Most studios know they need to soft launch. Fewer know what actually goes wrong when they do.

Of course, there is a playbook for your soft launch;

• Set your KPIs

• Confirm the technical setup

• Pick your test markets

• Optimize your store listing

• Build creatives for each channel

• And monitor retention 

All of it is technically correct and almost none of it prepares you for the moment you are staring at data that looks fine on the surface but is quietly worthless.

There are typical mistakes that kill soft launches through bad data, wrong conclusions, and product decisions made on a foundation that was never solid to begin with. 

So we're sharing how to avoid that. 

Before we start: What a soft launch?

A soft launch (“beta”) is when a game is released to a limited number of players in a test market before releasing it to a global audience.

There are typically two stages of soft launch: 

• Alpha is the earlier stage and is often used to test whether the core of the game is working, including the technology and core gameplay loop. 

• Beta is later and is used to test the meta gameplay loop, marketing acquisition, server scalability, and monetization. 

A soft launch is not a quiet version of your global launch. It is a controlled test designed to answer one question: do you have a game worth scaling?

You do not need product-market fit before you start. Finding product-market fit is the whole point. You put a limited amount of traffic into a limited set of markets, you watch what happens, and you use that data to decide whether to invest more or go back to the product team.

Ultimately, its about knowing if your game is worth it?

This framing matters

If you go in thinking the soft launch is a dress rehearsal, you will be tempted to make it look good. If you go in knowing it is a diagnostic test, you will be more honest about what the data is telling you. 

That shift in mindset is the difference between a soft launch that teaches you something and one that just costs money.

Here's an example of how Ex-Supercell team soft launched their debut game after $18m raise.

Mistake 1: Splitting a small budget across too many channels and markets

This is the most common mistake we see, and it makes everything else worse.

A studio comes in with a modest budget and wants to test three channels across three countries simultaneously. On paper it sounds thorough. In practice, the budget gets divided so thinly that no single channel ever reaches its learning phase.

Ad platforms need volume to optimize. Meta, Google, and AppLovin,  all require a minimum number of conversion events before their algorithms can start targeting effectively. If your budget runs out before you hit that threshold, you are not getting optimized traffic. You are getting whatever the algorithm serves before it has learned anything meaningful.

What this actually looks like in practice

The result is noisy data that looks like it means something but does not. Studios then make product decisions based on that noise, which is worse than having no data at all. We have seen games go back into six weeks of product rework because of retention numbers that turned out to be completely unreliable.

What to do instead: 

Pick two channels. Give each one enough budget and enough time to exit the learning phase before you draw any conclusions. 

Two channels is the minimum that lets you separate a channel problem from a game problem. If both are struggling, look at the game. If one is struggling, look at that channel.

Mistake 2: Choosing markets based on convention rather than objective

The classic advice is to soft launch in Canada, Australia, or the Nordics because they behave like US users and have high ARPU. That advice is not wrong. But studios often apply it without thinking about what they actually need from the data at each phase.

If your goal early on is simply to confirm nothing is broken, that users can get through onboarding without hitting a crash, that the core loop works, you do not need an expensive Tier 1 market. The Philippines, for example, has very low CPI and high volume. 

It is a perfectly reasonable place to run a basic technical and UX validation.

The fraud problem nobody talks about enough

The danger is when studios use Tier 3 markets for everything, including retention and monetization testing. Some of those markets carry significant fraud risk, particularly when you are optimizing for installs. 

We have seen situations where a studio ran install-optimized campaigns in a high-fraud country, got what looked like decent CPI numbers, and then spent weeks trying to understand why retention was catastrophic. 

The retention was catastrophic because a meaningful portion of the installs were not real users.

What to do instead: Match your market to your objective. Early bug testing can happen in cheaper markets. When you move to retention and monetization, choose markets that reflect your real audience. The UK and Canada are consistently underrated here, user behavior close to Tier 1, lower CPI than the US, and have high English penetration.

Mistake 3: Trusting early CPI numbers

Early CPI from a fresh campaign is almost always unreliable, and acting on it is a fast way to make the wrong call.

When a campaign first goes live, the platform is still figuring out who to target. The audience it reaches in the first few days is not the audience it will stabilize on once it has had time to learn. During that early window, CPI can swing dramatically in either direction.

The problem compounds with fraud

If you are running in markets with any fraud exposure and optimizing for installs, you will likely pick up fraudulent traffic. 

Fraudulent installs are cheap, which flatters your CPI. Your retention will look terrible, and you will spend time trying to fix a product problem that does not exist. 

According to GameAnalytics' benchmark data, median D1 retention across mobile games sits around 22%, with the top quartile reaching 26-28%. If your numbers are dramatically below that after a week of campaigns, ask whether you have clean data before you touch the product. 

What to do instead: Give campaigns time. Move to deeper funnel optimization as quickly as your budget allows. Install-optimized and purchase-optimized campaigns produce very different user quality. You cannot compare their retention numbers as if they mean the same thing.

Mistake 4: Changing UA and product at the same time

This one is subtle but it kills the diagnostic value of a soft launch completely.

A soft launch should answer a clear question. But if your UA team is changing targeting and creatives while your product team is updating game mechanics and rebalancing levels, you cannot attribute anything you observe to a single cause.

You end up unable to diagnose anything

D7 retention drops. Is it because the new creative attracted a different type of user? Is it because the difficulty spike in level four got worse after the last update? Is it because you switched audience segments on Meta? 

You cannot know, because you changed three things at once. As GameAnalytics notes in their guide on evaluating early game concepts, the discipline of isolating variables is as important in UA as it is in product development.

What to do instead: Coordinate deliberately. When product is in an iteration cycle, pause UA changes. When UA is testing a new creative direction, hold the product updates. It requires more planning upfront but it makes everything you observe actually actionable.

Mistake 5: Treating D1 retention as proof the game works

D1 retention tells you that users came back the day after they installed. That is a useful early signal, but it is only one data point, and studios consistently over-index on it.

A game can produce a decent D1 through strong onboarding and a well-crafted first session. But if the core loop does not sustain interest beyond day two, D7 will tell a very different story.

The shape of the curve matters more than the headline number

The benchmarks commonly cited are D1 at 40%, D7 at 20%, D30 at 10%. 

The Addict Mobile whitepaper on soft launch notes these are aspirational and rarely achieved in practice. 

What actually matters is the shape of the drop. 

A game at D1 28% and D7 14% is in a better position than a game at D1 35% and D7 4%. The second game has a cliff after day one that no amount of UA spend will fix.

What to do instead: Look at the full curve. A steep D1 to D3 drop is a core loop problem. Users making it to D7 but not D30 is a progression or content depth problem. Each pattern points to a different fix in the product.

Mistake 6: Starting paid campaigns before the store listing is ready

Before you do anything, optimize your app store (ASO)! 

Your store listing is doing conversion work before anyone ever installs your game. Weak screenshots, a generic icon, a description that does not communicate why the game is worth five minutes of someone's time, all of it inflates your CPI before your campaign has had a fair shot.

Studios put months into the game and two days into the store page. The result is that every ad click costs more than it should because fewer people convert after the click.

What to do instead: Treat the store listing as part of the product. Get it into its best state and test it before you spend anything on paid traffic.

Mistake 7: Running a soft launch that is too short to produce real data

A month-long soft launch is almost always too short.

You need time for campaigns to exit the learning phase. You need to wait at least a week after the first cohort installs before you have D7 data. You need D30 data if you are serious about long-term engagement. And you need time for the product team to act on what the data shows and run another iteration cycle.

What three months actually buys you

Compress all of that into four weeks and you end up making decisions on partial data from campaigns that have not optimized yet, with no room for a second pass. 

Three months is the baseline that gives you campaigns that have had time to learn, retention data out to D30, at least one full product iteration cycle, and enough volume to separate signal from noise. Some studios need longer if the first month surfaces product issues that need fixing before the data is meaningful.

What to do instead: Plan for three months from the start. Build it into your budget and your production schedule before the soft launch begins, not as an afterthought when the first month runs out.

How to actually soft launch your game?

The game soft launch: What you NEED to know!

Before you spend anything on paid traffic

Confirm your attribution and analytics setup is working correctly and make sure you've optimized the App Store (ASO). Check out our ASO Guide for how to do this.

Tracking errors in this phase corrupt everything downstream and they are surprisingly common. Make sure your MMP, Appsflyer, Adjust, Singular, or whichever tool you use, is recording events correctly, that funnel events are firing at the right moments, and that you can cleanly separate organic from paid traffic.

Get the store listing ready and tested before campaigns go live. Build creatives for each channel you plan to use. What works on Meta does not translate directly to TikTok or AppLovin. Resize-and-repurpose is not a creative strategy.

During the soft launch

Start with two channels. Do not make simultaneous changes to UA and product. Watch the retention curve, not just CPI.

Move toward deeper funnel optimization as quickly as the budget allows. Keep the product team informed of what UA is seeing. 

If retention drops off sharply after a specific in-game event, that is a product signal, not a UA signal. The two teams need to be sharing data and diagnosing together, where users drop off inside the game is as important as how many come back.

Reading the data correctly

The core question is always: is this a channel problem, a creative problem, or a game problem?

• Retention low across both channels and in organic: game problem. UA cannot fix it.

• Retention decent in organic but poor in paid: you are attracting the wrong users. That is a targeting or creative problem.

• One channel performing and one not: channel problem worth investigating before writing off the weaker one.

When to move forward

There is no perfect moment. A soft launch has done its job when you have clean retention data from optimized campaigns across two channels, a tested store listing, some monetization signal, and at least one product iteration cycle complete. At that point you have enough to make a confident call about whether to scale, extend, or stop.

What soft launch data cannot tell you

No soft launch gives you certainty. 

The test markets are not your global audience. The users you attract at small scale are not identical to the ones you will see at full volume. Early ROAS numbers are directional, not predictive.

What it can do

What a well-run soft launch does is reduce risk. It will clearly show you the weak points of a game release and whether the game has potential. 

It surfaces game problems before you spend global launch budget on them. It gives you a defensible CPI baseline from clean traffic. It tells you whether users are coming back, and where they stop coming back, so you can fix those points before they cost you at scale.

The studios that get the most out of soft launch are the ones that go in with a clear question, the discipline to test one variable at a time, and the willingness to act honestly on what the data shows, even when it means harder conversations with the product team.

FAQs

What a soft launch in mobile gaming?

A soft launch is a preliminary release of a game to a limited, restricted audience in specific test regions of the world.

What games are currently in soft launch?

Here's the list of 47 top mobile games in soft launch

Hubapps is a mobile marketing consultancy specializing in user acquisition, ASO, and soft launch for mobile games. We work as an embedded part of your team, not as an external agency. If you are preparing for a soft launch and want a second opinion on your setup, book a discovery call here.