MQL vs SQL: Definitions, Differences & How to Set the Threshold

An MQL is a lead that marketing has decided is worth a sales conversation — an SQL is a lead that sales has confirmed is worth pursuing. The distinction sounds simple, but the handoff between these two stages is where most B2B revenue gets lost. Poorly defined thresholds mean SDRs waste time on unqualified leads, or marketing keeps warm prospects too long waiting for a score that never comes.

What is a marketing qualified lead (MQL)?

A marketing qualified lead is a prospect who has demonstrated enough interest or fits enough demographic criteria that the marketing team believes they are worth handing off to sales. The word "qualified" is doing a lot of work here — it means the lead has cleared a minimum bar, not that they are confirmed ready to buy.

MQL status is typically assigned based on a combination of two factors: fit (does this person match your ICP in terms of company size, industry, role, and location?) and engagement (have they done something that signals intent, such as downloading a pricing guide, attending a webinar, or visiting the product page multiple times?). Most marketing teams use a lead scoring model that assigns point values to each action and attribute, and promotes a lead to MQL status when they cross a threshold score.

Common behaviours that trigger MQL status include: requesting a demo, downloading a bottom-of-funnel asset (ROI calculator, comparison guide), visiting the pricing page more than twice in a week, or engaging with a sequence of nurture emails in a short window. The specific triggers should reflect your actual buyers — not a generic template from a CRM vendor's onboarding doc.

What is a sales qualified lead (SQL)?

A sales qualified lead is a prospect that a sales rep has personally reviewed and confirmed meets the criteria for active pursuit. Where MQL status is assigned algorithmically, SQL status requires human judgment. The rep has typically made contact, confirmed budget and authority, and verified that the prospect has a problem your product actually solves.

The most widely used framework for determining SQL status is BANT: Budget, Authority, Need, and Timeline. A prospect becomes an SQL when a rep has confirmed they have the budget to purchase, the authority to make or influence the decision, a genuine need that the product addresses, and a timeline within which a purchase is plausible. Some teams add additional criteria — for example, that the prospect is actively evaluating solutions, not just vaguely interested.

The practical difference between MQL and SQL is accountability. Marketing owns MQL conversion; sales owns SQL conversion. That division of ownership is exactly why the handoff between them breaks down so often — when criteria are vague, each team blames the other for poor quality or slow follow-up.

"The MQL is a promise from marketing to sales: 'we think this is worth your time.' The SQL is sales confirming that promise was accurate. When those two conversations happen in the same room, conversion rates improve dramatically."

— Trish Bertuzzi, Author of The Sales Development Playbook

What is the difference between MQL and SQL in B2B sales?

The core difference between an MQL and an SQL is who has evaluated the lead and what criteria they used. An MQL is a marketing judgment call based on data signals; an SQL is a sales judgment call based on a direct conversation or deep qualification research.

A common misconception is that every MQL should become an SQL. It should not. Salesforce's State of Sales research consistently finds that only a fraction of MQLs are ever confirmed as qualified by sales — in most B2B organisations, the MQL-to-SQL conversion rate sits between 13% and 27%. If your conversion rate is dramatically below that range, the MQL definition is almost certainly too loose. If it is above 30%, the MQL bar may be so high that sales is missing opportunities early enough to influence them.

How do you set the threshold between MQL and SQL?

The threshold between MQL and SQL should be defined jointly by marketing and sales in a formal SLA (service-level agreement). It should not be set by marketing alone (which produces leads sales ignores) or by sales alone (which produces a threshold so strict that the pipeline starves).

Step 1: Agree on your ICP criteria

Before you can define what qualifies a lead, both teams need to agree on who your ideal customer is. Company size, industry vertical, tech stack, geographic market, and buyer persona role are the minimum. This is the fit side of the equation. If a lead does not fit the ICP on these dimensions, no amount of engagement score should promote them to MQL status.

Step 2: Identify your highest-intent behavioural signals

Look at your last 50 closed-won deals and ask: what did those prospects do before they became customers? What pages did they visit? What content did they download? Which sequences did they engage with? The signals that appear most frequently among closed-won deals are your highest-value engagement criteria. Assign your lead scoring points accordingly, not based on what your CRM vendor defaults to.

Step 3: Define SQL criteria explicitly and in writing

SQL criteria should be explicit enough that two different reps reviewing the same lead would arrive at the same conclusion. "The prospect has confirmed a budget of at least $X, has purchase authority or access to the decision-maker, has described a problem our product solves, and has indicated a decision timeline within 90 days" is explicit. "The prospect seems interested" is not.

Step 4: Set a feedback loop between sales and marketing

Every MQL that sales disqualifies should be tagged with a reason: wrong company size, wrong persona, no active need, competitor already locked in. That data goes back to marketing to tighten the scoring model. Without this loop, marketing keeps sending the same low-quality leads indefinitely, and sales keeps complaining about them indefinitely.

Why does MQL to SQL conversion break down in most sales orgs?

The most common cause of a broken MQL-to-SQL pipeline is misalignment on criteria — not process failure. According to Forrester's research on sales and marketing alignment, organisations where sales and marketing agree on the definition of a qualified lead achieve 24% faster revenue growth and 27% faster profit growth over three years. The data is unambiguous: definitional alignment is a revenue lever, not an administrative exercise.

A second common failure is speed. The probability of contacting a lead successfully drops by 10x if you wait longer than five minutes after they submit a form, according to Harvard Business Review research. MQLs that sit in a queue for 24–48 hours before SDR outreach have already cooled significantly. The threshold problem and the speed problem compound each other — when SDRs are buried in low-quality MQLs, they take longer to reach the high-quality ones.

The intent gap: MQLs that were never leads at all

A subtler problem is the intent gap. A prospect who downloaded a generic industry report and visited your homepage twice has expressed curiosity, not intent. Many organisations promote these to MQL status anyway, because they hit a threshold score, even though there is no evidence of purchase intent. The result is SDRs cold-calling people who have no idea why they are being contacted.

This is why some of the highest-converting lead sources never enter the MQL stage at all. Companies that are actively using a competitor product — and are potentially ready to switch — arrive with confirmed budget, a known problem, and category familiarity. They are effectively pre-qualified. Identifying them through tools like Stealery, which lets you search for companies currently using a specific competitor and filter by size, location, and hiring signals, means your SDRs start conversations at the SQL level rather than the MQL level. The qualification work is already done by the market.

What is a good MQL to SQL conversion rate?

A good MQL-to-SQL conversion rate in B2B SaaS is between 13% and 27%. The wide range reflects differences in how organisations define MQL status — a stricter MQL definition produces a higher conversion rate to SQL, while a looser definition produces a lower one. Neither extreme is optimal: too strict and you starve the pipeline; too loose and you waste SDR capacity.

Here is how to interpret your conversion rate:

• Below 10%: The MQL definition is too loose. Marketing is passing too many unqualified contacts. Tighten fit criteria first, then engagement scoring.
• 10–20%: Functional but with room to improve. Review which MQL sources convert at higher rates and invest more in those channels.
• 20–30%: Strong performance. Focus on MQL-to-SQL speed and SQL-to-close rates.
• Above 30%: Either your MQL bar is extremely high (and you may be missing early-stage opportunities) or your ICP is very precise. Audit for missed volume.

Track conversion rate by source (paid, organic, outbound, referral, event) as well as in aggregate. The sources with the highest conversion rates reveal where your best-fit buyers actually come from — and where you should be spending more budget and SDR time.

How do you improve MQL to SQL conversion rates?

The fastest lever for improving MQL-to-SQL conversion is fixing the definition, not adding more volume. More MQLs with a broken threshold just creates more waste. But once the definition is solid, three operational improvements consistently move conversion rates upward.

Shorten response time aggressively

Set an SLA of under 5 minutes for high-intent MQLs (demo requests, pricing page visits, direct contact form submissions) and under 4 hours for medium-intent MQLs. Automate the first outreach if needed, but make sure the automation is personalised enough that it does not feel automated. A routed Slack notification to the relevant SDR, triggered by the CRM on MQL status change, is a practical starting point.

Use lead routing to match fit, not just territory

Routing every MQL to the SDR who owns the geographic territory ignores the fact that some SDRs have deeper expertise in specific verticals, company sizes, or competitive situations. Route fintech leads to the rep who closed three fintech deals last quarter. Route leads from companies using a specific competitor to the rep who knows that competitor's weaknesses best. Fit-based routing consistently improves SQL conversion rates because the first conversation is more relevant.

Build a re-nurture path for disqualified MQLs

Not every MQL that fails to become an SQL is a bad lead forever. A company that was too early-stage six months ago may have grown into your ICP. A prospect who was not the decision-maker may have been promoted. Set disqualified MQLs to re-enter a nurture sequence with a trigger to re-evaluate after 90 days. Done correctly, this recaptures 5–8% of disqualified MQLs as future SQLs without any additional acquisition cost.