Sales-Ready vs. Marketing-Qualified: Why This Distinction Can Sabotage Your Pipeline

The Problem with Rigid MQL/SQL Definitions

You know the scenario: Marketing delivers hundreds of leads, Sales takes a look and just waves them off. Not qualified, they say. Or the other way around: Sales complains about lead quality while Marketing proudly presents its MQL numbers.

The classic distinction between Marketing-Qualified Leads (MQLs) and Sales-Qualified Leads (SQLs) was originally supposed to bring clarity. In reality, it often creates more problems than it solves.

Why?

Because it’s based on a fundamental misunderstanding: the belief that buying intent moves through clearly defined, separate phases. That there’s some magic moment when a lead shifts from “not ready yet” to “ready now.”

The Reality of B2B Sales Is Different

B2B buying decisions are no longer linear processes. .

Your potential customers are everywhere at once: reading your blog posts, comparing you on review platforms, debating solutions internally, visiting your pricing page—all in no particular order.

Yet many companies stick to rigid definitions:

• MQL = Lead filled out a form and reached a score of 50+
• SQL = Lead was contacted by SDR and meeting booked

The issue with that? These definitions ignore context entirely.

What Rigid Definitions Miss

A CEO who revisits your pricing page and thoroughly reviews your case studies may be far closer to buying than a junior marketing manager who simply downloaded an eBook—even if only the latter formally meets your MQL criteria.

But in many systems, the CEO is ignored (no form fill = not an MQL) while the junior manager gets passed along as qualified.

This creates friction at the most critical point in your pipeline: the handoff from Marketing to Sales.

This is exactly where the biggest losses occur. Not because your teams are underperforming, but because the system itself is broken.

The Hidden Costs of the MQL/SQL Split

Let’s look at the numbers—and they’re sobering.

The Conversion Rate Reality

In 2025, MQL-to-SQL conversion rates range from 12% to 21% depending on the industry, with top performers using advanced lead scoring reaching up to 40%.

To put it another way: Average MQL-to-SQL conversion is about 13%. That means 87% of your supposedly “qualified” marketing leads never become sales-qualified leads.

It gets even starker if you consider the full funnel: Roughly 98% of marketing-qualified leads never become paying customers—in other words, just 2 out of 100 MQLs end up as customers.

Let that sink in: 98% of your qualified marketing leads go nowhere.

Where Exactly Is the Lead Lost?

Friction occurs at multiple points:

1. Differences in Definitions
About half of sales and marketing teams have a joint definition of what constitutes a lead.

Marketing defines “qualified” differently from sales. This leads to endless debates about who’s right.

2. Tracking Gaps
Leads are lost between departments, though regular process audits can reduce loss.

Leads slip through the cracks because no one is truly tracking what happens between a marketing handoff and sales contact.

3. Timing Issues
Quick follow-up boosts conversion rates considerably – but many MQLs wait days or weeks for any kind of follow-up.

Why? Because sales doesn’t see the leads as “truly qualified” and thus gives them low priority.

The Financial Impact

Let’s run the numbers for a typical mid-sized B2B company:

The difference? Over half a million euros in pipeline value per month. Simply by using a better qualification methodology.

But it’s not just about lost pipeline. It’s about wasted resources, too.

The Invisible Costs

Most MQLs don’t turn into sales due to poor nurturing processes, while sales reps ignore a large percentage of the leads sent by marketing.

What this really means:

• Marketing spends budget on leads that sales doesn’t take seriously
• Sales wastes time on leads that aren’t truly ready
• Highly qualified leads fall through the cracks because they don’t fit the arbitrary model
• Both teams get frustrated and start blaming each other

The good news? There are alternatives. And they demonstrably work better.

Alternative Models: Ways Out of the Pipeline Trap

The rigid MQL/SQL divide isn’t set in stone. More and more successful B2B companies are breaking away from this model, choosing more flexible approaches instead.

The three most promising alternatives:

• Account-Based Scoring – Shifts the focus from individuals to accounts
• Intent-Based Qualification – Buying readiness determined through behavioral signals
• Hybrid Approaches – Marketing and Sales define qualification criteria together

Each of these approaches tackles the fundamental weaknesses of the classic MQL/SQL model—but in different ways.

Why Alternative Models Work Better

The key difference: They’re not built on rigid point systems or form-fills, but on real purchase signals and account context.

Instead of asking, Has this lead accumulated enough points? they ask:

• Is this account showing genuine buying interest?
• What signals indicate now is the right moment for a conversation?
• How can marketing and sales together decide when a lead is ready?

This may sound subtle, but in practice, it makes a massive difference.

The Paradigm Shift: From Leads to Accounts

The problem with traditional lead scoring: in B2B, you usually sell to companies, not individuals—one person downloading a whitepaper or using your free product is a signal that should feed into a score reflecting the company they work for.

This shift from lead-based to account-based thinking is fundamental. It recognizes that B2B purchases are made by buying committees, not individuals.

Let’s dive into the individual models in detail.

Account-Based Scoring: The Paradigm Shift

Account-based scoring turns traditional lead qualification on its head. Instead of evaluating individual contacts, it assesses entire accounts—using aggregated signals across all stakeholders in the target company.

How Account-Based Scoring Works

Account scoring is a data-driven method used by B2B revenue teams to evaluate, rank, and prioritize target accounts based on their likelihood of becoming high-value customers—it’s a quantifiable approach to filter thousands of potential accounts into a prioritized, actionable list for sales.

Imagine this: Five people from the same target company visit your website. The CTO checks out your technical documentation, the CFO visits your pricing page, a developer tries your demo, the VP Operations downloads case studies, and the CEO reads your ROI blog post.

In the classic MQL model? Five separate leads, each with different scores, who may never be recognized as belonging together.

In account-based scoring? One high-potential account with strong buying signals at all levels of the decision hierarchy.

The Three Dimensions of Account Scoring

Comprehensive account scoring unites fit, intent, and engagement—this blend ensures your scoring model reflects both long-term potential and short-term buying readiness, while sales always has up-to-date insight into which accounts are becoming active.

1. Fit Score: Is This Account Even a Match?

A fit score shows if a company is a good potential customer—calculated using fit data: details describing a company’s firmographic profile, such as industry, country, and employee count. It can also consider factors like a person’s role or department.

Example fit criteria:

• Company size: 50-500 employees
• Industry: SaaS, IT services, e-commerce
• Revenue: €5–50M
• Tech stack: using Salesforce, HubSpot, or similar
• Location: DACH region

2. Intent Score: Is the Account Showing Buying Interest?

Intent scores are based on behavioral data signaling interest and engagement—such as a hand-raise (request for a sales conversation), usage of your freemium product, reading your ebooks, or visits to your website. Website activity is a great source for intent data.

Intent signals might include:

• Multiple visits to the pricing page in a short period
• Downloads of multiple case studies
• Webinar attendance
• Competitor research on third-party platforms
• Engagement with email campaigns

3. Engagement Score: How Deep Is the Interaction?

This score measures the depth and quality of interaction. Not all website visits are equal.

Visitor website activity, including mouse movement and webpage clicks, signals deeper engagement—a visitor is exploring your site, with each page weighed differently as you define. A visitor actively consuming website content triggers a valuable intent signal, far beyond an inactive session.

Practical Implementation: An Account Scoring Model

Here’s what a simple account-scoring system could look like:

The Practical Benefits

In B2B, you often have several people at the same company evaluating your product—an advanced approach (sometimes called account scoring or product qualified accounts) is to aggregate usage signals at account level.

In practice, this means:

• You’ll never miss opportunities just because individual contacts fall below the MQL threshold
• You’ll identify buying committees early and target all the right stakeholders
• Your sales conversations are richer because you know the full account activity
• Marketing and sales talk about the same accounts, not disconnected leads

But account-based scoring is just one alternative. Let’s look at intent-based qualification.

Intent-Based Qualification: Buyer Intent Over Checkboxes

If account-based scoring shifts your perspective (from leads to accounts), intent-based qualification shifts your focus: from demographics and firmographics to real behavior.

The core question is no longer “Does this lead meet our criteria?” but rather “Is this prospect actively showing buying intent?”

What Is Intent-Based Qualification?

Intent data goes far beyond simple website visits and reveals deep insight into a prospect’s buying intent—it looks at a wide range of online behaviors: content downloads, whitepaper registrations, visits to solution-related pages, even interactions with competitor content. This helps you identify prospects actively researching solutions like yours.

Picture this: The CEO of a mid-sized manufacturing company (your ideal customer profile) acts as follows over the last 48 hours:

• Visits your website 4 times (twice to the pricing page)
• Spends 8 minutes reading a case study
• Downloads a product comparison PDF
• Clicks an email about your ROI calculator
• Third-party intent signal: searching Marketing Automation B2B on Capterra

In a classic lead-scoring model, maybe this person gets 45 points—too few to become an MQL (threshold: 50). They stay in nurturing.

With intent-based qualification? Its a clear Sales Ready signal. This prospect is ready to buy. Now.

The Two Types of Intent Data

First-party intent data is great for identifying sales-qualified leads, as it specifically relates to their interactions with your company and shows how likely they are to buy from you—third-party intent data is gathered from external sources, offering insight into their wider online behavior, like which solutions they’re researching elsewhere.

First-party intent data (from your own systems):

• Website behavior: visited pages, time on site, click paths
• Email engagement: open rates, clicks, replies
• Content consumption: resources downloaded
• Product interaction: demo requests, free trial usage
• Event engagement: webinars, workshops, events

Third-party intent data (from external providers):

• Topic research: searching review sites?
• Competitor research: looking at alternative solutions?
• Content consumption: reading industry publications?
• Social listening: discussing pain points in LinkedIn groups?

The Most Important Intent Signals for B2B

Key examples include: website visits to specific product or resource pages (deep engagement), content downloads (whitepapers, case studies signifying desire for detail), engagement with relevant content (blog posts, webinars indicating active research), social media activity, and competitor content research—all offering valuable insight.

But not all intent signals are created equal. Here’s a hierarchy:

Intent-Based Scoring in Practice

Intent data can supercharge lead scoring for marketing and sales teams on the hunt for sales-qualified leads—use it to prioritize high-intent prospects and tailor your outreach to their specific needs by integrating intent data into your existing models, which sharpens SQL identification and improves results.

For example: A SaaS company uses this intent scoring:

• Pricing page visited: +25 points (high intent)
• Case study read >3 min: +15 points (medium intent)
• Product comparison guide downloaded: +20 points (high intent)
• Attended webinar: +10 points (medium intent)
• Clicked product info email: +5 points (low intent)
• Third-party: Searched G2/Capterra: +30 points (very high intent)

Threshold for “Sales Ready”: 50 points within 7 days.

The Key Advantage: Timing

Intent signals are perishable—often their relevance fades within weeks. Timing is everything; if you act fast on this data, you’ll catch prospects while their interest is highest and before a competitor wins them over.

Intent-based qualification lets you strike exactly when your prospect is most receptive. Not three weeks later, after they’ve already signed elsewhere.

This is the game-changer.

Hybrid Approaches: Marketing and Sales at the Helm Together

Account-based scoring is powerful. Intent-based qualification is precise. But there’s a third approach—often the most pragmatic: hybrid models, where Marketing and Sales define qualification criteria together.

The basic idea: Instead of Marketing setting the MQL definition and Sales setting the SQL definition, both teams collaborate from the outset.

The Sales-Marketing Alignment Problem

In theory, sales and marketing have the same goal: connect the right buyers with the right solutions. In practice, the relationship often falls short, especially when marketing focuses on lead generation and sales on lead conversion, measured by different KPIs. Here’s where the classic MQL problem starts: Marketing generates tons of promising-sounding MQLs that don’t convert because sales sees them as irrelevant.

You may know this first-hand:

• Marketing celebrates 500 MQLs a month
• Sales reaches out to maybe 100
• Of those, 10 become opportunities
• Marketing gets frustrated (Sales ignores our leads!)
• Sales gets frustrated (Marketing sends us junk!)

The solution? Joint definitions and processes from the very beginning.

Developing Joint Lead Qualification Frameworks

The hybrid approach looks like this:

Step 1: Joint Workshop Sessions

Sales and marketing need to agree on their target audience and how those people make buying decisions—that means collaboratively defining an Ideal Customer Profile (ICP) and mapping the full buyer journey. This enables marketing to attract the right audience with the right content, while sales approaches leads with context, trust, and relevance—for smoother handoffs, better engagement, and a faster-moving pipeline.

In these sessions, clarify:

• Who is our ideal customer? (Shared ICP)
• What signals show true buying interest?
• When is a lead “ready” for sales outreach?
• What information does sales need to succeed?

Step 2: Service Level Agreements (SLAs) Between Teams

A Service Level Agreement (SLA) between sales and marketing formalizes the relationship, setting mutual expectations. For example, marketing commits to deliver a set number of qualified leads per month, while sales commits to follow up within a certain timeframe and provide feedback on lead quality—reducing ambiguity, boosting accountability, and fostering a culture of collaboration.

A typical SLA might include:

• Marketing commits to: delivering 150 qualified leads per month that meet jointly defined criteria
• Sales commits to: contacting every lead within 24 hours and providing feedback
• Joint commitment: Weekly review meetings on lead quality

Step 3: Establish Joint Metrics

Instead of simply tracking total leads or website visits, you should monitor KPIs like lead-to-customer conversion rate (efficiency of marketing-to-sales conversion) and pipeline velocity (average time for a lead to become a customer).

Both teams should measure:

• Qualified lead → opportunity conversion rate
• Opportunity → customer conversion rate
• Average time to close
• Pipeline value from marketing activities
• Customer acquisition cost (CAC)

The Impact of Alignment

The numbers speak for themselves:

Conversions from target buying groups to pipeline are consistently higher when marketing supports the sales process.

Specifically, that means:

• Higher lead quality: When teams are aligned, marketing focuses on generating high-quality leads that meet sales-defined criteria. Sales teams don’t waste time on irrelevant prospects, and a significant portion of sales professionals see improved lead quality when their teams are aligned.
• Shorter sales cycles: Efficient handoffs between sales and marketing reduce deal-close time, as marketing delivers qualified leads (MQLs) and sales follows up with personalized outreach, moving buyers through the funnel faster.
• Higher revenue: Alignment drives higher conversion rates and bigger deal sizes—studies show companies with aligned sales and marketing teams generate significantly more revenue.

Practical Example: ZoomInfo’s Success

ZoomInfo long struggled with warm MQLs and couldn’t push its conversion rate above 4%—so to improve sales and marketing alignment, ZoomInfo created a sales role 100% focused on calling warm marketing qualified leads, lifting conversion from 4% to 15%, with an ideal volume of 150 calls per day.

That’s nearly quadruple the conversion rate. All through better alignment and clear processes.

The Role of RevOps

Revenue Operations (RevOps) is a strategic approach that unifies sales, marketing, and customer success teams by optimizing processes, data, and technology to drive consistent revenue growth—it breaks down silos and ensures all teams share goals and metrics, improving collaboration and decision-making.

RevOps acts as “referee” between marketing and sales, making sure everyone is pulling in the same direction.

Case Studies: How Companies Optimized Their Pipeline

Enough theory. Let’s see how real companies implemented these approaches—and what results they saw.

Case Study 1: SaaS Company Boosts Conversion by 43%

Within six months, the company increased its conversion rate by 43% and shortened its sales cycle by nearly a quarter—this kind of transformation isn’t a pipe dream, but a result of intentional alignment.

Starting Situation:

• MQL-to-SQL Conversion: 8%
• Average Sales Cycle: 120 days
• Marketing and sales had different lead definitions
• Leads regularly slipped through the cracks

Measures Implemented:

1. Joint workshop sessions to define ICP
2. Implemented intent scoring alongside demographic scoring
3. Established marketing/sales SLA
4. Weekly pipeline review meetings
5. Shared dashboard for both teams

Results after 6 months:

• MQL-to-SQL conversion: 15% (↑87%)
• Average sales cycle: 90 days (↓25%)
• Pipeline value: +156%
• Sales satisfaction with lead quality: from 42% to 78%

Case Study 2: Technology Company with Product-Led Growth

SoftTechCo had traditionally relied on a marketing qualified lead model, with marketing scoring leads based on website visits, content downloads, and company fit to determine handoff to sales—by 2024, they noticed a problem: despite generating many MQLs, sales complained that “hot” leads weren’t actually ready to buy or were a poor fit, with disappointing conversions of just about 5% MQL to SQL.

Starting Situation:

• Freemium model with 10,000+ trial users per month
• MQL-to-SQL conversion: 5%
• Problem: Trial usage was not factored into qualification
• Sales contacted leads based on form submissions, not product usage

Measures Implemented:

1. Product usage scoring introduced (Product Qualified Leads – PQLs)
2. Triggers defined: e.g., “5+ team members invited” or “premium feature used 3x”
3. Combining fit score, intent score, and product usage score
4. Sales receives product usage context before outreach

Results:

• PQL-to-SQL conversion: 28% (was 5% MQL-to-SQL)
• Trial-to-paid conversion: +120%
• Average deal size: +35% (thanks to focusing on highly engaged users)
• Sales productivity: +40% (less time spent on unqualified leads)

Case Study 3: B2B Service Provider with Account-Based Approach

Starting Situation:

• Target accounts: 500 enterprise companies
• Problem: Individual contacts qualified, but buying committee not identified
• Many opportunities stalled because key stakeholders were missing

Measures Implemented:

1. Switched from lead-based to account-based scoring
2. Tracked account engagement across stakeholders
3. Multi-threading: proactively engaged different buying committee roles
4. Used intent data to identify “hot accounts”

Results:

• Account-to-opportunity conversion: +40%
• Opportunity-to-win rate: +32% (thanks to full buying committee involvement)
• Average deal size: +58% (more stakeholders = bigger deals)
• Sales cycle shortened by 18% (fewer setbacks)

What These Case Studies Have in Common

Three success factors show up in all cases:

1. Moving away from rigid definitions: All companies dropped the “one-size-fits-all” MQL definition
2. Behavior-based signals: Intent data and observed behaviors mattered more than demographic checkboxes
3. Sales/marketing alignment: Joint definitions, metrics, and processes were critical

The improvements ranged from 30% to 120% depending on starting point and the measures implemented.

But how do you get started?

Implementation: First Steps Toward Flexible Lead Qualification

You’re convinced your current MQL/SQL model isn’t optimal. You see the potential of alternative approaches. But where do you start?

Here’s a pragmatic 90-day action plan.

Phase 1: Analysis and Alignment (Weeks 1–3)

Step 1: Understand Current Performance

Before changing anything, you need to know your starting point:

• How many MQLs do you generate per month?
• What’s your MQL-to-SQL conversion rate?
• What’s your SQL-to-opportunity conversion rate?
• How long does the average sales cycle take?
• Where do most leads get lost?

Start by analyzing your current lead qualification process—check your conversion rates and gather sales team feedback to spot areas for improvement.

Step 2: Get Sales and Marketing in the Same Room

Run a half-day workshop with both teams:

• Present your current numbers
• Discuss: Where are the biggest pain points?
• Ask sales: What really makes a lead qualified?
• Ask marketing: What signals do we see that sales isn’t using?

The goal: build a shared understanding of the problem.

Step 3: Identify Quick Wins

You don’t have to revolutionize everything overnight. Look for quick, easy improvements:

• Are you already tracking high-intent signals you’re not using?
• Can you add 2–3 intent signals to your lead scoring now?
• Are there obvious communication gaps between teams?

Phase 2: Pilot Implementation (Weeks 4–8)

Step 4: Define the New Qualification Framework

Choose a qualification framework that fits your business, like BANT or MEDDIC, and tailor it to your criteria—include both demographic and behavioral elements as part of your lead scoring, guided by insights from your sales team.

Decide on an approach:

• Option A: Add an intent layer (easy starting point)
• Option B: Introduce account-based scoring (medium effort)
• Option C: Hybrid model with jointly defined criteria (highest impact, more effort)

For most mid-sized B2B companies, we recommend: Start with Option A, then gradually expand toward Option C.

Step 5: Start with a Subset

Pilot your new framework with:

• Only one lead source (e.g., website leads)
• Only one product or service
• Only one part of your sales team

This minimizes risk and enables quick learning.

Step 6: Adapt Your Tech Stack

You don’t need expensive new software. Start with what you have:

• CRM (Salesforce, HubSpot, etc.): Adjust your lead scoring rules
• Marketing Automation: Integrate new triggers based on intent signals
• Analytics: Make sure you can track website behavior

Companies that use lead scoring and qualification software achieve higher ROI than those relying on manual processes—and in 2025, a wide range of technologies is available, from AI-powered scoring to intent data providers, helping B2B teams qualify smarter and faster.

Phase 3: Optimization and Scaling (Weeks 9–12)

Step 7: Measure Results and Iterate

After four weeks of piloting:

• Compare conversion rates: pilot group vs. control group
• Collect qualitative feedback from sales
• Identify what works—and what doesn’t
• Refine the framework accordingly

Step 8: Gradually Scale Up

If the pilot is successful:

• Expand to more lead sources
• Involve more sales team members
• Continuously refine your scoring criteria

Review conversion metrics quarterly, rebalance lead scores using real results, and refine workflows based on SDR feedback and signal trends.

Step 9: Institutionalize the New System

After 90 days:

• Document your new framework
• Train all relevant teams
• Establish regular review processes
• Celebrate successes together

The Most Important Success Factors

1. Secure Executive Buy-In

Without top-level support, change is tough. Present leadership with the business case and numbers (see pipeline value comparison above).

2. Involve Both Teams Equally

Account scoring will fail if it’s seen as a “marketing-only” exercise—success requires early buy-in from every revenue-touching team: sales, SDRs, RevOps, marketing, and leadership. Discuss which signals matter most, how to weigh them, and what thresholds define a marketing qualified account (MQA).

3. Start Small, Think Big

You don’t need to overhaul everything on day one. Begin with a pilot, learn, and iterate.

4. Technology Is an Enabler, Not an Answer

The best tools in the world are useless without sales-marketing alignment. Fix the process first.

5. Be Patient

Change takes time. Expect 3–6 months before your new system runs smoothly.

Frequently Asked Questions

What’s the main difference between MQL and SQL?

A marketing qualified lead (MQL) has shown interest in your offering (e.g., by filling out a form or downloading content) and meets basic demographic criteria. A sales qualified lead (SQL) has been verified by sales, shows concrete buying intent, and meets budget, authority, and timeline criteria. The issue: this rigid split often ignores key contextual signals and creates friction.

What’s a good MQL-to-SQL conversion rate?

The average MQL-to-SQL conversion rate is about 13%, while top performers using advanced lead scoring and fast follow-up hit up to 40%. If you’re well below 10%, you likely have room to improve in lead quality or follow-up processes.

What is account-based scoring?

Account-based scoring evaluates not individual contacts, but entire companies (accounts) by aggregating signals from all stakeholders. It combines fit score (does this company match our ICP?), intent score (are they showing purchase intent?), and engagement score (how strong is the interaction?). This is especially valuable in B2B, where buying decisions are made by committees.

How does intent-based qualification work?

Intent-based qualification uses behavioral signals to measure buying readiness. First-party intent data (like visits to your pricing page, demo requests, repeat website visits) is combined with third-party intent data (like product research on review sites, competitor comparisons). The focus is on “What is the prospect doing right now?”—not just “Do they fit our checklist?”

Do we need new software for more flexible lead qualification?

Not necessarily. Most modern CRM and marketing automation platforms (HubSpot, Salesforce, Marketo, etc.) already support intent-based scoring and account-level tracking. The key isn’t technology, but new processes and better sales/marketing alignment. Start with your existing tools and expand as needed.

How do we convince sales and marketing to try a new approach?

Let the numbers speak: show current conversion rates, lost pipeline, and wasted resources. Calculate potential pipeline gains from better qualification. Start with a joint workshop where both teams can share frustrations, then pilot a small initiative to demonstrate quick wins.

How long does it take to roll out a new qualification framework?

Expect 4–6 weeks for an initial pilot. For full rollout across all teams and lead sources, 3–6 months is realistic. The key is to start small, learn fast, and scale iteratively—rather than trying to change everything at once.

What are the most common mistakes during a shift to new qualification?

The top three mistakes: 1) changing too much at once instead of piloting, 2) prioritizing technology over process, 3) letting one team (usually marketing) define the new system without real sales buy-in. Successful rollouts always begin with joint alignment and clear, simple first steps.

How do we measure the success of a new qualification methodology?

Focus on these core metrics: MQL-to-SQL conversion rate, SQL-to-opportunity conversion rate, average time to close, pipeline value generated from marketing activity, and qualitative sales feedback on lead quality. Compare metrics between pilot and control groups, and before/after implementation.

Does flexible lead qualification work for small companies too?

Absolutely. Smaller teams benefit even more from focusing limited resources on truly qualified leads. You don’t need a complex account-based marketing setup—start by adding 2–3 key intent signals (like pricing page visits, repeat website activity) to your scoring and hold regular sales/marketing meetings.

Should we abolish MQL/SQL completely?

Not necessarily the terminology, but rethink the definitions behind them. Many successful companies still use MQL/SQL as pipeline stages, but with a more flexible approach: A SQL isn’t just “form filled out + 50 points,” but “shows clear buying signals + fits ICP + has engaged with us.” The wording matters less than building flexibility into your criteria.