A/B Testing
Test different versions and see what works best
A/B Testing is a method to determine which of two variations is the most effective in an experiment, where one variation is control and the other experimental. The Experimentation can be done on event conversion, retention, sessions, revenue, viral coefficient, and much more. This A/B testing tool automatically determines sample size and how many days your experiment should run. It also provides conversion lift (relative conversion improvement) and helps decide if the economic impact is big enough to implement the new variation
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Shows which A/B version is the winning one and with how much significance.
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Experiment on event conversion, retention, sessions, revenue, viral coefficient, and much more.
-
Automatically determines sample size and how many days your experiment should run
-
Shows conversion lift (relative conversion improvement), and helps decide if the economic impact is big enough to implement the new variation
To track experiment data, use the Set-Up Data option in the projects page.
To A/B test viral coefficients, you also need to track user referrals.
For more information see Referring Users / Virality in the Setup Data page of your project.
A/B Testing is a method to determine which of two variations is the most effective in an experiment, where one variation is control and the other experimental. The Experimentation can be done on event conversion, retention, sessions, revenue, viral coefficient, and much more. This A/B testing tool automatically determines sample size and how many days your experiment should run. It also provides conversion lift (relative conversion improvement) and helps decide if the economic impact is big enough to implement the new variation
-
Shows which A/B version is the winning one and with how much significance.
-
Experiment on event conversion, retention, sessions, revenue, viral coefficient, and much more.
-
Automatically determines sample size and how many days your experiment should run
-
Shows conversion lift (relative conversion improvement), and helps decide if the economic impact is big enough to implement the new variation
To track experiment data, use the Set-Up Data option in the projects page.
To A/B test viral coefficients, you also need to track user referrals.
For more information see Referring Users / Virality in the Setup Data page of your project.