Anastasia Braitsik stands at the forefront of the digital marketing landscape, navigating the intricate world of Meta Ads with a precision that blends rigorous data analytics with creative intuition. As a global leader in SEO and performance marketing, she has transformed how brands interpret their digital footprints, moving beyond surface-level metrics to uncover the underlying narratives of consumer behavior. Her approach treats the Meta Ads Manager not as a simple scoreboard of wins and losses, but as a complex ecosystem where every data point is a vital signal. By shifting the focus from reactive “toggle-switching” to proactive system diagnosis, she helps advertisers scale performance by understanding the “why” behind every conversion. In this discussion, we explore her methodology for diagnosing campaign health and her vision for the future of AI-driven advertising.
The following conversation delves into the evolution of media buying, exploring the transition from traditional reporting to a sophisticated “team metrics” framework. We discuss the hidden relationship between creative quality and auction costs, the technical vulnerabilities that drain budgets, and the precise art of isolating variables to fix broken conversion funnels.
Advertisers often focus on ROAS as the ultimate measure of success. How can viewing metrics as an interdependent system rather than just a scoreboard change your optimization strategy, and what specific data points help tell the story of a campaign’s health? Please include step-by-step details for this transition.
When you treat your dashboard like a scoreboard, you are essentially staring at the final score of a game without ever watching the actual play, which leaves you powerless to influence the next match. To break out of the “scoreboard trap,” you must transition to a diagnostic mindset where you view metrics as a sports team where every player has a unique responsibility. The first step in this transition is to consciously ignore the ROAS column during your initial daily check-in to avoid the emotional impulse to kill a campaign that might just be experiencing a temporary hiccup. Instead, you trace the “path of the ball” through the funnel: start with the “scouts” like CPM and Reach to gauge market resonance, then look at the “midfielders” such as CTR and Hook Rate to see if your creative is successfully passing the user to your site. Finally, analyze the “strikers”—your CVR and AOV—which tell you if your landing page is actually closing the deal. This system-wide view allows you to see that a low ROAS might actually be caused by a “midfielder” failing to pass the ball rather than a “striker” missing the goal, giving you a clear roadmap for which specific lever to pull.
High CPMs are frequently blamed on expensive audiences or market competition. How can creative quality directly influence these costs, and what steps should a media buyer take to determine if the creative is causing Meta to charge a premium for a poor user experience?
It is a common misconception that a high CPM is purely a result of a crowded auction or a high-value audience segment, but in the modern Meta ecosystem, it is often a direct tax on poor creative quality. Meta’s primary goal is to keep users engaged on their platform, so if your ad provides a jarring or low-value experience, the algorithm will essentially charge you a “penalty fee” to show that content. To diagnose this, a media buyer should look at the CPM relative to their historical account averages; if you see a sudden spike while targeting the same audience, it is a loud signal from the auction that your creative isn’t resonating. You should also evaluate the estimated action rates, because if Meta’s AI predicts that users won’t interact with your ad, it will devalue your “total value” score, forcing your bid to work much harder and more expensively. By refining the visual hook and ensuring the content feels native to the platform, you can actually lower your CPMs, effectively earning a “quality discount” from the algorithm for contributing to a positive user journey.
A strong hook rate doesn’t always lead to a high click-through rate. When an ad successfully stops the scroll but fails to drive traffic, how do you diagnose the breakdown in the creative’s narrative, and what specific adjustments can bridge that gap between grabbing attention and eliciting a click?
This is a classic case of an ad that is great at shouting for attention but terrible at telling a persuasive story, much like a striker who can dribble past everyone but refuses to take the shot. When you see a high hook rate paired with a low click-through rate, it means your opening three seconds are doing their job, but the “hold rate”—the middle portion of your video—is likely falling flat or feeling disconnected from the initial promise. To diagnose this, I look at the drop-off points in the video retention graph to see exactly where the audience is losing interest; if they leave immediately after the hook, your transition is likely too abrupt or feels like “bait.” To bridge this gap, you must strengthen the middle narrative by reinforcing the value proposition and ensuring the video ends with a clear, high-friction call to action that makes clicking feel like the natural next step. It is about moving from simple attention-grabbing to genuine interest-building, ensuring that the energy established in the first few seconds is carried all the way through to the final frame.
Significant gaps often exist between link clicks and actual landing page views. What technical vulnerabilities or infrastructure issues usually cause this discrepancy, and how can teams differentiate between a creative failure and a server-side issue like slow load speeds?
The gap between link clicks and landing page views is one of the most overlooked “leaks” in an advertising budget, and it is almost always a technical failure rather than a creative one. If you see a scenario where you have 1,000 link clicks but only 450 landing page views, you are essentially losing over half of your potential customers to a “digital black hole” before they even see your offer. This massive 55% drop-off is a classic symptom of a slow server or a poorly optimized mobile site; in a world of instant gratification, a user who clicks an ad will rarely wait more than two or three seconds for a page to load. To differentiate this from a creative failure, you simply have to look at the intent: the click itself proves the creative did its job of sparking interest. If the landing page view doesn’t follow, the friction is happening at the infrastructure level, requiring a deep dive into page speed scores, tracking pixel firing sequences, or potential redirects that are frustrating the user’s journey.
Rising CPAs and high frequency often signal creative fatigue. How do you distinguish between an audience that is simply exhausted and a bidding strategy that needs adjustment, and what is your process for swapping assets without disrupting the entire campaign ecosystem?
When you see CPAs climbing alongside a rising frequency, it’s like watching a neighborhood get tired of seeing the same billboard every morning on their commute; the message has simply become invisible. To distinguish this from a bidding issue, I look for a decline in CTR and engagement alongside that rising frequency; if people are seeing the ad more but clicking less, the audience is fatigued, whereas a bidding issue usually presents as a sudden drop in delivery or impressions regardless of creative performance. My process for swapping assets involves a “surgical” approach to avoid sending the entire campaign back into the volatile learning phase. Instead of a total overhaul, I introduce one or two fresh creative assets into an existing ad set or use a dedicated testing environment to validate new hooks before moving them into the main “proving ground.” This allows the system to maintain its historical data stability while slowly replacing the “tired” assets with new “scouts” that can reinvigorate market resonance and bring those CPAs back down to earth.
Identifying a bottleneck is only the first step. When you see low conversion rates despite high engagement, why is it vital to isolate only one variable at a time, and what is the framework for deciding whether to overhaul the ad or the landing page?
The reason we must isolate a single variable is to avoid the “scattergun” effect, where you change five things at once and have no idea which one actually fixed the problem—or made it worse. If your ad has high engagement and a strong click-through rate but your conversion rate is dismal, the “midfielders” have done their job perfectly, and the friction is clearly with the “striker,” which is your landing page. In this scenario, overhauling the ad would be a mistake because you’d be breaking the one part of the system that is actually working. Instead, look at the landing page experience: are you sending someone from a broad, multi-product ad to a hyper-specific product page, or vice-versa? If the ad features a collection of items, you should consider a product collection landing page to reduce friction, ensuring the user sees exactly what they expected when they clicked. By fixing only the landing page, you can definitively prove that the offer or the UX was the bottleneck, allowing you to engineer growth with scientific certainty rather than guesswork.
What is your forecast for Meta Ads?
My forecast for Meta Ads is a transition where the role of the media buyer shifts from a manual “operator” to a high-level “system architect” as Meta’s AI, specifically through systems like Andromeda and GEM, takes full control over granular targeting. We are moving into an era where the creative is the targeting; the algorithm is now so sophisticated that it can find your ideal customer based purely on how they interact with the visual and narrative cues of your ads. This means that success will no longer be found in “hacking” the dashboard or finding a secret interest group, but in the ability to interpret the complex interplay of metrics to diagnose systemic friction. I expect to see even more automation in budget allocation and creative testing, which will reward those who can think like engineers—identifying why a funnel is leaking and using creative assets to plug those holes. The future belongs to those who stop looking at Meta as a scoreboard for quick wins and start treating it as a dynamic, interconnected system that requires constant diagnostic refinement to scale.
