I’m thrilled to sit down with Milena Traikovich, a powerhouse in the world of demand generation and performance marketing. With her deep expertise in analytics, campaign optimization, and lead nurturing, Milena has guided countless businesses to outpace their competition through innovative paid media strategies. In this conversation, we dive into the evolving landscape of digital advertising, exploring the pitfalls of conventional approaches, the power of bold experimentation, and the critical mindset shift needed to stand out in a crowded market. Join us as we unpack actionable insights on creative testing, data interpretation, and strategic activation.
How did you first come across the concept of the “Edge Gap” in performance marketing, and what does it mean to you?
I started noticing the Edge Gap a few years back while working with brands that seemed to hit a wall despite doing everything “right.” It’s the divide between those who stick to best practices—tweaking and optimizing within a familiar framework—and those who break away by exploring uncharted territory. The ones stuck in iteration mode often see diminishing returns, while the explorers gain a real competitive advantage. It became clear to me that following the rulebook can only take you so far; true growth comes from daring to rewrite the rules.
Why do you think so many brands get comfortable with just iterating on best practices instead of pushing boundaries?
A lot of it comes down to risk aversion. Best practices are safe—they’re backed by data, endorsed by industry leaders, and easy to justify to stakeholders. When you’re a CMO or marketing lead, sticking to the playbook checks boxes for governance and accountability. But the trap is that it keeps you in the middle of the pack. Brands get comfortable because iteration feels like progress, even when it’s just maintaining the status quo. Breaking out requires a willingness to fail, and not every team or organization is ready to embrace that uncertainty.
What are some of the hidden downsides you’ve seen when brands rely too heavily on standard best practices?
The biggest downside is the hidden cost of sameness. When everyone in a category follows the same strategies—whether it’s targeting the same audiences or using similar campaign structures—costs like CPMs and CPCs skyrocket due to auction competition. You’re essentially paying a premium to blend in. Plus, there’s the diminishing returns factor: best practices might give you a quick lift at first, but over time, they stop delivering standout results because the market catches up. It’s a cycle that benefits ad platforms more than advertisers.
How do advertising platforms gain when brands all use the same tried-and-true methods?
Platforms like Meta and Google thrive when advertisers adopt uniform strategies because it drives up competition within their ecosystems. If every brand is chasing the same audience signals or running identical campaign setups, the auctions get crowded, and costs naturally climb. Higher CPMs and CPCs mean more revenue for the platforms, while brands are left fighting over the same slice of the pie. It’s a clever business model for them, but it puts advertisers in a tough spot, paying more for less differentiation.
Why do you believe experimentation is the key to overcoming this plateau and closing the Edge Gap?
Experimentation is the only way to break out of the cycle of sameness. While optimization focuses on refining what you already know, exploration is about discovering what you don’t. It’s the difference between tweaking an ad’s headline for a 2% lift and testing a completely new concept that could 10x your results. Brands that experiment—whether through creative, data signals, or activation tactics—find first-mover advantages and set new benchmarks. It’s not about reckless gambles; it’s about structured curiosity layered on a solid foundation.
When it comes to creative testing, why do you emphasize velocity and diversity over chasing perfection in a single ad?
Polishing one “hero” ad is a trap. It assumes you already know what will resonate, which is rarely true. Velocity and diversity mean launching multiple, distinct concepts quickly to see what cuts through. I’ve seen brands test wildly different ideas—think contrasting tones, formats, or messaging—and stumble on breakthroughs they’d never have predicted. The focus should be on learning fast, not perfecting slow. You’re better off with a portfolio of bets than a single polished piece that might miss the mark.
Shifting to signal and attribution, why shouldn’t brands take attribution data at face value when measuring performance?
Attribution data often misleads because it shows correlation, not causation. Just because a click happened before a purchase doesn’t mean it drove the sale—there could be other factors at play. I’ve seen brands pour budget into channels that look good on paper but don’t actually move the needle. That’s why incrementality tests are crucial; they help isolate the true impact of a campaign by comparing test and control groups. It’s about building your own understanding of what drives revenue, not blindly trusting platform reports.
On the topic of activation, what do you mean by looking beyond broad automation to find unique opportunities?
Broad automation, like some of the AI-driven tools out there, can be powerful, but it’s not a silver bullet. If everyone’s using the same plug-and-play features, you’re back to sameness. I encourage brands to dig deeper—explore first-mover windows, tweak conversion data in unconventional ways, or experiment with parameters most competitors overlook. For instance, some leading brands are now using synthetic data to refine bidding strategies. It’s about finding edges in the gray areas, not just following the default settings.
What kind of mindset shift does a brand need to prioritize exploration over pure optimization?
It starts with embracing failure as a learning tool. Exploration means some ideas won’t work, and that’s okay if you’re structured about it—think ring-fencing a small budget like 5-10% for testing new channels or formats. Brands also need to foster curiosity over comfort, asking “What if?” instead of “What’s proven?” It’s about building a culture where experimentation is a habit, not a one-off, and where teams are rewarded for insights gained, not just immediate wins.
Looking ahead, what’s your forecast for the future of performance marketing and the role of experimentation in it?
I think we’re heading toward an era where experimentation isn’t just an option—it’s a necessity. As AI and automation make best practices even more accessible, the gap between average and exceptional will widen further. Brands that don’t experiment will struggle to differentiate, while those who do will redefine the game. I expect we’ll see more tools and methodologies emerge to support structured testing, especially around creative and incrementality. My forecast is clear: the future belongs to the bold, the ones willing to explore without permission.
