An MQL is a lead marketing believes is worth contacting based on behaviour and fit. An SQL is a lead sales has spoken to and confirmed is worth pursuing. The difference is who did the qualifying and on what evidence.
A marketing qualified lead (MQL) is a contact who has met a scoring threshold combining fit criteria, such as company size and job title, with behavioural signals, such as a demo request or pricing page visit. Qualification happens without a human conversation.
A sales qualified lead (SQL) is a contact a sales rep has engaged with and validated against explicit criteria, usually some version of need, authority, budget and timeline. Qualification requires a conversation.
The two labels describe the same person at two different points, separated by a handoff and a verdict.
Most teams use four stages, not two. Skipping the middle one is where the arguments start.
[table]
Stage | Qualified by | Evidence required | Typical conversion to next stage
Lead | Nobody | Contact details captured | 20% to 40% reach MQL
MQL | Marketing, via scoring | Fit attributes plus a behavioural trigger | 10% to 30% reach SQL
SAL (sales accepted lead) | Sales, on review | Sales agrees the lead is worth a call | 40% to 60% reach SQL
SQL | Sales, after contact | A conversation confirming need and fit | 15% to 30% reach opportunity
[/table]
Sales accepted lead is the stage most teams leave out, and it is the one that ends the recurring argument about lead quality. An SAL means sales looked at the lead and agreed to work it. Without that stage, marketing counts a lead as delivered and sales counts the same lead as never worth calling, and neither number is wrong.
An MQL is a prediction. An SQL is an observation.
Marketing qualifies on the evidence available without talking to anyone: the person works at a company matching the ICP, holds a relevant title, and did something that historically correlates with buying. Every part of that is inference from patterns.
Sales qualifies on the evidence only a conversation produces: does this person actually have the problem, do they have budget, is there a timeline, can they bring the rest of the buying committee. None of that is visible in a form fill.
This is why an MQL-to-SQL conversion rate below 10% is almost always a definition problem rather than a lead volume problem. The scoring model is predicting something the conversations keep disproving.
This is the part that matters for anyone running Google or LinkedIn Ads.
Automated bidding optimises toward whatever conversion signal you send it. If the signal is MQLs, the algorithm gets very good at finding people who look like past MQLs. That is only useful if MQLs reliably become revenue, and in most B2B SaaS accounts they do not.
The consequence is predictable and slow. Month one the MQL volume looks good and cost per MQL falls. Month four sales is complaining about lead quality. Month six the account has produced hundreds of MQLs, a handful of opportunities, and no defensible CAC number.
Feeding SQLs back into the ad platforms as the primary conversion action changes what the algorithm learns. Instead of optimising toward form-fillers, it optimises toward the people who survived a sales conversation. That requires offline conversion import, and it is the single highest-leverage tracking change available to a B2B SaaS advertiser.
Two failure modes cover nearly all of it.
The definition was set once and never revisited. A scoring model built when the company sold to 50-person startups still runs after the ICP moved to mid-market. It keeps producing MQLs that no longer match who closes. Review the definition against closed-won data every two quarters.
Marketing and sales never agreed on it. Marketing defines MQL, sales defines SQL, and nobody defined what has to be true for a lead to cross between them. This produces the familiar standoff where marketing reports a strong month and sales reports an empty pipeline. Both are measuring honestly against definitions that do not connect.
Define MQL and SQL together, in one document, signed off by both teams, and feed SQLs back to the ad platforms.
The definition needs three things to be usable: the fit criteria that make a company worth pursuing, the behavioural trigger that indicates timing, and the explicit condition under which sales agrees to work the lead. Anything vaguer produces the standoff.
Once the definitions hold, connect the CRM back to Google Ads and LinkedIn so that SQL status flows into the platforms as a conversion action. The bidding algorithms then optimise against the outcome that matters instead of the proxy that is easy to measure.
A team that reports on MQLs in a board meeting is reporting on a prediction. A team that reports on SQLs and pipeline created is reporting on what happened.
An MQL is qualified by marketing using fit attributes and behavioural scoring, with no conversation involved. An SQL is qualified by a salesperson after actually speaking with the contact and confirming need, authority, budget and timeline. One is a prediction, the other is a verified observation.
Roughly 10% to 30% in most B2B SaaS categories, varying with ACV and how strict the MQL definition is. Consistently below 10% points to a scoring model that is predicting the wrong thing, rather than to a shortage of leads at the top of the funnel.
A sales accepted lead is a lead sales has reviewed and agreed to work, before any conversation happens. It is worth adding because it separates leads sales rejected outright from leads sales worked and disqualified. Without it, lead quality disputes have no evidence on either side.
SQLs, once the account has the volume to support it. Optimising toward MQLs teaches the bidding algorithm to find more form-fillers. Optimising toward SQLs teaches it to find people who survive a sales conversation, which is what actually produces pipeline.
Through offline conversion import, which matches CRM stage changes back to the original click using a stored click identifier. Once configured, an SQL created 45 days after the click is still attributed to it, which is essential given B2B sales cycles.
Related: Product Qualified Lead · Pipeline Velocity · Offline Conversion Import · Smart Bidding
If your MQL numbers look healthy and your pipeline does not, the definition is usually the place to start rather than the campaigns.
ScalixAI is a performance-driven Google Ads agency specializing in helping high-growth, AI-first companies scale with predictable, profitable customer acquisition. Founded by an ex-Googler with 9 years of insider advertising experience, we manage the entire Google Ads lifecycle—from campaign strategy and account setup to conversion tracking, analytics, and ongoing optimization. Our data-centric, AI-powered approach ensures you know exactly which campaigns are working, why they’re working, and what to do next to outpace your competitors.ScalixAI is a performance-driven Google Ads agency specializing in helping high-growth, AI-first companies scale with predictable, profitable customer acquisition. Founded by an ex-Googler with 9 years of insider advertising experience, we manage the entire Google Ads lifecycle—from campaign strategy and account setup to conversion tracking, analytics, and ongoing optimization.
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