MQL (Marketing Qualified Lead) and SQL (Sales Qualified Lead) are the two most-used qualification statuses in B2B revenue operations. The distinction matters because it defines when marketing hands off to sales and what each function is measured on. The mistake most teams make: defining MQL and SQL based on internal artifacts rather than buyer signal. This guide covers what the labels actually mean, how to define them well, and how to operationalize the handoff.
What an MQL actually is
An MQL (Marketing Qualified Lead) is a contact who has demonstrated enough engagement and fit signal to warrant sales attention. The threshold is set by the company — typically a combination of demographic match (right title, right company size, right industry) plus behavioral signals (downloaded content, attended webinar, requested demo, hit a lead score threshold).
The keyword is “warrants sales attention.” An MQL is not a buying signal — it is the marketing team’s judgment that this contact is worth a sales conversation. Whether the contact actually buys is a separate question that gets answered by sales qualification.
What an SQL actually is
An SQL (Sales Qualified Lead) is a contact that sales has reviewed and confirmed is worth pursuing as a potential customer. The qualification criteria typically include budget, authority, need, and timing — the BANT framework, or its successors (CHAMP, ANUM, MEDDIC).
The handoff: marketing flags a contact as MQL; sales reviews and either accepts (SQL) or rejects (returns to nurture, not a real prospect). The disagreement rate between marketing and sales on this handoff is one of the most-tracked revenue-ops metrics.
The MQL-to-SQL handoff in practice
What works: clear, mutually agreed criteria for what an MQL is. Time-bound sales response (typically 24 hours or less). Feedback loop where sales rejection reasons are tracked and feed back to marketing. Joint review of MQL-to-SQL conversion rate at a regular cadence.
What fails: vague MQL criteria (“they downloaded something”). Slow sales response. No feedback loop. Marketing and sales meeting only at executive level. The result is the classic marketing-sales dysfunction — marketing flags leads, sales rejects most of them, neither side updates the other on why.
The handoff in modern CRMs
Modern CRMs handle the MQL-to-SQL workflow with status fields, automated routing, and SLA tracking. When a lead hits MQL criteria, it gets routed to the right sales rep within seconds. SLA timers track how long sales takes to respond. Acceptance or rejection is captured with reason codes. The data flows back to marketing automatically.
Conduyt’s sales automation handles all of this in one system — including the lead scoring that feeds MQL definition, the routing logic, the SLA tracking, and the feedback flow back to marketing. The CRM lead management guide covers the full lead lifecycle.
Common MQL/SQL mistakes
The biggest mistake: defining MQL by content download alone. “Anyone who downloaded a whitepaper is an MQL” floods sales with low-quality leads and burns out the SDR team. Better: behavioral threshold (multiple engagements over a period) plus demographic fit.
The second mistake: no agreement between marketing and sales on the definition. If marketing thinks MQL means “engaged” and sales thinks MQL means “ready to buy,” every handoff is a fight. Quarterly joint review of criteria and conversion data fixes this.
The third mistake: no recycling path. Leads that sales rejects should not just disappear — they should return to nurture for further marketing engagement. Without recycling, marketing loses visibility into what happens to its work and stops trusting the sales feedback.
Beyond MQL and SQL
Modern teams increasingly add intermediate statuses. PQL (Product Qualified Lead) for product-led-growth motions where in-product behavior is the signal. SAL (Sales Accepted Lead) for cases where sales has acknowledged but not yet qualified. Each addition needs operational discipline — adding statuses without managing them just creates noise.
Frequently asked questions
What’s the typical MQL-to-SQL conversion rate?
Varies widely by segment and definition. SMB B2B SaaS typically sees 15-30% MQL-to-SQL conversion. Enterprise B2B sees 8-20%. Below 10% suggests MQL criteria are too loose; above 50% suggests MQL criteria are too tight.
Who owns the MQL definition?
Jointly between marketing and sales. Marketing typically drafts; sales sign-off is required. Quarterly review with both teams keeps the definition aligned with reality.
Should every MQL get a sales call?
Yes — that is the definition. If your team treats some MQLs as worth a call and others as not worth a call, the MQL definition is broken.