MQL vs. SQL

The difference lies in who makes the judgement. Marketing declares a contact an MQL when company size, industry and role fit and behaviour signals interest: a whitepaper downloaded, the pricing page visited repeatedly, a demo requested. Sales declares the same contact an SQL once need, budget and a timeframe have been confirmed in conversation. Between the two judgements sits a phone call, not an algorithm.

This handover is exactly where it breaks in practice. Marketing reports numbers upwards, sales rejects a substantial share, and both sides keep separate statistics about the same contacts. The cause is rarely bad faith but a missing shared definition. If nobody has written down which criteria an MQL must meet and how quickly sales has to respond, you end up arguing about numbers instead of customers.

Technically the classification usually hangs on a lead scoring model in the CRM: points for company attributes, points for behaviour, status changes above a threshold. That threshold is not a mathematical quantity but an agreement between two departments. It needs recalibrating after the first hundred handovers, otherwise you are optimising a model whose assumptions were never tested.

One confusion worth naming: in a sales context SQL means Sales Qualified Lead, in the database world it means Structured Query Language. If you cover both topics in one document, spell the term out. Some teams also insert a stage called SAL (Sales Accepted Lead) between the two, separating acceptance from qualification. That pays off as soon as sales starts routinely handing leads back.