Anastasia Braitsik has established herself as a titan in the digital marketing landscape, specifically within the high-stakes world of mobile gaming. With a background that seamlessly blends granular data analytics, sophisticated SEO strategies, and high-impact content marketing, she has spent years decoding the behavioral patterns of players across the globe. Today, she joins us to pull back the curtain on the rigorous processes that drive a successful game launch, from the initial spark of a gameplay mechanic to the complex operations of a global rollout. Our conversation navigates the critical stages of a game’s infancy, examining how developers can move beyond gut feelings to make data-driven decisions that ensure long-term viability.
Throughout this discussion, we explore the strategic selection of traffic channels and why certain platforms are better suited for the early, volatile days of testing. We delve into the non-negotiable benchmarks for the idle and tycoon genres, specifically focusing on the retention numbers that separate a hobby project from a commercial powerhouse. Anastasia also sheds light on the delicate transition from testing basic engagement to validating actual return on investment, the necessity of building immense content depth before scaling, and the psychological shift required when moving from small-scale experiments to six-figure monthly budgets.
When launching a new idle game, why is Facebook often favored over other ad networks during the initial testing phase?
Facebook serves as a remarkably reactive channel, which is exactly what a studio needs when they are standing at the starting line with a relatively unproven product. In the early stages of 2026, where efficiency is everything, Facebook allows us to find a broad audience quickly without the agonizingly long learning periods required by other algorithmic networks. It tends to produce far fewer false positive signals; you don’t have to spend a fortune “training” the network to find your players because its baseline targeting is already so sophisticated for the idle and tycoon demographics. When we launch a test in a massive market like the United States using gameplay-specific creatives, we can almost immediately gauge the balance between audience size and the cost per install. It provides a clean, fast environment to test the core mechanics and the setting’s market fit before we commit to more complex, expensive platforms.
What specific benchmarks should developers be hitting in terms of retention and costs to feel confident about a project’s future?
In the current landscape for tycoon and idle titles, we are looking for very specific, high-performance milestones to justify further investment. For a typical project, Day 1 retention should ideally hit 40% or higher, assuming you are pulling homogeneous traffic from a major region like the United States. As the player journey continues, we look for Day 3 retention to hold at 20% to 25%, Day 7 to remain above 8% to 12%, and by the time they reach Day 30, we want to see a solid 6% of those players still engaged. On the financial side, we generally aim for a cost per install that stays below $1.50 during these initial tests to ensure the acquisition funnel isn’t leaking money from the start. These numbers aren’t just dry statistics; they are the pulse of the game, telling us whether the core loop is satisfying enough to keep a player coming back in an era of endless entertainment options.
How do you determine if you have enough data to make a decision without overspending on “perfect” statistical significance?
Chasing academic perfection in data can be a trap that drains a small studio’s budget before they even find their footing. We’ve found that for a primary test focusing on Day 1 retention and cost per install, a volume of 300 to 500 installs is usually sufficient to understand the general direction of the project. While some might argue for thousands of installs to reach a classic confidence level, treating these early metrics as “orders of magnitude” rather than absolute truths allows us to iterate faster and cheaper. We combine these numbers with our subjective view of the team’s speed and the product’s inherent potential to decide if we should scale or pivot. This pragmatic approach saves thousands of dollars that are better spent on improving the product once the initial “market fit” hypothesis is validated.
Once a game shows promising early engagement, what does the transition to a full-scale ROAS test actually look like?
The shift from testing “can we get people into the game” to “can this game make money” is a high-pressure moment that often requires a dedicated budget of $15,000 or more. At this stage, we are no longer just looking at installs; we are obsessing over the Return on Ad Spend and the quality of those players. We typically return to Facebook for this phase to leverage its targeted optimization goals, which allows us to validate payback for specific days and build a predictive model for the lifetime value of the user. We find that once we reach approximately 50 paying users, we have enough behavioral data to start sketching out the first real forecast curves for the next several months. It is a sensory experience for the marketing team, watching those first few dozen payers to see if their spending habits align with the retention curves we observed in the previous weeks.
How critical is content depth when moving from these early tests toward a global launch?
Content depth is the absolute backbone of a successful idle game, and it is often where developers underestimate the sheer volume of work required. Ideally, by the time you are running a Day 30 ROAS test, your game should actually have enough content to last 60 days, because paying players tend to consume your levels and mechanics much faster than the average user. Before we even consider a full-scale global launch, we aim to have a content pipeline that stretches between 90 and 180 days to ensure we don’t spend a fortune on traffic only to have players “finish” the game in a week. If the content isn’t ready, we might maintain traffic at a lower level, perhaps $30,000 to $50,000 a month, just to keep the data flowing and the optimization “warm” while the developers race to build out the world. You cannot scale a product that is only three weeks deep; the UA team is a multiplier, but they need a deep, engaging product to multiply, otherwise, you are just throwing money into a void.
When a project is ready to scale beyond Facebook, what is the strategic order for adding new ad networks?
Our standard operating procedure, once we’ve proven the model on Facebook, is to move toward AppLovin, especially since we utilize their MAX mediation layer which provides a seamless transition for our data. After AppLovin is stabilized, we look toward expanding into Unity or Google, though Google requires a very different mindset and a significantly larger budget to push through its initial learning phase. Each network has its own “personality” and threshold for efficiency, and we have to be careful not to expand too quickly before we’ve calibrated our internal benchmarks for each source. We constantly monitor the actual ROAS dynamics against our predictions, and if we see a significant divergence where the curve is lower than expected, we aren’t afraid to pause and re-evaluate the product’s monetization hooks. Scaling is as much about knowing when to hold back as it is about knowing when to spend, and we only move to that $100,000+ monthly global launch phase when every signal is green.
What is your forecast for the mobile game marketing landscape over the next two years?
I believe we are entering an era where the “magic” of operations will become even more dependent on the symbiosis between the product team and the marketing team, rather than seeing them as separate silos. We will see a shift where companies have to choose their path more clearly—either focusing on high-volume, lower-margin organic growth or leaning into high-precision, predictable profit from paid traffic. The era of the “lone wolf” UA manager who can launch a campaign and watch it fly to the top of the charts is largely over; success now requires a deep, historical database and the ability to iterate on dozens of gameplay creatives simultaneously. My forecast is that the cost of entry will continue to rise, making the “step-by-step” testing logic we discussed today not just a suggestion, but a survival requirement for any studio hoping to break through the noise. Readers should focus on building their own internal benchmarks and expertise now, because the market’s tolerance for unoptimized, “guess-based” spending is effectively zero.
