Sales Qualified Leads

What it measures

A point-in-time count of every lead sales has formally accepted and is actively working, where each lead satisfies your written SQL criteria (budget, authority, need, and timeline fit, or your own equivalent rubric). This is a funnel-stage snapshot, not a cumulative tally of every lead ever qualified. A lead enters the count when sales accepts it and exits when it converts to an opportunity, closes, or is disqualified. Count the prospect once — one account or opportunity, not one record per contact at the same company.

Why it matters

SQLs are the handoff point where marketing's output becomes sales' input, and they are the leading indicator of future customers and revenue. Sales leadership uses SQL volume and conversion to know whether the pipeline is healthy, whether coverage is sufficient to hit quota, and whether marketing is delivering the right caliber of prospect. Without a clean SQL count you cannot diagnose whether a revenue slowdown comes from too few leads, a weak marketing-to-sales handoff, or sales execution — you are flying blind on the most expensive part of the funnel.

How to read it

Read SQL as a trailing funnel metric, never as a single number. Always compare this period's closing count to the prior period and to your plan, and always pair volume with SQL-to-Customer conversion rate over the same cohort — volume alone is meaningless. High SQL volume with stalling conversion points to loose qualification or sales execution, not a pipeline shortage; low volume with strong conversion means the constraint is lead supply. Break the count by source (marketing-sourced, sales-sourced, inbound) and by age to find which engine is working and whether leads are sitting stale.

What good looks like

Watch-outs

• Conflating SQL count with SQL age. A steady count of 30 says nothing about whether each lead is 10 days old (fresh, engaged) or 60 days old (stale, at risk). Track age distribution and velocity — new SQLs per week and conversion per cohort — alongside the headline number.
• Letting the SQL pool become a junk drawer. If sales disqualifies leads rarely or inconsistently, the count bloats and loses predictive power. Set an explicit disqualification policy (for example, no activity in 90 days triggers automatic removal) and enforce it every month.
• Counting leads that do not actually meet the bar. Marketing sometimes pushes leads to SQL status before they truly fit on budget, authority, need, and timeline. Define the SQL rubric in writing with sales, and audit a sample each month — misaligned criteria quietly destroy the metric's forecasting value.
• Reading the total without source and quality cohorts. If most SQLs come from one event or one rep and conversion is weak, the problem is concentrated, not systemic. Segment by source, rep, industry, and company size so you fix the real bottleneck instead of the average.

Worked example