In mobile games, at any one time, countless variables have an impact on each player’s experience. That variability features an effect and makes optimization a way more complex undertaking than it might seem.
If you would like to improve any aspect of your game, you would like technology that isolates the aspect in question and tests your “improvements” effectively. If rigorous A/B testing bears out your hypothesis in specific conditions, on a subset of your total audience, you'll roll it out on a larger scale.
An A/B test essentially compares different versions of an equivalent variable to work out the impact of changes to that variable. The thought is to run concurrent tests during which conditions are as near to identical as possible, but the variable being tested.
The control group continues to experience the “default” behavior and the treatment group receives the new behavior, so you can compare the results and determine the impact of fixing your chosen variable.
Testing changes during this way is very important for several reasons: You can statistically prove the effects of your proposed change, instead of relying on gut feeling. By selecting what constitutes change before running the test, you'll be objective in your assessment.
Testing is an iterative process. The diagram below breaks down A/B testing step-by-step.A key factor is crucial to running a successful A/B test. continue the following rules, and you can’t go wrong.
Ending a test early, because you perceive a significant outcome appearing, won't give the most effective results. wait for the test to run its full course.
You can draw reliable conclusions from tests that action one change at a time. As soon as multiple variables are at work, you can not accurately evaluate the precise impact of any of them. It’s one at a time, or not in the least.
Your control and treatment groups must be made of identical audiences, or the entire exercise is pointless. Demographic, geolocation and play style are useful markers for segmentation because such things differentiate player profiles. If you want to understand the actual impact of a change, it must be tested on identical subjects.
We already have A/B testing functionality within the delta DNA platform, and it's an integral part of optimizing everything – from game difficulty to in-app purchase (IAP) offer value.
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