A sales qualified lead, or SQL, is a contact that a salesperson has looked at and decided deserves real selling time. Marketing may have generated the lead, scored it and passed it along, but it only becomes an SQL once someone on the sales side agrees that the company can buy, needs what you sell and is likely to decide within a reasonable window.
The term sits one step after the marketing qualified lead in a typical B2B pipeline. You will see it in CRM stages, in monthly reports and in the agreement between marketing and sales about what a good lead looks like. Because each company writes its own criteria, an SQL at one business can mean something quite different at another.
Mechanics
How a lead earns the label
Most teams qualify with a short checklist. A common one covers budget, authority, need and timing: does the buyer have money set aside, is the person able to sign off or influence the decision, is there a problem your product solves, and is a purchase planned in the next few months. Some teams replace this with their own questions about company size, industry or current tools.
The handoff usually works like this. Marketing collects a form fill or a demo request, a scoring rule flags the contact as an MQL, and a sales rep or a sales development rep then reviews the record, often with a short discovery call. If the answers pass the checklist, the rep changes the stage to SQL and opens an opportunity. If not, the lead goes back to nurturing with a note explaining why.
The share of MQLs that become SQLs depends on lead source, form design and how strict the checklist is. Tightening the criteria lowers the count but usually raises the win rate further down.
Example
A worked example of SQL math
Assume a software company spends 9,000 on paid search in a quarter and collects 300 form submissions, a cost per lead of 30. Scoring flags 120 of them as MQLs. Sales reviews all 120 and accepts 36 as SQLs, so the MQL to SQL rate is 30 percent and the cost per SQL is 9,000 divided by 36, which is 250.
Now suppose sales closes 9 of those 36 deals, a close rate of 25 percent. The acquisition cost per closed customer from this channel is 9,000 divided by 9, or 1,000. Compare a second channel that gets the same 9,000 and brings 450 forms at a cost per lead of 20. If only 18 of those leads pass the sales review, 4 percent of the forms, its cost per SQL is 9,000 divided by 18, or 500, twice the first channel.
The lesson is simple: judge channels on cost per SQL and cost per closed deal, because a low cost per form can still produce a costly customer.
Use
When the SQL count helps and when it misleads
- Use it to compare lead sources on quality, since two channels with the same form volume can send very different numbers of accepted prospects to sales.
- Use it as the shared goal between marketing and sales, written down with clear criteria so both teams count the same thing each month.
- It misleads when reps mark leads as SQL just to hit a quota, which inflates the number and hides a weak pipeline further down.
- It says little on its own about revenue; pair it with close rate and deal size, as covered in marketing KPIs by channel.
Watch out
Common mistakes with sales qualified leads
- Never writing down the qualification criteria, so each rep applies a personal standard and the monthly SQL figure cannot be compared over time.
- Letting leads sit unreviewed for days after the MQL flag, which cools interest and makes the acceptance rate look worse than the lead quality really was.
- Rejecting leads without a reason code, which leaves marketing guessing about what to fix in targeting, forms or messaging.
- Treating every demo request as an SQL automatically and skipping the conversation that would reveal a student, a competitor or a buyer with no budget.