.svg)
Glossary:
Lead Management & Qualification
Master the essential revenue and financial metrics that drive B2B SaaS success. From ARR and MRR to retention metrics and customer economics, these terms are critical for understanding pipeline health, forecasting growth, and making data-driven decisions.
Marketing Qualified Lead (MQL)
Short Definition
Definition
A Marketing Qualified Lead (MQL) represents a lead who has shown enough engagement with marketing content and fits the Ideal Customer Profile (ICP) well enough to justify sales outreach. MQLs are created through lead scoring models that combine explicit criteria (job title, company size, industry) and implicit behaviors (content downloads, webinar attendance, email engagement).
The MQL represents a critical handoff point between marketing and sales, where marketing signals "this lead is worth your time" and sales validates through discovery conversations. MQLs are distinct from Sales Qualified Leads (SQLs), which have confirmed buyer intent through live qualification.
Why MQLs Matter
MQLs serve as the quality filter that determines sales productivity and pipeline health:
- Sales Capacity Protection: Only 20-40% of raw leads become MQLs, preventing SDRs from wasting time on low-fit contacts.
- Marketing Accountability: MQL-to-SQL conversion measures marketing's ability to generate sales-ready leads.
- Revenue Attribution: MQL source analysis reveals which channels (content, paid, events) drive downstream pipeline value.
- Forecasting Signal: Consistent MQL volume provides early visibility into pipeline generation trends.
- GTM Alignment: Joint MQL definition forces marketing-sales agreement on lead quality standards.
How to Score MQLs
- Define Fit Criteria: Firmographics (company size 50-5000, target industries, senior titles).
- Define Behavior Criteria: Minimum engagement profile (3+ pages viewed, content download, webinar).
- Assign Scores: Explicit (job title VP+=30, target industry=20) + Implicit (webinar=50, email open=5).
- Set Threshold: Typically 50-75 combined score triggers MQL status.
- Automate Routing: MQLs trigger instant SDR assignment (<5 min SLA).
Example Scoring Model
MQL Lifecycle Progression
Industry Benchmarks
Common Mistakes
- Overly Lenient Thresholds: Volume-focused MQLs that waste sales time (target 30-50% MQL-to-SQL).
- No Negative Scoring: Failing to filter out bad-fit leads (competitors, students, wrong geo).
- Static Models: Not recalibrating scores based on actual pipeline outcomes quarterly.
- Definition Misalignment: Marketing and sales disagreeing on MQL criteria.
- Slow Routing: MQLs sitting >30min before SDR contact.
The Fix: Quarterly score calibration using pipeline data, joint MQL definition workshops, <5min routing automation, regular conversion analysis.
Frequently Asked Questions
What qualifies someone as an MQL versus SQL?
MQLs meet automated scoring thresholds indicating fit and interest. SQLs have confirmed buyer intent, budget, timeline, and decision process through live sales qualification.
How often should MQL scoring be recalibrated?
Quarterly, using actual MQL-to-SQL and MQL-to-opportunity conversion data to ensure scores predict sales outcomes.
Should PLG companies use MQLs?
Yes, but usage-based (e.g., trial activation, feature adoption, seat growth) signal MQL status rather than traditional content engagement.
What MQL-to-SQL conversion is realistic?
Median: 25-35%
Best-in-Class: 40-50%
Below 20% indicates poor scoring or ICP fit.
Who owns MQL definition, marketing or sales?
Joint ownership. Marketing owns score execution, sales defines qualification outcomes that calibrate the model.