PQL vs SQL
A PQL means the product has shown this account its value. An SQL means a salesperson has confirmed a real buying opportunity. In a product-led funnel the PQL often comes first. Plenty of PQLs, though, buy on their own and never need to become SQLs at all.
Key differences
| Question | PQL | SQL |
|---|---|---|
| Who qualifies it? | Product usage data, against a rule the company sets. | A salesperson, against agreed acceptance criteria. |
| What evidence is used? | Value moments in the product, such as teammates invited or limits reached, plus customer fit. | Confirmed need, buyer authority, budget and timing from a conversation. |
| Where in the funnel? | Inside a free plan or trial, before or instead of sales contact. | After a lead is passed to sales and before an opportunity is opened. |
| Does it need a salesperson? | No. Many PQLs upgrade through self-serve checkout. | Yes, by definition. |
One scenario, two measures
Hypothetical worked scenario
A fictional product has 300 PQLs in a quarter. 120 are companies above 200 employees and are offered a sales call; sales accepts 60 as SQLs. 70 of the remaining 180 smaller accounts upgrade on their own.
PQL to SQL (large accounts) = 60 ÷ 120 = 50%. Self-serve upgrades from smaller PQLs = 70 ÷ 180 ≈ 39%.
Only some PQLs should become SQLs. Routing all 300 to sales would have meant chasing accounts that were going to buy anyway.
Which should you use?
- Use the PQL count to judge whether the product turns sign-ups into engaged accounts, and the PQL-to-paid rate to test the PQL definition.
- Use SQLs to measure the sales pipeline. Report PQL-sourced SQLs separately from MQL-sourced ones, because they convert differently.
- Send a PQL to sales only when the account is large enough, or the purchase complex enough, to benefit from a conversation.
Where comparisons can mislead
- Neither label has a standard definition. State your criteria next to any PQL or SQL figure you report.
- If you count PQLs by user and SQLs by account, the conversion rate between them means nothing.
Read the full guides: PQL and SQL.
Sources and methodology
These references explain the methods. The worked scenarios use hypothetical figures. They are not company results or current market benchmarks.
- Pocus: The Definitive PQL Guide, Part 1
How to build a PQL definition from customer fit, product usage and buying intent, tested against real conversions.
- HubSpot Knowledge Base: Use contact and company lifecycle stages
Defines lead, MQL, SQL and opportunity as successive stages, with the qualifying criteria left to each company.
Found an error or a definition that differs from your reporting? Send a correction with the page URL and the method you use.