introducing airflux mobile game ad solution

What if a casual game could pull in half a billion dollars? 

That’s exactly what Survivor.io did, crossing $500 million in lifetime revenue in 2024. For a tap-to-play title, that’s a staggering number—and a clear sign of how massive the casual gaming market has become.

We’re in a high-growth window for mobile casual games, where a single well-made title can deliver outsized returns.

But the flip side? Competition is tougher than ever. User acquisition (UA) costs are rising, traffic is plateauing, and teams are under growing pressure to drive more revenue from the players they already have. That’s no easy task.

That’s exactly why the team behind Airbridge built Airflux—a platform designed to help casual game studios maximize ad revenue faster, smarter, and more efficiently. It’s also Airbridge’s first standalone product since the company was founded.

To understand how Airflux came to life—and why it matters more than ever—we sat down with Noel Son, Data Scientist, and Roi Nam, CEO and Co-founder of Airbridge.

1. Can you briefly introduce yourselves?

Roi: I’m Roi, CEO and Co-founder of Airbridge. I started the company about ten years ago with Airbridge, our MMP solution, which now supports over 500 companies across 10 countries—including Korea, the U.S., China, Vietnam, and Turkey. Most recently, we launched Airflux, and I’m excited to share the story behind it.

Noel: I’m Noel. I’ve been leading the Data Science team at Airbridge for the past three years. We pitched the idea for Airflux internally last year, worked hard to bring it to market, and now I’m leading the project as the product owner. It’s been exciting to see it come to life.

Noel Son (left) and Roi Nam (right)

2. What is Airflux?

Noel: Before I get into Airflux, we need to take a step back and look at how mobile games monetize.

Most mobile games—especially casual ones—make money through two core models: in-app ads (IAA) and in-app purchases (IAP). The revenue mix depends heavily on the game genre.

Casual games lean on IAA, while hardcore titles rely more on IAP. Some games stick to one model entirely. For example, a hyper-casual title might monetize 100% through ads, whereas a MMORPG game could be fully IAP-driven.

Midcore games like idle titles often use both, showing a few rewarded videos (RV) to boost engagement, while IAP remains the main revenue driver. But in casual games, purchases are minimal. The gameplay is light, and there aren’t many purchase-worthy moments—so ads become critical. If users aren’t buying or watching ads, the game doesn’t make money.

Here’s where it gets tricky. Most casual games rely on rewarded videos and interstitials—the latter often referred to as “forced ads” because they interrupt gameplay and aren’t user-initiated. Show too many and players churn. Show too few and you leave revenue on the table.

To manage that balance, monetization managers often hardcode the timing and frequency of interstitial ad displays—like showing one after every one or two stage clears, or triggering them 50% of the time after each stage clear.

But here’s the catch: increasing effective CPM (eCPM) by a few cents—say, bumping your bid from 3¢ to 4¢—adds a small 1¢ margin. Getting a user to watch just two more ads without churning? That has a far bigger impact on revenue.

However, tolerance for interstitials varies by region, device, play style, and player progression; essentially, it eventually varies by each individual user. Despite that, most games still use coarse rules and broad segments for interstitial ad display frequency and timing, leaving a lot of value untapped.

This is where true optimization needs to happen—dynamically adjusting ad frequency and timing so players watch more ads without getting annoyed. But getting to that level of personalization, with automated split testing and smart user segmentation? Very difficult. 

That’s where Airflux comes in.

Airflux automatically personalizes interstitial delivery—adjusting frequency and timing by user segment. It groups players by country, device, behavior, in-game engagement and progress, then dynamically optimizes when and how often ads are shown. For example, high-IAP potential users may see fewer interstitials, while non-spenders who tolerate ads may see more.

The result? More ad views for potential non-payers, less ad views for potential payers, better retention, and higher lifetime value (LTV).

Roi: Noel and our data science team have statistically proven that when users see an interstitial—and how often—has a direct impact on LTV.

We validated this causal relationship across multiple models. Get the timing and frequency right, and you get higher LTV.

This is especially critical for hyper-casual and hybrid-casual studios, where even small LTV gains can significantly affect profitability. As mobile game development gets easier and competition intensifies, retention will define long-term revenue.

Airflux automates what used to be a manual process in monetization and LiveOps (live operations, including post-launch updates and ongoing tuning)—using personalization and automated A/B testing to replace guesswork with data-driven precision.

That’s why we say this with confidence: Airflux’s biggest value to game studios is driving revenue and profitability.

3. There are plenty of industries—why focus on mobile games?

Noel: Right now, casual games need this kind of SaaS platform more than any other sector.

Many casual studios are lean. Some generate $20–30 million in annual revenue with fewer than 50 employees. That’s impressive efficiency, but it also means non-dev teams—monetization, LiveOps, data analysis—haven’t scaled much over the past decade. 

In our meetings with studios, we often see data analysts maintaining BI (business intelligence) dashboards, but we rarely see dedicated data scientists focused full-time on modeling.

E-commerce, by contrast, is packed with data scientists, data-driven growth marketers and CRM marketers focused on LTV. They use tools like Amplitude and Braze to scale and automate personalization.

In gaming—especially ad monetization—the tooling still lags behind. Most of the infrastructure is built for UA or dev workflows. And honestly, plenty of teams are still running everything out of Excel.

Roi: Like Noel said, most studios don’t have the in-house resources to deeply analyze their data. So even when there’s clear potential to grow revenue, it often goes untapped.

Mobile gaming is massive. According to Statista, in-game ad revenue is projected to hit $124 billion by 2025. Yet due to the lack of ad optimization, studios are missing out on a big chunk of that.

What we’re building is a way for studios to use AI and machine learning to boost ad revenue from the traffic they already have. That’s the value we want to deliver—helping teams earn more from the users they already have.

Noel: This is exactly where a SaaS platform becomes the solution.

Building and testing optimization models requires a full data science team—experts in math, stats, and machine learning. But most studios don’t have that luxury.

So we asked ourselves: what if a SaaS platform could do that work instead? If we handle the complexity, studios can boost revenue without needing to scale their teams.

4. Are mobile game studios seeing this as a real problem, too?

Noel: Absolutely. We’re currently running Airflux tests with over ten studios, and the early results are promising.

Even before launch, studios told us they wanted to try it as soon as it was ready. All free trial slots filled up quickly, and we’ve already moved on to paid POCs (Proofs of Concept). Despite that, the response has been extremely positive—which has been incredibly motivating for our team.

There aren’t many tools like this out there, so we’re doubling down on building something that can truly help studios grow revenue and improve profitability. 

Roi:  We’ve seen especially strong interest from hyper-casual studios, where tight margins mean they need to drive high volume to stay profitable. These teams are always looking for ways to squeeze more value from their ad inventory.

We’re also seeing more traction from hybrid-casual studios, especially as we expand optimization into balancing payouts for the rewarded videos.

One of the big drivers behind this is that eCPM growth in 2024 has been underwhelming. Most teams have already tried the usual levers—more placements, higher bid floors, longer videos, added competition—but those tactics just don’t move the needle like they used to.

Ad monetization is getting harder. So more teams are turning to smarter, automated solutions—like Airflux.

No studio has unlimited resources. Every monetization touchpoint has to count. In the short term, we’re helping optimize interstitials and rewarded videos. Long term, we’re expanding into IAP optimization, all powered by our proprietary machine learning and automation engine.

Because once LTV goes up, teams can reinvest more into UA, bring in more players, and keep the growth loop going non-stop. It’s a classic case of LTV driving CAC (customer acquisition cost). 

5. How is Airflux different from running A/B tests manually with tools like Firebase?

Roi: With Firebase, studios need to define their own hypotheses, manually set up tests, and configure variables—like whether to show an interstitial after Stage 1 or Stage 2, and at what probability (say, 50 percent vs. 70 percent).

These tests are one-dimensional but still require a lot of internal effort. And in mobile games, where you’re dealing with hundreds of variables, manual A/B testing just doesn’t scale.

You also have to manage test duration, sample size, and post-test analysis. Doing this across 100+ countries and multiple platforms with varying player behaviors? That’s hundreds or even thousands of variants. It’s almost impossible to keep it under control. 

That’s exactly what Airflux solves. It automates testing across granular segments—no dev work, no custom code. It’s plug-and-play and built to scale in ways traditional tools can’t.

Firebase is great for testing major changes, like game design updates or new IAP offers, such as a welcome pack. But when it comes to optimizing ad delivery at scale, Airflux is purpose-built for the job.

Noel: Running statistically meaningful A/B tests is more complex and time-consuming than most people think. There are just too many variables to manage manually, especially in mobile games.

Airflux uses machine learning to automate the entire process—from generating hypotheses to running tests, analyzing results, and applying the insights. The AI segments users, runs thousands of tests in parallel, and continuously optimizes for revenue.

For studios, this means no need to hire a data science team or burn hours on manual testing. It saves time and money.

More importantly, Airflux helps unlock incremental revenue that static setups simply can’t.

Roi: As a quick recap, here’s the core value Airflux brings to game studios: 

• Automatically adjusts interstitial ad frequency and timing based on user attributes and behavior
• Increases LTV from your existing traffic, with results proven through statistically reliable incrementality measurement
• Automates LiveOps tasks that typically require hours of manual work
• Replaces manual A/B testing on monetization with automated, scalable experimentation
• Optimizes ad exposure to each user’s tolerance threshold—maximizing revenue without hurting UX or retention
• Delivers clear, measurable, data-backed results

6. Have you seen real-world success with Airflux?

Noel: Absolutely. We’re currently working with over ten game studios, and the early results have been really strong.

For one zombie-themed FPS (first-person shooter) with over 10 million global downloads, we saw a 50% increase in user LTV. In another case, a studio with more than 10 million MAU (monthly active users) saw a 14% uplift in just two weeks.

One of our larger partners is currently seeing a 16% uplift, which is incredibly promising.

7. What strategies do mobile game studios need to stay competitive in the market?

Roi: In e-commerce, the early focus was all about driving new user acquisition and direct-response sales. But over time, the strategy shifted toward increasing LTV by leveraging CRM, data analytics, hyper personalization, and loyalty programs to maximize value from existing users. We’re now seeing the same shift in mobile gaming. The days of cheap user acquisition and fast scaling are behind us. What matters now is how much value you can generate from each player.

That’s why optimizing your ad monetization system with machine learning will be critical—maximizing revenue and lifting LTV on a per-user level.

And in my opinion, Airflux is the solution best equipped to do exactly that.

Noel: I completely agree with Roi. As the barrier to entry drops, mobile gaming is only getting more competitive.

That’s why the focus needs to shift to growing LTV without sacrificing the player experience. At its core, it’s simple. You’ve already spent big to acquire these users—now treat them like the high-value assets they are. Protect their experience, but don’t miss a single chance to monetize over the long run.