Marketing Qualified Leads (MQLs) (MQL)¶

Definition¶

Marketing Qualified Leads (MQLs) are leads that have shown enough interest or engagement with your brand to be considered potential customers. They meet specific criteria that indicate they are ready to be handed over to the sales team for further nurturing.

Description¶

Marketing Qualified Leads (MQLs) are a key indicator of top-of-funnel performance and lead readiness, reflecting how many leads meet your criteria for likely conversion, based on engagement and fit.

This metric plays different roles depending on GTM maturity:

In B2B SaaS, it’s defined by content interaction + firmographic filters
In enterprise, it may include intent signals, job titles, and tiered scoring
In product-led models, it blends usage signals and persona fit

A steady or rising MQL volume signals effective demand gen, while a drop—or too many low-quality MQLs—may indicate scoring misalignment or campaign fatigue. By segmenting by channel, campaign, or industry, you can optimize content, budget allocation, and lead handoff to sales.

MQLs inform:

Strategic decisions, like targeting refinements and funnel structure
Tactical actions, such as adjusting scoring models or content mapping
Operational improvements, including handoff processes or SDR SLAs
Cross-functional alignment, helping marketing, RevOps, and sales work off a common definition of “qualified”

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 Scoring Model Calibration: If your MQL definition is too loose or strict, you're either flooding or starving sales.
Content and Campaign Alignment to Buyer Stage: Mid-funnel, pain-solving content generates more MQLs than top-funnel fluff.
Channel Targeting Precision: Paid and organic efforts that hit the right personas yield better MQLs — not just more.

Improvement Tactics & Quick Wins¶

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

If MQL volume is low, revisit your scoring model and lower thresholds tied to real sales conversion signals.
Add lead capture gates to high-intent content (e.g., templates, tools, ROI guides).
Run a test offering interactive content (quizzes, calculators) to increase opt-ins and lead score.
Refine paid targeting to map more tightly to ICP titles, company sizes, and intent signals.
Partner with sales to validate what really converts from MQL to SQL — and revise scoring accordingly.

Required Datapoints to calculate the metric
- Lead Engagement: Actions such as form submissions, email clicks, webinar attendance, or content downloads.
- Demographics or Firmographics: Data such as job title, company size, or industry to match your ideal customer profile.
- Scoring Criteria: A predefined scoring system that assigns points to specific behaviors and attributes.
Example to show how the metric is derived

A SaaS company identifies MQLs based on:
- Behavioral Actions: 3 website visits, content download, and email engagement.
- Demographics: Job titles like “IT Manager” at companies with 50+ employees.
- Leads meeting these criteria are flagged as MQLs and routed to sales.

Formula¶

Formula

\[ \mathrm{MQL\ Conversion\ Rate} = \left( \frac{\mathrm{Number\ of\ MQLs\ Converted\ to\ SQLs}}{\mathrm{Total\ Number\ of\ MQLs}} \right) \times 100 \]

Data Model Definition¶

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

Leads

cube('Leads', {
  sql: `SELECT * FROM leads`,

  measures: {
    mqlCount: {
      sql: `id`,
      type: 'count',
      title: 'MQL Count',
      description: 'Count of Marketing Qualified Leads based on engagement and scoring criteria.'
    }
  },

  dimensions: {
    id: {
      sql: `id`,
      type: 'string',
      primaryKey: true,
      title: 'Lead ID',
      description: 'Unique identifier for each lead.'
    },

    engagementType: {
      sql: `engagement_type`,
      type: 'string',
      title: 'Engagement Type',
      description: 'Type of engagement action performed by the lead.'
    },

    jobTitle: {
      sql: `job_title`,
      type: 'string',
      title: 'Job Title',
      description: 'Job title of the lead.'
    },

    companySize: {
      sql: `company_size`,
      type: 'string',
      title: 'Company Size',
      description: 'Size of the company the lead is associated with.'
    },

    industry: {
      sql: `industry`,
      type: 'string',
      title: 'Industry',
      description: 'Industry of the lead's company.'
    },

    score: {
      sql: `score`,
      type: 'number',
      title: 'Lead Score',
      description: 'Score assigned to the lead based on engagement and demographics.'
    },

    createdAt: {
      sql: `created_at`,
      type: 'time',
      title: 'Created At',
      description: 'Timestamp when the lead 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 Scoring Model Calibration: An improperly calibrated lead scoring model can either flood the sales team with unqualified leads or starve them of potential MQLs, negatively impacting the MQL count.
- Content Misalignment: Content that does not address the needs or stage of the buyer can lead to disengagement, reducing the number of MQLs.
- Poor Channel Targeting: Ineffective targeting of marketing channels can result in reaching the wrong audience, decreasing the quality and quantity of MQLs.
- Low Engagement Rate: Low engagement with marketing efforts suggests a lack of interest, reducing the likelihood of leads becoming MQLs.
- High Bounce Rate: A high bounce rate on landing pages can indicate that the content or offer is not resonating with visitors, leading to fewer MQLs.
Positive influences

Factors that push the metric in a favorable direction, supporting growth or improvement.
- Lead Scoring Model Calibration: A well-calibrated lead scoring model ensures that only leads with a high likelihood of conversion are marked as MQLs, increasing the quality and potential conversion rate of MQLs.
- Content and Campaign Alignment to Buyer Stage: Creating content that aligns with the buyer's journey, particularly mid-funnel content, increases engagement and the likelihood of leads becoming MQLs.
- Channel Targeting Precision: Accurate targeting of marketing channels to reach the right personas results in higher quality leads that are more likely to become MQLs.
- Engagement Rate: Higher engagement rates with marketing content indicate a greater interest from leads, increasing the likelihood of them becoming MQLs.
- Conversion Rate Optimization: Improving the conversion rate of landing pages and forms can lead to more leads qualifying as MQLs.

Involved Roles & Activities¶

Involved Roles

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

Demand Generation
Marketing
Product Marketing (PMM)
Sales Manager
Activities

Common initiatives or actions associated with this KPI:

Lead and Demand Generation
ICP Targeting
Nurture Campaigns

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 crucial early signal that indicates users have engaged deeply with the product and are highly likely to convert. An increase in PQLs often precedes and drives increases in MQLs, as product engagement often leads to marketing qualification.
- Unique Visitors: Unique Visitors reflects the top-of-funnel audience size and is a fundamental driver of new Marketing Qualified Leads (MQLs). Higher unique visitor volumes increase the pool of potential leads who can be engaged and qualified by marketing.
- Trial-to-Paid Conversion Rate: Trial-to-Paid Conversion Rate signals the effectiveness of nurturing and converting trial users, which can inform the quality and readiness of MQLs. Improvements in this metric often correlate with better-quality MQLs entering the funnel.
- Activation Rate: Activation Rate measures how many users reach a meaningful engagement milestone. High activation rates indicate effective onboarding and increased likelihood that engaged users will become MQLs.
- Lead Quality Score: Lead Quality Score assesses the conversion potential of leads, providing critical context to MQLs. A high lead quality score among incoming leads increases the chance that MQLs are sales-ready and will progress 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.
- Opportunity Creation Velocity (from MQL): This metric measures how quickly MQLs convert into sales opportunities, allowing marketing teams to recalibrate their lead qualification criteria and processes based on actual sales progression and feedback.
- Percent of MQLs Meeting Qualification Criteria: This directly quantifies the alignment between marketing's MQL criteria and sales' requirements, offering feedback to improve lead scoring and targeting for future MQL generation.
- Lead Response Time (Post-Onboarding): Measures the speed at which sales or success teams respond to newly onboarded MQLs, providing operational feedback that can improve lead handoff processes and MQL nurturing strategies.
- SQL-to-Opportunity Conversion Rate: Tracks the effectiveness of converting Sales Qualified Leads (which originate from MQLs) into opportunities. Trends in this metric inform marketing on whether their MQLs are adequately sales-ready.
- Conversion Rate: The overall conversion rate from MQL to customer quantifies and validates the effectiveness of MQL criteria and campaign quality, helping marketing optimize targeting and qualification rules.