Why MQL Is Not A Good Funnel Step
If you’re wondering why MQLs should not be part of a funnel, here’s the short answer: MQLs measure attention, not revenue. A Marketing Qualified Lead is a weak proxy for buying intent and a poor step in a funnel because it rewards volume over value. To grow predictably, replace MQLs with revenue metrics: opportunities created in €, stage movement, marketing-attributed sales, and pipeline coverage.
In this guide, we’ll show you why “MQL is dead” for modern B2B and e‑commerce teams, how MQL vs SQL became a battleground, and what to adopt instead. You’ll get practical definitions, formulas, and dashboards you can implement immediately, plus a step-by-step transition plan to build sales‑marketing alignment and end the lead-to-revenue gap.
Expect specifics: how to define opportunities, how to calculate pipeline coverage, which attribution models work, and the exact KPIs to track weekly. This is about outcomes, not optics.
The MQL Became The Source Of So Much Dysfunction Between Sales And Marketing
Because it was never really about revenue, it was about a handoff. Marketing optimized for form fills, content downloads, and webinar signups, while Sales cared about real opportunities. The gap between “someone who downloaded an ebook” and “someone ready to buy” created the friction. MQLs gave Marketing a metric to celebrate, but left Sales drowning in noise.
You don’t fix MQLs with new math, you replace them with pipeline accountability. That means Marketing is measured on sourced and influenced pipeline, not hand-raises. The focus shifts from generating leads to creating revenue opportunities. Instead of debating lead scores, Marketing and Sales work from the same pipeline targets, aligned to stages everyone agrees on.
How MQLs Turned Into A Handoff, Not Revenue
MQLs were invented to mark when a lead looked “interesting,” then pass it to SDRs. Over time, the symbol became the goal. Teams gamed content gating, giveaways, and webinar signups to hit MQL goals. Handoffs increased. Pipeline did not.
Here’s the pattern we see when we audit teams: MQLs spike after campaigns, but accepted opportunities (SQLs/SAOs) barely move. Lead nurturing sequences get longer. SDRs chase low-intent leads. Sales blames Marketing. Marketing blames lead follow-up speed. Nobody blames the metric.
When “marketing-sourced revenue” becomes “marketing-sourced MQLs,” the system optimizes for the wrong outcome. The result is a full top of funnel that doesn’t convert.
Volume Metrics Versus Opportunity Metrics: The Misalignment
Volume metrics (MQL counts, CTR, impressions) tell you if people saw or clicked. Opportunity metrics (pipeline in €, opportunities created, stage conversion) tell you if revenue is likely. Conflating the two is how forecasts drift away from reality.
Consider MQL vs SQL: an SQL (or SAO, sales accepted opportunity) implies a verifiable problem, budget potential, and a next step. An MQL implies someone filled a form. One is a revenue step. The other is a marketing touch.
Volume metrics optimize for reach, often at the expense of relevance.
Opportunity metrics optimize for intent, fit, and value creation.
When you compensate teams on volume, you get volume, then a clogged pipeline.
Lead scoring tries to bridge the gap, but if the goal is still MQL creation, the scoring model becomes a gameable filter—another conversation about the math, not the money.
Real Consequences: Wasted SDR Time, Longer Sales Cycles, Forecast Noise
The cost of MQL-driven funnels is tangible:
Wasted SDR time: Chasing low-intent “hand-raises” that never become opportunities. We often see 60–80% of MQLs never convert to pipeline, weeks lost each quarter.
Longer sales cycles: Pushing unready leads into sales conversations forces unnatural timing. Buyers retreat, cycles stretch, and conversion rates fall.
Forecast clutter: MQLs inflate “pipeline coverage” when teams confuse activity with opportunity, making forecasts optimistic and difficult to trust.
Meanwhile, marketing attribution becomes a political fight. Without clean opportunity creation and stage movement data, it’s hard to credit channels fairly. That erodes trust just when you need tight sales‑marketing alignment.
Why MQLs Fail As A Funnel Step
Funnels Are Linear; Buying Is Not
Buying journeys are non-linear. Your funnel is not. Prospects bounce between research, evaluation, and internal alignment. They discover you via search, social, partner referrals, and community content, often simultaneously. Forcing a “lead → MQL → SQL” sequence misreads how modern B2B buying actually happens.
Think funnel vs flywheel. A funnel treats customers as outputs. A flywheel treats them as inputs, accelerating growth through advocacy, expansion, and network effects. MQLs belong to funnel-think; opportunity creation belongs to flywheel-think, where the signal is whether value is being created at every stage.
In product-led or content-rich motions, people will try, use, and self-serve long before they talk to Sales. Your model must respect that reality. Track stage movement across the journey, not just a single lead promotion event.
The False Comfort Of Lead Counts
High lead counts feel safe. They’re not. If 1,000 MQLs convert to 10 opportunities, the “volume” is a distraction. The only reliable predictor of revenue is qualified pipeline in € plus stage conversion and cycle length. Everything else should ladder into those outcomes.
Gated content, generic newsletters, and loosely targeted ads create fake momentum. It shows up as vanity metrics: opens, CTR, downloads. None of these pay salaries unless they turn into pipeline and revenue. Many teams realize this too late, usually when pipeline coverage is thin halfway through the quarter.
We’re not anti lead generation. We’re anti lead worship. The goal is to generate demand that converts to revenue, not to farm email addresses.
When Better Lead Scoring Isn’t The Answer
Lead scoring helps triage interest. It cannot fix a broken objective. If Marketing is rewarded for MQL volume, any scoring model will eventually be bent to hit the number. New rules, new thresholds, new disputes, same outcome.
Scoring also suffers from model drift. Buyer behavior changes, product positioning evolves, and ICP expands. Scores lag reality. Revenue teams end up debating points while deals stall. That’s time you could spend moving opportunities forward.
Use scoring tactically: routing, prioritizing, and triggering targeted lead nurturing. But measure Marketing against opportunity creation, stage movement, and revenue, not a score that approximates readiness.
Replace It With Revenue Metrics
Replace “leads” with a short, aligned set of revenue metrics that everyone trusts. Four pillars work consistently across B2B and e‑commerce: € opportunities created, stage movement, marketing-attributed sales, and pipeline coverage. They’re lag‑less, hard to game, and directly connected to outcomes.
€ Opportunities Created — What To Count And Why
Count opportunities that meet a minimum value threshold and a light qualification standard. The unit is € value at “Opportunity Created,” not a form fill. This is the clearest bridge from lead-to-revenue and the most reliable north star for demand teams.
Definition: A new opportunity in the CRM with confirmed business pain, rough value, buying role identified, and an agreed next step.
Thresholds: Set a floor (e.g., €5k ARR or €10k TCV) to avoid clutter. Adjust by segment.
Source tracking: Tag “marketing-sourced,” “sales-sourced,” “partner,” and “product-led.” You’ll need this for accountability and optimization.
Quality control: Sales must accept the opportunity (SAO). Measure creation-to-acceptance conversion weekly.
Why it works: it aligns demand generation with revenue production. You create the right incentives for campaigns, content, and channels to work together to open real deals, not inflate MQLs. Over time, you’ll see clearer patterns of which investments create pipeline and which just collect emails.
Stage Movement — Track Progress, Not Promises
Stage movement captures how opportunities advance through your revenue process. It’s a velocity and conversion discipline. If opportunities aren’t moving, revenue won’t either. This metric exposes bottlenecks and focuses teams on progress.
Track entry and exit: For each stage (Discovery, Evaluation, Proposal, Committed), measure time-in-stage and conversion to next stage.
Diagnose friction: High drop-off after Discovery? Revisit ICP or qualification. Stalled in Proposal? Improve pricing clarity, social proof, or ROI modeling.
Run experiments: A/B sales collateral, change demo flow, adjust follow-up cadence, then measure stage conversion deltas.
Report consistently: Weekly stage movement tells you if you’re building momentum or building a backlog.
Stage movement is where marketing and sales truly meet. Great education content, clear case studies, and targeted sequences should show up as faster movement and higher conversion, not just more MQLs.
Marketing‑Attributed Sales — Models That Work
Marketing attribution should be decision-grade, not perfect. Pick an approach that the team understands and can act on. For most B2B motions, multi-touch models beat last click and avoid over-crediting any single campaign.
Position-based (W‑shaped): Shares credit between first touch, lead creation, and opportunity creation. Simple, fair, and behaviorally sound.
Time-decay: Gives more weight to touches closer to opportunity creation. Useful for long cycles and complex buying committees.
Self-reported attribution: Add a “How did you hear about us?” field. It catches community, word-of-mouth, and dark social that tracking misses.
Guardrails: Single source of truth is the CRM. Define your attribution window (e.g., 90 days to opp creation) and stick to it.
The goal is not to crown a winner; it’s to identify scalable patterns. When you see which channels consistently contribute to opportunity creation and stage movement, budget allocation becomes obvious and defensible.
How These Metrics Reduce Debate And Create Joint Accountability
These four metrics defuse the MQL vs SQL fight. They make the conversation about shared revenue outcomes, not departmental output. Everyone rallies around the same scoreboard.
Marketing knows the pipeline number they must create and influence.
Sales sees clear stage conversion and opportunity quality.
RevOps enforces definitions, data hygiene, and reporting rhythm.
Leadership gets forecast stability and fewer surprises.
When the team aligns on opportunity creation, stage movement, marketing attribution, and pipeline coverage, MQLs stop mattering. The business moves from debate to delivery.
Pipeline Coverage: How Much Pipeline Do You Actually Need?
What Pipeline Coverage Means (Definition & Formula)
Pipeline coverage is the ratio between your open pipeline value and your revenue target (quota) for a given period. It de-risks forecasts by ensuring you have enough qualified opportunities to hit goal despite natural win-rate variance. Formula: Pipeline Coverage = Open Pipeline (€) / Quota (€).
Example: If your quarterly quota is €1M and your open pipeline is €3M, your coverage is 3x. This buffer matters because not every deal closes, and cycles rarely follow a neat schedule. Coverage prevents over-reliance on heroic win rates.
Most teams target 3x coverage when average win rate is around 30%. That’s a rule of thumb, not a law. Your ideal multiple depends on win rate, cycle length, stage distribution, and concentration risk.
Example: 3x Coverage With 30% Win Rate (Numeric Scenario)
Here’s a simple scenario to make coverage tangible and forecast-friendly.
Quota: €1,000,000 for the quarter.
Average win rate: 30% across opportunities.
Target coverage: 3x, so you aim for €3,000,000 in qualified pipeline.
Distribution: Ensure at least 60–70% of that pipeline sits in mid-to-late stages to be realistic for in-quarter close.
Now apply a weighted view: if you weight stages by historical conversion (e.g., Discovery 20%, Evaluation 40%, Proposal 70%), your weighted pipeline should still exceed quota. If weighted pipeline is only €700k against a €1M quota, you’re exposed—even if “raw” coverage looks like 3x.
Two levers fix this quickly: increase opportunity creation early in the quarter, and accelerate stage movement with better enablement, stronger offers, and clearer next steps per deal.
Sales Cycles And Win‑Rate Variability
Coverage needs change with your motion. New segments, new geographies, and new products require higher coverage because win rates are unproven and cycles are longer. Enterprise deals need heavier buffers than SMB.
Three practical adjustments we recommend:
Seasonality: Build extra coverage entering slow months. Don’t rely on end-of-quarter miracles.
Mix matters: Track coverage by segment and stage. A “3x” number can hide a late-stage drought.
Concentration risk: If one deal is 30% of your quota, you don’t have 3x—no matter what the math says. Spread exposure.
Coverage is a risk instrument. Use it to make proactive decisions: launch targeted campaigns, shift AE time to late-stage deals, or spin up account-based plays to fill a stage gap.
Who Owns Coverage — Marketing, Sales, Or Both?
Both. Coverage is a company number, not a department number. Marketing owns a share of coverage through sourced and influenced pipeline. Sales owns a share through outbound and expansion. RevOps owns the definitions and reporting cadence.
Set explicit coverage targets by source and segment. Example:
Marketing-sourced: 35% of new pipeline.
Sales-sourced (outbound): 45%.
Partner/product-led/expansion: 20%.
Run a weekly pipeline council where Marketing, Sales, and RevOps review coverage by stage, by source, and by segment. Adjust quickly. This is how you operationalize sales‑marketing alignment.
If you want a structured way to align GTM plans to coverage goals, consider building your plan from a growth framework. Our perspective on planning is distilled in our growth marketing approach—you can explore a strategic canvas for aligning experiments to revenue outcomes in our article on the Growth Marketing Canvas.
How To Transition From MQLs To Pipeline Accountability
Step 1: Agree On Unified Stage Definitions
Start with language. If Sales and Marketing use different definitions for lead, SQL, SAO, and Opportunity, you’ll never agree on the numbers. Document each stage, entry criteria, exit criteria, and owner. Do it together.
Lead: A person or account with contact info—no implied intent.
MQL: Optional. If you keep it, define it as a routing signal only.
SAL: Sales accepted lead—meets ICP, valid contact, and conversation started.
SAO/SQL: A qualified opportunity with next step and € estimate.
Stages: Discovery → Evaluation → Proposal → Committed → Closed Won/Lost.
Keep it simple. The more complex your taxonomy, the more oxygen it takes to maintain—and the more room for debate. Your goal is clarity, speed, and predictability.
Step 2: Change Reporting And Dashboards
Replace the MQL dashboard with a pipeline and revenue dashboard. If a metric doesn’t forecast revenue, relegate it to a channel-level view. Your executive dashboard should answer four questions within seconds: Are we creating enough opportunities? Are they moving? Are we covering quota? Are we winning fast enough?
Core tiles: Opportunities created (€), coverage ratio, stage conversion, win rate/cycle time.
Source view: Pipeline by source with marketing influence overlay.
Aging: Deals stalled by stage with SLA-based alerts.
Attribution: Position-based or time-decay, visible and agreed.
If you’re building reports, standardize on a single source of truth (your CRM) and share a simple view in a BI tool. For a practical starting point, see our guidance on building dashboards in Looker Studio—the principles translate directly to revenue reporting.
Step 3: Set SLAs And Joint Targets (Sourced vs Influenced Pipeline)
Define how fast leads are worked, when they convert to SAL/SAO, and who is responsible at each step. Create both sourced and influenced pipeline targets for Marketing. Create outbound and expansion targets for Sales.
Response times: Inbound SAL within 1 hour during business hours. Outbound hand-raisers within 24 hours.
Acceptance criteria: ICP fit, need articulated, meeting booked or next step defined.
Sourced vs influenced: Marketing owns sourced pipeline and a share of influenced revenue measured via your agreed attribution model.
Automation: Use routing rules, alerts, and sequences to enforce SLAs and reduce manual work.
Strong SLAs combine people and systems. If you need help implementing the mechanics—routing, scoring-as-a-signal, and automated handoffs—our team can support with automation and workflow design.
Step 4: Update Compensation And Incentives
Compensation cements culture. If you pay Marketing on MQLs, you’ll get MQLs. Shift variable comp to pipeline and revenue to force alignment with business outcomes.
Marketing: Variable tied to € opportunities created and marketing-attributed revenue.
SDRs: Variable tied to SAL → SAO conversion and opportunity value, not meetings alone.
Sales: Variable unchanged, but add recognition for pipeline hygiene and accurate forecasting.
Leadership: Review incentives quarterly to prevent gaming or unintended behavior.
When everyone’s pay depends on pipeline, vanity metrics lose their appeal. You get better collaboration, stronger handoffs, and less finger-pointing.
Step 5: Pilot, Measure, Iterate
Don’t flip the whole company in one go. Run a 60–90 day pilot with one segment, product, or region. Move from MQLs to pipeline accountability, then compare outcomes.
Baseline: Last two quarters by MQLs, opportunities, coverage, win rates, and cycle time.
Intervention: Replace MQL goal with € opportunities created and stage movement targets.
Enablement: Update playbooks, sequences, landing pages, and routing.
Review: Weekly pipeline council, monthly attribution review, end-of-quarter results.
Success looks like fewer leads, more opportunities, faster movement, and better forecast accuracy. Keep what works. Scale it across the business. Sunset the MQL goal.
Sample Reports & KPIs To Replace MQL Dashboards
Minimal Dashboard: 4 Revenue‑Focused Tiles
Build a single-screen view that leaders and operators both trust. Four tiles beat a zoo of charts.
Opportunities Created (€): New pipeline value this quarter vs target. Trend by week. Split by source.
Pipeline Coverage: Open pipeline (€) / quota (€), with a stage-weighted variant.
Stage Movement: Conversion rates and average time-in-stage. Highlight bottlenecks in red.
Win Rate & Sales Cycle: Rolling 90-day figures by segment and source.
Add two supporting charts: marketing influence on pipeline (multi-touch) and pipeline aging (stalled deals by stage). Everything else can sit in channel or campaign reports.
If you want a primer on making KPI views useful (and not just pretty), our take on reporting the metrics that matter lays out how to focus on decision-making, not dashboard design.
Weekly Sales‑Marketing Handoff Report
This report turns alignment into a habit. It’s short, consistent, and action-biased.
Inbound SALs: Count, acceptance rate to SAO, average response time.
Outbound sequences: Meetings booked → opportunities created → value created.
Top sources: New opportunity creation by channel and campaign with trend vs last week.
Pipeline hygiene: Stalled opportunities by stage with owner and next step.
Include a “nudge list” of high-fit accounts showing buying signals (site intent, product usage, content engagement) and a set of next-best-actions. This is where lead nurturing belongs—helping live opportunities progress, not feeding the MQL machine.
Attribution Window And When To Credit Marketing
Agree on credit rules once, then stop arguing. Attribute marketing influence if there’s a qualified marketing touch inside a defined window leading to opportunity creation.
Window: 90 days to opportunity creation is common; shorten for short-cycle products.
Touch types: Ads, search, content, events, partner webinars, email—logged in CRM.
Model: Use position-based or time-decay plus self-reported attribution for dark social.
Exclusions: Don’t double-count. One opportunity, one revenue pot, transparent splits.
This approach respects reality without overcomplicating the math. You’ll get fair recognition for marketing impact while keeping the focus on revenue outcomes.
Quick Wins To Prove Impact In 30–60 Days
If you need early proof that the “MQL is dead” approach wins, run these fast plays:
Ungate low-intent content: Keep the brand value, remove fake hand-raises, and retarget high-intent behaviors instead.
Fix speed-to-lead: Route by ICP and channel, alert owners instantly, and add backup rules. Speed shows up as higher SAL → SAO conversion.
Retarget to opportunity creation: Build audiences from product pages, pricing, ROI tools, and case studies. Optimize to pipeline, not clicks.
Upgrade landing pages: Clarify ICP, outcomes, proof, and pricing signals. We’ve seen 20–40% more opportunity creation with focused pages—if you’re reworking experiences, see our guidance on effective landing page design.
Shift budget to high-intent demand: Invest in search terms, comparison content, and competitor alternatives. Our paid media and SEO and SEA services focus on these revenue levers.
Pair these with a weekly pipeline review and you’ll have clean evidence that fewer, better leads create more, faster revenue.
Ready To Replace MQLs? Talk To 6th Man
If the goal is predictable growth, MQLs are the wrong step to optimize. Replace them with opportunity creation in €, stage movement, marketing-attributed sales, and pipeline coverage. That’s how you operationalize sales‑marketing alignment, strengthen your lead-to-revenue engine, and forecast with confidence.
What 6th Man Can Do: Audit, Pilot, Embed
We help growth-minded teams move from vanity metrics to revenue metrics—quickly and without the agency bloat. As an embedded team, we audit your funnel, rebuild your reporting, and run a 60–90 day pilot that proves impact on pipeline and revenue.
Audit: ICP and stage definitions, attribution setup, routing, dashboards, and SLA health.
Pilot: Replace MQL targets with pipeline targets in a focused segment, then scale.
Embed: Align content, paid, automation, and sales enablement around opportunity creation and stage movement.
Want to see how we operate across B2B motions? Explore our B2B solutions and browse case studies for examples of predictable growth in action. If you’re ready to pressure-test your pipeline and replace MQLs for good, contact our team. We’ll bring the senior talent, the playbooks, and the dashboards—so you can bring the results.
Prefer to start with a broader assessment of where your digital strategy stands? Our digital marketing services are built to plug in fast and focus on what moves revenue, not just what moves metrics.
