📊 Quantitative Method- the collection and analysis of numerical data to measure user behavior

• Playtest Pixie Chatbot

What is A/B Testing in Games?

A/B testing is a method used to compare two versions of a feature, mechanic, or interface in your game to determine which performs better. By splitting your players into randomized groups and showing each group a different version (A or B), you can collect data on how each version impacts player behavior. This approach enables data-driven decision-making, helping developers optimize the player experience.

A/B Testing can be applied in various scenarios, not limited to:

• Testing different UI layouts or placements to improve user experience.
• Experimenting with different mechanics, difficulty levels and progression to increase retention.
• Investigating different item prices/pricing models to maximize revenue.
• Analyzing the impact of new features on user experience.
• Testing different ad placements to improve ad revenue.

How It Works: Step-by-Step

1. Identify a Variable to Test

   Choose a single change to measure. For example:

   • Will a larger “Buy” button increase purchases?
   • Does a daily login reward keep users coming back?
   • Do players prefer a fast or slow progression curve?

2. Create Two Versions

   • Version A (Control): The original or current implementation.
   • Version B (Variant): The new idea or change you want to test.

3. Segment Your Audience

   Randomly split your players into two (or more) groups. Make sure groups are statistically similar to avoid bias. (see Determining Sample Size)

4. Run the Test

   Deploy each version to its corresponding player group while keeping all other variables constant.

5. Collect Data

   Measure key metrics over a meaningful period of time, this might include:

   • Session length
   • Retention rate
   • Conversion rate
   • Revenue per user
   • Engagement with specific features

6. Analyze the Results

   Compare the data from both groups to see which version performed better. Use statistical significance testing to ensure your results are reliable.