Definition
A Sales Qualified Lead (SQL) is a lead that sales has reviewed and accepted — they meet the budget, authority, need and timing thresholds the sales team uses to define a real prospect.
If MQL is marketing's bar, SQL is sales's. The handoff between the two is where most B2B funnels leak. Marketing celebrates passing leads over; sales rejects them; marketing thinks sales isn't trying; sales thinks marketing is sending junk. The cure is a written, agreed-on definition of SQL — and a feedback loop where marketing learns which MQL signals correlate with SQL acceptance.
BANT (Budget, Authority, Need, Timing) is the classic SQL framework but increasingly outdated for modern B2B sales — buying committees and consensus selling don't fit BANT cleanly. Newer frameworks (MEDDIC, ChAMP) extend the criteria but the core idea is the same: is this a deal worth working?
Origin
BANT originated at IBM in the 1950s as a sales-qualification framework. SQL as a distinct concept emerged in the late 2000s alongside MQL and the rise of marketing automation, when the marketing-to-sales handoff became operational software.
How it works
- Define SQL criteria with sales (BANT, MEDDIC, or your own framework).
- Marketing passes MQLs into sales with all the context.
- Sales reviews each MQL — accept (becomes SQL), reject (with reason), or recycle (back to marketing).
- Track MQL → SQL rate by source, segment and rep.
- Loop the rejection reasons back to marketing weekly to tune scoring.
When to use it
Use when
- In sales-led B2B with multi-touchpoint deals.
- When the sales cycle is long enough that filtering matters more than speed.
- When marketing and sales have agreed on a written SLA.
Skip when
- In transactional, high-velocity sales where filtering is overhead.
- In product-led companies where in-product behaviour is the qualification signal.
Key metrics
- SQL acceptance rate (% of MQLs accepted as SQLs).
- SQL-to-opportunity conversion.
- SQL-to-Closed-Won conversion.
- Time from SQL to closed deal.
Examples
- SQL acceptance rate climbed from 38% to 67% after we tightened the MQL criteria.
- We need a written SQL definition. Right now every rep is using their own bar.
- 70% of SQLs come from one channel. Concentration risk worth knowing.
In practice at Makreate
Makreate's lead-gen work for B2B clients is judged on SQLs accepted, not raw leads delivered. We instrument the MQL-to-SQL handoff and meet weekly with the sales team to review rejected leads — what looked like an MQL but didn't meet sales's bar. Three weeks of that loop usually tightens the scoring enough to lift acceptance rate by 15-25 points. A recent fintech client went from 29% to 58% SQL acceptance in two months without changing volume, just by fixing what marketing was sending.
LinkedIn Outreach Automation →Common mistakes
- Treating SQL as marketing's metric. It's a sales judgement.
- Not feeding rejection reasons back to marketing. Without the loop, MQL scoring never improves.
- Different reps using different bars. Standardise the definition.
- Optimising for SQL volume instead of SQL-to-Closed-Won rate.
Frequently asked
What's a good MQL→SQL conversion rate?
50–60% is healthy. Below 40% means MQL criteria are too loose. Above 80% means they're too strict and you're missing demand.
BANT, MEDDIC, ChAMP — which framework?
BANT is fine for transactional B2B. MEDDIC suits enterprise sales where 'metrics, economic buyer, decision criteria, decision process, identified pain, champion' matters. Pick what your team will actually use.
Who owns the SQL definition?
Sales owns it; marketing aligns to it. The SLA between the two should be documented and reviewed quarterly.