SQL (Sales Qualified Lead)

An SQL (Sales Qualified Lead) is a contact that the sales team has evaluated and accepted as a legitimate business opportunity, ready to move forward in the sales process. Unlike an ordinary lead or an MQL (which marketing considers interested), an SQL has already passed a stricter filter: sales confirmed that the person has a concrete need, purchasing power and the right timing to make a decision.

The SQL is the handoff point between marketing and sales. When an MQL gets follow-up from a sales rep or SDR and they verify that it is worth investing time, the lead is "promoted" to SQL and usually becomes an opportunity in the pipeline. Managing that transition well is part of the work that Sellium organizes on top of the sales CRM, where every qualification stage is logged and measurable.

The core idea is to separate interest from real purchase intent: not everyone who downloads an ebook or asks for information is ready to talk about pricing, and treating everyone the same wastes the sales team's time.

Why the distinction matters

In any B2B team, the most expensive resource is the reps' time. If marketing passes them every contact unfiltered, reps end up chasing people who only wanted information, while the prospects who are truly ready wait. The SQL concept exists to protect that time: only the leads that pass sales validation get the full sales effort. When the definition of an SQL is clear and shared between the two teams, the conversion rate to a sale goes up because every conversation starts with a prospect who has already shown real signals.

How a lead is validated as an SQL

The move from MQL to SQL is almost always made by a person (an SDR, a BDR or the rep themselves) in a call or a short exchange, based on a qualification framework. The most common ones in the region are:

• BANT: you confirm Budget, Authority (who decides), Need and Timing. It is the simplest and most popular among small and midsize companies in Argentina.
• MEDDIC or CHAMP: more detailed frameworks for complex, high-ticket sales with several decision-makers.
• Custom criteria: many companies define their own checklist (for example, "has more than 50 employees, uses a competing tool and replied in less than 24 hours").

The key is not which framework you choose, but that marketing and sales agree in writing on what counts as an SQL. That agreement, sometimes formalized in an internal SLA, prevents the never-ending fight of "marketing sends bad leads" versus "sales does not work the leads".

MQL vs SQL: how they differ

They are two stages of the same funnel, but they are separated by who validates and what is being measured:

A good flow is: anonymous lead, then MQL when behavior passes a lead scoring threshold, then SQL when sales accepts it, and finally an opportunity when formal negotiation begins.

A concrete example (Latin America)

A management software company in Buenos Aires receives 400 leads a month from campaigns and downloads. Its scoring system flags 90 as MQLs. The SDR team calls those 90 and, applying BANT, confirms that 25 have budget allocated for the quarter and a problem the product solves. Those 25 become SQLs and go to the account executives as opportunities. The other 65 go back into a nurturing campaign until they are ready. The result: reps work 25 quality conversations instead of 90 scattered ones, and their close rate improves because they no longer waste hours on people who are just curious.

Common mistakes

• Not having a shared definition: if marketing and sales understand different things by "SQL", the funnel breaks at the handoff.
• Promoting under quota pressure: marking lukewarm leads as SQLs to inflate the pipeline only moves the problem and muddies the sales forecast.
• Not sending feedback back: if sales rejects an SQL, that information should go back to marketing to refine targeting; without that loop, the same mistakes repeat.
• Confusing an SQL with an opportunity: the SQL is the accepted lead; the opportunity is the deal already in negotiation, with an amount and an estimated date. They are not the same.

Measuring how many MQLs become SQLs and how many SQLs become customers gives an honest picture of the health of the funnel and of how aligned the two teams are.