MQL to SQL Conversion Calculator
Calculate marketing qualified lead to sales qualified lead conversion rates, and work backwards from a revenue target to the lead volume required.
🎯 Marketing📐 SQL rate = SQLs accepted / MQLs delivered💼 Business
MQLs delivered
SQLs accepted by sales
Opportunities created
Closed won deals
Average deal value
Revenue target (to work backwards)
Please enter valid values.
Formula & Reference
| Variable | Symbol | Formula | Units |
|---|---|---|---|
| MQL to SQL Conversion Calculator | — | SQL rate = SQLs accepted / MQLs delivered | percent |
Step-by-Step Examples
Example 1
Full Funnel
850 MQLs, 221 SQLs, 132 opportunities, 33 won, deal value 18,000, target 1.2M.
- MQL to SQL = 221 / 850 = 26.0%
- SQL to opportunity = 59.7%
- Opportunity to won = 25.0%
- MQL to customer = 33 / 850 = 3.88%
- Revenue = 594,000, revenue per MQL = 698.82
- For 1.2M: 67 deals needing about 1,725 MQLs
✓ 26.0% MQL to SQL, 3.88% to customer
Example 2
Loose MQL Definition
1,500 MQLs, 180 SQLs.
- MQL to SQL = 12.0%
- Well below 20% — sales is rejecting most of what marketing passes
- Usually a definition or scoring problem, not a sales problem
✓ 12.0% — MQL bar too loose
Example 3
Tight Definition
400 MQLs, 290 SQLs.
- MQL to SQL = 72.5%
- Very high acceptance suggests marketing is over-qualifying
- Volume may be being left on the table
✓ 72.5% — possibly over-qualifying
Real-World Applications
Marketing and Sales Alignment
MQL to SQL rate is the clearest quantitative measure of whether the two functions agree on what a good lead looks like.
Capacity Planning
Working backwards from a revenue target through the funnel gives the required lead volume.
Lead Scoring Calibration
A persistently low acceptance rate is usually a scoring model problem rather than a sales effort problem.
Revenue Per Lead
Dividing revenue by MQLs gives a value per lead that makes marketing spend decisions concrete.
Common Mistakes to Avoid
⚠️
Blaming sales for a low acceptance rate
A rate under 20% almost always means the MQL definition is too permissive, not that sales is failing to follow up.
⚠️
Optimising for MQL volume
Generating more loosely qualified leads inflates a vanity metric while lowering acceptance and wasting sales time.
⚠️
Ignoring the time lag
Leads generated this month convert over subsequent months. Comparing same-period MQLs against same-period SQLs understates the rate in a growing funnel.
Frequently Asked Questions
What is the difference between an MQL and an SQL? ▾
An MQL meets marketing's qualification criteria based on behaviour and fit. An SQL has been reviewed and accepted by sales as worth pursuing.
What is a good MQL to SQL conversion rate? ▾
Commonly 20 to 40%, though it depends heavily on how each is defined. Consistency of definition matters more than the benchmark.
What does a very low rate indicate? ▾
Usually that the MQL threshold is too low, letting through leads that do not fit. It can also indicate a definition disagreement between teams.
How do I work backwards from a revenue target? ▾
Divide target revenue by average deal value to get deals needed, then divide by the MQL-to-customer rate to get required lead volume.
Should MQL definitions change over time? ▾
Yes. As the product, market, and ideal customer profile evolve, scoring models need recalibration against actual conversion data.