Vertigo Games made Ktrl their competitive edge on Critical Strike
Running UA for one of mobile gaming's most competitive titles, Vertigo Games used Ktrl to close the gap between noticing a change in performance and acting on it , replacing slow, manual cohort rebuilds with fresh daily predictions, granular geo coverage, and a 24/7 AI analyst.
In user acquisition, we believe the team that wins is not the team with the most data. It is the team that can act on it first. Running growth for a competitive title like Critical Strike, we see this every day. There is always a gap between noticing a change in performance and actually doing something about it, and this gap usually decides whether you scale a winner at the right time or lose the opportunity.
Closing this gap is the reason we started working with Ktrl.
How we worked before
Before Ktrl, we mapped LTV curves from our historical cohort data and used those assumptions to project where our active campaigns were going.
To be fair, it worked. Over a long period, our models were quite accurate. But accuracy was never the problem. Speed was.
The workflow was simply not efficient. We could not get fresh predictions every day, and it was not realistic to run detailed, dimension-by-dimension analysis as fast as we needed. In a category as competitive and fast-moving as ours, this delay has a real cost. Audiences fatigue, algorithms change, bid landscapes move, and a projection that takes days to rebuild always describes the market from last week, not the market you are in today. Geo level analysis was the clearest example. Refreshing predictions across that many dimensions, often enough to act on them, was not feasible manually.
There is always a gap between noticing a change in performance and actually doing something about it… this gap usually decides whether you scale a winner at the right time or lose the opportunity.
What changed for us
The first thing Ktrl gave us back was time. Instead of rebuilding cohort models from zero, we now get fresh predictions every day. This clearly reduced the time my team spends checking and optimizing campaigns, and it allowed us to spend that time on decisions instead of spreadsheets.
But the bigger change was about confidence and coverage, not only speed.
For the first time, we could review everything daily. With the detailed dimensions available frequently instead of occasionally, we could finally look closely at the areas we used to miss. The biggest improvement for us was at the geo level. By looking at granular breakdowns every day, we started to find under-the-radar areas to scale or cut that we would have missed before, or noticed too late.
The integrated AI assistant Angus became part of our daily routine very quickly. Instead of building a new report every time a question comes up, we simply ask it about a specific campaign, a specific breakdown, or anything we need, and it returns recommendations and context immediately. We see it as a new team member who is available and working 24/7.
And the reassurance is as valuable as the recommendations. Ktrl works as an objective, automated sanity check next to our own optimization logic. It is a second opinion that gives us much more confidence before we make major budget changes. This trust did not come automatically. The baseline predictions were close to our historical models, and after we fine-tuned the targets against our own LTV curves, seeing Ktrl track performance accurately across volatile cohorts is what earned our trust.
The bigger picture
To be honest, our experience does not include a single dramatic success story. There is no single campaign that saved our quarter. The value for us is quieter than that, and probably more durable. It is the result of making better decisions, earlier, every day.
Forecasting changed from something we checked occasionally to something built into how we work. We notice areas to scale or cut earlier. We enter new geos with a clearer understanding of where the opportunities are. And we make our important budget decisions with a second opinion already in hand.
For a team working in one of the most competitive areas of mobile gaming, this change from reacting to anticipating is very important.
The way we think about it is simple. Speed to insight is speed to scale.
Key outcomes
Time back to the team. Daily predictions replaced rebuilding cohort models from zero, cutting the time spent checking and optimizing campaigns , so the team spends it on decisions instead of spreadsheets.
Coverage they used to miss. Reviewing granular breakdowns every day surfaced under-the-radar geos to scale or cut that would previously have been missed or caught too late.
An always-on analyst. Angus became a daily team member, returning recommendations and context on any campaign or breakdown immediately, with no new report to build.
Confidence before big bets. An objective, automated second opinion gives the team more conviction before major budget changes , earned by tracking performance accurately across volatile cohorts.
About Vertigo Games and Critical Strike
Vertigo Games is a mobile game development and publishing studio specialising in midcore, real-time social multiplayer games. Founded in Istanbul, Turkey, with a presence in Tallinn, Estonia, the studio builds fast-paced competitive titles for a global player base.
Critical Strike (CS: Online FPS) is Vertigo's flagship real-time multiplayer shooter , fast matchmaking, sub-four-minute battles, 40+ weapons, and multiple modes and maps. The title has surpassed 153 million installs, holds a 4.7-star rating, and posted roughly 5x year-over-year revenue growth, making it one of the most competitive UA environments in mobile gaming.