MQL Query: How to Define, Score & Convert MQLs

What Is an MQL Query, Actually?

When people search "MQL query," they usually mean one of two things: either they want to understand what a Marketing Qualified Lead is and what criteria define one, or they want to know how to query their CRM (HubSpot, Salesforce, etc.) to surface and report on MQLs. This article covers both - because if you don't understand the criteria, your CRM query is meaningless, and if you can't pull the data, the criteria just live in a slide deck.

Let's start with the foundation. An MQL is a prospect your marketing team has flagged as more likely to become a customer than a generic lead - based on a defined set of behavioral and firmographic criteria. That's it. It's not magic. It's a filter you define, apply consistently, and then hand off to sales at the right moment.

The problem most teams have isn't that they don't know what an MQL is. It's that they've never written the criteria down precisely enough to actually build a working query. Marketing says "they showed interest" and sales says "that's not good enough." That gap costs real deals. And the data backs this up: research shows that 67% of lost sales opportunities stem directly from leads not being properly qualified before pursuit. That's not a sales problem - that's a definition problem.

Get the definition right, build the query around it, and the whole system starts working. Let's walk through how to do that from the ground up.

The Four Pillars of a Solid MQL Definition

Before you open your CRM and start building filters, you need a written definition. Here are the four criteria that almost always matter:

• Firmographic fit: Does this person work at the type of company you sell to? Job title, company size, industry, location - all of it. If you sell to marketing directors at SaaS companies with 50-500 employees, anyone outside that box shouldn't be burning your sales team's time.
• Behavioral engagement: What actions did they take? Page visits, content downloads, webinar registrations, email clicks, pricing page views - these signal intent. A blog reader is not the same as someone who downloaded your pricing guide and visited your case studies page twice.
• Lead score threshold: Assign point values to each action and firmographic match. A contact who hits a defined score threshold qualifies as an MQL automatically. This makes the handoff systematic instead of subjective.
• Recency: Engagement that happened eight months ago is not the same as engagement that happened last week. Build recency into your query. If someone scored 80 points but hasn't opened an email in 90 days, they shouldn't be auto-promoted to MQL.

Once you have these four elements documented and agreed upon by both marketing and sales, you can actually build a query that means something.

Explicit vs. Implicit MQL Criteria: Know the Difference

One thing most MQL articles gloss over is the difference between explicit and implicit scoring criteria - and this distinction is critical when you're building your query logic.

Explicit criteria is information you gather directly from the lead. Job title, company size, industry, location, annual revenue - data the prospect tells you, or that you verify through enrichment. This is your firmographic layer. It tells you whether this person could be a buyer based on who they are.

Implicit criteria is behavioral. It's inferred from what the lead does: pages visited, content downloaded, emails opened, webinar attended, pricing page viewed. This tells you whether this person is acting like a buyer right now.

Strong MQL queries combine both layers. Explicit criteria alone gives you a list of people who fit the profile but might not be interested yet. Implicit criteria alone gives you engaged visitors who might be completely wrong for your product - like a student researching your industry for a class project. You need both working together before a lead earns MQL status.

When you build your CRM filter, think of it as an AND gate: firmographic fit AND behavioral threshold AND recency. Remove any one of those legs and the stool falls over.

Building the MQL Query in Your CRM

The mechanics differ slightly by platform, but the logic is the same whether you're in HubSpot, Salesforce, or a CRM like Close.

In HubSpot

HubSpot uses a Lifecycle Stage property. The MQL stage sits between Lead and SQL in the standard pipeline. You can qualify contacts as MQLs two ways: manually (a rep or marketer updates the field) or automatically via a workflow triggered by lead score thresholds, form submissions, or behavioral criteria.

To build an MQL report in HubSpot, go to Reports - Create Report - Journey Reports, then select Lead, Marketing Qualified Lead, and Sales Qualified Lead from the Lifecycle Stage list. This gives you a funnel view so you can see exactly where contacts are dropping off between stages.

A practical HubSpot MQL query for a B2B SaaS company might look like this:

• Lifecycle Stage = MQL
• Company size = 50-500 employees
• Job title contains: Director, VP, Head of, Manager
• Last activity date = within last 30 days
• Lead score greater than or equal to 40

That query surfaces people who fit your ICP, are actively engaged, and scored above your threshold - not just anyone who once clicked a link.

One HubSpot-specific tip: once a contact meets your MQL criteria, set up a workflow that changes the Lifecycle Stage to MQL automatically AND creates a task for the assigned sales rep. Don't rely on reps to manually check a report. Push the notification to them so speed-to-follow-up stays tight. Research consistently shows that following up with a qualified lead within the first hour dramatically increases conversion rates compared to waiting 24 hours or more.

In Salesforce

Salesforce uses the Lead object as a top-of-funnel bucket. To report on MQLs, you typically create a custom field (like MQL_Status__c) or use the Lead Status picklist to tag records as MQL. Then build a report filtering on that field, filtered by whatever firmographic and behavioral properties you've captured.

A Salesforce MQL report setup that works well for most B2B teams:

• Object: Leads
• Filter: Lead Status = Marketing Qualified
• Filter: Lead Source not equal to Employee Referral (unless you track those separately)
• Filter: Last Activity Date within the last 45 days
• Filter: Company (not blank)
• Group by: Lead Source - so you can see which channels are producing MQL-quality traffic

One important thing to handle in Salesforce: duplicate lead records. If the same person registers for a webinar and also downloads a whitepaper, your system may create two separate lead records. That leads to two reps reaching out to the same person. Clean your data before the handoff. Salesforce's built-in Duplicate Management helps, but you may also want a dedicated enrichment layer.

Another Salesforce-specific consideration: if you're using Pardot (now Marketing Cloud Account Engagement) alongside Salesforce, your lead scoring happens in Pardot and syncs back to the CRM. Make sure your MQL threshold in Pardot matches what you've agreed on with sales - otherwise the automation promotes leads that your reps immediately reject.

In Close CRM

Close takes a simpler approach. There's no native Lifecycle Stage field the way HubSpot has it, but you can build the same logic using custom activity types, lead statuses, and smart views. Create a lead status called "MQL" and build a Smart View that filters by that status plus any custom fields you've set up for firmographic data. The result is a live-updating list of MQL-quality leads your reps can work from directly inside their outreach workflow.

The Outbound Angle (Most Articles Skip This)

Everything above assumes inbound - leads coming to you. But if you're running outbound, the "MQL" concept still applies; it just looks different. Instead of behavioral signals from your website, you're using firmographic filters to pre-qualify a prospect list before anyone has engaged at all.

In outbound, your MQL query is essentially your prospecting filter: Who fits the ICP well enough to be worth a cold email or a call? You're making that determination before engagement, not after.

To build that list, you need a solid B2B lead database. I use ScraperCity's B2B lead database to filter by job title, seniority, industry, and company size - the same filters you'd apply in a CRM MQL query, but applied upstream when you're building the list. If the firmographic fit isn't there, they don't make the outbound prospect list, period. That's your outbound MQL filter in action.

For finding verified email addresses once you've built your prospect list, this email finding tool can pull contact data so your outreach actually lands in the inbox. No point defining great MQL criteria if you can't reach the people who match them.

MQL vs. SQL: Where the Line Actually Is

The MQL is marketing's responsibility. The SQL is sales'. The line between them is where intent becomes explicit.

An MQL has taken actions that suggest interest: downloaded content, visited key pages, hit a lead score threshold. An SQL has gone further - requested a demo, asked about pricing, responded to a sales email, or been vetted by a rep and confirmed as a good fit.

Some teams add a middle layer called a Sales Accepted Lead (SAL). An SAL is a lead that's passed certain acceptance criteria but still needs further qualification before a senior rep treats it as a true opportunity. If your sales team is large enough that SDRs handle initial qualification before AEs take over, adding the SAL stage makes sense. It creates an accountability checkpoint so leads aren't just passed to AEs cold.

The practical handoff usually works like this:

• Marketing identifies MQLs via lead score or form submission
• A notification triggers in the CRM and assigns a sales task
• A sales rep reviews the contact, confirms ICP fit, and either accepts (- SQL) or rejects (- back to nurture)
• If accepted, the contact moves into the formal sales pipeline as an opportunity

The rejection step matters. When sales rejects an MQL, that's signal. If the same types of contacts keep getting rejected, your MQL criteria are too loose. Tighten the firmographic filter, raise the lead score threshold, or add a recency requirement. The criteria aren't static - they should evolve as you learn what actually converts.

Build a structured rejection reason into your CRM: "Wrong industry," "Wrong title," "No budget signal," "Too small," etc. That data is how you refine your query over time. Without it, marketing keeps sending the same types of bad leads and sales keeps rejecting them - and nobody fixes anything because neither side has the data to prove what's wrong.

MQL vs. PQL: When Product Signals Matter More

If you're running a SaaS product with a free trial or freemium tier, you have a third lead type worth knowing: the Product Qualified Lead (PQL). A PQL is someone who has already used your product and taken actions inside it that predict conversion - things like hitting a usage threshold, inviting teammates, or using a core feature multiple times.

PQLs are often higher-intent than MQLs because they've experienced actual product value, not just consumed marketing content. The scoring logic is similar but the data source is different: you're pulling behavioral signals from your product analytics tool (Mixpanel, Amplitude, or similar) rather than from web behavior and email engagement.

For most B2B teams that don't have a product-led motion, MQLs remain the primary qualification mechanism. But if you're running a PLG (product-led growth) model, you'll want to build PQL criteria alongside your MQL query and track them separately - because the conversion rates and deal sizes can be very different.

Lead Scoring: The Engine Behind the MQL Query

Lead scoring is what makes MQL queries automated and scalable. The basic concept: assign point values to actions and characteristics, set a threshold, and let the system promote leads automatically when they hit the number.

Here's a simple scoring framework you can adapt:

• +15 points: Requested a demo or contacted sales
• +10 points: Visited pricing page
• +10 points: Downloaded a high-intent content piece (case study, ROI calculator)
• +8 points: Attended a webinar live
• +5 points: Opened 3+ emails in a sequence
• +5 points: Visited the website 3+ times in 14 days
• +10 points: Job title matches ICP (Director/VP/C-Suite)
• +8 points: Company size in target range
• +5 points: Industry matches target verticals
• -10 points: Personal email domain (Gmail, Yahoo, etc.)
• -10 points: Company size outside target range
• -15 points: No activity in 60+ days

Set your MQL threshold somewhere in the 35-50 range and run it for 60 days. Then check: what percentage of MQLs are being accepted by sales? What percentage are converting to opportunities? If sales is rejecting more than 40% of your MQLs, your threshold is too low or your firmographic filters are too broad.

Score Degradation: The Part Most Teams Forget

One of the biggest mistakes I see in lead scoring setups is treating scores as permanent. A contact scores 60 points in one month from a flurry of activity, then goes dark for four months. They're still showing as a 60-point lead in your CRM. That's stale data masquerading as current intent.

Build score degradation into your model. A practical approach: subtract 5 points for every 30 days of inactivity after 45 days of no engagement. Once a lead drops below your MQL threshold because of inactivity, they should automatically move back to a nurture status - not remain in the MQL queue taking up sales bandwidth.

Most marketing automation platforms (HubSpot, Marketo, Pardot) support score degradation natively. If yours doesn't, a simple workflow that checks last activity date and resets the stage field can accomplish the same thing.

Multi-Threshold Scoring: Warm, Hot, and MQL

Rather than a single binary cutoff (MQL or not MQL), consider building multiple thresholds. A tiered system might look like this:

• Score 20-39: Warm lead. Enters an automated nurture email sequence. No sales touch yet.
• Score 40-59: Engaged lead. Marketing continues nurturing with more targeted content based on the specific pages and topics they've engaged with.
• Score 60+: MQL. Sales is notified and assigned a follow-up task within 24 hours.

This prevents your sales team from being overwhelmed with mediocre leads while also making sure warm leads don't fall through the cracks just because they haven't hit the MQL threshold yet. The middle tier gets marketing love until they're ready for sales.

How to Use Intent Data to Strengthen Your MQL Query

Behavioral signals from your own website are valuable, but they only capture what people do on your properties. Intent data adds a third layer: what are prospects researching across the web right now?

Tools like Dealfront (formerly Leadfeeder) show you which companies are visiting your website even when the individual hasn't filled out a form. You can see the company name, the pages they visited, and how often - even for anonymous traffic. That data can trigger an MQL flag or at minimum flag the account for outbound follow-up.

Third-party intent platforms go further. They track keyword research behavior across millions of B2B content sites and identify accounts actively researching topics relevant to your product - even if they've never visited your site. When you layer that intent signal on top of your firmographic filters, you get a much tighter outbound MQL list than firmographics alone would produce.

The practical setup: pull your ICP-fit accounts from a B2B lead database, cross-reference with intent signal data to prioritize which accounts are actively researching right now, and use that as your outbound prospecting queue. That's your pre-intent MQL filter working upstream of your CRM.

Building the Prospect List That Feeds Your MQL Query

If you're running outbound alongside inbound, you need a way to source the right contacts before your nurture sequences even start. A few tools worth knowing:

• ScraperCity B2B Email Database: Filter by job title, seniority, industry, location, and company size to build prospect lists that match your MQL firmographic criteria from the start.
• Clay: Powerful for enriching prospect lists with intent data and building automated outbound sequences that respond to firmographic and behavioral triggers.
• Lemlist: Solid for running multi-channel outbound sequences to your MQL-quality prospect lists.
• Smartlead: Good for high-volume cold email sending with deliverability baked in.
• Reply.io: Handles multichannel sequences (email, LinkedIn, calls) and integrates with CRMs so MQL status can update automatically when a prospect responds.

Before you run any outbound sequences, validate your email list. Sending to bad addresses tanks your deliverability and can get your domain flagged. Use an email validator to clean the list before your first send. This is not optional if you care about inbox placement.

If you want help building this entire system - from defining MQL criteria to building the list to running the sequences - I put together a free resource that covers the fundamentals: grab the GPT Lead Gen Prompts to speed up the research and targeting side.

What Good MQL-to-SQL Conversion Looks Like

Benchmarks vary a lot by business model and industry, but here's how the numbers stack up across B2B:

• Lead-to-MQL conversion rate: The cross-industry average sits around 31%, with B2B SaaS companies outperforming at roughly 39%. Organic search leads tend to convert at 41% because of the inherent intent in search behavior.
• MQL-to-SQL conversion rate: The typical range is 12-21% across B2B industries. Teams with advanced behavioral scoring and fast follow-up protocols push toward the higher end; teams with loose MQL criteria or slow handoff processes sit at the bottom.
• MQL to opportunity rate: 10-25% depending on deal complexity and sales cycle length.
• MQL to closed-won rate: 1-5% across the full funnel is common for B2B.

One benchmark worth paying close attention to: the impact of follow-up speed. Research consistently shows that following up with a qualified lead within the first hour after MQL conversion increases conversion rates dramatically compared to waiting 24 hours or more. Build your CRM automations around this. If a contact hits MQL status at 2pm on a Tuesday, a rep should be in their inbox or on the phone by end of business that day - not three days later when the lead has moved on.

If your MQL-to-SQL rate is below 12%, the issue is usually one of three things: criteria that don't match your actual buyers, a broken handoff process between marketing and sales, or a nurture sequence that isn't building enough intent before the handoff.

The fix for the first two is what this article covers. The fix for the third is building better sequences - and if you want to pressure-test your specific setup, the Target Finder Tool can help you get clearer on exactly who you should be targeting before you spend another dollar on ads or sequences.

The Marketing-Sales Alignment Problem (And How to Fix It)

Here's a stat that should make every marketing leader uncomfortable: 84% of business leaders acknowledge that the marketing-to-sales handoff is one of the most significant challenges they face in aligning their teams. And yet most companies treat the MQL handoff like it's someone else's problem to solve.

The root cause is almost always a definition gap. Marketing defines MQLs by engagement signals alone. Sales defines a good lead by whether they can close it. Those two definitions are not the same, and they never will be unless you sit in a room together and write down one shared definition that both teams commit to.

Here's how to build that alignment in practice:

Step 1: Run a historical win analysis. Pull the last 50-100 closed-won deals from your CRM. Look at what the contacts did before they became opportunities: which pages did they visit, what content did they download, what was their lead score at the time of handoff? This tells you what MQL behavior actually predicts revenue for your specific business - not generic advice from a blog post.

Step 2: Get sales to define rejection reasons. Have your sales team categorize every rejected MQL from the last 90 days into buckets: wrong industry, wrong title, no budget, no urgency, too small, etc. The most common rejection reason is the thing you need to add as a disqualifying filter in your MQL query.

Step 3: Set a shared SLA. Marketing commits to a specific MQL volume target (quality-gated, not raw count). Sales commits to responding to every MQL within a defined timeframe. Write it down. Review it monthly.

Step 4: Build a closed-loop report. Every week, marketing should see: how many MQLs were accepted vs. rejected, the rejection reasons, and how many MQLs from 30 days ago have progressed to opportunity. Without this visibility, marketing is optimizing for MQL volume instead of MQL quality.

The teams that get this right don't just convert more MQLs - they build a feedback loop that continuously improves both the marketing campaigns that generate leads and the criteria that filter them. That's what separates pipeline that grows quarter over quarter from a lead list that just sits in a queue.

Common MQL Query Mistakes (And How to Fix Them)

Mistake 1: Using engagement signals without firmographic filters

Just because someone spent 12 minutes on your site doesn't mean they're a good lead. A student writing a paper about your industry could exhibit the same behavioral signals as your best-fit buyer. Always combine behavioral data with firmographic fit before tagging someone as an MQL.

Mistake 2: Never updating the criteria

Your ICP shifts. Your product evolves. Your market changes. MQL criteria that worked when you were targeting SMBs might be completely wrong now that you're moving upmarket to enterprise. Review and update your MQL definition quarterly, minimum. Look at which MQLs from the last quarter actually converted to customers, and work backward from there. If your MQL-to-conversion rate is declining, there's a good chance your target customer profile has shifted and your scoring model needs adjustment.

Mistake 3: No feedback loop from sales

If marketing is tagging MQLs and dropping them in a queue without ever hearing from sales about quality, you're flying blind. Build a structured rejection reason into your CRM: "Wrong industry," "Wrong title," "No budget signal," etc. That data is how you refine your query over time.

Mistake 4: Treating outbound prospects like inbound MQLs

A cold prospect you identified through prospecting is not the same as someone who visited your pricing page three times. Don't put them in the same MQL bucket or report them the same way. Track inbound-sourced MQLs and outbound-sourced MQLs separately. The conversion rates, velocity, and deal sizes are almost always different, and mixing them makes your reporting meaningless.

Mistake 5: Setting the threshold too low to avoid hurting lead volume numbers

This one is political, and it's more common than people admit. Marketing gets measured on MQL volume, so there's an incentive to set the threshold low enough that the numbers look good. But flooding sales with low-quality leads destroys the relationship between teams and tanks pipeline quality. Set your threshold based on what actually converts, not what makes the marketing dashboard look impressive. A lower volume of higher-quality MQLs is always more valuable than a big number that sales ignores.

Mistake 6: Ignoring the time-to-conversion metric

Most teams track MQL volume and MQL-to-SQL rate, but fewer track how long it takes an MQL to convert. The average time for an MQL to convert to a SQL or opportunity typically falls in a 30-90 day window for B2B, depending on deal complexity and sales cycle length. If your average is 60 days, looking at monthly MQL-to-SQL conversion rates will give you a misleading picture because many of those MQLs haven't had time to move yet. Factor your sales cycle length into how you read your conversion metrics.

MQL Reporting: The Metrics That Actually Matter

Once your MQL query is live and your scoring is running, you need a reporting setup that tells you whether the whole system is working. Here are the five reports every team should be running:

• MQL volume by source: Which channels are producing the most MQLs? Which channels produce MQLs that actually convert? These are not the same question. A channel might produce high MQL volume but low SQL conversion - that's a signal to either adjust the criteria for that source or de-prioritize the channel.
• MQL acceptance rate: What percentage of MQLs is sales accepting? If it drops below 60%, something is wrong with your criteria or your source quality.
• MQL rejection breakdown: Why are MQLs being rejected? The most common rejection reason is the most important thing to fix in your query.
• MQL-to-opportunity velocity: How many days does it take from MQL creation to opportunity creation? Track this over time. If it's getting longer, your nurture sequences or follow-up SLAs are breaking down somewhere.
• MQL-sourced revenue: What percentage of closed-won revenue originated from MQL-stage contacts? This is the ultimate test of whether your MQL program is actually connected to business outcomes.

Run these reports monthly, review them with both marketing and sales in the same meeting, and adjust criteria when the numbers drift outside acceptable ranges. The teams that generate consistent pipeline aren't just running better ads - they're running tighter qualification criteria that mean sales only touches people who are actually worth their time.

Want a full system for building this, including the outbound side? My Free Leads Flow System walks through how to connect prospect list building, lead qualification, and outreach into a single repeatable process.

The Bottom Line on MQL Queries

An MQL query is only as good as the definition behind it. Get marketing and sales in a room, define the firmographic fit and behavioral signals that predict conversion for your specific business, assign point values, set a threshold, and build the filter in your CRM. Then review it every 90 days using real conversion data.

Don't treat MQL criteria as a one-time setup. Markets shift, ICPs evolve, and what converted six months ago might be completely wrong for where your business is today. The teams winning on pipeline are the ones treating their MQL query as a living system - constantly fed by sales feedback, constantly calibrated against actual revenue outcomes.

The teams that generate consistent pipeline aren't just running better ads - they're running tighter qualification criteria that mean sales only touches people who are actually worth their time. That's the whole game.

If you want to go deeper on building a full outbound and inbound lead system that connects MQL criteria to actual revenue, I cover the full approach inside Galadon Gold.