Cost per SQL is what a company pays for one lead that sales has inspected and accepted as worth working. The formula divides acquisition spend by SQL count for the same period. The metric matters because it prices the handoff, which is the first point in the funnel where a second team agrees the lead is real.
The formula and what goes in the numerator
Cost Per SQL = Total Acquisition Spend / SQLs Created in PeriodThe numerator decision drives the number more than the denominator does. A media-only numerator produces a flattering figure. A fully loaded numerator that carries media, agency fees, marketing salaries, sales development compensation, and the tooling both teams use produces a number finance can reconcile to the P&L. Pick the fully loaded version for planning, and keep the media-only version for channel tuning.
Cost per SQL prices the acceptance rate
The metric folds two things together: what leads cost and what share of them survive qualification. A team paying $200 per MQL with a 25% acceptance rate has an $800 cost per SQL. Lift acceptance to 40% without touching spend and cost per SQL falls to $500. That is why acceptance rate deserves as much attention as media efficiency. Cheaper leads and better-qualified leads move the same number, and only one of them also improves win rate later.
Cohort the spend or the number lies
Spend and SQLs land in different months. A campaign funded in March produces SQLs across April and May, so a March-over-March division mixes this month's cost with last month's output. Assign each SQL back to the month its originating spend occurred, then let the cohort mature for a full qualification cycle before you report it. Teams that skip this step see cost per SQL fall in months when spend drops, which reads as efficiency and is actually a timing artifact.
Carry it to revenue
Cost per SQL is an input, not a verdict. Divide it by the SQL-to-close conversion rate to get cost per closed-won customer, then compare that against average contract value to see whether the channel pays for itself. A channel with a high cost per SQL and a strong close rate can beat a cheap channel that stalls at proposal. Tie the ratio into the pipeline coverage plan so demand spend is set against the pipeline the quarter actually needs, and check the assumption again whenever forecast accuracy drops on marketing-originated deals.
Frequently Asked Questions
How is cost per SQL different from cost per MQL?
Cost per MQL measures what marketing pays to hit its own scoring threshold. Cost per SQL measures what the company pays for a lead sales agreed to work. The gap between the two is the MQL-to-SQL conversion rate, so a team with a 20% acceptance rate has a cost per SQL five times its cost per MQL.
Should sales development salaries be in the numerator?
If SDRs qualify the lead before it becomes an SQL, yes. Excluding them makes inbound look cheaper than it is and makes outbound look free. Publish the numerator definition next to the metric, because a company that includes SDR cost cannot compare its number to one that does not.
What makes cost per SQL rise without any change in spend?
Usually a change in the acceptance bar. When sales tightens what it will work, SQL counts drop while spend holds, and cost per SQL climbs even though lead quality improved. Annotate the metric whenever the qualification definition changes so the finance team reads the movement correctly.
Is cost per SQL a better budget metric than cost per lead?
It is closer to revenue, so it is harder to game with volume. It is also slower, since SQLs arrive weeks after the spend that created them. Use cost per SQL for quarterly budget decisions and cost per lead for weekly channel management.
Put these metrics to work
ORM builds custom revenue forecast models that turn concepts like cost per sql into prescriptive action for your team.
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