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SQL-to-Win %

What is SQL-to-Win %?

SQL-to-Win % measures the percentage of Sales Qualified Leads (SQLs) that successfully convert into closed-won deals. It’s a critical sales effectiveness metric, showing how efficiently your sales team turns qualified opportunities into revenue.

High SQL-to-Win % indicates strong alignment between marketing and sales qualification, while a low percentage highlights potential gaps in lead quality, sales execution, or process consistency.

How to Calculate SQL-to-Win %?

You can calculate it by dividing the number of SQLs that converted to closed-won deals by the total number of SQLs in a given period, then multiplying by 100.

Steps to Calculate SQL-to-Win %

  • Step 1 – Define SQLs clearly. Ensure marketing and sales agree on what qualifies a lead as SQL.
  • Step 2 – Count total SQLs. Track the number of leads that reached the SQL stage in your CRM during a time period.
  • Step 3 – Identify closed-won deals from SQLs. Filter those SQLs that resulted in a successful sale.
  • Step 4 – Divide closed-won SQLs by total SQLs, then multiply by 100 to get a percentage.

Formula to Calculate SQL-to-Win %

SQL-to-Win % = (Closed-Won ÷ SQLs) × 100

Benchmark for SQL-to-Win %

Benchmarks vary widely by industry, deal size, and sales motion (SaaS vs. enterprise vs. transactional). General ranges lie between 20–30%.

Related Metrics for SQL-to-Win %

  • Lead-to-SQL %
  • SQL-to-Opportunity %
  • Win Rate (Opportunity-to-Win %)
  • Sales Cycle Length

FAQ's

SQL-to-Win % tracks the percentage of Sales Qualified Leads that convert into closed-won deals, while Win Rate measures the overall success of opportunities regardless of how they originated. SQL-to-Win % gives a sharper view of the effectiveness of lead qualification and sales execution starting from the SQL stage.

A low SQL-to-Win % often signals issues such as weak lead qualification, poor alignment between marketing and sales, or challenges in sales execution. It may also reflect long or complex buying cycles that reduce the efficiency of closing SQLs.

Quarterly measurement is recommended to account for sales cycle variability and provide a stable view, though monitoring monthly trends can help detect early shifts in performance.