Sales Qualified Lead (SQL)
What is SQL?
An SQL (sales qualified lead) is a prospect that sales has vetted as a real opportunity, typically confirming budget, authority, need, and timeline before it enters the pipeline as an opportunity.
An SQL is a lead a salesperson has looked at and accepted as a real potential customer. It sits between the lead handed over by marketing or the product and the opportunity, which carries a deal value. The stage only works if sales applies the same written criteria every time. Otherwise SQL counts and conversion rates drift in meaning from month to month.
Also known as: Sales Qualified Lead
How to use it
- Write down the acceptance criteria sales will use. Usually that’s a confirmed need, a buyer who can approve the purchase, an available budget and a realistic timeline. BANT and MEDDIC are places to start; you don’t have to adopt either.
- Record every lead that sales reviews as accepted (SQL) or rejected, with a reason for each rejection. The reasons show marketing and product which leads not to send.
- Track SQLs by the source that produced them (MQL, PQL, outbound, referral) and follow how many become opportunities and closed deals. That’s how you find out which sources are worth the effort.
Worked example
Hypothetical worked scenario
In a fictional quarter, marketing passes 400 MQLs to sales and the product passes 60 PQLs. Sales accepts 80 of the MQLs and 36 of the PQLs as SQLs. 24 of the MQL-sourced SQLs and 18 of the PQL-sourced SQLs become opportunities.
MQL to SQL = 80 ÷ 400 = 20%. PQL to SQL = 36 ÷ 60 = 60%. SQL to opportunity: MQL source 24 ÷ 80 = 30%; PQL source 18 ÷ 36 = 50%.
PQLs are fewer, but more of them make it through each stage. One blended SQL number would hide the fact that the two sources need different amounts of sales effort.
Turning rejections into fixes
In a fictional quarter, sales reviews 500 MQLs, accepts 90 as SQLs (18%) and records a reason for each of the 410 it rejects.
| Rejection reason | Leads | Share of rejections | Who acts | What to change |
|---|---|---|---|---|
| Outside the ideal customer profile (company too small, wrong industry) | 250 | 61% | Marketing | Add company-size and industry requirements to the MQL rule. |
| Good fit, but no project this year | 120 | 29.3% | Marketing | Return the lead to nurture with a review date. Do not count it as lost. |
| Duplicate or existing customer | 25 | 6.1% | Sales operations | Match leads against existing CRM accounts before routing. |
| Student, job seeker or competitor | 15 | 3.7% | Marketing | Filter by form answers and email domain before scoring. |
| All rejections | 410 | 100% |
The acceptance rate alone would suggest sales is not following up. The reasons show that 61% of rejections are about fit, which is fixed in the MQL rule, not by sales working harder. Leads with no current project are not failures either; counting them as lost understates next year's pipeline.
What the result tells you
When the MQL-to-SQL rate falls, marketing and sales usually disagree about who the ideal customer is, or someone has tightened the criteria without saying so. Lots of SQLs but few opportunities suggests acceptance is too easy. Read SQL numbers next to the rejection reasons. The reasons explain the trend.
Assumptions and common mistakes
- Be careful comparing SQL conversion rates across companies. Each one sets its own qualification criteria, so the same rate can describe very different funnels.
- Don’t let reps mark leads as SQLs to hit an activity target. Tie the stage to the written criteria and check a sample every month.
Compare related measures
Frequently asked questions
What is the difference between an SQL and an opportunity?
An SQL is a lead sales has accepted as worth pursuing. An opportunity is a qualified prospect with a specific deal in progress, usually with an expected value and close date.
Who decides whether a lead is an SQL?
Sales does, using criteria agreed with marketing. Without that shared definition, the MQL-to-SQL conversion rate doesn’t tell you much.
Sources and methodology
These references explain the methods. The worked scenarios use hypothetical figures. They are not company results or current market benchmarks.
- HubSpot Knowledge Base: Use contact and company lifecycle stages
Defines lead, MQL, SQL and opportunity as successive stages, with the qualifying criteria left to each company.
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