In mobile games, understanding player behavior can feel like a guessing game. Causal analysis remains a relatively untapped strategy.

Unreliable data and limited resources make it difficult to understand why players act the way they do. To find the root causes, having the right infrastructure and tools is key.

To explore this further, we gathered some insights from industry professionals. How is causal analysis changing mobile games? What are its hurdles? How can newcomers get up to speed? The results are in.

Causal Analysis in Mobile Games: Key Findings

Based on a survey of 132 game analysts and developers, we learned that:

• On a scale of 1-5, 65% of respondents rate the impact of causal analytics on their games as either 4.5 or 5 out of 5.
• 42.42% think that a lack of clean, reliable data is the biggest challenge of conducting casual analysis in mobile games.
• 26.6% think that adding new features is the game element most impacted by causal analysis. Running special promotions (20.4%) and user interface changes (15.8%) were the next most mentioned.

These figures clearly show how industry professionals currently feel about causal analysis. But what exactly is it? And why is it becoming essential in mobile game development?

What is Causal Analysis in Mobile Games?

In mobile games, causal analysis goes beyond showing what players do in a game. It helps game analysts understand why players do what they do.

Analysts may use this method to see which decisions keep players engaged and which retain them. 

Typical techniques used in causal analysis include:

• A/B testing
• Regression models
• Randomized controlled trials (RCTs)
• Causal inference models.

By understanding why players make certain decisions, causal analytics identify specific game elements that influence engagement and retention.

Game Elements Most Impacted By Causal Analysis

Based on our survey, here’s how developers are using causal analysis to refine their games. The most impacted elements are:

• Adding new features (26.6%).
• Running special promotions (20.4%).
• User interface changes (15.8%).
• New in-game reward systems (14.2%).
• Game design changes (12.6%).

Other impacted elements (10.4%) include difficulty spikes, daily challenges, in-game ad frequency, and limited-time events.

To conduct causal analysis, you must use game analytics solutions to track player interactions. Some examples include:

Solution	Best for…
Do Why (Open-source Python library)	Causal inference.
Keewano	AI-driven agent that understands players.
Amplitude	Event-based data tracking.
Mixpanel	Analyzing engagement funnels and user actions.
Unity Analytics	Real-time insights in Unity-based games.
Game Analytics	Tracking player progression.

While the possibilities of causal analysis seem limitless, it’s not without its fair share of hurdles.

The Challenges of Causal Analysis in Mobile Games

Game analysts and developers face several challenges when conducting causal analysis. Based on our findings, here are the three most common and how to overcome them.

1 – Lack of Clean, Reliable Data (42.42%)

You need high-quality, detailed data to conduct effective causal analysis. Incomplete datasets and inconsistent event tagging will make your results unreliable. If your data is unorganized, then it’ll be nearly impossible to make sense of it.

How to overcome this:

• Use relevant data-tracking tools.
• Track the correct information from the start.
• Regularly review data pipelines and align event tracking across all game features.
• Segment data into cohorts to ensure more accurate results.

2 – Misinterpreting Results (25.76%)

It’s important to make a clear distinction between causation and correlation. Many analysts confuse the two, often leading to skewed results and ill-informed decisions.

How to overcome this:

• Train analyst teams on causal modeling basics.
• Validate results with external experts and control groups.

3 – Resource Limitations (20.45%)

Those working at upcoming game studios might not have the best tools for causal analysis. You may also lack the expertise to effectively implement it.

How to overcome this:

• Use tools like Google Analytics or free trials of causal inference libraries. This will help you find the most effective tool for your causal analysis before scaling.

Despite these challenges, newcomers to causal analytics can navigate them effectively by taking some simple steps.

How to Start Using Causal Analytics: Key Tips

Just starting on your causal analysis journey and not sure where to start? Here are some basic tips to get it up and running:

1. Start with one detail you think might improve your game (e.g. a new feature or offer).
2. Focus on a key metric that you think might be important (e.g. Day 1 Retention).
3. Build a basic hypothesis that is player behavior-focused, and use simple models to test it.
4. Collect data and interpret it to see the impact it made.
5. Use visualization tools like Looker or Tableau to make causal relationships clearer.
6. Cross-collaborate: Maybe you have data analysts, UX designers, and game developers in your company. If so, get them collaborating on causal analysis to build a holistic interpretation of the results.
7. Continuously iterate your testing based on data, results, and player feedback.

If you’ve already started conducting causal analysis on your game, you might already begin to see its impact.

How Effective Is Causal Analysis in Mobile Games?

In our survey, we asked game analysts how effective causal analysis has been compared to other analytics methods (on a scale of 1-5).

So far, the overwhelming majority of respondents have found causal analysis to be extremely effective. About 65% of respondents gave a rating of 4.5-5.

The Bottom Line on Causal Analysis

We believe that causal analysis is a real game-changer (literally) for:

• How mobile games are developed and improved.
• Pinpointing not just what players do, but why they act the way they do. 
• Optimizing game promotions, features, user interfaces, and many other game elements.

Of course, its effectiveness depends on how prepared your studio or company is to implement it. You need a solid data infrastructure (and the right tools) to make causal analysis truly impactful.

Have your say in the poll below. We we made it a bit simpler this time round (No decimals):