Adoption of the MQL as a primary marketing metric fell from 72.0% to 55.3% in a single year, a 16.7-point drop (6sense, 2024). At the same time, up to 75% of US buy-side leaders now say their core ad measurement approaches underperform (IAB State of Data, 2026). The MQL isn’t dead. It’s being demoted, because it counts interest and the business needs a number that counts intent.

Is the MQL dead?

16.7 percentage points: that is how far MQL adoption as a primary metric fell in a single year, from 72.0% to 55.3% (6sense, 2024). The MQL is not dead, but it lost its status fast, and that drop marks the collapse of a metric that ran B2B marketing for a decade. What changed is not the definition of a lead. It is what teams trust the number to tell them.

An MQL was always a proxy. It said a person crossed a scoring threshold, downloaded the thing, opened the emails. That was useful when the buying journey ran through forms and gated content. The proxy held. It is holding less well now because most of the buying journey happens before a form is ever touched, which means the MQL is measuring the visible slice of a mostly invisible process.

So the honest read is a demotion, not a funeral. Plenty of teams still generate MQLs and still find them useful as an early signal. The high performers stopped treating that signal as the scoreboard.

Why does the MQL mislead demand gen teams?

MQLs convert to SQLs at just 13% on average, so hitting 100% of an MQL goal can deliver only about 30% of the pipeline target (The Digital Bloom, 2025). That gap is the core problem. An MQL number can climb while the pipeline the business needs stays flat, because the two are only loosely connected.

The MQL rewards volume at the top of the funnel, where volume is cheap and easy to manufacture. Lower the score threshold, run a content offer, and MQLs climb. None of that guarantees a single additional deal. When marketing hits its MQL goal and sales still misses quota, the argument that follows is predictable: marketing says it delivered leads, sales says the leads were junk, and nobody can settle it because the metric everyone agreed to doesn’t map to revenue.

We have watched this play out inside enterprise demand programs more than once. The dashboard is green, the MQL target is met, and the CRO opens the quarterly review by asking how much pipeline marketing sourced. The MQL number cannot answer that question. It was never built to.

What should B2B teams measure instead of MQLs?

Cost per opportunity replaces the MQL as the primary target: one B2B security company (Armorblox) switched from MQL-based lead scoring to a cost-per-opportunity model and cut CPO from roughly $40,000 to about $800, a 50x reduction, while lead-to-conversion rose from around 2% to over 30% (Metadata.io / Armorblox, 2024). The metric they optimized against changed, and the economics followed.

The shift is from counting interest to counting intent and money. Instead of “how many people crossed a score threshold,” the questions become: how much qualified pipeline did we create, what did each opportunity cost to generate, and how fast are deals moving between stages. Those metrics are harder to game because they only move when something real happens in the CRM.

The Armorblox result is not a promise that every team gets a 50x cut. It is proof that moving the optimization target changes what the media buys. When you optimize toward opportunities rather than form fills, the algorithms and the budget chase the audiences that actually buy, and cost per lead in that case fell from about $1,000 to roughly $50 as a byproduct.

MQL model vs. pipeline and opportunity model

Both models measure something real. The difference is what each one lets you optimize and what the board sees.

Why did B2B ad measurement break in the first place?

Up to 75% of US buy-side leaders say their core measurement approaches, including attribution analysis, incrementality tests, and marketing mix models, underperform (IAB State of Data, 2026). The MQL problem is one symptom of a broader measurement crisis: the tracking got harder and the buying got more complicated at the same time.

The buying side moved first. A typical B2B buying group is now 6 to 10 decision-makers, each gathering 4 to 5 pieces of research independently (Gartner, 2023). No single form fill, and no single scored lead, represents that group. The MQL captures one person raising a hand while the other eight members of the committee research silently and never fill anything out. The metric sees a fraction of the buying reality and reports it as the whole.

Measurement also got structurally harder as signal loss, longer cycles, and fragmented channels piled up. That is why so many leaders now rate their own attribution and mix models as underperforming. The response we are seeing is not a better lead score. It is a move toward outcome metrics that hold up even when the path to them is impossible to fully trace.

How do you make the internal case to move off MQLs?

13% is the MQL-to-SQL conversion rate that makes the case on its own: at that rate, an MQL goal can be fully met while pipeline lands near 30% of target (The Digital Bloom, 2025). That comparison reframes the debate from “is the MQL bad” to “does our goal predict the revenue we owe the board.” It almost never does.

Then propose a parallel-run, not a rip-and-replace. Keep generating and scoring leads, but add cost per opportunity and sourced pipeline as the metrics you report up and optimize the media against. Run both for a quarter and let the CRO see which number tracks the deals that actually closed. The MQL becomes a diagnostic signal; pipeline economics becomes the scoreboard.

One move: rebuild this quarter’s marketing dashboard so the top line is sourced pipeline and cost per opportunity, and demote MQLs to a supporting row. If the top line can’t answer “how much revenue did marketing influence,” it isn’t the right top line.

This is the work we do with enterprise demand teams: re-anchoring the measurement model on pipeline and opportunity economics, then rewiring the media and the CRM stages so the numbers the board sees are the numbers the business runs on. Moving Parade builds the optimization target first, then buys against it, because the metric you optimize toward is the one you get more of.

Frequently Asked Questions

Is the MQL completely obsolete?

No. Adoption of the MQL as a primary metric fell from 72.0% to 55.3% in one year, but that still leaves a majority of teams using it in some form (6sense, 2024). The MQL is being demoted from scoreboard to supporting signal, not deleted. It remains a useful early indicator when paired with pipeline metrics.

What is cost per opportunity and why does it matter?

Cost per opportunity measures what it costs to generate one qualified sales opportunity, and it can move dramatically when it becomes the optimization target: one company cut CPO from roughly $40,000 to about $800 after switching off MQL-based scoring (Metadata.io / Armorblox, 2024). CPO matters because it ties spend directly to pipeline rather than to form fills.

Why don’t MQLs reflect how B2B buying actually works?

A typical B2B buying group now includes 6 to 10 decision-makers, each doing 4 to 5 independent pieces of research (Gartner, 2023). An MQL captures one person crossing a score threshold while the rest of the committee researches without ever raising a hand. The metric sees a sliver of the buying group and reports it as the whole.

Isn’t attribution a better answer than the MQL?

Not on its own. Up to 75% of buy-side leaders say attribution analysis, incrementality tests, and marketing mix models underperform (IAB State of Data, 2026). Fixing the MQL by leaning harder on attribution swaps one struggling proxy for another. The durable move is optimizing toward outcome metrics like sourced pipeline and cost per opportunity.

Meta description: Is the MQL dead? MQL adoption fell 16.7 points in a year. What high-performing B2B teams measure instead: pipeline and cost per opportunity.