Why Players Bounce Before Level One? A Data-Driven Guide for Hyper-Casual Game Retention

The hyper-casual game market remains one of the top-performing genres by downloads in 2025—but keeping players engaged is still a major hurdle.

Industry benchmarks paint a tough picture: average Day 1 retention for hyper-casual games is under 25%, Day 7 drops below 8%, and by Day 14, only 2%–5% of players typically remain. These numbers reflect the brutal nature of a genre where low CPI meets high churn, and where most users bounce within minutes—often before they even finish level one.

So what causes players to “click in and drop out”? Why do so many never make it past the first level? For hyper-casual games, early experience is everything—and when that experience breaks, so does your growth trajectory.

This article focuses on pinpointing and fixing that exact moment: when users churn before completing the first level. We’ll show how to break down key metrics like open rate and first-level completion, analyze real player paths, and segment user behavior to turn early leaks into long-term wins.

Open Rate × First-Level Completion: Break It Down to Find the Leak

The first step is to measure open rate and first-level completion rate side by side.

Open rate refers to the percentage of users who click your ad and actually open the game. This is often measured by: users who launch the app ÷ users who clicked the ad. If open rate is low, it may be due to misleading ad creatives, long load times, or confusing redirects.

For example, one casual puzzle game showed strong ad click rates but an open rate below 30%. SolarEngine's analytics showed many users got stuck on a 3+ second loading screen, during which privacy prompts and login popups appeared. The team revised the flow to load directly into the game homepage, deferring permissions—open rate nearly doubled.

First-level completion, on the other hand, shows whether users actually engaged with the core gameplay. If open rate is healthy but few complete the first level, the problem may lie in the tutorial: pacing, clarity, difficulty, or missing cues.

In one merge-style game, many users failed to progress beyond the tutorial. Path analysis revealed they repeatedly tapped on a non-interactive item, misled by unclear prompts. After optimizing instructions and tweaking level goals, first-level completion jumped from 51% to 74%.

What matters most is whether users understand how to play. Clear steps, intuitive goals, and responsive feedback drive successful first sessions. With SolarEngine's path analysis tools, you can map out sequences like "homepage → start button → complete tutorial → level one clear" and then filter by user device, traffic source, or version to optimize for specific cohorts.

Funnel Analysis is used to track the conversion rate and churn rate between each step along the user journey

Where Are They Dropping Off? Reconstruct Real User Paths

Even if open rate and first-level completion are decent, drop-offs can still occur within the flow. That’s why it's crucial to visualize the actual player journey.

In a hyper-casual runner game, SolarEngine’s path analysis revealed that many users completed the tutorial—but never started level one. Instead, they were shown a reward popup that redirected them to a skin shop, after which they exited.

This behavior wouldn’t show up in basic retention charts. Only a path view like “tutorial complete → shop opened → exit” would surface the problem.

So when auditing early UX, always clarify the core funnel—e.g., "homepage → start game → tutorial → level one"—and separate it from secondary flows like skins, gacha, or bundles. Non-core elements should be introduced after gameplay begins, or at least offer a clear “continue” option so users aren’t lost or confused.

SolarEngine’s custom path builder helps teams compare different user journeys across device types, languages, and campaigns to decide: where are users lost? Which flow steps are worth changing?

Who’s Bouncing? Use Behavior-Based Tags to Prioritize Fixes

After finding the drop-off points, the next step is identifying which users are leaking out—and whether they share traits.

With SolarEngine’s behavioral tagging, you can define audience segments based on real actions. For instance:

• "Opened but no event" = users who loaded the app and did nothing.

• "Watched 3+ ads but no gameplay" = possible ad-spam induced churn.

• "Exited within 5 minutes of first level" = gameplay didn’t hook.

One idle game team found users who exited shortly after beating level one. Behavior tags identified these as players who didn’t perform any second interaction and quit within five minutes. Reviewing the path, they saw no clear next task was provided. New users had to wait passively for idle rewards to unlock more content—an anti-climactic end to a first session.

To fix this, the team triggered a one-time bonus task for “low-action users” immediately after level one. With the change, Day 2 retention for that segment rose by 9%.

Prebuilt tags in SolarEngine help reduce manual setup—like “new users who didn’t complete a level” or “high ad viewers with low gameplay”—making it easy to test hypotheses and push targeted changes.

Fixing First-Day Drop Starts with These Three Steps

If your hyper-casual game is leaking users before or during level one, try this checklist:

1. Map core actions using event funnels to monitor conversions.

2. Trace real paths with behavior charts to identify drop-off triggers.

3. Segment by behavior to target solutions at the right users.

Retention is the first barrier in this ultra-competitive genre. Fixing those early leaks before players even “get to the fun” is the first win on the path to long-term monetization.

SolarEngine’s behavioral analytics tools—event funnels, path analysis, and audience tags—are built to help studios uncover why users quit, validate fixes fast, and turn data into better player experiences.

This article is part of SolarEngine’s Growth Guide for Game Ops series. In each piece, we unpack real-world operational issues, explore actionable metrics, and highlight how behavioral analytics can turn problems into progress. Stay tuned as we tackle the trickiest pain points in UA, monetization, and retention—one metric at a time.