Here's a confession that might get my CMO card revoked: for years, I watched companies buy intent data like it was a magic potion. Sprinkle some buyer signals on your campaigns, and pipeline would rain from the sky. Spoiler alert: it didn't work that way. The data sat in dashboards, sales teams ignored it, and marketing blamed the vendor.
Then I came across Qualified's case study with Metadata, and it stopped me mid-scroll. Not because $6.9M in influenced pipeline is a small number (it isn't), but because the story underneath reveals something most intent data conversations miss entirely: the gap between having signals and actually doing something useful with them.
The Problem Nobody Wants to Admit
Qualified, a mid-market SaaS company with about 200 employees, had invested in their own intent data through their Signals product. They knew which accounts were surging with buying intent. They had the data. What they didn't have was a way to turn that data into targeted advertising that actually reached the right people.
Sound familiar? According to recent research from DemandScience, 87% of organizations report that their marketing investments produce unreliable or inflated intent signals. Only 26% of those signals convert into qualified opportunities. The industry has an intent data problem, and it's not about the data itself.
Qualified's challenge was specific: their third-party intent data didn't align cleanly with what they saw in Salesforce. The performance metrics from their intent provider told one story; their CRM told another. And when they tried to activate that intent data on Facebook and LinkedIn, the native targeting options couldn't match the specificity of their signals.
In other words, they had a Ferrari engine but no transmission.
The Execution Gap
This is where the case study gets interesting for anyone who's ever tried to operationalize intent data. Qualified used Metadata to bridge what I'd call the activation gap, the space between knowing something about buyer behavior and actually reaching those buyers with paid media.
The mechanics were straightforward: build a Salesforce list view report segmenting surging accounts from Signals, connect that account list to Metadata, add contact-level audience criteria, and launch campaigns across Facebook and LinkedIn. URL parameters tracked when visitors from surging accounts arrived on the site from sales outreach.
What made this work wasn't the technology alone. It was the workflow. As Nick Duke, Qualified's Senior Director of Digital Marketing, put it:
Metadata gives us the ability to build custom audiences and leverage them on ad channels that typically don't let you get that granular like Facebook and LinkedIn.
Nick Duke, Senior Director of Digital Marketing at Qualified
The results: 2,000+ surging target accounts reached, $6.9M in influenced pipeline, and 703 hours of manual work automated.
Why Most Intent Data Initiatives Fail
Let me be blunt about something. The B2B intent data market is projected to hit $20.89 billion by 2035, growing at a 16.6% CAGR. Yet only 24% of teams report exceptional ROI from their intent data investment. That's a lot of money chasing mediocre outcomes.
The problem isn't the data. It's the activation layer.
Most marketing teams buy intent data, plug it into their existing workflows, and expect magic. But intent signals are only as valuable as your ability to act on them quickly and precisely. According to 6sense's 2025 Buyer Experience Report, 95% of deals land with a vendor already on the buying group's day-one shortlist. If you're not reaching surging accounts while they're actually surging, you're not in the consideration set.
Qualified's approach worked because they closed the loop between signal and action. The intent data identified accounts showing buying behavior. Metadata turned those accounts into targetable audiences. The campaigns reached the right people at the right companies. And the tracking connected ad interactions back to pipeline influence.
The Uncomfortable Truth About Timing
Here's a stat that should keep every demand gen leader up at night: 70% of accounts active in a target segment this quarter will not be active next quarter. Intent is perishable. The window between researching solutions and made a decision is shrinking.
This is why the automation piece of Qualified's story matters. Those 703 hours of manual work they automated weren't just about efficiency. They were about speed. When you're manually uploading account lists, building audiences by hand, and waiting for campaigns to launch, you're losing the timing advantage that intent data is supposed to provide.
The companies winning with intent data aren't the ones with the most sophisticated signals. They're the ones who can act on those signals fastest.
What This Means for Your Stack
If you're sitting on intent data that isn't translating into pipeline, the Qualified case study offers a diagnostic framework. Ask yourself:
- Can you turn account-level intent signals into contact-level targeting? Most intent data tells you which companies are in-market. But companies don't click ads; people do. If you can't layer persona data onto your account lists, you're advertising to buildings, not buyers.
- Can you activate across channels that matter? LinkedIn's native targeting is powerful but limited. Facebook's B2B targeting is notoriously weak. If your intent data can only be activated on one channel, you're missing reach.
- Can you close the attribution loop? Intent-influenced pipeline is only valuable if you can prove it. URL parameters, Salesforce integration, and proper tracking aren't optional.
The Bigger Picture
Metadata's 2026 Buyer's Guide makes a provocative claim: Intelligence without execution is just expensive data. It's a marketing line, sure, but it captures something real about where the industry is headed.
The next generation of B2B marketing isn't about having more data. It's about having systems that can act on data autonomously, at scale, and in real time. Qualified's $6.9M pipeline result wasn't about buying better intent signals. It was about building an execution layer that could turn those signals into targeted impressions before the buying window closed.
For CMOs evaluating their martech stack, the question isn't do we have intent data? It's can we activate it fast enough to matter?
The answer, for most teams, is still no. And that's the real opportunity hiding in plain sight.
