How do you audit marketing-sourced pipeline quality and spot rotten SQL sources?

Brief

Track SQL→Opportunity close rate by source. Below 20% kills your deal math; above 50% questions your SQL gate.

Detail

Marketing sourced 100 SQLs last month. Sales closed 12 of them. That's 12% close rate. Is that good or terrible? Depends on the source:

• High-intent webinar (gated, vertical-focused): 35–45% SQL→Opp
• Organic content (blog, SEO): 22–28% SQL→Opp
• Paid ads (brand keyword): 18–25% SQL→Opp
• List/outbound nurture: 8–15% SQL→Opp

If your paid ads are only hitting 12%, that's below the band. Your MQL gate is too loose.

Quality Audit Framework

For each source, calculate over 90 days:

The *Paid Search (Brand)* source is bleeding money. Why? Three tests:

1. Is the form too loose? (Landing page accepts anyone)
2. Is the audience wrong? (Targeting SMB instead of Enterprise)
3. Is the message misaligned? (Ad promises X, product is Y)

Root Cause by Data Pattern

Run this audit monthly. Kill sources dropping below 18% SQL→Opp unless they're volume plays (high MQL count, lower quality acceptable).

TAGS: pipeline-quality,SQL-sources,source-audit,marketing-sourced,conversion-by-channel

Source Stack

• Andreessen Horowitz "16 Startup Metrics": https://a16z.com/16-startup-metrics/
• OpenView Expansion SaaS Benchmarks: https://openviewpartners.com/expansion-saas-benchmarks/
• Bessemer "10 Laws of Cloud": https://www.bvp.com/atlas/10-laws-of-cloud
• First Round Review: https://review.firstround.com/
• Lenny\'s Newsletter benchmark archive: https://www.lennysnewsletter.com/
• HubSpot State of Sales Report: https://www.hubspot.com/state-of-marketing

Verified Financial Benchmarks (2024-2025)

Verified Financial Benchmarks (2024-2025)

The Bear Case (Customer-Side Adoption Friction)

Three friction vectors:

1. Budget reallocation in downturn — services/SaaS get aggressive cuts. 20-30% pipeline compression, 90-day cash buffer.
2. Buying-committee expansion — Gartner: 6 → 11 stakeholders/decade. Each adds 30-45 days.
3. Procurement-driven price compression — 20-40% discounts are closing condition, not opener.

Mitigation: ACV-expansion tiers, exec-sponsor motions, renewal escalators 5-7% annual.

See Also (related library entries)

Cross-references for adjacent operator topics drawn from the current 10/10 library set, ranked by tag overlap with this entry:

• q251 — How do you design a sales contest that doesn't tank pipeline quality after it ends?
• q9502 — How do you scale a workshop-led senior tech-training business in 2027 — what's the proven path past the single-operator ceiling?
• q9559 — How should a CRO calibrate qualification rigor when cash position and runway are forcing a choice between conservative organic growth and ag
• q9558 — What's the framework for a CRO to decide whether to build two separate sales motions (organic vs M&A/upmarket) with distinct qualification r

Follow the q-ID links to read each in full.

FAQ

What SQL→Opportunity close rate does the article use as the floor for killing a source? The article says to kill sources dropping below 18% SQL→Opp unless they are volume plays with high MQL count where lower quality is acceptable. It also frames below 20% as a level that breaks your deal math.

Above 50% is flagged as a sign your SQL gate may be too tight.

What SQL→Opp ranges does the article give for each marketing source type? High-intent gated, vertical-focused webinars run 35–45% SQL→Opp and organic content (blog, SEO) runs 22–28%. Paid ads on brand keywords run 18–25%, while list/outbound nurture runs only 8–15%. The article notes paid ads hitting just 12% are below the band, indicating a loose MQL gate.

In the 90-day audit table, which sources were marked KILL or NO? The Event Booth source — 18 SQLs, 2 opportunities, an 11% opp rate, and a $21K average deal — was marked KILL. Paid Search (Brand), with 91 SQLs, 15 opportunities, a 16% rate, and a $31K average deal, was marked NO. Webinar and ABM Account (Cold) were both marked YES.

What three tests does the article apply to the bleeding Paid Search (Brand) source? The article asks whether the form is too loose (a landing page accepting anyone), whether the audience is wrong (targeting SMB instead of Enterprise), and whether the message is misaligned (the ad promises X but the product is Y).

These three tests diagnose why a high-volume source produces low-quality SQLs. The mindmap expands them into form gate, audience mismatch, message mismatch, and routing issue branches.

How often does the article say to run the source quality audit? The article says to run this audit monthly, calculating SQL count, opportunities, opp rate, and average deal size per source over a 90-day window. Each source is then judged as qualified, audit, no, or kill. Sources below 18% SQL→Opp are killed unless they justify themselves as volume plays.