Retaining players can be a real headache without understanding player flow. This is especially true considering that retention rates drop by a huge 26.25% from Day 1 to Day 30.
It doesn’t matter if players are onboarding, in the middle of the action, or planning on leaving. These figures can worsen quickly if you don’t notice how they navigate your game.
But these issues will vanish if you take the right steps to analyze your game’s player flows.
Player flows are sets of actions or steps a player takes in a game, usually to perform a common task. Flows of this kind usually show how players navigate the game. This can range from onboarding to level progression. It especially applies when engaging with specific game features.
Player flow can often be confused with other common terms such as game flow and player journey. Let’s break down the differences.
We have captured the main differences between these three terms in the table below:
| Category | Player Flow | Game Flow | Player Journey |
| Definition | A set of interactions that present ideal steps for a player to accomplish a typical game task. | Overall design and pacing of the game. Provides experiences and challenges to engage players. | Steps customers take to reach a high-level goal with a game. Typically done across channels and over time. |
| Focus | Micro: Granular, specific | Meso: Structural, experience-based | Macro: High-level, broad |
| Perspective | Player-centric | Game-centric, design | High-level view of player and game. |
| Key Elements | Onboarding, gameplay, monetization flow, exit flow | Immersion, storytelling, difficulty progression, mechanics | Marketing, onboarding, engagement, churn prevention. |
| Objective | Optimizing user experience. | Keep players immersed in the game. | Maintain long-term engagement and retain players. |
| Examples | – Navigating an open world. – Completing a mission | – Balancing low and high-tension moments. – Progression of difficulty. | – Marketing efforts (emails, ads) – Reward systems – Updates. |
Some typical types of player flows include:
There are many reasons why player flow is integral to the success of mobile games.
In 2024, there was a huge drop in retention from Day 1 (29.46%) to Day 30 (3.21%). In monetization flows, engagement rates of rewarded ads can reach 92%. And about 80% of players prefer to see rewarded ads during gameplay.
By analyzing and optimizing player flows, you can maximize player engagement and retention.
79% of mobile games implement in-app purchases. So it’s essential to understand how and why players engage with your game’s monetization elements.
In player flows, you can analyze how players interact with ads and in-app purchases. This strengthens game monetization efforts while maintaining positive player experiences.
Player flows help developers understand player preferences better. It also shows how players interact with the game from start to finish.
For example, a player might lose and instead of retrying the level, they could leave. So this could suggest adjusting the level’s difficulty.
Player flows also show which features are worth keeping or improving. And it helps refine gameplay.
We’ve broken down the analyzing and optimizing of player flows into five basic steps.
There are various aspects of your game you can analyze and optimize. Typical examples include onboarding drop-offs and level completion rates. Whether for player engagement, retention, or monetization, you need to decide which metrics to track.
Game analysts typically use heatmaps, A/B testing platforms, and funnel analysis to gain insights into player behavior.
But it’s worth considering alternatives and next-generation tools. Keewano’s an ideal example, simplifying the understanding of player behavior across large populations. It provides clear, real-time visualizations that scale effortlessly, even for massive gaming audiences.
Equipped with advanced features, Keewano highlights and tracks events that caused churn, automatically characterizing the impacted player population. All of this is achieved seamlessly on a single screen, regardless of your user base size.
The next step is collecting relevant player data to analyze. Many tools need manual data aggregation or rely on existing data sources. This might cause performance issues if the data source isn’t fast enough. Also, partial or imprecise data might lead to inaccurate insights.
For example, Keewano runs on an AI-first real-time database. This allows analysts to collect and process 256 million events in under a second. This is essential if you want to validate multiple hypotheses quickly and draw the right conclusions.
We also advise segmenting players to identify which groups you want to analyze. In this case, Keewano excels too, providing automated user segmentation based on their actions.
Today’s tools leverage AI to map player flows effortlessly and in high resolution. Keewano doesn’t just present the player flow. It also highlights and translates the game’s main player trends.
Let’s say you’re analyzing your puzzle game. Keewano could show that 25% of players are more likely to retry a level if they use a power-up at a specific point. It will then provide actionable recommendations.
After implementing the recommendation, you can A/B test it and see which change works best. And from the outcomes, you can optimize your player flow. Then, integrate your tool into your development process and automate future analysis.
Furthermore, certain tools can generate predictive insights to anticipate future player behavior. This helps your player flow evolve, increasing the chances of longer engagement.
Want to boost engagement, retention, and monetization in your game? Then you need to fully control your player flows. Leveraging next-gen tools and following these steps will help you refine gameplay and engage players for longer.
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