A/B testing is a data-driven approach that involves comparing and contrasting different variants of your game to determine which version performs better. By conducting controlled experiments, you can make informed decisions based on real-world data rather than relying on assumptions or guesswork.
In the context of game development, A/B testing allows you to test and optimize various aspects of your game, such as player behavior, feature performance, and user flows. By systematically testing different variables, you can identify the most effective changes to enhance player engagement, retention, and monetization.
The Power of A/B Testing
A/B testing provides many advantages for game developers and gaming studios. It helps game designers make data-driven decisions, minimizes negative impacts on game performance, and provides tangible proof to support optimization efforts instead of depending on subjective opinions or guesses for results.
A/B testing is a useful tool for game studios to gain insight into the behaviors and preferences of their players. By testing different versions of their game, studios can gather valuable data on players’ actions, spending habits, and interaction patterns. This data can then be used to make informed development decisions, ensuring that future versions of the game provide an engaging and enjoyable experience for players.
Why Should You Implement A/B Testing in Your Game?
A/B testing offers numerous benefits in the game development process. Here are a few of them:
Data-Driven Decision Making
A/B testing empowers you to make data-driven decisions. By analyzing the results, you can identify the most ROI-efficient game changes to optimize your game and improve player experience.
Maximizing Player Behavior
A/B test data provides valuable insights into different player behaviors, such as spending habits, gaming patterns, and retention rates. By leveraging this data, game developers can optimize their strategies to better cater to player preferences and enhance overall engagement. Additionally, various game elements can be strategically implemented to encourage specific behaviors.
Optimize Feature Performance and Adoption
A/B testing provides an efficient way to assess both new and existing features to measure their performance and adoption rates among players. By testing different variants, A/B testing helps identify which options resonate best with your target audience.
Improve User Flows
You can improve specific user flows in your game, from the first-time user experience to in-game store navigation, level progression, or reward pacing — it all matters regarding player satisfaction and engagement. A/B testing helps identify the most efficient and enjoyable flows to increase player engagement and satisfaction levels.
The Importance of Control Variants in A/B Testing
In A/B testing, control variants play a crucial role in accurately determining the impact of changes. A control variant is a group of users who remain unaffected by the experimental changes. By comparing the performance of the experimental variants with the control variant, you can isolate and measure the true impact of the variations.
Control variants are essential for detecting any lifts or drops in key metrics caused by external factors or other changes in the gaming environment. They help ensure that the observed improvements or declines are genuinely attributable to the changes being tested rather than external influences.
How to Conduct A/B Testing in Games
To successfully conduct A/B testing in games, you need a well-defined process and clear goals. Here are the key steps to conduct effective A/B tests:
Define Your Goals
Before starting an A/B test, defining your goals and identifying the specific metrics you want to measure is crucial. Clearly articulate the business objective you want to achieve with the test. Whether it’s improving retention rates, increasing in-app purchases, or enhancing user engagement, having a clear goal will ensure you collect relevant data and make informed decisions.
For example, if you are testing a new player’s starting currency balance, your goals might include improving retention rate, average revenue per daily user (ARPDAU), and conversion rate. By defining these goals upfront, you can focus your analysis on the relevant metrics and make informed decisions based on the results.
Example A/B Test: Starting Currency Balance
| Audience | New Users |
| Variant A (enabled) | 100 gold |
| Variant B (control) | 0 gold |
| KPIs to measure | Retention rate (D1, D3, D7, D30), ARPDAU, conversion rate |
Create Variants
Create different variants of the game element or feature you want to test. These variants should represent different configurations, designs, or mechanics. For example, you can create variations of the starting currency balance for new players or test different pricing options for in-app purchases.
Random Assignment
Randomly assign users to each variant to eliminate biases and ensure accurate results. Random assignment helps distribute user characteristics evenly across variants and prevents external factors from influencing the test outcome.
Measure Key Performance Indicators (KPIs)
Identify the KPIs that align with your goals and measure the impact of each variant on those metrics. For example, if your goal is to improve retention rates, measure daily, three-day, seven-day, and thirty-day retention rates for each variant.
Analyze and Interpret Results
Analyze the collected data and interpret the results to determine the performance of each variant. Look for statistically significant differences in the measured KPIs between variants. Statistical significance indicates that the observed differences are not due to chance but are genuinely driven by the variations in the game elements.
Implement Changes
Based on the results of the A/B test, implement the changes that showed positive impact and improved performance. Iterate and optimize your game based on the insights gained from A/B testing to enhance player satisfaction and drive business growth.
A/B Testing Tips
Determining Statistical Significance
Determining statistical significance is of utmost importance in A/B testing. By setting a significance level (a) and calculating your probability value (p-value), you can accurately ascertain whether your results are statistically significant. A lower p-value indicates greater significance, providing confidence when making decisions based on test results.
Example A/B Tests in Games
Various examples of A/B tests can be run within games, with first-time user experiences (FTUEs) often used as an experiment to increase player retention early. Also, testing different In-App Purchase (IAP) price points to maximize revenue and user engagement is often employed as a successful experiment.
Example 1: First-Time User Experience (FTUE)
- Audience: New Users
- Variant A (variant): Normal FTUE (10 steps)
- Variant B (control): Short FTUE (5 steps)
- KPIs to measure: Retention rate (D1, D3, D7)
Example 2: In-App Purchase (IAP) Bundle
- Audience: Spenders
- Variant A (enabled): $5 bundle
- Variant B (control): $20 bundle
- KPIs to measure: ARPDAU, LTV (long-term value)
A/B Testing: Dos and Dont’ts
Do:
- Always have an A/B test running to optimize your game continuously.
- Perform tests on various metrics and variables to uncover areas for improvement.
- Ensure that the sample sizes of your variable groups are similar to obtain accurate results.
- Analyze the impact of the treatment on user behavior and metrics.
Don’t:
- Test too many variables simultaneously, as it can lead to inaccurate results.
- End tests prematurely, as it may result in unreliable data and low statistical significance.
- Be afraid to target specific user segments for more granular experiments.
Game A/B Testing Tools
Game A/B testing tools offer various capabilities, such as advanced targeting, integrations, and different types of testing. Here are some examples of such tools:
Helika
Helika is a leading data analytics, marketing, and game management platform designed to empower game studios with the insights to make strategic data-driven decisions. It provides powerful advanced analytics and tooling to help teams create more robust marketing campaigns, drive user acquisition, optimize game performance with A/B testing, increase user revenue, and more.
Apptimize
Apptimize is a cross-platform tool for experimentation, optimization, and feature releases. It allows you to run A/B tests across multiple platforms, including mobile apps, through a centralized dashboard. This tool focuses on mobile app A/B testing and provides powerful feature flagging features to help you roll out new features to specific users.
Taplytics
Taplytics is another A/B testing tool available for iOS and Android app testing. It lets you make live changes without App Store updates, enabling fast iterations for mobile apps. Taplytics is designed to be easy to use and comprehend, supports localization, and allows you to test everything from button colors to game physics.
Conclusion
A/B testing can be invaluable to game developers looking to optimize their games for success. By conducting well-designed A/B tests, game designers can make data-driven decisions to increase player behavior, optimize feature performance, and enhance user flows. Remember to set clear goals, create variants, measure KPIs, analyze results, and implement changes based on insights gained from A/B testing.
If you seek expert guidance and support with game optimization, consider signing up for Helika Accelerate. This cohort-based accelerator provides in-depth education, mentorship, and access to Helika’s Solution Suite, which includes Game Management, Growth Analytics, LiveOps, and AI Tooling.
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