Why Player Behavior Matters More Than Churn Analysis

It’s crucial to track how many players are leaving your game. That’s where churn analysis shines. But it doesn’t answer the million-dollar question: why are they leaving?

The harsh reality is that churn metrics only get you so far. Without insights into player interactions and preferences, developers can’t address underlying issues behind the user journey and player satisfaction. 

The next logical step is player behavior analysis, which fills in some huge blind spots. Let’s explore why player behavior analysis is a game-changer, helping developers create more tailored, engaging experiences that drive retention and revenue.

What is churn analysis?

Churn analysis examines how many users a product is losing and highlights where they are dropping off.

Typical metrics in churn analysis include:

• Churn rate: Percentage of players leaving a game.
• Drop-off points: Specific stages where players are likely to churn.
• Session length: Average time players spend on a game in one session.
• Session frequency: How many sessions the average player engages in.
• Retention rate: Percentage of players returning to the game over specific periods.

There are several ways that churn analysis can be quite limited in its impact. While this type of analysis provides tangible, quantitative data on how many players you are losing, it doesn’t provide insights about player motivations. 

Also, while it provides a high-level view of player drop-off, it doesn’t focus on behavioral patterns. Consider the mechanics that keep players engaged or what features they’re enjoying most.

While churn analysis generates important data to show how players are leaving *and at which stages), it doesn’t tell you why. This is where player behavior analysis comes in.

What is player behavior analysis?

Player behavior analysis is the collecting and interpreting of in-game data to understand player actions and motivations. Focusing on gameplay aspects like in-game activities, player preferences, and performance metrics explains why players behave the way they do.

This process is less concerned with metrics and more on insights. Here are some key areas that player behavior analysis focuses on.

Tracking gameplay patterns

Those who track user behavior typically dive into how players navigate a game (e.g. progression paths, frequency of play). The results show what keeps players returning and why they might lose interest.

Engagement with specific features

Which features might be more popular than others? And why? This helps developers optimize features and add new content to improve player satisfaction, and ultimately player retention.

In-game decision-making

Studying what choices players make uncovers their motivations. Seeing what skill upgrades they choose, what items they purchase, and what character paths they take facilitates stronger customization and new content creation.

In player behavior analysis, it’s important to focus on several key data types to build a holistic view of player experience. These include:

• Player motivations: Focusing on factors like exploration, social interaction, and competition, developers can better understand why players engage with a game and tailor content and gameplay accordingly.
• Player interactions: How players treat the in-game world, including the environment and social dynamics. For example, in The Elder Scrolls VI, players interact with NPCs and adopt special roles, highlighting engagement preferences.
• Engagement levels: Calculating how much time players spend on a game and how many times they open the game over a set period. This helps pinpoint drop-off points, and high-interest areas, explaining what works and what needs improvement.
• Progression and achievement: Looking into moments of player progression (like unlocking free skins in Fortnite or picking up a new weapon in Halo). With this data type, you can adjust difficulty levels and reward systems.
• In-game purchases: Analyzing monetization data to understand what players spend money on, including power-ups and cosmetic items. This helps to optimize in-game economies and boost satisfaction.

You can use a mix of these data types to get a clear overview of player experience, which paves the way for more effective gameplay optimization, monetization, and engagement. So how does player behavior analysis stack up against churn analysis?

Comparing player behavior analysis to churn analysis

While there is an overlap between player behavior analysis and churn analysis, there is one key difference that separates the two: And that’s the result.

Churn analysis (and standard game analytics solutions, in general) shows you where churn and attrition take place in a game. These tools will even pinpoint specific drop-off points, while also showing you retention numbers (both at different game stages and overall). 

Then there is player behavior analysis, which answers an even more critical question: Why do players churn? This process reveals the motivations and frustrations that drive them to leave. 

According to a study, retaining users is 10 times more cost-effective than acquiring new ones. This shows how critical it is for gaming companies to focus on retention strategies that dive deeper than standard churn metrics.

But that’s not all. Player behavior analysis reveals the why of many other essential player actions that impact gameplay, engagement, and monetization. These include:

• Stuck points: Why players struggle to progress at certain points of the game (e.g. unengaging content or high difficulty levels).
• IAPs and spending patterns: Understanding why players make (or don’t make) in-game purchases.
• Session length: Uncovering why players spend less time in a game. Common reasons include poor reward systems and gameplay fatigue.
• Event participation: Reasons for how much players engage with in-game events.
• Social engagement: Why players engage more in community-based formats (guilds and multiplayer mode).

Uncovering these actions helps developers improve their games in a targeted way, boosting key metrics like engagement, satisfaction, and revenue. Let’s dig deeper into why it makes sense to make player behavior analysis a top priority.

The benefits of prioritizing player behavior over churn analysis

Placing greater emphasis on player behavior analysis over other processes like churn analysis brings many benefits. These include:

• Identifying root causes of churn: This type of analysis helps developers pinpoint obstacles and player frustrations, including challenging game stages and reward shortages.
• More personalized user experiences: According to Deloitte Digital, brands with strong personalized user experiences are 71% likelier to report improved customer loyalty. Developers can leverage player behavior analysis to customize player experiences more effectively—personalizing content, adjusting difficulty, and tailoring reward systems.
• Predicting future player behavior: Knowing how your players are currently behaving can give you an idea of what they might do next. You are also better prepared to produce content better aligned with user preferences.
• Improving game retention strategies: Analyzing player behavior provides a strong platform for planning better retention strategies.

Prioritizing player behavior analysis gives developers an edge—the ability to interpret powerful insights to build a gaming experience that is more engaging and long-lasting. But this doesn’t mean that churn analysis should be ignored completely.

Implement both strategies for a comprehensive analysis 

While we have clearly picked a winner in this “battle,” we should make the following clear: Player behavior analysis and churn analysis can work in tandem together.

By combining these analytics types, developers can track key metrics like churn rates, retention rates, and session lengths, while also identifying the reasons behind all of them.

Using both creates a more comprehensive and actionable strategy for improving player engagement, satisfaction, and retention.