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MQL to SQL Conversion Rate Calculator

Measure how effectively marketing qualified leads convert to sales accepted leads.

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Marketing Qualified Leads in the period
Sales Qualified Leads accepted by sales
SQLs that became active opportunities
Opportunities that closed as won
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Frequently Asked Questions

Everything you need to know about mql to sql conversion rate calculator.
What is the MQL to SQL conversion rate?
The MQL-to-SQL conversion rate is the percentage of Marketing Qualified Leads (MQLs) that are accepted by sales as Sales Qualified Leads (SQLs). Formula: MQL-to-SQL Rate = (SQLs ÷ MQLs) × 100. This metric is a key indicator of lead quality and sales-marketing alignment. A high rate suggests marketing is generating well-qualified leads; a low rate suggests a disconnect between marketing’s lead definition and sales’ qualification criteria.
Industry benchmarks vary, but typical B2B SaaS MQL-to-SQL rates are 20–40%. Top-performing teams achieve 40–60%. If your rate is below 20%, it typically indicates one of three problems: your MQL definition is too loose (marketing is passing too many unqualified leads), your lead scoring model needs recalibration, or there is a misalignment between marketing and sales on what constitutes a qualified lead.
The most effective improvements are: (1) Tighten your MQL definition — add more qualification criteria like company size, industry, and engagement depth; (2) Implement lead scoring that weights intent signals and firmographic fit; (3) Create a formal SLA between marketing and sales defining what constitutes an MQL and how quickly sales must follow up; (4) Implement a lead recycling process for MQLs that sales rejects, so they re-enter nurture rather than being lost.
An MQL (Marketing Qualified Lead) is a lead that marketing has determined meets the criteria to be passed to sales, typically based on a combination of firmographic fit and engagement behaviour. An SQL (Sales Qualified Lead) is a lead that sales has reviewed and confirmed meets their qualification criteria — typically using a framework like BANT (Budget, Authority, Need, Timeline) or MEDDIC.

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