MQL

An MQL is a lead whose behaviour and fit suggest that speaking to sales would feel natural rather than intrusive. In other words, marketing has collected enough signals—page views, downloads, role, company size—to believe the person could buy within a sensible timeframe.

• A SaaS finance director who attended a live ROI webinar and downloaded the pricing sheet.
• A manufacturing COO who spent ten minutes on your case-study library and booked a product-tour slot.
  

Both have crossed the invisible line between casual interest and genuine consideration, so they earn the “Marketing-Qualified” badge in the CRM.

What is not an MQL?

• A student using a generic Gmail address to grab your ebook.
• A competitor’s intern subscribing to your newsletter.
• A CEO who skimmed one blog post and vanished.
  

Those contacts may stay in nurture lists, but until they show stronger intent or a closer ICP fit, they remain ordinary leads.

HubSpot lets you build smart lists: “Lifecycle stage → Lead” AND “Page views ≥ 3” AND “Persona = Finance leader”.

Pipedrive makes you push a Lead into a Pipeline stage once it earns MQL status—simple but stops leakage.

Aligns marketing and sales expectations

When both teams agree on the exact engagement and fit required, hand-offs become smooth and political friction drops. Sales trusts the list because it contains budget-holding roles from the right industries; marketing enjoys faster feedback because reps actually follow up.

Protects sales bandwidth for high-value conversations

SDRs and account executives have finite capacity. Flooding them with every ebook downloader wastes prime call hours. A clear MQL filter—say, score ≥ 65 and seniority director+—means sales spends time only on contacts likely to progress, keeping morale and close rates healthy.

Turns vague interest into predictable pipeline metrics

Because MQLs sit one rung below SQLs and Opportunities, their weekly count offers an early revenue forecast. If MQL creation slows in July, you know September pipeline will look lean. Corrective action—new campaigns, bigger ad spend—can start weeks earlier than if you waited for SQL or deal signals.

Improves message personalisation

Scoring criteria (industry, role, behaviour) segment contacts automatically. A management consultancy can send CFO-specific case studies to finance MQLs while emailing “team performance audit” offers to HR MQLs, lifting conversion without manual list slicing.

Feeds remarketing and look-alike audiences

Uploading only MQL email hashes to LinkedIn or Google builds high-quality look-alike seed lists. Ads then reach prospects who resemble proven engagers, stretching paid budgets further than broad targeting ever could.

Define crisp fit and engagement criteria

Start with your ICP spreadsheet: target industries, firm size, tech stack, job titles and regions. Add engagement milestones that show genuine intent—pricing page view, webinar attendance, asset download, chatbot question. Publish the matrix so everyone can reference it.

Example (digital agency):

• Fit: SaaS, 10–200 employees, C-level or VP Marketing.
• Engagement: Viewed portfolio page + downloaded case study OR booked a 15-min discovery via Calendly.
  

Build a transparent lead-scoring model

Assign numeric weights. Fit traits might contribute 60 %, engagement 40 %. A finance director from a 150-employee SaaS gets 50 fit points; attending a live demo adds 40 intent points, totalling 90—above the 75-point MQL threshold. Use HubSpot’s native scoring or a Zapier pipe feeding Pipedrive custom fields. Keep the formula public so sales trusts the math.

Automate alerts and routing

When a lead crosses the threshold, trigger:

• Slack ping to the assigned SDR.
• Auto-creation of a task “Call within 24 h”.
• Removal from generic nurture sequences.
• Speed matters—responses within one hour can double connection rates. Automations guarantee no hot lead languishes in a spreadsheet.
  

Nurture leads falling just short of MQL status

A CTO who scored 60/75 may need a nudge: invite to a technical webinar, send a focused case study, or offer a no-obligation audit. Marketing automation (HubSpot workflows, ActiveCampaign, Customer.io) drips value until the lead self-qualifies.

Review and refine every quarter

Pull conversion data: Lead → MQL, MQL → SQL, SQL → Closed-Won. If MQL-to-SQL is under 20 %, fit criteria are too loose; tighten industry filters or raise the score threshold. If conversion is over 60 % but MQL volume starves sales, loosen criteria or create fresh engagement hooks. Continuous calibration keeps quality and quantity in balance.

Practical examples

Architecture firm: Fit—property developers with ≥ £5 m build budget; Engagement—download BIM integration checklist.

Bookkeeping agency: Fit—UK SaaS, ARR £1–10 m, Finance Lead; Engagement—uploads trial data + views pricing.

Cyber-security MSP: Fit—Healthcare or fintech; Engagement—attends “Zero-trust roadmap” webinar + requests breach-cost calculator.

Documenting these rules in the CRM and living by them ensures every MQL handed to sales is truly worth a call—raising pipeline accuracy, win rates and team trust across the funnel.