SQL

SQL is a sales-qualified lead that sales has validated real potential. The exact criteria vary, but they normally include fit, need, access, and timing.

For example, need, fit, access, and timing are sufficiently established. Definitions vary by company. In a real marketing operations environment, that scenario gives the team a concrete way to recognize when SQL applies and what should happen next.

A practical SQL example is this: Need, fit, access, and timing are sufficiently established. Definitions vary by company. The example translates the definition into an observable action, record, decision, or outcome rather than leaving the concept abstract.

In a real workplace, someone might say, “Sales confirmed a real problem and a plausible buying process, so this is no longer only a marketing-qualified lead.” That conversation is a practical signal that the team is dealing with SQL, even if nobody uses the formal label.

Teams should connect SQL creation to opportunity rules and report conversion from MQL and SAL. Otherwise SQL becomes another ambiguous label that cannot guide forecasting.

For example, need, fit, access, and timing are sufficiently established. Definitions vary by company. Making that scenario explicit helps the team connect SQL to a measurable process instead of treating it as vocabulary with no operational consequence.

Common mistakes with SQL are using SQL as a synonym for any lead sent to sales. Another frequent mistake is allowing individual reps to apply incompatible qualification standards.

For example, a team may say it uses SQL while different people apply incompatible rules or check only the easiest part of the process. The result is a label that looks consistent in a meeting but produces unreliable execution or reporting.

SQL is sales-qualified lead: sales has validated real potential. By contrast, MQL is marketing-qualified lead: meets the agreed marketing threshold for sales consideration. The distinction matters because the two concepts answer different operational questions.

For example, need, fit, access, and timing are sufficiently established. Definitions vary by company. A contrasting MQL scenario is: Fits the ICP and shows enough engagement to pass to sales. Seeing both situations together makes the boundary easier to apply in real work.