How an SQL actually works
An SQL is the handshake where sales takes ownership of a lead. After marketing passes an MQL, a salesperson or an SDR reviews it, often with a quick qualifying conversation, and either accepts it as a real opportunity or sends it back. Acceptance usually means the lead has a genuine need the product fits, roughly the right timing, and the authority or budget to buy. The SQL is the moment a lead stops being a marketing metric and becomes a sales commitment, which is why it is a more honest signal of value than any marketing score.
The MQL-to-SQL rate is your quality gauge
The single most revealing number in a sales-led funnel is the share of MQLs that sales accepts as SQLs. A healthy rate means marketing and sales agree on what a good lead looks like. A low rate means marketing is passing leads sales does not want, usually because the MQL bar is too loose or the targeting is off. iClick reads a falling MQL-to-SQL rate as an early warning that the ad account is buying volume over quality, long before it shows up as wasted budget in the cost-per-customer math.
SQL vs MQL: opinion vs acceptance
The difference is who is making the judgement and how much is at stake. An MQL is marketing saying, based on scoring, this lead looks ready. An SQL is sales saying, having looked, this lead is worth my time. Marketing is rewarded for volume and can be optimistic; sales pays the cost of chasing bad leads and is ruthless. That tension is healthy. The gap between the two counts is not a problem to hide but a measurement to use, because it quantifies exactly how much of your lead flow is real.
Why SQL is the better bidding target
Because an SQL is closer to revenue than an MQL, it is usually the better conversion to optimise ads toward, once you have the volume to support it. Feeding SQL and closed-won data back into Google or Meta as offline conversions lets the bidding model learn which clicks lead to real opportunities, not just form fills. The trade-off is data volume: SQLs are rarer than MQLs, so thin accounts may have to optimise to MQLs first and layer SQL feedback in as the numbers grow.
