What's the right conversion rate from SQL to closed-won at our stage?

Short answer: Bridge Group&#39;s 2024 SaaS AE Metrics Report pegs median SQL-to-close at 17% across SaaS, but segment dispersion is brutal: Enterprise (ACV >$100K) lands at 6-9%, Mid-Market ($25-100K) at 12-18%, SMB/Velocity (<$25K) at 18-28%. Pavilion&#39;s 2024 GTM Benchmarks confirm top-quartile teams hit 22%+ blended while bottom-quartile sit at <8%. RepVue&#39;s Q4 2024 State of Sales shows median SMB AE close rate at 22% on $720K quota. Carta&#39;s 2024 SaaS Benchmarks flag that Series B-D companies with <12% SQL-to-close burn 1.6x faster than peers. Your number lives in your own data - pull 6 trailing quarters and own it.

How to actually calculate it (CRO-grade)

• SQL definition: First *completed* discovery meeting (not booked, not no-showed, not email reply). Anchor to a CRM stage gate with MEDDPICC fields populated. See [/knowledge/q4](/knowledge/q4) for SDR-to-AE handoff spec.
• Closed-won: Revenue recognized in-period, ARR-weighted. Strip deals that churned within 90 days (RepVue 2024: ~6% of SMB closed-wons cancel inside Day 90 - CSM problems, not pipeline wins).
• Cohort window: Cohort SQLs by *creation date*, follow them 2x median cycle. ENT at 129-day median needs an 8-month observation window. Same-month closed-won/SQL reporting is the #1 metric malpractice in SaaS - artificially deflates conversion 30-50%.
• Comp implications: levels.fyi 2024 SaaS sales data shows AE OTE at $240K-$340K for MM; if SQL-to-close is 8% but comp model assumes 14%, expect attainment crisis in 2 quarters.

Worked example (HubSpot-style funnel)

Using HubSpot&#39;s 2024 DEF 14A disclosed metrics as a template: ~$2.5B ARR, ~150K customers, MM-skewed mix. Reverse-engineering implies a blended SQL-to-close near 15-17%. Apply to your funnel:

`` 10,000 SQLs (trailing 6Q) -> 5,500 Stage 2 (55% advance) -> 2,750 Stage 3 (50%) -> 1,650 Stage 4 (60%) -> 660 Closed-Won (40%) = 6.6% SQL-to-close (ENT-heavy mix) ``

If you're at 6.6% but think you're MM: your stage definitions are mislabeled or your ICP is drifting upmarket without comp-plan adjustment.

Stage-gate math (why ENT looks worse than reality)

40% Stage-4-to-close decomposes to ~7% SQL-to-close: 0.60 x 0.50 x 0.60 x 0.40 = 7.2%. Gong&#39;s 2024 Revenue Intelligence Report shows 62% of ENT deals slip at least one quarter - fallout is baked in.

Segment dispersion (why blended numbers lie)

Enterprise (6-9%): Bridge Group median cycle 129 days, 5.4 stakeholders. BVP State of the Cloud 2024 shows ENT CAC payback 18-24 months.

Mid-Market (12-18%): 60-90 day cycle, 2-4 stakeholders. SaaStr&#39;s 2024 benchmarks: top-quartile MM clears 18%.

SMB (18-28%): 30-45 day cycle, 1 buyer. RepVue 2024: median 22% on $720K quota. CAC payback gotcha: SMB 22-28 months vs MM 14-18 (BVP).

Bear Case (read this before celebrating)

1. Your great number is fake. SMB at 26%? SDRs may be rejecting 30%+ of legitimate first-meetings to protect AE conversion stats. Audit SDR-to-AE handoff rejection rate. >15% = your conversion rate is fiction. Pull rejection reasons; if 'not ICP' is >40%, ICP doc is stale or SDRs are gaming.

2. Stable = stagnant. Flat across 4+ quarters means zero funnel learning. Healthy orgs see SQL-to-close move 200-400 bps/year as ICP tightens. See [/knowledge/q19](/knowledge/q19).

3. Conversion rate masks a CAC problem. A 22% SMB rate with 28-month CAC payback is worse than a 14% MM rate with 16-month payback. Former is a treadmill; latter compounds. See [/knowledge/q23](/knowledge/q23).

4. Public-company benchmarks are survivor-biased. DEF 14A filings only capture companies that made it to IPO. Bridge Group/Pavilion samples skew funded SaaS. Bootstrapped or pre-PMF? Expect 30-50% lower until ICP locks.

5. The number is a CFO trap. CFOs love SQL-to-close because it's a single ratio - but optimizing it in isolation drives AE risk-aversion. Reps will refuse harder ICP-fit deals to protect their personal conversion. Healthy orgs measure conversion x ACV x cycle-time as a triplet, not conversion alone. See [/knowledge/q12](/knowledge/q12) on rep-level diagnostics.

Diagnostic playbook

1. Trend. QoQ delta >10% = ICP or market regime change.
2. By rep. Median 12% but P90 22% = coaching arbitrage worth millions. [/knowledge/q12](/knowledge/q12).
3. By source. Inbound demos convert 2-3x outbound cold. If they don't, inbound qualification is broken.
4. By segment. Cross-reference [/knowledge/q23](/knowledge/q23) on segment CAC and [/knowledge/q8](/knowledge/q8) on pipeline coverage.
5. By cohort age. Plot conversion vs SQL-creation-month; newer cohorts underperforming = TAM saturating or competition tightened.

See also: [/knowledge/q31](/knowledge/q31) on SDR economics, [/knowledge/q4](/knowledge/q4) on handoff specs, [/knowledge/q19](/knowledge/q19) on ICP refinement, [/knowledge/q8](/knowledge/q8) on pipeline coverage.

TAGS: sql-conversion,sales-metrics,lead-quality,crm-ops,sales-benchmarks