SQL (Sales Qualified Lead): Definition and Meaning

What does SQL stand for?

SQL stands for Sales Qualified Lead. It’s the stage in the B2B pipeline where a lead stops being marketing’s responsibility and becomes sales’ commitment. An MQL is marketing saying “this one looks promising.” An SQL is sales saying “I’ve talked to them, and yes, this is real.”

The distinction matters because it creates accountability. Marketing can generate thousands of MQLs, but if sales reviews them and only qualifies a handful as SQLs, something is broken upstream — either the lead scoring model, the ICP definition, or both. The MQL-to-SQL conversion rate is one of the most revealing metrics in revenue operations for exactly this reason.¹

How a lead becomes an SQL

There’s no universal checklist. What qualifies a lead as an SQL varies by company, deal size, and sales motion. But the process usually follows a pattern.

A lead enters the system through marketing — form fills, content downloads, webinar signups, demo requests. The marketing automation platform scores them based on firmographic fit and behavioral signals. When they cross the MQL threshold, they get routed to an SDR or AE for outreach.

This is where qualification happens. The sales rep conducts a discovery call and evaluates the lead against frameworks like BANT (Budget, Authority, Need, Timeline). Some organizations use a Sales Accepted Lead (SAL) stage in between, where sales acknowledges receipt before doing deeper qualification.

If the rep confirms the lead has a real need, can make or influence the buying decision, and has some level of timeline or urgency, the lead gets tagged as an SQL and moves into the active pipeline.

Why does the human check matter? Because lead scoring models are imperfect. A VP at a target-size company who downloaded your pricing guide looks great on paper. But if they were researching for a competitor analysis and not evaluating vendors, no scoring model would catch that. The SQL designation exists because some qualification requires a conversation.

SQL vs. MQL: where sales takes the wheel

The MQL-to-SQL handoff is the most friction-prone junction in B2B revenue operations. Marketing says “we sent you qualified leads.” Sales says “those leads were junk.” The argument has been running since the first CRM was installed.

The root cause is usually a definition gap. Marketing and sales are working from different criteria for what “qualified” means. Forrester’s B2B Revenue Waterfall framework addresses this by creating shared stages with explicit entry and exit criteria that both teams agree to.²

Here’s a practical distinction: an MQL is a signal. An SQL is a commitment. When marketing flags an MQL, they’re saying “the data suggests this lead is worth your time.” When sales confirms an SQL, they’re saying “I’ve verified this is a real opportunity and I’m putting it in my pipeline.” That commitment changes the economics — the rep is now investing hours in demos, proposals, and follow-up calls.

The gap between the two stages is also where Product Qualified Leads (PQLs) fit in for product-led growth companies. Instead of relying on marketing engagement signals alone, PQLs use actual product usage data as a qualification input. A user who’s hit feature limits or invited teammates is a different kind of “qualified” than someone who downloaded an ebook.

How marketing ops tracks SQL performance

For revenue operations teams, SQLs aren’t just a pipeline stage — they’re a measurement point. Here’s what to watch:

MQL-to-SQL conversion rate. This is the headline metric. Industry benchmarks vary widely (13% to 50%+ depending on how you define each stage), but the trend matters more than the absolute number. If your conversion rate is dropping month over month, either lead quality is declining or your qualification criteria need adjustment.

SQL-to-opportunity conversion. Once a lead is tagged as an SQL, how often does it become a real deal with a dollar amount attached? Gartner’s benchmark: top-performing SDR teams convert 59% of SQLs to opportunities.¹ If you’re well below that, the SQL definition might be too loose.

Speed to follow-up. How quickly does sales respond to new MQLs that need qualification? Research consistently shows that response time within the first hour dramatically impacts conversion rates.

Rejection reasons. When sales declines to qualify a lead as an SQL, why? Track the reasons — bad fit, no budget, wrong timing, already in pipeline. This data feeds directly back into scoring model refinements and helps marketing send better leads over time.

The feedback loop between SQL outcomes and lead scoring is where marketing ops earns its keep. Without it, you’re optimizing a scoring model with no signal about whether it actually predicts revenue.

Frequently Asked Questions

What does SQL stand for?

SQL stands for Sales Qualified Lead. It refers to a prospect that has moved past the marketing qualification stage and has been reviewed by the sales team as a real buying opportunity. Unlike a Marketing Qualified Lead (MQL), which is scored based on engagement and fit data, an SQL has been validated through direct conversation — typically a discovery call where a rep confirms need, authority, and buying intent. The SQL designation is sales saying this one is worth my time.

What is the difference between an SQL and an MQL?

An MQL (Marketing Qualified Lead) is qualified by marketing based on engagement signals and firmographic fit — things like content downloads, pricing page visits, and company size. An SQL (Sales Qualified Lead) is qualified by the sales team after direct outreach, usually a discovery call. The key difference is who owns the judgment: marketing qualifies MQLs using data and scoring models, while sales qualifies SQLs through conversation. The MQL-to-SQL conversion rate is one of the most watched metrics in B2B revenue operations because it reveals whether marketing and sales agree on what qualified actually means.

Who decides when a lead becomes an SQL?

The sales team makes the final call. Typically, an SDR (Sales Development Representative) or AE (Account Executive) reviews inbound MQLs, conducts initial outreach or a discovery call, and then decides whether the lead meets the company's SQL criteria. Most organizations use a qualification framework like BANT (Budget, Authority, Need, Timeline) to standardize this decision. The important part is that marketing and sales agree on the criteria before leads start flowing — otherwise you get the classic finger-pointing where marketing claims they sent great leads and sales says they were all junk.