Alex Collmer has spent more than a decade pushing a point the industry now repeats with confidence: creative does most of the work. As founder of Vidmob and this year’s Startup Marketer of the Year at the New York Ad Club, he built his company around that imbalance at a time when most investment was flowing into media optimization.
“Every marketer will say creative is the majority of results,” Collmer says. “Then they’ll base decisions on models that have zero factor for creative quality.”
The Creative vs. Media Imbalance
| Aspect | Current State |
|---|---|
| Creative Impact | Drives 50-70% of performance |
| Media Optimization Focus | Receives most investment and attention |
| The Gap | Systems used to measure and optimize campaigns largely ignore creative |
“We had to keep saying it over and over, creative is 50-70% of performance. And yet all the focus was on optimizing the other 30%,” Collmer says.
That imbalance still shows up in how campaigns are run day to day. Teams rely on media models and bidding systems that track delivery and conversion, but offer limited visibility into what is happening inside the creative itself.
The Problem: Awareness Without Action
| Issue | Description |
|---|---|
| Awareness | Every marketer agrees creative drives results |
| Inaction | That belief doesn’t change behavior once campaigns are live |
| Result | Budget shifts and audiences get refined, but creative stays in place until performance drops |
“Everyone agrees on it,” he says. “But the systems we use don’t reflect it.”
That disconnect is what Vidmob set out to address. The company has moved from production into analytics and now into what Collmer describes as “creative data” — a structured view of how creative decisions influence outcomes and how they can be improved over time.
The AI Advantage: From Volume to Intelligence
| Old Paradigm | New Paradigm |
|---|---|
| Creative as fixed asset | Creative as continuously optimizable |
| Volume of content | Intelligence about what to create |
| Set-and-forget approach | Real-time refinement based on audience response |
The rapid adoption of AI has made it possible to produce content at a scale that would have been difficult to manage even a few years ago. But that hasn’t made the job easier.
“It’s making it easier to create,” Collmer says. “That doesn’t mean it’s making it easier to be excellent.”
“When the ability to create is no longer the barrier, the knowledge of what to create becomes the advantage.”
The Middle Ground: Craft Meets Performance
For years, brand storytelling and performance marketing have been treated as separate disciplines, each with its own priorities. As expectations increase, that separation becomes harder to maintain.
“There’s a middle ground,” he says. “Craft still matters. But it has to drive business performance.”
Vidmob’s work with platforms such as Meta, TikTok, and YouTube feeds into that process by giving marketers a clearer view of how creative performs across audiences and environments.
The Next Phase: Bringing Intelligence to Generative AI
| Direction | Details |
|---|---|
| Goal | Bring creative intelligence directly into generative AI systems |
| Scope | All major generative AI platforms, not just one |
| Outcome | Influencing how content is produced as well as how it is measured |
“We’re going to bring intelligence into generative AI platforms,” he says. “Not just one, all of them.”
As content production continues to scale, the advantage comes from something more precise than output alone. It comes from knowing what will resonate before a campaign goes live.
