The report introduces a sharp phrase for a familiar frustration: the Marketing Data Mirage. It describes a growing disconnect between marketing signals that look successful on the surface and the revenue outcomes executives actually care about. In short, campaigns appear to perform well, but the money doesn’t follow.

And this isn’t a niche problem. DemandScience surveyed 750 senior marketing leaders across industries including technology, financial services, healthcare, manufacturing, and professional services. The companies represented range from $100 million to well over $5 billion in annual revenue. Two-thirds of respondents say their dashboards regularly show “success” that fails to translate into revenue.

That gap, the report argues, is now one of the biggest threats to modern B2B performance marketing.

Performance marketing has long been framed as a discipline of optimization: pick the right channels, fine-tune spend, measure everything. But DemandScience’s findings suggest the bigger issues are happening before ads ever run or emails ever deploy.

The real cracks are upstream—in the quality of intent signals, the reliability of data, the explosion of disconnected tools, and the overuse of AI-generated content that looks polished but fails to persuade real buyers.

“Marketers are working harder than ever,” said Bill Hobbib, CMO at DemandScience, “yet their campaigns are dragged down by signals, AI-generated content, and metrics that look promising on the surface but fail to translate into real outcomes.”

It’s a familiar scenario: dashboards glow green, lead goals are exceeded, impressions scale effortlessly. Then sales steps in—and conversion rates collapse. The Mirage makes tactical execution look healthy while masking the structural issues quietly draining revenue.

Intent data has become one of the most heavily marketed categories in martech, promising to reveal which buyers are “in market” before they ever raise a hand. DemandScience’s data suggests those promises are being oversold.

A striking 87% of organizations say their marketing investments produce unreliable or inflated intent signals—things like clicks, downloads, and behavioral scores that don’t reflect real buying intent. Only 26% of those so-called intent signals convert into qualified opportunities.

In other words, marketers are swimming in signals but starving for substance.

This helps explain why 66% of leaders report that their campaign metrics frequently look successful yet fail to drive revenue. When weak signals are treated as strong buying indicators, entire campaigns can be optimized around noise.

Misleading metrics don’t just distort reporting—they burn real money.

Respondents estimate that 25% of their marketing budget is wasted on efforts that fail to drive outcomes. For organizations plagued by frequently misleading metrics, that waste climbs to 30%. Companies with clearer, more reliable measurement still lose about 23% of budget, but the gap highlights how costly bad data can be.

That level of inefficiency is especially painful as marketing budgets face tighter scrutiny. CFOs increasingly expect marketing to defend spend with revenue impact, not vanity metrics. The Mirage makes those conversations harder, not easier.

If the instinctive response to underperformance is “add another tool,” the data suggests that strategy is backfiring.

Organizations using between 11 and 25 marketing tools report nearly 90% unclear ROI. By comparison, those with 6 to 10 tools report unclear ROI at a lower—but still troubling—62%.

The takeaway isn’t that technology is the enemy. It’s that fragmentation kills visibility. As stacks grow, data gets harder to reconcile, attribution becomes fuzzier, and teams spend more time managing systems than improving campaigns.

Ironically, the very tools meant to increase performance are often reinforcing the Mirage.

Content remains central to B2B marketing, but DemandScience’s research suggests much of it is built on shaky foundations.

Seventy-six percent of organizations admit they create content without verified buyer signals, intent data, or performance analytics. Instead, content is often shaped by assumptions, competitor mimicry, or generic personas that don’t reflect how real buyers make decisions.

That helps explain why so much B2B content struggles to engage. Without credible signals guiding creation, teams are essentially guessing—then measuring success with the same flawed metrics that caused the problem in the first place.

AI has made content creation faster than ever, but speed may be undermining differentiation. According to the report, 72% of marketing leaders believe AI-generated content is actively harming brand distinctiveness.

The performance data backs that up. Eighty-one percent of respondents say half or less of their content drives meaningful buyer engagement—defined as outcomes that lead to sales conversations, pipeline, or revenue.

AI isn’t inherently the villain here. The issue is overreliance on generic outputs, often disconnected from real buyer intent. When everyone uses the same tools trained on the same data, sameness becomes inevitable.

Perhaps the most sobering insight: marketers are spending more time repairing broken systems than building new ideas.

Eighty-five percent of respondents say their teams spend more than half their time fixing issues instead of creating campaigns. Seventy-eight percent spend at least 21% of their time on manual tasks like data cleanup, list building, troubleshooting campaigns, and reconciling systems.

That’s a massive drain on creativity and morale—and another hidden cost of the Mirage.

Despite the bleak picture, the report ends on a note of opportunity. Respondents estimate they could unlock 32% more annual revenue if their data, signals, content, and orchestration were better connected and more effective.

“These potential gains are hiding in plain sight,” said Derek Schoettle, CEO and chairman of DemandScience. For organizations operating at scale, that upside represents hundreds of millions—or even billions—of dollars.

The implication is clear: fixing performance marketing isn’t about chasing the next channel or doubling down on spend. It’s about rebuilding trust in data, prioritizing signal quality over volume, simplifying stacks, and treating AI as an assistant—not a replacement for insight.

In an era where marketing is expected to prove its value with precision, the biggest risk may be believing the numbers too easily.