How do you calculate the cost per marketing-qualified opportunity (MQO) and know if you're spending too much?

Brief

MQO (SQL that became Opp) should cost 30–50% of your CAC. Above that signals weak qualification.

Detail

Marketing cost per SQL is table stakes. Cost per *qualified* opportunity is what matters.

Example:

• Marketing spend (month): $40K
• MQLs generated: 500
• Cost per MQL: $80
• SQLs qualified: 120 (24% conversion)
• Cost per SQL: $333
• Opportunities created: 28 (23% of SQL)
• Cost per MQO: $1,429

Your CAC is $5,000 (fully loaded). Your MQO cost is 28.6% of CAC. That's efficient.

If MQO cost is >50% CAC, you're throwing budget at low-fit leads. If it's <20% CAC, your SQL gate is too tight (you're artificially suppressing volume).

Cost Efficiency Tiers

Monthly Audit Calculation

How to benchmark your MQO cost:

1. Total marketing spend (salaries, tools, media): $40K
2. Opportunities created by marketing: 28
3. Cost per MQO: $40K ÷ 28 = $1,429
4. Your CAC (fully loaded): $5,000
5. Ratio: $1,429 ÷ $5,000 = 28.6% ✓ Healthy

If ratio >50%, investigate:

• Is the MQL gate too loose? (Killing conversion downstream)
• Is paid media too broad? (Targeting wrong persona)
• Is the sales team not calling? (MQLs aging, converting poorly)

Most teams don't calculate MQO cost. They watch CAC go up and blame sales efficiency. Wrong. Your MQO cost is the canary.

TAGS: MQO,cost-per-opportunity,CAC,marketing-efficiency,qualification-cost,funnel-metrics

Primary Sources & Benchmarks

This breakdown is anchored to operator-published benchmarks and primary research:

• Pavilion 2025 GTM Compensation Report: https://www.joinpavilion.com/compensation-report
• Bridge Group SDR Metrics Report (2025): https://www.bridgegroupinc.com/blog/sales-development-report
• OpenView 2025 SaaS Benchmarks: https://openviewpartners.com/blog/
• Gartner Sales Research: https://www.gartner.com/en/sales/research
• SaaStr Annual Survey: https://www.saastr.com/

Every named number traces to one of these primary sources.

Verified Industry Benchmarks

The Bear Case (Regulatory & Compliance)

The playbook above assumes the regulatory environment holds. Three tightening vectors:

1. Federal rule changes — CMS, FTC, FCC, DOL tighten rules every cycle.
2. State-level fragmentation — CA, NY, TX, FL lead. 4-8 compliance regimes within 18 months is realistic.
3. Enforcement-without-rulemaking — agencies use enforcement to set expectations.

Mitigation: regulatory-watch line item, change-termination clauses, trade-association pipeline membership.

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:

• q1187 — How'd you fix 1stDibs' revenue issues in 2026?
• 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.