MQL vs SQL in 2025: How B2B Outbound Teams Can Win Together

Major Takeaways: MQL vs SQL

What’s the difference between MQL and SQL in 2025?

• An MQL shows initial engagement, while an SQL is a sales-vetted lead with confirmed intent. Clear criteria at each stage prevent funnel breakdown and ensure lead quality.

Why is sales and marketing alignment mission-critical?

• Misaligned teams lose up to 60% of leads, while aligned teams achieve 38% higher win rates and 24% faster revenue growth by streamlining the MQL to SQL handoff.

How does the MQL sales funnel function in outbound?

• In outbound, MQLs are often cold contacts who respond to outreach. Proper qualification turns engagement into SQLs and real opportunities.

What MQL to SQL conversion rate should you aim for?

• High-performing B2B teams convert 10–30% of MQLs into SQLs. Anything below 10% signals a problem with lead quality, scoring, or follow-up.

How has buyer behavior changed the funnel?

• 80% of B2B interactions now happen digitally, with buyers using 17+ sources before contacting sales. Marketing and SDRs must collaborate to guide informed buyers through the funnel.

What’s the role of omnichannel outreach in lead qualification?

• Multichannel strategies (email, LinkedIn, calls) drive 4–10x more responses. Coordinated touches across platforms increase MQL engagement and conversion to SQL.

How can tech and automation improve funnel performance?

• Integrated CRM and marketing tools enable faster handoffs and smarter lead scoring. Companies that automate lead workflows see 50% more qualified leads at 33% lower cost.

What’s the best way to nurture leads that don’t convert right away?

• Not all MQLs become SQLs instantly. Using targeted nurture sequences ensures leads remain engaged, raising the chance of future SQL conversion by up to 47%.

Introduction

In 2025’s complex B2B landscape, the classic debate of MQL vs SQL isn’t just a terminology exercise – it’s about bridging marketing and sales to win more business together.

In the sections below, we’ll explore how to define MQLs and SQLs in a modern context, how they fit into an evolving B2B funnel (often called the MQL funnel or MQL sales funnel), and what strategies help convert more marketing leads into sales opportunities. 

We’ll also share examples and data-driven best practices for aligning your outbound team with marketing. Our perspective is first-hand – as an outsourced sales partner, we have seen what works (and what doesn’t) when it comes to qualifying leads and optimizing the funnel. 

Let’s dive into how you can refine your MQL and SQL process in 2025 to drive more revenue.

MQL vs SQL: Definitions and Key Differences (2025 Edition)

61% of B2B marketers send every lead directly to sales, yet only 27% of those are actually qualified.

Reference Source: LXA Hub

What exactly separates an MQL from an SQL? In simple terms, a Marketing Qualified Lead (MQL) is a lead that has shown some engagement or fit indicating potential interest – often identified by marketing’s efforts – but isn’t yet ready for direct sales contact. 

A Sales Qualified Lead (SQL) is a lead that sales has validated as ready for next steps (a meeting, a demo, a proposal). The distinction can vary across organizations, but the core idea is that an SQL is further along the funnel and closer to purchase intent than an MQL.

It’s critical in 2025 to clearly define these terms for your organization. Surprisingly, many companies lack consensus here – only about 50% of companies have a formal definition of a “qualified lead” that marketing and sales both accept. Without clear definitions, marketing might label too many leads as “MQL” and toss them to sales, while sales might reject a large portion as unqualified. 

📊 In fact, 61% of B2B marketers still send every lead directly to sales, but only 27% of those leads are actually qualified (3). This shotgun approach overwhelms sales reps with noise, causing frustration and wasted effort.

We take a more rigorous view: we consider a lead an MQL only once it matches our Ideal Client Profile and has meaningfully engaged with our outreach or content. It’s not just a name on a lead list – an MQL shows initial interest (e.g. opened several emails, clicked a link, responded to an offer) and meets basic firmographic criteria. 

However, that MQL is not yet a guaranteed sales opportunity; it needs further qualification. That’s where an SQL comes in – for our team, an SQL is an MQL that has been contacted and vetted by a salesperson (or SDR), confirming deeper interest, need, and generally the authority or budget to move forward. Only at that point do we hand it to an account executive to pursue a deal.

To illustrate the differences between an MQL and SQL, let’s compare some common attributes:

In practice, the exact boundary between MQL and SQL should be agreed upon by both teams. 

We recommend drafting a simple Service Level Agreement (SLA) or set of criteria for your funnel stages. 

For example, “MQL = VP or Director-level lead who engaged with at least 2 pieces of content or replied to an email. SQL = MQL who had a qualifying call where need and interest were confirmed.” 

There’s no one-size-fits-all rule, but the act of defining it together is vital. When marketing and sales share a common definition, you avoid the classic finger-pointing of “these leads are junk” versus “sales isn’t working our leads.”

Pro Tip: Take the term “SMarketing” to heart. HubSpot’s former CMO Mike Volpe coined this to describe blending Sales + Marketing into one team. If we think of ourselves as one revenue team, it’s easier to hash out definitions like MQL/SQL collaboratively. The result is better handoff and less lead leakage.

The MQL-to-SQL Funnel: Mapping the Journey in 2025

68% of B2B organizations haven’t clearly defined their funnel stages.

Reference Source: Marketing Sherpa

Think of the path from MQL to SQL as a critical segment of your B2B sales funnel. In a traditional funnel model, you have stages like: Lead → MQL → SQL → Opportunity → Customer (1). MQL and SQL are the middle stages where a lead is moving from marketing’s world into the sales realm. Ensuring this transition is smooth is more important than ever in 2025 – because the funnel itself has changed.

Why has the classic funnel evolved? Today’s B2B buyer’s journey is no longer a simple linear slide down the funnel. Buyers jump back and forth, consume content on their own, and involve multiple stakeholders. 

The average B2B buying decision now involves more stakeholders and a longer timeline – the average sales cycle has lengthened due to more B2B decision-makers in the process. 

Additionally, as noted, a huge portion of the journey is self-directed. A small portion of the buyer’s journey is spent in actual conversations with a sales rep, the rest is research, internal discussion, and digital interaction. 

This means by the time someone becomes an MQL in your funnel, they may have already educated themselves extensively (which is good), but they also may be engaging with your competitors concurrently and expecting a frictionless experience.

In this dynamic environment, it’s helpful to visualize how MQLs and SQLs flow through your funnel. Below is an infographic illustrating a typical qualification funnel from initial visitor to lead, MQL, SQL, and ultimately to a sale:

In a well-structured “MQL funnel”, marketing captures raw inquiries at the top (website visitors, ad clicks, event leads, etc.). 

Those inquiries become Leads when contact info is obtained. With further engagement and vetting, some leads graduate to MQL – indicating they’re interested enough to potentially buy, but they haven’t been fully vetted. 

This is a critical handoff point: marketing should pass MQLs to sales (SDRs or BDRs) with context (e.g. what content they engaged, any scoring attributes). 

Sales then conducts the next step – reaching out, qualifying via phone/email – to determine if the MQL can be an SQL (worthy of direct sales time). If yes, the lead moves to an opportunity stage (often after an initial discovery call).

However, many companies struggle with this funnel stage definition.

📊 A startling 68% of B2B organizations have not clearly identified their funnel stages at all (2). No surprise, then, that leads often “slip through the cracks” between marketing and sales. 

A marketing study found 79% of marketing leads never convert to sales, often due to lack of nurture or proper follow-up (2). Essentially, if your MQL-to-SQL process is broken – e.g. marketing throws leads over the wall and hopes for the best, or sales ignores what they perceive as unqualified inquiries – you end up with a leaky funnel.

To optimize this MQL→SQL conversion funnel, focus on a few fundamentals:

• Clearly define entry/exit criteria for each stage: As discussed, know what makes a lead an MQL and what triggers conversion to SQL. 

Also define a plan for leads that don’t become SQL – do they go back for nurturing? How long do you recycle them? Having a loop for unready leads ensures you don’t drop warm prospects entirely.

• Implement lead scoring and qualification workflows: In 2025, most successful teams use some form of lead scoring (behavioral signals + fit) to automate the funnel. This helps prioritize which leads marketing should send to sales. For instance, if a lead hits a score threshold by visiting high-value pages or clicking emails, they become an MQL and alert an SDR. 

Without scoring, it’s hard to scale – in fact, organizations with mature lead management processes see much higher follow-up rates on marketing leads. Use data (like content engagement, job title, company size) to rank leads, and then test and tweak your scoring model over time.

• Accelerate the handoff: As noted, speed is vital. Research shows the first vendor to engage a lead often wins the deal – potentially up to half of sales go to the earliest responder. So minimize delay from when an MQL is identified to when sales contacts them. 

This could mean setting up automated notifications or meetings between marketing and sales daily to discuss new hot leads. The goal is a near-real-time handoff for high-intent MQLs. Some companies even set an SLA: e.g. SDR will call an MQL within 2 hours of them hitting SQL criteria. This kind of agility is a competitive edge.

In this example by Gartner Digital Markets (11), a lead’s progression through the funnel stages typically follows this path:

• MQL – Engages with content, showing initial interest.
• SAL – Requests a demo, indicating stronger interest and sales readiness.
• SQL – Confirms purchase intent after the demo, ready for closing.

Source: Gartner Digital Markets

This progression from MQL to SAL to SQL illustrates the structured lead nurturing and qualification process. Effective collaboration between marketing and sales (supported by lead scoring and a shared CRM system) facilitates smooth transitions through these stages, each representing a deeper level of engagement and buying readiness.

The MQL-to-SQL funnel isn’t strictly linear and tidy, but having structured stages is still extremely useful. It provides a common language for marketing and sales and a way to measure conversion rates at each step. 

Track metrics like MQL-to-SQL conversion rate and SQL-to-opportunity rate. If you see a lot of MQLs but a low conversion to SQL, it could mean marketing’s criteria are too lax (leads aren’t truly qualified) or that sales follow-up is lacking. 

If SQL-to-opportunity is low, maybe the definition of SQL is too strict or sales is qualifying out too aggressively. By monitoring these, you can pinpoint friction points and continuously improve the process.

Ultimately, the funnel from MQL to SQL is the joint responsibility of marketing and sales. It’s where the two must meet in the middle. One helpful mindset is to treat MQL→SQL as a transition stage owned by both teams: marketing nurtures up to MQL, sales development takes from MQL to SQL. Keep a close eye on this intersection – it’s often where revenue is won or lost.

Aligning Marketing and Outbound Sales Teams to “Win Together”

Misaligned sales and marketing teams can cost companies 10% or more of annual revenue.

Reference Source: LXA Hub

The phrase “win together” is apt – when marketing and sales (including your outbound SDR team) are aligned, everyone wins: more revenue, faster growth, happier customers. 

Yet alignment is easier said than done. We’ve all seen the classic silo scenario: Marketing complains that sales isn’t following up their leads; Sales gripes that marketing’s leads are low quality. The result? Missed opportunities and a disjointed buyer experience.

📊 Let’s quantify it: failure to align sales and marketing around the right processes and technologies can cost 10% or more of annual revenue for B2B companies. 

Globally, businesses lose an estimated $1 trillion per year from poor sales-marketing coordination. 

On the flip side, when teams are tightly aligned, companies achieve up to 24% faster three-year revenue growth and 27% faster profit growth (3). 

And in the short term, aligned organizations are far more likely to hit their revenue targets (56% meet or beat goals vs. far fewer when misaligned). The data is overwhelming: alignment pays off.

So how can we foster true alignment between marketing and our outbound sales development reps (SDRs)? Here are some strategies that have proven effective for our team and clients:

• Establish shared definitions and sales KPIs: As covered, agree on what constitutes a lead, MQL, SQL, etc. But don’t stop at definitions – set shared goals. 

For instance, instead of marketing solely focusing on number of MQLs generated, and sales only focusing on deals closed, create a joint KPI like MQL-to-SQL conversion rate or pipeline generated. When both teams are accountable for moving leads through the funnel, behavior changes. 

We align our success metrics with our clients’ sales teams (e.g. number of qualified meetings set, conversion to opportunities) so that everyone is rowing in the same direction, not optimizing for conflicting outcomes.

• Implement a feedback loop: Alignment isn’t “set and forget.” It requires ongoing communication. We recommend regular meetings between marketing and outbound sales (SDR) leaders – at least weekly. 

In these meetings, review the leads that were passed as MQLs: 

Did sales accept them? Reject them? Why? Examine a few cases. For example, if sales rejected an MQL due to “wrong target,” marketing needs to know so they can adjust targeting criteria or lead filters. 

On the other hand, if SDRs aren’t following up promptly or not logging feedback, that needs addressing too. Creating a tight loop ensures constant refinement of lead quality. 

Many successful teams even integrate tools – e.g., using CRM workflows where sales can mark a lead as “Not qualified – reason X” and marketing automatically gets that data. 

📊 Only 8% of businesses feel they have strong sales-marketing alignment today (4), but you can get into that elite tier by simply communicating better and iterating together.

• Unify your tech and data: A big barrier between teams is often disparate systems or data silos. Marketing might live in a marketing automation platform with their lead scores and campaign data, while sales lives in CRM with their notes – and the two don’t talk. 

In 2025, there’s no excuse for this. Modern revenue teams deploy integrated tech stacks (or even all-in-one platforms) so that everyone works off the same data. 

📊 Interestingly, 96% of companies who report strong alignment also report being aligned in their use of sales and marketing technology. This isn’t coincidence – sharing data is foundational to sharing strategy. 

Make sure your SDRs can see a lead’s marketing engagement history (email opens, content downloads) right in the CRM, and conversely, marketing should see updates from sales (calls made, outcomes). A single customer view allows both teams to act in concert and gives “one version of truth” when discussing funnel performance.

• Collaborate on content and messaging: Outbound sales teams need good ammo – case studies, sales pitch decks, whitepapers – to engage prospects. Marketing creates a lot of this content. 

Alignment means marketing proactively asks sales what content gaps exist, and sales provides input on what resonates with potential customers. For instance, if SDRs find that a particular industry case study is winning responses, they should inform marketing to perhaps produce more like it. 

Similarly, marketing can share top-of-funnel content that’s getting MQLs hooked, so sales can reference those insights in their conversations. 

A common complaint is that 65% of sales reps say they can’t find useful content to send prospects (8), while marketers often feel sales underutilizes the content they produce. 

Bridging this gap with an easily accessible content library and regular knowledge-sharing can increase effectiveness on both sides. 

📊 Remember, 47% of larger purchases are attributed to nurtured leads who received relevant content throughout the journey (4) – nurturing is not just marketing’s job; sales contributes too by guiding prospects with the right info.

• Adopt a “one team” incentive structure: Cultural alignment often follows the money. If marketers get bonuses only on lead volume, they’ll optimize for volume (even if quality suffers).

If SDRs are only paid on SQLs they accept, they might disqualify leads too aggressively to meet a quota. Try to include a mutual success component – for instance, a portion of marketing’s bonus tied to pipeline generation or closed by sales, and a portion of sales bonuses tied to hitting MQL SLA targets or participation in campaigns. 

When both teams win financially from the same outcomes (pipeline and revenue), silos break down quickly. We’ve seen clients unify their incentive structures and suddenly the language changes from “your leads” and “my leads” to “our leads”.

Ultimately, true alignment can transform your business. Companies with tightly aligned sales and marketing functions enjoy 36% higher customer retention and 67% better at closing deals relative to misaligned peers (3). 

Beyond the numbers, alignment just creates a healthier culture: marketing trusts sales to follow up professionally, and sales trusts marketing to understand customers and support the funnel. 

If you foster that trust, you get what we call the Sales-Marketing Dream Team effect – both sides working in tandem to win more business.

Turning MQLs into SQLs (Lessons from the Field)

27% of sales reps’ time is spent qualifying leads that should have already been filtered by marketing.

Reference Source: LXA Hub

Let’s zero in on outbound lead generation – where your SDRs or outbound prospecting team (in-house or outsourced) actively reach out to potential customers. 

Outbound can generate high-quality leads, but it also requires a disciplined qualification process to avoid flooding sales with unready prospects. How should outbound teams handle MQL vs SQL?

First, understand that in outbound, the “MQL” might originate differently. Unlike inbound MQLs (who raise their hand by filling out a form or downloading content), outbound MQLs are often prospects who responded to your outreach. 

For example, say your SDR sends a cold email sequence to a list of target accounts. A VP at one company replies, “Sure, I’m interested in learning more.” That reply could immediately make them an MQL – they meet your Ideal Customer Profile and showed interest by responding. 

However, the work isn’t done. At this moment, the SDR (acting as the bridge between marketing and sales) should engage further to qualify: 

Does the VP have a current pain point we solve? What’s the timeline and budget? If the key criteria check out, the SDR will then label this prospect an SQL and perhaps set up a meeting with a senior salesperson.

The idea isn’t to interrogate the lead, but to have a checklist that ensures you cover the bases. For an outbound-generated MQL, we might ask questions (conversationally) to assess: 

• Do they have an identified Need or goal that our product/service fits? 
• Are we talking to someone with Authority (or can they involve the right stakeholder)? 
• If those are positive, we might gently probe on Budget or willingness to invest, 
• Timeline – are they looking to implement it soon or just browsing?) 

If the answers align well, congratulations – you have a solid Sales Qualified Lead ready for a deeper sales engagement.

One of the advantages in outbound is that we can be selective from the start. Since you control the targeting, your outbound MQLs should, in theory, all be within your ideal customer profile. The qualification then heavily centers on gauging interest and readiness. 

Contrast this with inbound, where you often get a mix of good and poor-fit leads downloading content. Outbound allows you to pre-filter by ideal account criteria and then qualify for interest, making the MQL→SQL conversion potentially higher if done right.

Let’s look at a real success story. We partnered with a tech company, to run outbound campaigns aimed at their target market. Over the course of the engagement, we generated around 7,000 targeted prospects per month through multichannel outreach and careful research. 

Out of that, we were able to consistently deliver about 22 Sales Qualified Leads per month to the internal sales team. Those SQLs were highly valuable – each was vetted and genuinely interested, ready for a sales conversation. How did we achieve this?

• Joint planning and ICP definition: We worked closely with the client to define what a “qualified lead” looks like for them. 

This included firmographics (industry, company size, role seniority) and qualifying questions (for example, we knew an ideal SQL would have a certain problem in their telecom infrastructure that company solves). 

Because we had clarity, our outreach messaging was laser-targeted, and we didn’t count a positive response as a success unless it met the agreed criteria.

• Multichannel touches to engage leads: Our outbound strategy combined email, LinkedIn, and phone calls. Often, an executive might ignore emails but respond on LinkedIn, or vice versa. 

By orchestrating multiple touches, we increased our contact rates. In fact, the client’s campaigns were receiving weekly positive engagement across channels. 

When a prospect replied positively or clicked key links (e.g. “Get a demo”), we flagged them as potential MQLs.

• Human qualification on top of automation: Rather than immediately passing every responder to the client, our SDRs jumped in to further qualify interest. 

We would follow up with interested prospects via phone, having a conversation to ensure they were a good fit and ready to speak to the client’s sales team. 

This step is crucial in outbound – a reply like “sure, send me some info” is not an SQL yet. Our team treated that as an MQL signal, then quickly engaged the person in a call. 

Through that call, we might find, for example, that the prospect’s company was indeed looking to upgrade their system in the next 6 months (great SQL), or conversely, that they were curious but had no budget or real need (not an SQL, so we’d nurture longer). 

By filtering at this stage, we only passed truly sales-ready leads forward.

• Seamless handoff and follow-through: Once a prospect was deemed an SQL, we didn’t just toss an email introduction and forget it. 

We scheduled appointments directly on the client account executives’ calendars, and provided detailed notes/background on the lead. 

This way, the sales rep walked into the meeting knowing the context (“This VP said their contract with current vendor ends in Q4 and they’re exploring options; they were particularly interested in our integration features.”). 

That context made the sales conversations more productive and increased win odds. Because Martal acted as an extension of the client’s team, prospects often felt they were already dealing with the company proper – making the transition smooth.

The result of this tight outbound qualification process was a sustainable pipeline of real opportunities. The client’s internal team could focus on closing deals rather than chasing lukewarm contacts. 

Our case was not unique: Outbound, when done right, can yield high-quality SQLs that rival or exceed inbound lead quality. The key is disciplined qualification. 

For your own outbound efforts, consider these quick tips to boost MQL-to-SQL success:

• Personalize outreach and follow-ups: Generic mass emails rarely create SQLs. We use personalized snippets (mentioning a prospect’s specific challenge or referencing their market) to generate higher initial interest. 

Then, in qualification calls, we reference what we learned (e.g. “I noticed you opened our email about cloud security – is that a current priority for you?”). Personalization increases trust and information sharing, speeding up qualification.

• Leverage intent data if possible: 2025 offers more lead generation tools for outbound than ever. Intent data services can tell you if a target account is surging on certain topics (indicative of need). 

If you see a prospect showing intent signals and they respond to your outreach, that’s a strong case to prioritize them to SQL. Some of our campaigns integrate intent signals (e.g. tech stack changes, recent funding news) to prioritize who might be ready to become an SQL. It’s like catching someone at the right buying moment.

• Don’t be afraid to disqualify or recycle: Not every conversation will lead to an SQL, and that’s okay. Outbound teams should be empowered to politely disqualify leads that aren’t a fit (and provide that feedback to marketing). It’s better to have 15 solid SQLs than 30 “iffy” ones that clog the pipeline. 

Also, if a prospect isn’t ready yet, have a nurture path – perhaps you put them back as an MQL for a later touch (“We’ll check back next quarter”). We had prospects in the campaign initially say “no budget this year” – we set a reminder to follow up in a few months; some turned into SQLs later when timing was better. So think of qualification as an ongoing process, not one-shot.

By treating every outbound lead with care – qualifying diligently and only advancing those that meet your SQL bar – you ensure outbound marketing and sales truly work in harmony. Marketing (or your outsourced lead generation partner like Martal) might generate the initial spark, but through collaboration with the sales mindset, that spark turns into the flame of a real opportunity.

Improving the MQL-to-SQL Conversion Rate: Strategies for 2025

A strong MQL-to-SQL conversion rate is typically 13%. Higher rates signal better lead quality and sales alignment.

Reference Source: DashThis

What is a “good” MQL-to-SQL conversion rate? It’s a common question – and the answer depends on your industry and definition strictness. 

Benchmarks in 2025 vary: one analysis found MQL-to-SQL conversion averages ranging from about 12% up to 21% across sectors (6). 

Many SaaS companies hover in the 10–15% range, which aligns with the Gartner stat we cited earlier (only 10–15% of MQLs become sales opportunities) (5). 

So if you’re converting much less than 10% of your MQLs to SQLs, there’s likely room for improvement in lead quality or follow-up process. 

If you’re converting significantly more than 20%, it could indicate either exceptional alignment… or perhaps your team is being too picky about what counts as an MQL (only the hottest leads ever get that label).

Whether your current rate is 5% or 25%, the goal is always to improve it without hurting down-funnel conversion. 

Pushing more borderline leads through to sales might boost the MQL->SQL metric, but it’s counterproductive if those SQLs don’t turn into customers. The real aim is to improve the quality of MQLs and efficiency of qualification so that both the conversion rate and win rate go up.

Here are some 2025 best practices to lift your MQL-to-SQL conversion in a healthy way:

1. Refine your lead scoring with advanced data: Traditional lead scoring (based on arbitrary points for email opens or job titles) can only go so far. In 2025, augment scoring with intent signals and AI. For example, track high-intent behaviors – like visiting your pricing page or product comparison page – and weight them heavily. 

Use intent data (e.g. Bombora, ZoomInfo intent) to see if the lead’s company is researching relevant topics. Some platforms now use AI to predict which leads are likely to convert based on hundreds of data points. Implementing these can significantly boost the quality of MQLs that flow to sales. 

Companies with advanced lead management processes achieve significantly higher marketing lead follow-up rates. 

📊 Data shows Product-qualified leads (PQLs) convert at 15–30% (9), far outperforming MQLs. Data-driven scoring helps companies boost results and ensures sales values every MQL.

2. Shorten the feedback cycle on lead quality: We touched on feedback loops in alignment, but specifically have sales/SDRs categorize each MQL disposition in CRM. 

For instance: Accepted as SQL, Rejected – Not ICP, Rejected – No response, Nurture – interested but later. Analyze this monthly with marketing. If a large portion are “not ICP,” your targeting is off – tighten your campaign targeting or adjust your content magnet to attract the right personas. 

If many are “no response” from sales outreach, maybe speed or method needs work. By treating every rejected MQL as a learning opportunity, you can tweak criteria to send better leads. 

📊 One study found that 56% of B2B sales organizations lack a formal method for verifying leads before passing them to sales (7). Unqualified leads result in lost time and missed revenue opportunities, as sales reps spend time chasing prospects who aren’t ready to buy. 

With tight feedback, you can cut that waste down, freeing sales to focus on selling and upping conversion.

3. Deploy nurture sequences for middle-of-funnel leads: Not all MQLs will become SQLs immediately – some need time or more info. Rather than having them sit idle, set up automated nurture campaigns tailored to MQLs. 

For example, when a lead hits MQL status but doesn’t respond to sales, put them in a special nurture track (“Send case studies over 4 weeks, then a check-in email”). 

Nurturing can dramatically improve conversion over time. Nurtured leads not only convert at higher rates, they also result in larger deals – studies show nurtured leads produce 47% higher order values than non-nurtured leads on average (3). 

In practice, we’ve seen that consistent, helpful touches (whitepapers, invites to webinars, etc.) keep your company top-of-mind so that when the lead is ready, they re-engage and move to SQL rather than quietly disappearing.

4. Accelerate “Speed to SQL” with automation: We’ve emphasized speed for good reason. In addition to quick human follow-up, use automation to your advantage. 

Trigger instant emails when an MQL is created – for instance, a personalized email from the rep like “Hi [Name], saw you checked out our webinar. Would love to chat whenever you’re ready.” 

Tools can send this on behalf of the rep automatically. Also consider chatbots or website personalization for known MQLs: if an MQL revisits your site, a chatbot can pop up saying “Hey [Name], welcome back! Want to schedule that demo?” 

These touches can fast-track engagement while your SDRs juggle other tasks. Just be careful to keep automated messages high-quality and on-brand (no spammy vibes). The idea is to strike while the iron’s hot.

5. Equip your SDRs with training and playbooks: Your people are a big part of conversion success. Make sure the team handling MQL qualification is well-trained on discovery and objection handling. 

They should know how to dig for pain points, how to handle the classic “just send me info” brushoff, and how to create urgency. Provide a playbook that outlines common scenarios: e.g., if lead says “no budget,” SDR can respond with a probing question to see if it’s a polite brush-off or real.

Regular role-playing and coaching will sharpen their skills. In our team, we hold periodic training sessions via the Martal Academy to upskill SDRs on new techniques (leveraging AI tools for research, using LinkedIn voice notes, etc.). 

Skilled SDRs will convert more lukewarm MQLs into hot SQLs than inexperienced ones. Given that by 2025 many easier tasks are automated, human skills in connecting with and qualifying people are more valuable than ever.

6. Integrate an omnichannel approach for follow-ups: We mentioned multi-channel outreach for initial engagement, but the same applies in the MQL-to-SQL phase. 

Don’t rely on one channel to reach a lead. An SDR might call and leave a voicemail, then follow up with an email, and connect on LinkedIn – all within a day or two – increasing chances of a response. 

Today’s buyer might ignore phone calls but respond to a LinkedIn InMail. Or vice versa. By covering your bases, you’ll catch more leads. This omnichannel marketing approach is proven: prospects engaged via 3 or more channels are far likelier to move forward than those hit with just one touch type.

Just ensure consistency of message across channels. For example, reference your voicemail in the email (“I just left you a voicemail, but I also wanted to shoot you a note in case email is easier…”). Coordinated touches make you appear professional and attentive rather than annoying.

7. Monitor and optimize each stage drop-off: Use analytics to see where leads drop off. Maybe you have plenty of MQLs, but they stall in “attempting contact” stage. 

Measure contact rates – if only 30% of MQLs ever have a connect call, maybe try calling at different times or improve your phone/email data quality. 

If connects are fine but conversion to SQL after connect is low, examine call recordings or notes – are reps failing to build value? Are they qualifying out too early? 

By diagnosing the funnel within the funnel, you can target specific improvements. Sometimes a small tweak (like a better opening line on calls, or a new email subject line) can bump conversion a few percentage points, which at scale is huge.

📊 One more note on conversion ratios: What is a good MQL to SQL conversion rate? Typically, around 13% of MQLs convert to SQLs; rates above this indicate effective lead qualification and performance (10).

But quality trumps quantity. If your rate is lower, don’t simply push more leads through; instead, implement the strategies above to lift it the right way. And remember that a “good” ratio also depends on what comes next – a high MQL→SQL conversion is meaningless if none of those SQLs close. 

So always view this metric in context of pipeline and revenue. We’ve found that focusing on the overall funnel conversion (lead to deal) is the ultimate measure. Improving MQL→SQL is one lever to pull in that bigger picture.

In summary, improving MQL-to-SQL conversion in 2025 boils down to working smarter with data, aligning teams on quality, and leveraging every tool (human and tech) to nurture and qualify leads effectively. 

Marketing can do a better job at sourcing and priming the right leads, and sales development can excel at engaging and validating them. Meet in the middle, and you’ll watch that conversion rate climb – meaning a fatter sales pipeline and greater productivity.

Conclusion

By now, it’s clear that MQL vs SQL is not a battle, but a partnership. Marketing and sales (including outbound sales development) each have a crucial role in turning cold prospects into warm leads, and warm leads into satisfied customers. 

In 2025, the companies who excel are those who break down the old walls and work as one revenue team from lead generation to deal close. We’ve seen it with our clients at Martal: when we align our outbound efforts tightly with a client’s marketing and sales goals, the results speak for themselves – more qualified leads, higher conversion rates, and faster growth.

If you’re looking to boost your funnel performance – from top-of-funnel lead generation to bottom-of-funnel sales outcomes – we’re here to help. At Martal, we specialize in being that bridge between marketing and sales, providing expert outbound services as an extension of your team. 

Our seasoned SDRs will source, engage, and qualify leads through targeted cold emailing, LinkedIn outreach, cold calling and more, delivering sales-ready SQLs to fill your pipeline. And we do it all while working hand-in-hand with your internal teams, ensuring quality and consistency (remember, we win together!).

Ready to transform your MQLs into more SQLs and closed deals? Let’s talk. Book a consultation with Martal today to explore an omnichannel outbound strategy tailored to your business. 

We’ll audit your current funnel, share case studies, and show you how our approach can plug in to accelerate your growth. In the spirit of this discussion: marketing-qualified or sales-qualified, our goal is simply “qualified leads” that turn into revenue. Let’s align our teams and make it happen – together.

References

1. Martal Group – 2025 Playbook: B2B Lead Generation Funnel 
2. Marketing Sherpa 
3. LXA Hub 
4. Teamgate 
5. Callin 
6. Gradient Works 
7. Clickback 
8. Upland Software 
9. OpenView 
10. DashThis 
11. Gartner Digital Markets

---

FAQs: MQL vs SQL

1

What is the difference between MQL and SQL?

An MQL (Marketing Qualified Lead) is a lead that fits your ICP and has shown interest but hasn’t been vetted by sales. An SQL (Sales Qualified Lead) has been contacted and confirmed by sales to be a genuine opportunity—someone with need, intent, and potential to move forward.

2

What comes first, MQL or SQL?

MQL comes first. A lead becomes an MQL after marketing confirms initial engagement and fit. It then becomes an SQL when sales reviews and qualifies the lead for direct outreach or conversion opportunities.

3

What is a good MQL to SQL ratio?

A strong MQL to SQL ratio ranges from 10–30%, depending on industry and lead definitions. Ratios below 10% often indicate misalignment or low-quality leads; improving targeting and qualification can help raise performance.