Every dashboard said the campaigns
were working. Enrollment disagreed.
Every dashboard said the campaigns were working. Strong click-through rates, real traffic, real spend, all pointing in the right direction. But enrollment lagged behind targets across several concurrent F100 pharmaceutical trial recruitment programs, including Pfizer, Eli Lilly, Merck, Johnson & Johnson, and AbbVie.
The engagement metrics and the enrollment numbers were telling two different stories, and nothing in the existing reporting explained why.
Treating the funnel as one system,
not four separate reports
I tracked the full funnel, media spend, click-through rate, landing page behavior, and screener responses, as one connected system instead of four separate reports, using month-over-month and quarter-over-quarter comparisons to separate real shifts from noise.
That connected view surfaced a consistent pattern I've since come to call the Engagement-Conversion Gap: the space between what a channel earns in attention and what it actually converts into enrollment. Paid social was the clearest case, often strong on every engagement metric, weak at the one that mattered, the completed screener.
A channel can win every engagement metric on the dashboard and still lose at the one metric that was ever the point.
Not what's earning attention,
but where we're losing people
Once I could see the gap clearly, it gave message testing and budget decisions a sharper target: not "what's getting engagement" but "where exactly are we losing people."
Sometimes the gap traced back to the message itself, creative that performed well on click-through but failed to build the trust a screener completion actually requires. Those messages got reworked, not because they were underperforming on engagement, but because engagement was never where they were losing people.
Other times the gap was a budget problem wearing an efficiency disguise: a channel with a low cost per click and a high volume of engagement, sitting on a screener conversion rate that didn't justify the spend. That budget moved to channels where the gap was smaller.
Attention and action
are not the same metric
Applied across concurrent tracking programs, closing that gap drove an average 28% lift in screener conversions and directly shaped site selection, messaging, and budget decisions for ongoing campaigns.
screener conversions
One example made the pattern concrete. On a major pharmaceutical client's recruitment campaign, paid social posted a 4.55% click-through rate, nearly double paid search's 2.69%. But its click-to-screener conversion rate was 0.06%, compared to paid search's 5.51%. The channel that looked best on engagement was converting almost no one.
This wasn't a tracking upgrade.
It was a shared definition of success.
The program could have been scoped as a reporting improvement with better dashboards, faster turnaround, and cleaner data. That framing would have been accurate but incomplete. What actually mattered was giving creative, media, and account teams a shared, evidence-based answer to a question they'd been answering differently: what does "working" actually mean for this campaign.
Finding the Engagement-Conversion Gap wasn't the harder part. Turning it into something creative, media, and account teams could act on, without a researcher in the room to walk them through it each time, was.
The 28% average wasn't one campaign's lucky break. It came from applying the same lens consistently across multiple concurrent client programs over time, the kind of pattern that only shows up when a framework gets used across a portfolio, not chased opportunistically on whichever campaign is underperforming that month.
Three things I'd approach
differently in retrospect
The Engagement-Conversion Gap became clear because I was cross-referencing engagement and conversion numbers across separate weekly reports before that comparison was built into one shared, standing tracker. Designing the reporting around that comparison from day one, rather than assembling it after the pattern was already suspected, would have surfaced it faster and made it visible to stakeholders without me having to make the case each time.
Different teams on our side, creative, media, and account services, were reading the same engagement numbers as a win before the enrollment data caught up. Getting explicit, upfront agreement on which metric actually defined a campaign's success, before results started coming in, would have prevented some of the friction that came with reframing a channel everyone had already mentally filed as "working."
No single role on the account naturally saw both halves of the funnel at once, media performance lived with the buying team, enrollment outcomes lived with the client-facing side. The pattern changed our decisions as soon as I connected the two, budget and message testing shifted immediately. What took longer was turning that connection into something the team could see without me personally bridging the two data sources every time.
What this case study demonstrates
The 28% is an average across multiple concurrent tracking programs, not one campaign's before-and-after.