If you have worked in B2B marketing for more than a minute, you have probably been measured by MQLs.
Marketing qualified leads were straightforward to count, easy to report, and felt like a useful bridge between marketing and sales. Give us the leads, the story went, and sales will do the rest.
That confidence is cracking. MQL, as a single, sacred metric, is showing its limits. It was designed for a simpler time, when buyers behaved predictably, purchase journeys were linear, and a form submit meant genuine interest.
Today buyers research privately, buying decisions involve multiple stakeholders, intent signals are fragmented across channels, and the path from awareness to revenue is rarely a straight line.
The result is a growing mismatch between the legacy metric and the reality it is supposed to represent.
This piece explains why MQL is becoming obsolete, what to use instead, and how to reorganize your processes so marketing and sales actually accelerate revenue together.
How MQL Became the Standard
MQL rose to prominence because it solved a practical problem. Marketing needed a clear handoff point to pass leads to sales.
A checkbox approach simplified operations: define behaviors and thresholds, track conversions, and feed the list to SDRs. For many companies this worked. It created accountability, made performance tangible, and built a predictable pipeline of conversations.
But the conditions that made MQLs useful have eroded. The metric was optimized for volume and funnel velocity, not for the nuanced buying experiences that define modern B2B purchases. It measured a transaction rather than a relationship. It rewarded form fills, not readiness to buy.
Why MQL is Losing Relevance
1. Buying is Collective and Multi-Staged
B2B purchases are rarely made by one person. Decisions now involve cross-functional groups, procurement processes, and often external consultants.
A single person filling a form rarely signals a company-level intent to buy. MQLs treat individuals as proxies for accounts, which introduces noise and low quality signals.
2. Journeys are Fragmented Across Channels
Buyers shift between vendor websites, LinkedIn, peer communities, podcasts, and private channels. They consume content across weeks or months, often anonymously.
MQL thresholds based on a single click or download miss the broader context of that behavior, and therefore misclassify many prospects.
3. Intent Signals are Noisy and Privacy Constrained
First-party intent is valuable, but third-party cookies have faded and platform targeting has changed. Buyers are more privacy conscious, and intent data vendors are less reliable than they once were.
MQL frameworks that depend on brittle third-party signals crumble when the data is incomplete or inconsistent.
4. Quantity Can Mask Low Quality
When teams are rewarded for MQL count, there is an incentive to optimize for the metric rather than for pipeline health.
This creates a treadmill of acquiring more leads that never turn into opportunities, while the underlying cost of acquisition rises.
5. Sales and Marketing Misalignment
MQLs were supposed to be a handoff. In practice they often become a blame game. Marketing says they delivered leads, sales says they were unqualified.
Without richer signals and shared definitions that extend beyond a binary pass, collaboration stalls.
6. Buyers Expect Relevance and Personalization
A form submit is a weak signal for intent when buyers are used to highly tailored experiences.
Sales conversations that start from a generic MQL often feel cold and miss the context buyers expect, which reduces conversion and wastes both sides’ time.
What Replaces MQL
Moving beyond MQL does not mean abandoning measurement. It means replacing a single proxy with multi-dimensional signals that align with revenue outcomes.
Here are metrics and frameworks that modern B2B teams should consider.
1. Account Involvement Metrics
Track engagement at the account level, not just the individual level. Account engagement scores should combine signals from multiple people within the same company, weighted by role and influence. This reduces noise and elevates company-level buying intent.
2. Engagement-Based Scoring
Replace binary thresholds with engagement depth metrics. Look at repeat visits, time spent on key pages, content completion rates, webinar attendance, and product trial interactions. These signals paint a fuller picture of readiness.
3. Content-Assisted Pipeline
Measure which pieces of content assist pipeline creation. Instead of counting the top of funnel form fills, track content that appears frequently in the buyer’s journey prior to opportunity creation. This shows which assets actually help close business.
4. Opportunity Conversion Velocity
Track how marketing-influenced accounts move through the funnel, and measure their velocity versus other cohorts. If a group exposed to brand-led content moves faster, that is a meaningful signal of impact.
5. Revenue Influenced Metrics
Track revenue influenced by marketing activities. Multi-touch attribution is imperfect, but blending it with cohort analysis and time-based attribution gives you a much clearer readout of marketing’s true business impact.
6. Sales Accepted Contacts with Context
Rather than handing over a raw lead, deliver a sales accepted contact (SAC) with contextual intelligence. Include which content they consumed, key pain points inferred from behavior, and which colleagues at the same account showed interest.
How to Operationalize the Shift
Shifting away from MQL is partly about measurement, and partly about process and culture. Here are practical steps to help the transition.
1. Redefine the Handoff
Create a shared definition that includes not just contact-level criteria but account and engagement signals. Agree what a qualified account looks like, and what evidence sales needs to take the next step.
2. Invest in Account-Level Data
Use tooling that unifies individual behaviors into account profiles. Enrich these profiles with firmographics, technographics, and relationship maps. The richer the account signal, the less you rely on one-off form fills.
3. Build a Content to Conversion Map
Document which pieces of content correspond to each stage of the buyer journey. Map content to the behaviors that should increase an account’s readiness score. Use this map to inform both paid and organic distribution.
4. Align Compensation and KPIs
Ensure both marketing and sales are incentivized by outcomes that matter. Tie a meaningful portion of marketing compensation to pipeline and revenue influenced, not just to raw lead count. For sales, reward conversion velocity and quality, not just meetings.
5. Improve SDR Workflows
Equip SDRs with context and conversation starters. Instead of a generic outreach script, provide personalized insights drawn from the account profile: which content they read, which competitors they compared, and which team members are engaged.
6. Run Experiments and Measure Fast
Treat the shift as an experiment. Run parallel paths where one cohort is handled by the old MQL model and another by the new account-engagement model. Measure conversion rates, deal size, and sales cycle length.
7. Communicate Continuously
Change is cultural. Share wins and failures openly. Use weekly readouts to show how account-based signals are improving lead quality and pipeline efficiency.
Technology and Data Considerations
You cannot do account-based engagement without reliable data and orchestration. Invest in a stack that can unify digital touchpoints into account views, connect to CRM, and enable orchestration across channels.
Tools that provide intent, but also allow you to validate and contextualize signals, will reduce false positives.
Beware of over-relying on any single vendor or black box score. The best systems combine human judgment with algorithmic signals.
Build dashboards that show both the raw behaviors and the derived scores so teams can audit and trust the data.
Stories that Illustrate the Difference
One B2B SaaS business of mid-market finance solutions used to push hundreds of MQLs per month into sales, with a conversion rate to closed deals below one percent.
They switched to an account-engagement model that prioritized accounts where three or more stakeholders had consumed a specific case study and attended a webinar within a 90 day window.
The result was a 3x increase in deal close rate for that cohort and shorter sales cycles.
Another professional services firm replaced the MQL handoff with a co-created sales playbook.
Marketing supplied SDRs with content bundles and suggested opening lines, tied to the signals the account showed. Outreach became consultative from the first touch, and response rates improved dramatically.
Common Pitfalls and How to Avoid Them
1. Expecting Immediate Perfection
Moving to an account-based, engagement-driven model takes time. Start small and iterate.
2. Ignoring the Human Element
Don’t let scores replace conversations. Human insight is essential to interpret signals.
3. Over-Complicating the Scoring
Keep scoring transparent and explainable. Complex black box models reduce trust and slow adoption.
4. Letting Tools Dictate Strategy
Tools enable your strategy, they do not replace it. Define the approach first, then pick vendors that support it.
Conclusion
MQL served its purpose. It helped standardize handoffs and create measurable marketing outputs. But the modern buying environment is more complex, and the costs of relying on MQL as a single source of truth are higher than ever.
The future belongs to teams that measure at the account level, value engagement depth, align incentives across marketing and sales, and treat qualification as a context-rich conversation rather than a checkbox.
If you are ready to evolve your demand engine and replace noisy MQLs with signals that actually predict revenue, Growinity can help.
We design account-engagement frameworks, content strategies, and measurement systems that move marketing from volume to value.
Reach out to Growinity and let us help you build a system where marketing and sales win together, predictably.
