Percent of MQLs Meeting Qualification Criteria¶

Definition¶

Percent of MQLs Meeting Qualification Criteria measures the proportion of Marketing Qualified Leads that meet your company’s agreed-upon criteria for sales follow-up (e.g., ICP fit, budget, intent). It helps assess lead quality and marketing-to-sales alignment.

Description¶

Percent of MQLs Meeting Qualification Criteria is a vital check on lead quality and pipeline efficiency, reflecting how well marketing-generated leads match the standards and expectations of the sales team — beyond surface-level scoring.

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

In B2B SaaS, qualification might include firmographics (e.g., industry, company size), behavioral signals (e.g., demo requests, high-intent content), and role relevance
In eCommerce or SMB markets, it might include purchase intent, budget alignment, or industry fit
In hybrid funnels, it surfaces how well marketing and sales definitions of “qualified” are aligned

A high percentage suggests strong ICP targeting, refined scoring, and efficient funnel mechanics. A low percentage = wasted budget and frustration for sales. By segmenting by campaign, content type, or geography, you can fine-tune both lead generation and qualification processes.

Percent of MQLs Meeting Qualification Criteria informs:

Strategic decisions, like evolving ICPs or refining lead definitions
Tactical actions, such as reallocating budget or adjusting scoring logic
Operational improvements, including alignment workshops between marketing and sales
Cross-functional alignment, by syncing teams around high-quality, conversion-ready pipeline creation

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 Source and Intent Signal Strength: Leads from content downloads behave differently than leads from intent-based sources like G2 or demo requests. Quality varies by source — and so should your expectations.
Accuracy of Marketing Filters and Targeting: If you're not filtering by ICP or firmographic data, you're passing leads that aren’t truly qualified. Better targeting leads to better MQL-to-SQL conversion.
Alignment Between Marketing and Sales on MQL Criteria: If teams don’t agree on what “qualified” means, you're setting yourself up for finger-pointing and inefficiency. Criteria must be shared and actionable.

Improvement Tactics & Quick Wins¶

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

If too many MQLs are disqualified by sales, audit top lead sources and update forms or targeting to collect richer qualification data (e.g., role, company size).
Add progressive profiling to capture missing MQL criteria over time, especially if your forms are short by design.
Run a test scoring MQLs differently by source type, and track downstream performance (e.g., content lead vs. product sign-up).
Refine lead handoff SLAs with sales, ensuring timely follow-up for MQLs that do meet criteria — otherwise quality is wasted.
Partner with RevOps or Demand Gen to rerun lookalike modeling on your best-converting MQLs, and update targeting based on that cohort.

Required Datapoints to calculate the metric
- Total MQLs: All leads flagged as marketing-qualified.
- MQLs Meeting Sales Criteria: Number that match agreed sales qualification (e.g., passed BANT/CHAMP filters).
- Qualification Framework: Shared criteria used by sales/marketing.
Example to show how the metric is derived

Over one month:
- Total MQLs: 500
- Qualified by Sales: 275
- Formula: (275 ÷ 500) × 100 = 55%

Formula¶

Formula

\[ \mathrm{Percent\ of\ MQLs\ Meeting\ Criteria} = \left( \frac{\mathrm{Qualified\ MQLs}}{\mathrm{Total\ MQLs}} \right) \times 100 \]

Data Model Definition¶

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

MqlsCube

cube('MqlsCube', {
  sql: `SELECT * FROM mqls`,

  measures: {
    totalMqls: {
      sql: `total_mqls`,
      type: 'sum',
      title: 'Total MQLs',
      description: 'Total number of Marketing Qualified Leads.'
    },
    mqlsMeetingCriteria: {
      sql: `mqls_meeting_sales_criteria`,
      type: 'sum',
      title: 'MQLs Meeting Sales Criteria',
      description: 'Number of MQLs that meet the sales qualification criteria.'
    },
    percentMqlsMeetingCriteria: {
      sql: `100.0 * ${mqlsMeetingCriteria} / NULLIF(${totalMqls}, 0)` ,
      type: 'number',
      title: 'Percent of MQLs Meeting Qualification Criteria',
      description: 'Percentage of MQLs that meet the sales qualification criteria.'
    }
  },

  dimensions: {
    id: {
      sql: `id`,
      type: 'number',
      primaryKey: true,
      title: 'ID',
      description: 'Unique identifier for each MQL.'
    },
    qualificationFramework: {
      sql: `qualification_framework`,
      type: 'string',
      title: 'Qualification Framework',
      description: 'Criteria used by sales and marketing for qualification.'
    },
    createdAt: {
      sql: `created_at`,
      type: 'time',
      title: 'Created At',
      description: 'Timestamp when the MQL was created.'
    }
  }
});

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 Source and Intent Signal Strength: Leads from low-intent sources such as generic content downloads often do not meet the qualification criteria, reducing the overall percentage of MQLs that are considered qualified.
- Accuracy of Marketing Filters and Targeting: Inaccurate targeting and filtering can result in a higher number of unqualified leads being passed as MQLs, negatively impacting the percentage of MQLs meeting the qualification criteria.
- Alignment Between Marketing and Sales on MQL Criteria: Lack of alignment between marketing and sales on what constitutes a qualified lead can lead to discrepancies in qualification, reducing the percentage of MQLs meeting the criteria.
- Lead Quality from Non-ICP Sources: Leads that do not fit the Ideal Customer Profile (ICP) are less likely to meet qualification criteria, thus lowering the percentage of qualified MQLs.
- Inconsistent Data Collection Processes: Inconsistent or inaccurate data collection can lead to misclassification of leads, negatively affecting the percentage of MQLs that meet the qualification criteria.
Positive influences

Factors that push the metric in a favorable direction, supporting growth or improvement.
- Lead Source and Intent Signal Strength: Leads from high-intent sources such as demo requests or intent-based platforms are more likely to meet qualification criteria, increasing the percentage of qualified MQLs.
- Accuracy of Marketing Filters and Targeting: Effective targeting and filtering by ICP and firmographic data ensure that only truly qualified leads are passed as MQLs, improving the percentage meeting the criteria.
- Alignment Between Marketing and Sales on MQL Criteria: Strong alignment between marketing and sales on MQL criteria ensures that only leads meeting agreed-upon standards are qualified, increasing the percentage of MQLs meeting the criteria.
- Use of Advanced Lead Scoring Models: Implementing advanced lead scoring models that accurately assess lead quality can enhance the percentage of MQLs meeting qualification criteria.
- Continuous Feedback Loop Between Marketing and Sales: A continuous feedback loop between marketing and sales allows for ongoing refinement of qualification criteria, positively impacting the percentage of MQLs that meet the criteria.

Involved Roles & Activities¶

Involved Roles

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

Marketing Operations
Product Marketing (PMM)
Revenue Operations
Sales Manager
Sales Operations
Activities

Common initiatives or actions associated with this KPI:

Lead Scoring
Campaign Optimization
Funnel Health Reviews
ICP Alignment

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.
- Lead Quality Score: Lead Quality Score provides an early, quantitative assessment of leads' fit and intent before MQLs are formally qualified. Improving this score typically results in a higher Percent of MQLs Meeting Qualification Criteria, as higher-scoring leads are more likely to meet downstream sales requirements.
- Product Qualified Leads: The volume and quality of Product Qualified Leads (PQLs) act as an upstream signal for the likelihood that MQLs will meet sales qualification criteria. More PQLs indicate that users have engaged meaningfully with the product, which correlates with higher-quality, sales-ready MQLs.
- Marketing Qualified Leads (MQLs): The overall count and definition of MQLs directly influences the denominator for the Percent of MQLs Meeting Qualification Criteria. Changes in how MQLs are identified or in their volume will impact the proportion that meet further qualification thresholds.
- Activation Rate: A high Activation Rate indicates users are quickly reaching product value milestones, suggesting that leads are highly engaged and more likely to meet sales-defined qualification criteria as MQLs.
- Deal Velocity: Faster Deal Velocity for leads accepted into the pipeline signals clearer fit and readiness, reflecting that MQLs are well-qualified. When deal velocity increases, it's often because a higher share of MQLs meet required criteria and move efficiently through the funnel.
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: Conversion Rate quantifies how many leads (including MQLs) actually complete a desired action, such as becoming customers. It confirms the impact of MQL qualification quality on overall funnel efficiency.
- Opportunity Creation Velocity (from MQL): This metric measures how quickly MQLs convert to sales opportunities. A higher percent of qualified MQLs typically results in faster opportunity creation, validating the alignment of qualification criteria with sales-readiness.
- SQL-to-Opportunity Conversion Rate: Tracks the progression of Sales Qualified Leads (which originate from MQLs) into opportunities. If more MQLs meet qualification criteria, a higher SQL-to-Opportunity Conversion Rate follows, reinforcing the effectiveness of initial qualification.
- Customer Churn Rate: While longer-term, poor MQL qualification can lead to more churned customers due to misaligned expectations or poor fit. An increase in the percent of MQLs meeting criteria is expected to reduce downstream churn.
- Revenue Growth: Ultimately, better-qualified MQLs drive higher win rates and better-fit customers, which translates to stronger revenue growth. This metric quantifies the business impact of improvements in MQL qualification processes.