sales, inside an agreed response window measured in hours, not days. | The same salesperson, who now owns the outcome and the stall date. |
| How it fails | A volume target pulls the definition wider until the count rises and the close rate falls. | Leads sit accepted and untouched because nobody agreed what accepted obliges you to do. |
The one page both teams sign
A qualification contract is a single page, agreed by whoever runs marketing and whoever runs sales, that settles four questions in writing. It is not a policy document and it is not a workshop output. If it does not fit on one page and live in the CRM where the statuses actually change, it will not survive a busy quarter.
| Field | The question it settles | A weak answer, guaranteed argument | A workable answer you can audit |
|---|---|---|---|
| 01Who counts as in-ICP | Which accounts and which roles are eligible at all? | B2B companies that need what we sell. | A written filter anyone can apply without asking: size band, country, industry, named job titles, plus an explicit disqualifier list. |
| 02What action promotes a lead | What exactly turns a contact into an MQL? | The lead shows interest or engages with our content. | A named event with its context: replied to an outbound message asking about price, scope or timing; requested a quote; booked a slot. One list, no exceptions added by hand. |
| 03What sales must do, and within how many hours | What does the receiving team owe every promoted lead? | Sales will follow up. | First touch within four working hours. Three attempts across two channels inside five working days. Every outcome logged, including no answer. |
| 04What happens to a rejected lead | Where does a no go, and who hears about it? | It goes back to marketing, probably. | A reason picked from a fixed list, returned to nurture or recycled to a named owner, and counted weekly. Rejection reasons are the only input allowed to change fields 01 and 02. |
The signature block
- Signed by
- One name from marketing, one from sales. Not a committee.
- Reviewed
- Monthly, against rejection reasons. One field changed at a time.
- Lives in
- The CRM field that sets the status, not a slide deck.
Field 04 is the one teams skip and the one that does all the work. Without a fixed rejection list, a rejected lead is simply a lead that vanished, and marketing carries on producing more of exactly the same thing. With it, you get a weekly count of why sales said no: wrong company size, wrong role, no stated problem, unreachable, already a customer, competitor. Three or four weeks of that data will tell you more about your targeting than any benchmark you can buy.
Field 03 is the one that needs a number rather than an adjective. “Fast” is not a commitment, four working hours is. And if your CRM cannot record a rejection reason against a lead today, that is the first job, not the last: we cover it in sales pipeline and CRM setup, because a contract nobody can measure is a conversation, not a contract.
The same 1,000 contacts, two definitions
Here is why a higher MQL count can be a worse month. Both columns below are the same 1,000 contacts, worked in the same month by the same team. The only difference is where the promotion line sits. The numbers are round on purpose: this is arithmetic showing the shape of the problem, not a benchmark, and your own figures will differ.
| Line | Loose definition | Strict definition |
|---|---|---|
| Contacts worked in the month | 1,000 | 1,000 |
| Flagged as MQL | 180 | 65 |
| MQL rate | 18% | 6.5% |
| Accepted by sales | 72 | 59 |
| Acceptance rate, the SAL moment | 40% | 91% |
| Reached SQL | 32 | 30 |
| MQL to SQL conversion rate | 17.8% | 46.2% |
| Closed deals | 6 | 6 |
| SQL to closed deal | 18.8% | 20.0% |
| MQL to closed deal | 3.3% | 9.2% |
| Sales hours spent qualifying, at 20 minutes per MQL | 60.0 | 21.7 |
| Qualifying hours per closed deal | 10.0 | 3.6 |
The left hand column is the one that gets celebrated in the monthly review. It shows 180 MQLs against 65, nearly three times the lead volume, on a chart that goes up. It also shows sales accepting fewer than half of them, spending 60 hours qualifying instead of 22, and reaching 32 SQLs against 30. That is two extra real opportunities bought with 115 extra promoted leads, and both columns close exactly the same six deals. The lead count marketing reports improves. Every rate that ends in revenue gets worse: 17.8% of loose MQLs reach SQL against 46.2% of strict ones, and 3.3% become deals against 9.2%.
Price the gap and it stops being an abstraction. The 20 minutes per MQL is a salesperson’s time rather than an SDR’s, so costing it at the market rate for an in-house SDR, $5,500 to $8,500 per month fully loaded, is deliberately the cheapest hour we can defend putting on it. Across a 160 hour month that is roughly $34 to $53 an hour, so the 38 extra hours in the left hand column are something like $1,300 to $2,000 of selling time spent to produce nothing, and the true figure rises wherever a closer’s hour costs more than an SDR’s. That is market range arithmetic rather than a published benchmark, and the underlying ranges are set out in our B2B lead generation cost breakdown.
There is a second cost, slower and more expensive. Once sales has rejected 108 leads in a month, it stops reading the queue carefully, and the good leads sitting in it get treated exactly like the bad ones. That is the point at which the handoff is genuinely broken, and no amount of extra volume at the top fixes it. It is also precisely the failure we get called in for: marketing handing over MQLs that sales cannot close, on both sides of an argument nobody wrote down.
MQL to SQL conversion rate, and the benchmark nobody publishes
The formula is trivial. Take the leads a named salesperson qualified into SQL in a period, divide by the MQLs created in the same period, multiply by 100. In the worked example above, the loose definition converts 32 of 180, or 17.8%, and the strict one 30 of 65, or 46.2%. Neither figure means anything on its own, because the denominator was chosen by whoever wrote the promotion rule.
Then watch the numerator, because that is where the substitution happens. The acceptance rate, the SAL moment when sales takes a lead off marketing’s hands, reads 40% and 91% on those same two columns, and it gets reported as an MQL to SQL rate often enough to be worth naming: it is the flattering number and it arrives weeks earlier. The two measure different things. Acceptance says somebody agreed to look. SQL says somebody agreed to forecast. A rate quoted without saying which of the two it counts is not a rate, it is a headline.
So here is the honest answer to “what is a good MQL to SQL conversion rate”. There is no reliable published industry benchmark for it, and there is none for reply to meeting either. Vendors quote figures for both, but they arrive without a stated sample or a stated denominator, which is how two agencies can quote you numbers two or three times apart, as the two columns above are, and both be accurately describing their own arithmetic.
Compare that with the outbound numbers that at least carry some of their working. LinkedIn connection acceptance runs at 28.5% in Expandi’s data across 13,218,869 requests, and at 26% in Belkins’ 2025 dataset. Reply rates come in at 7.2% for Belkins across 15.1 million contacts and 10.4% for Expandi across 6,730,447 messages, both counted as replies per message sent rather than per accepted connection. Acceptance itself ranges from 17.5% to 40.1% across industries in Expandi’s data, which is why even a well sourced blended average is a weak planning input. All five figures name a source and a denominator, checked August 2026. Three of them also name a sample size. The Belkins 26% and the Expandi industry range are published without one, and we have left that gap showing rather than fill it with a number nobody can check. Ask for all three the next time an MQL to SQL rate is quoted at you, and watch how quickly the number becomes negotiable.
The practical answer is to instrument your own. Run the contract for one quarter without touching the promotion rule, record acceptance and rejection reasons against every lead, and the trend line you end up with is worth more than any industry average, because it is measured against your definition and your buyers. We take the same denominator problem apart stage by stage on the LinkedIn lead generation funnel page, including which stages have published data and which have none at all.
Which definition should you write?
There is no universally correct strictness. There is a correct strictness for your deal size and the shape of your team, and it follows one rule: the more a salesperson’s hour is worth, the stricter the promotion line should be. Deal size sets the value of the hour, team shape decides whether a handoff exists at all, and volume decides whether anyone can still sanity check leads by hand.
Write a looser definition if
- Your average deal is under roughly $5,000 and a call costs little to run
- You are still learning who actually buys, so a wrong conversation is research
- Sales capacity is idle and a missed lead costs more than a wasted hour
- One person does both the marketing and the selling, so there is no handoff to break
- You get fewer than about 20 conversations a month and can read every one by hand
Write a stricter definition if
- Your average deal is comfortably above $5,000 and a discovery call costs real selling time
- Two or more closers exist and their calendars are the binding constraint
- Marketing and sales report to different people, so the handoff is a real boundary
- Volume is high enough that nobody reads every lead individually any more
- Your lead count is rising while your close rate falls, the signature of a drifting promotion line
Whichever side you land on, review the line monthly against rejection reasons and change one field at a time, so you can tell what actually moved. If the handoff is the problem rather than the volume, that is the work itself: B2B appointment setting hands over meetings already qualified against a written rule, and an outsourced SDR puts a named person on the response window so the four hour clock is somebody’s actual job rather than an aspiration. If the top of the funnel is thin as well, fix the definition while the volume builds: the conversations we start on LinkedIn are promoted against the same written rule, which is why what we report back is the acceptance rate with its denominator printed next to it, and the SQL rate your own salespeople set underneath it, rather than a lead count on its own. On the evidence above, an acceptance rate quoted without both of those is the flattering half of the story, and we would rather hand you the half that ends in revenue.
Thirty minutes, free either way. We will read your current definitions and rejection reasons and tell you which of the four fields is missing. Any engagement is scoped and quoted after that audit.
When a contract is the wrong fix
A qualification contract fixes a definition problem. It does not fix an empty pipeline. If you are producing five MQLs a month, arguing about which three were real is the wrong meeting: you have a demand problem, and tightening the definition will shrink the dashboard and change nothing else. Create more qualified conversations first, then write the contract once there is enough volume to argue about.
WeeGrow is also the wrong answer in two specific cases. If your market closes self serve without a human conversation, none of this applies to you. And WeeGrow works done with you, which means somebody on your side has to own the sales half of the contract: the response window, the acceptance call, the rejection reasons. If nobody can own that, a done-for-you agency at the $1,500 to $5,000+ per month market rate is a more honest fit than we are, and we will say so on the call.
Asfand, who runs WeeGrow, spent 8+ years leading B2B sales, most recently as Head of Corporate Sales at InvoZone, with $30M+ in cumulative closed revenue and enterprise deals up to $3M+ across SaaS, technology, fintech, healthcare and telecom. Most of that time was spent on the receiving end of this handoff, which is the entire reason this page argues for the contract rather than the definitions.
MQL vs SQL, answered
An MQL is a contact marketing has promoted because they fit the profile and did something observable that suggests a sales conversation is worth having. An SQL is that same contact after a named salesperson has spoken to them, confirmed the problem, the authority and some sense of timing, and agreed to forecast the deal. Accepting a lead off marketing's hands is an earlier and weaker event than that: acceptance says somebody agreed to look, not that somebody agreed to forecast. The difference that matters is ownership: marketing sets the MQL, sales sets the SQL, and neither team gets to set the other's status. If your CRM lets marketing promote a contact straight to SQL, you do not have two stages, you have one stage with a longer name.