The marketing qualified lead vs sales qualified lead distinction sounds like internal process housekeeping. In practice, it is the boundary where most of the tension between marketing and sales teams lives. Marketing generates MQLs that sales considers unqualified. Sales cherry-picks leads and ignores others, leaving marketing unable to track what actually converted. Everyone has a different definition of what a ‘good lead’ looks like, and none of those definitions are written down anywhere.
Getting the MQL/SQL framework right does not require sophisticated technology. It requires a clear conversation between marketing and sales, an honest look at what actually predicts conversion, and the discipline to enforce definitions consistently.
What an MQL actually is
A Marketing Qualified Lead is a contact who has demonstrated sufficient interest and demographic fit to warrant nurturing attention, but who has not yet reached the threshold of sales readiness. The ‘qualified’ here means qualified for marketing investment — further nurturing, targeted content, lead scoring — not qualified for a sales conversation.
MQL criteria typically combine fit signals (job title, company size, industry, geography — characteristics that indicate the contact could be a buyer) with engagement signals (content downloads, webinar attendance, repeat site visits, email engagement — behaviours that indicate active interest). A contact who fits the profile but has never engaged is not an MQL. A contact who has engaged heavily but doesn’t fit the profile may still not be one.
What an SQL is and why the distinction matters
A Sales Qualified Lead is a contact who has been assessed — either automatically through lead scoring or through a marketing-to-sales handoff process — and determined to meet the threshold for direct sales engagement. This threshold should be defined in terms of both fit and intent: the contact has the profile of a potential buyer and has demonstrated signals suggesting they are actively evaluating solutions.
The SQL definition matters because it is the contract between marketing and sales. Marketing commits to only pass contacts that meet the SQL criteria. Sales commits to follow up with all contacts that meet the SQL criteria within a defined window. Without a shared, enforced definition, both sides have legitimate grievances: marketing generates volume that sales ignores, sales complains about lead quality, and nobody can diagnose the problem because nobody can agree on what a good lead looks like.
How to define both for your specific business
The most reliable way to define MQL and SQL criteria is to look backwards from your closed won deals. What did those contacts look like when they first engaged? What was their job title? Their company size? Which content did they consume before requesting a demo or making an enquiry? How many interactions did they have before converting? The patterns in your own historical data are more informative than any generic framework.
If you don’t have enough historical data to be analytical, the next best approach is a joint workshop with marketing and sales to define criteria based on shared experience. The key questions are: what characteristics make a company an ideal customer, what job titles or roles indicate buying authority, and what behaviours indicate someone is actively considering making a change.
Lead scoring: the mechanics of operationalising definitions
Lead scoring is the process of assigning point values to fit and behaviour signals and defining score thresholds that trigger MQL or SQL status. A contact might receive points for job title match, company size, industry fit, website visits, content downloads, email clicks, and webinar attendance — with more points for signals that more strongly predict conversion based on historical data.
The initial scoring model will be imperfect. Calibrate it over three to six months by reviewing whether contacts that reached SQL threshold actually converted, and adjusting scores for signals that predicted well and signals that didn’t. The model improves with iteration.
ThynkrSystems builds lead generation programmes that include MQL/SQL framework definition, lead scoring setup, and the reporting infrastructure to measure performance at every stage of the funnel. Getting the definitions right is the foundation everything else depends on.
