SQL-to-Opportunity Conversion Rate¶

Definition¶

SQL-to-Opportunity Conversion Rate measures the percentage of Sales Qualified Leads (SQLs) that progress to become sales opportunities. It reflects how effectively your sales team converts qualified leads into actionable opportunities.

Description¶

SQL-to-Opportunity Conversion Rate is a key indicator of lead quality and sales readiness, reflecting how often sales-qualified leads progress into real pipeline opportunities.

The relevance and interpretation of this metric shift depending on the model or product:

In SaaS, it reflects how tightly aligned the MQL→SQL→Opportunity criteria are between marketing and sales
In B2B services, it shows how well discovery calls or demos turn into scoped opportunities
In PLG, it tracks PQLs or activated users that receive sales outreach and enter a deal cycle

A high conversion rate suggests qualification criteria are working. A low rate might mean misaligned definitions or weak sales handoff. By segmenting by acquisition source, SDR, region, or vertical, you can improve targeting, messaging, and sales enablement.

SQL-to-Opportunity Conversion Rate informs:

Strategic decisions, like redefining SQL thresholds or persona prioritization
Tactical actions, such as better training SDRs or refining objection handling
Operational improvements, including lead scoring models and handoff SLAs
Cross-functional alignment, ensuring sales and marketing focus on high-fit, high-converting pipeline

Key Drivers¶

These are the main factors that directly impact the metric. Understanding these lets you know what levers you can pull to improve the outcome

Lead Fit and Intent Scoring: SQLs that look good on paper but lack buying signals won’t convert.
Sales Follow-Up Speed and Context: Slow, generic outreach kills momentum.
Alignment on Qualification Criteria: If SDR and AE definitions differ, the funnel breaks.

Improvement Tactics & Quick Wins¶

Actionable ideas to optimize this KPI, from fast, low-effort wins to strategic initiatives that drive measurable impact.

If conversion is low, audit top lost SQLs and identify common disqualifiers.
Add enriched data (intent, tech stack, revenue) at handoff to help AEs close faster.
Run sales–SDR alignment sessions to unify definitions and ICP triggers.
Refine SDR playbooks to focus discovery on urgency and timeline — not just fit.
Partner with RevOps to measure conversion rate by source and SDR.

Required Datapoints to calculate the metric
- Total SQLs: The number of leads deemed sales-ready and passed to the sales team.
- Converted Opportunities: The number of SQLs that transition into sales opportunities.
- Time Period: The timeframe during which conversions are measured (e.g., monthly, quarterly).
Example to show how the metric is derived

A B2B SaaS company tracks the following in Q1:
- Total SQLs: 1,000
- Converted Opportunities: 400
- SQL-to-Opportunity Conversion Rate = (400 / 1,000) × 100 = 40%

Formula¶

Formula

\[ \mathrm{SQL\text{-}to\text{-}Opportunity\ Conversion\ Rate} = \left( \frac{\mathrm{Converted\ Opportunities}}{\mathrm{Total\ SQLs}} \right) \times 100 \]

Data Model Definition¶

How this KPI is structured in Cube.js, including its key measures, dimensions, and calculation logic for consistent reporting.

LeadsOpportunitiesSQLToOpportunityConversionRate

cube('Leads', {
  sql: `SELECT * FROM leads`,
  measures: {
    totalSQLs: {
      sql: 'id',
      type: 'count',
      title: 'Total SQLs',
      description: 'The number of leads deemed sales-ready and passed to the sales team.'
    }
  },
  dimensions: {
    id: {
      sql: 'id',
      type: 'string',
      primaryKey: true
    },
    createdAt: {
      sql: 'created_at',
      type: 'time',
      title: 'Lead Created At'
    }
  }
})

cube('Opportunities', {
  sql: `SELECT * FROM opportunities`,
  measures: {
    convertedOpportunities: {
      sql: 'lead_id',
      type: 'countDistinct',
      title: 'Converted Opportunities',
      description: 'The number of SQLs that transition into sales opportunities.'
    }
  },
  dimensions: {
    id: {
      sql: 'id',
      type: 'string',
      primaryKey: true
    },
    conversionDate: {
      sql: 'conversion_date',
      type: 'time',
      title: 'Opportunity Conversion Date'
    }
  }
})

cube('SQLToOpportunityConversionRate', {
  sql: `SELECT
    leads.id AS lead_id,
    opportunities.id AS opportunity_id,
    leads.created_at AS lead_created_at,
    opportunities.conversion_date AS opportunity_conversion_date
  FROM
    leads
  LEFT JOIN
    opportunities ON leads.id = opportunities.lead_id`,
  measures: {
    conversionRate: {
      sql: `CASE WHEN COUNT(opportunities.id) > 0 THEN COUNT(opportunities.id) / COUNT(leads.id) ELSE 0 END`,
      type: 'number',
      title: 'SQL-to-Opportunity Conversion Rate',
      description: 'Measures the percentage of Sales Qualified Leads (SQLs) that progress to become sales opportunities.'
    }
  },
  dimensions: {
    leadId: {
      sql: 'lead_id',
      type: 'string'
    },
    opportunityId: {
      sql: 'opportunity_id',
      type: 'string'
    },
    leadCreatedAt: {
      sql: 'lead_created_at',
      type: 'time',
      title: 'Lead Created At'
    },
    opportunityConversionDate: {
      sql: 'opportunity_conversion_date',
      type: 'time',
      title: 'Opportunity Conversion Date'
    }
  }
})

Note: This is a reference implementation and should be used as a starting point. You’ll need to adapt it to match your own data model and schema

Positive & Negative Influences¶

Negative influences

Factors that drive the metric in an undesirable direction, often signaling risk or decline.
- Lead Fit and Intent Scoring: Poor scoring leads to unqualified SQLs, reducing conversion rates.
- Sales Follow-Up Speed and Context: Delayed or impersonal follow-ups decrease the likelihood of conversion.
- Alignment on Qualification Criteria: Misalignment causes unqualified SQLs to enter the funnel, lowering conversion rates.
- Lead Source Quality: Low-quality lead sources result in SQLs that are less likely to convert.
- Sales Team Turnover: High turnover disrupts relationships and reduces conversion effectiveness.
Positive influences

Factors that push the metric in a favorable direction, supporting growth or improvement.
- Lead Fit and Intent Scoring: High-quality scoring ensures SQLs have genuine buying signals, increasing conversion likelihood.
- Sales Follow-Up Speed and Context: Quick, personalized follow-ups maintain momentum and improve conversion rates.
- Alignment on Qualification Criteria: Consistent criteria between SDRs and AEs ensures SQLs are genuinely qualified, enhancing conversion.
- Sales Training and Enablement: Well-trained sales teams are more effective at converting SQLs into opportunities.
- CRM Data Accuracy: Accurate data helps in targeting the right leads, improving conversion rates.

Involved Roles & Activities¶

Involved Roles

These roles are typically responsible for implementing or monitoring this KPI:

Marketing
Revenue Operations
Sales Manager
Activities

Common initiatives or actions associated with this KPI:

Sales Enablement
Lead and Demand Generation
MQL-to-SQL Process Optimization

Funnel Stage & Type¶

AAARRR Funnel Stage

This KPI is associated with the following stages in the AAARRR (Pirate Metrics) funnel:

Acquisition
Type

This KPI is classified as a Lagging Indicator. It reflects the results of past actions or behaviors and is used to validate performance or assess the impact of previous strategies.

Supporting Leading & Lagging Metrics¶

Leading

These leading indicators influence this KPI and act as early signals that forecast future changes in this KPI.
- Product Qualified Leads: Product Qualified Leads (PQLs) are a direct precursor to SQLs becoming opportunities. A high volume or quality of PQLs increases the pool of leads meeting usage or engagement criteria, which enhances the likelihood of higher SQL-to-Opportunity conversion rates.
- Lead Quality Score: Lead Quality Score provides an early signal of how likely SQLs are to convert to opportunities. Higher-quality leads that become SQLs are more apt to progress, directly influencing conversion rate trends.
- Sales Qualified Leads: The volume and quality of Sales Qualified Leads are the denominator for the SQL-to-Opportunity Conversion Rate. An increase in well-qualified SQLs provides more 'ready' leads that can be worked into opportunities, boosting conversion rates.
- Deal Velocity: Deal Velocity measures the speed at which SQLs move through the pipeline to become opportunities. Faster-moving deals often signal more engaged or better-qualified SQLs, forecasting higher conversion rates.
- Lead-to-Opportunity Conversion Rate: This is a direct upstream indicator of the SQL-to-Opportunity Conversion Rate, as a higher lead-to-opportunity conversion suggests strong lead nurturing and qualification, positively impacting SQL progression.
Lagging

These lagging indicators confirm, quantify, or amplify this KPI and help explain the broader business impact on this KPI after the fact.
- Conversion Rate: Overall Conversion Rate helps recalibrate expectations for SQL-to-Opportunity progression. If general conversions are trending down, it can signal market or funnel issues that could lower SQL conversion rates.
- Percent of MQLs Meeting Qualification Criteria: Examines the quality of MQLs that eventually become SQLs. If fewer MQLs meet criteria, it may indicate that SQLs are also less qualified, requiring recalibration of SQL-to-Opportunity expectations.
- Opportunity Creation Velocity (from MQL): Measures how quickly MQLs become opportunities. Slower velocity may signal process or qualification issues, informing adjustments to SQL-to-Opportunity conversion strategies.
- Percent of Accounts Reaching Product-Qualified Lead (PQL) Status: Provides feedback on the effectiveness of product-led qualification. If fewer accounts reach PQL status, the SQL pool may shrink or become less qualified, prompting adjustments to upstream indicators.
- Signup Completion Rate: If fewer users complete signup, the pool of leads entering the funnel shrinks, eventually impacting SQL volume and conversion rates. Insights on signup completion can prompt re-evaluation of lead generation and SQL criteria.