SQL.

SQL, properly understood

An SQL is the output of a qualification step, not just a label sales applies whenever they feel like it. The MQL-to-SQL conversion happens through a defined check — usually a framework like BANT (Budget, Authority, Need, Timeline) or MEDDIC for more complex B2B sales — applied during a discovery call or through lead-scoring rules that gate a lead's status automatically. A lead becomes an SQL when it passes that check and a salesperson formally accepts ownership of it in the CRM; everything before that point is marketing's responsibility, everything after is sales'. The rate itself, MQL-to-SQL percentage, is simply SQLs accepted divided by MQLs handed over in the same period, and it's one of the cleanest read-outs of whether marketing's targeting matches what sales can actually work.

In Gulf B2B sales cycles, the qualification conversation often starts on WhatsApp rather than a booked call — a prospect messages a business number, a rep replies, and real qualifying information (company size, decision-maker status, budget signals) surfaces in a chat thread instead of a form field. That means SQL criteria need to be explicit enough to apply consistently whether the lead came through a form, a WhatsApp conversation, or a referral, or different reps will draw the SQL line in different places and the rate becomes meaningless for comparison. Ramadan and the summer slowdown both compress the sales calendar in the region, so timeline-based qualification criteria ("will decide within this quarter") need a seasonal adjustment, or leads get wrongly disqualified for a timeline delay that's cultural and predictable, not a sign of low intent.

The most damaging failure mode is sales and marketing disagreeing on the SQL definition without saying so out loud — marketing believes a lead is qualified because it hit a lead-score threshold, sales rejects it in a call because it lacks budget authority, and both sides walk away distrusting the other's numbers. Fix this with a written, mutually agreed SQL definition reviewed quarterly, not an assumption. A second trap is over-optimizing the MQL-to-SQL rate in isolation: a team can inflate it by only sending sales the easiest, most obviously-qualified leads, which looks great on this one metric while starving the pipeline of volume and eventually revenue. And a low or falling SQL rate doesn't always indict marketing — it can just as easily mean sales capacity constraints are causing reps to reject leads they'd normally accept, so check both sides before reallocating budget.

Track SQL rate alongside SQL-to-win-rate and sales cycle length so you can see the whole handoff, not just the first gate — a healthy MQL-to-SQL rate paired with a collapsing win rate usually means the SQL bar itself needs tightening, not the top-of-funnel targeting. Review the SQL definition in the same meeting where sales and marketing agree on lead scoring, and revisit both together whenever the product, pricing, or ideal customer profile changes meaningfully, since an SQL bar calibrated for last year's ICP quietly stops working as the business moves.