SQL-to-Close Rate
The proportion of sales-qualified leads that eventually become closed-won revenue.
What it measures
SQL-to-close rate is the proportion of sales-qualified leads that eventually become customers. It is the single most useful conversion figure in most funnels because it spans the whole sales process: it captures qualification accuracy and selling effectiveness in one number.
That breadth is also its weakness as a diagnostic. A falling SQL-to-close rate tells you something is wrong between qualification and close without telling you where, which is why it should be read alongside stage-to-stage conversion rather than instead of it.
Measure it on a cohort, not on a period
How to calculate it properly
Take a fixed cohort of SQLs from a period far enough back that most have resolved — at least one and a half sales cycles.
Follow that cohort forward and count how many reached closed-won, regardless of when.
Divide by the cohort size including those still open, and report the unresolved share alongside. A rate quoted without it can look strong simply because the losses have not landed yet.
Segment by source and by segment. A blended rate moves with mix and hides the channel that is actually underperforming.
Compare against the definition of SQL in force at the time. If qualification criteria tightened, the rate should rise and that is not an improvement in selling.
Where it misleads
Tightening the SQL definition raises the rate without improving anything, and loosening it lowers the rate without anything getting worse. The metric is only comparable under a stable definition.
It is a lagging indicator by a full sales cycle, so it responds to changes long after they happen and is a poor tool for steering within a quarter.
Low volume makes it noisy. At a handful of SQLs per rep per month the rate swings on individual deals and carries almost no information.
It says nothing about deal size. A rising close rate on shrinking deals can coincide with falling revenue.
RELATED TERMS
Conversion Rate
The proportion of items moving from one defined state to the next. The arithmetic underneath every funnel and forecast.
Sales Cycle Length
The elapsed time from an opportunity's creation to its close. The time dimension underneath capacity planning and forecasting.
Sales Qualified Lead (SQL)
A lead that sales has examined and accepted as a genuine opportunity worth working. The point at which pipeline responsibility formally transfers.
Stage-to-Stage Conversion
The proportion of opportunities moving from each stage to the next. The most localised view of where pipeline dies.
Win Rate
The proportion of opportunities that close won, measured against a defined denominator — which is where most of the disagreement lives.
COMMON QUESTIONS
- What is SQL-to-close rate?
- The proportion of sales-qualified leads that eventually become customers. It spans the whole sales process, capturing both qualification accuracy and selling effectiveness in one figure — which makes it the most useful single conversion number in most funnels and a poor diagnostic on its own, because it cannot tell you where the loss occurred.
- How do you calculate SQL-to-close rate?
- On a cohort: take a fixed set of SQLs from far enough back that most have resolved, follow them forward, and divide those that reached closed-won by the cohort size. Dividing this month's wins by this month's new SQLs compares two different populations and is wrong whenever volume is changing — which is most of the time.
- What is a good SQL-to-close rate?
- Entirely dependent on how strictly you define a sales-qualified lead, which is why cross-company comparison is meaningless. A strict definition produces a high rate on low volume and a loose one the reverse, and neither is better. Track your own rate under a stable definition; a change in the rate with an unchanged definition is the signal worth acting on.
- Why did our close rate improve without anything changing?
- Most often because the SQL definition tightened, which raises the rate arithmetically without any improvement in selling. The second common cause is a mix shift towards a segment that converts better. Check both before concluding performance improved — and check whether unresolved deals from the cohort are simply still open.
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