SQL (sales qualified lead)

What is an SQL?

An SQL, or sales qualified lead, is a lead that your sales team has checked and agreed is a real potential deal. It's the handoff point where a lead stops being marketing's job and becomes a sales opportunity.

Let's say you sell a contract management tool. A legal ops manager downloads your guide, then books a demo a week later. That demo request makes her an MQL, a lead marketing thinks is worth a conversation. On the first call, your SDR learns her team of eight lawyers is stuck in shared drives, she has budget this quarter and her general counsel wants a shortlist by month end. The SDR marks her as an SQL. Now there's a real deal to work.

Most B2B SaaS teams decide that with a short set of questions on the first call. Some use BANT (budget, authority, need, timing). Others use their own list, like team size, current tool and when they want to switch.

Why SQLs matter for B2B SaaS

SQLs are where marketing numbers start to mean money. A thousand leads look good on a dashboard, but only the ones sales accepts can turn into pipeline.

The gap between leads and revenue is wide. Forrester found in 2023 that fewer than 1% of leads turn into closed deals. That's why I push teams to report on SQLs and pipeline instead of raw lead counts. Our post on lead to MQL conversion rate covers the step before this one.

Here's why the SQL stage deserves a clear definition:

• It keeps marketing honest: When marketing is judged on SQLs, it stops counting cheap downloads as wins and starts building pages and offers that bring buyers.
• It shows which channels work: Two channels can bring the same number of MQLs and very different numbers of SQLs. The SQL count shows which one brings real buyers.
• It gives sales and marketing one language: A written SQL rule ends the "your leads are bad" argument, because both teams agreed on what good looks like.

How to define an SQL

A good SQL definition is short, written down and checked by both teams. Here's my process:

• Start from won deals: Look at your last 20 closed won deals and note what was true at the first call. Company size, role, the problem and the timeline usually repeat.
• Write three to five rules: Turn those patterns into simple checks, like "company has 50+ employees", "talking to the budget owner or someone reporting to them" and "wants a tool within six months".
• Agree on who decides: Usually the SDR or account executive marks the lead after the first call. Write down how fast that has to happen, like within two working days.
• Log the reason when a lead is rejected: Give sales a short dropdown in the CRM, like "too small", "no budget" or "student". Those reasons tell marketing what to fix.
• Review the rules every quarter: As you move upmarket or launch a new plan, the profile of a good deal changes. Update the rules with sales.

An SQL example

Here's how the stages could look for a hypothetical contract management company over one quarter:

Now say the team splits SQLs by source. Comparison and pricing pages bring 35 SQLs from 60 MQLs, while a gated industry report brings 6 SQLs from 70 MQLs. The report looks great on an MQL dashboard. The comparison pages are what fill the pipeline, so they get the next quarter's budget.

These numbers are made up to show how the math works. Your rates will depend on how strict your MQL and SQL rules are.

How to measure SQLs

Count SQLs, but spend more time on the rates around them. Here's what I track:

• MQL to SQL rate: Divide SQLs by the MQLs they came from in the same period. A sudden drop usually means a channel started bringing the wrong people or sales changed how it qualifies.
• SQLs and SQL rate by source: In your CRM, report SQLs by original source and first landing page. That shows which channels and pages bring buyers.
• SQL to closed won rate: This is close to your win rate on new business. If it falls while SQL counts rise, the bar for SQL may have slipped.
• Speed to SQL: Track the time from demo request to SQL. Leads that wait days for a first call often go somewhere else.
• Rejection reasons: Count the reasons sales gives for rejecting MQLs. If "too small" leads the list, tighten your targeting or your form.

Common SQL mistakes

• Letting the definition drift: If each rep decides their own way, your SQL numbers mean nothing month to month. Write the rules down.
• Marking SQLs before a conversation: A demo request alone isn't an SQL. Someone from sales has to confirm the fit.
• Measuring marketing on MQLs only: It rewards volume. Add SQLs and pipeline to marketing's scorecard.
• Ignoring rejected leads: Rejected MQLs are free feedback on your targeting and content. Read the reasons every month.
• Slow follow-up: A buyer who requested a demo on Monday and hears back on Friday has often booked a competitor's demo already.

Here's a good first step. Ask your sales lead to write down, in five lines or fewer, what makes a lead worth their time. Does it match the rule your CRM uses today?