MQL -> SQL conversion
Where marketing volume becomes real pipeline.
- Formula
- SQLs / MQLs (cohort-lagged)
- Unit
- %
- Models
- SaaS
| SaaS | ~39% central estimate (First Page Sage proprietary); wide observed spread of 5–40% driven entirely by MQL-definition variance across organizations. | First Page Sage |
What it is
MQL-to-SQL conversion rate is the percentage of Marketing Qualified Leads that sales subsequently accepts as Sales Qualified Leads. The formula is: SQLs generated divided by MQLs in the same cohort, ideally measured with a lag to account for the time sales takes to review and accept or reject leads.
How to calculate it
Count all MQLs handed off to sales in a given period, then count how many were accepted as SQLs — typically within a defined review window of days or weeks. Divide SQLs by MQLs and multiply by 100. Cohort-lagging (measuring SQL acceptance against the MQL cohort that entered the pipeline, rather than SQLs in the same calendar period) produces a more accurate rate.
Why it matters
MQL-to-SQL conversion is the critical handoff metric between marketing and sales at the acquisition stage of B2B SaaS funnels. A low rate signals either that marketing is generating poor-quality or poorly targeted leads, or that the MQL definition is too loose. A high rate may indicate the MQL bar is too high, leaving pipeline opportunities on the table.
Benchmarks & pitfalls
First Page Sage reports a central estimate of ~39%, but this is a proprietary figure with no disclosed methodology. The observed real-world spread runs from 5% to 40%, almost entirely driven by how strictly each organization defines an MQL — making cross-company comparison nearly meaningless without aligning definitions. This is a directional rule of thumb, not a rigorously validated industry study. No asOf date was available for this figure. Before benchmarking externally, audit your own MQL definition: if sales rejects more than 60% of MQLs, the definition is typically the problem, not the volume.