A/B Testing and Experiment Analysis

A/B testing and experiment analysis are used to evaluate how changes in a product impact user behavior. In an experiment, users are typically divided into groups (such as variant A and variant B), and each group experiences a different version of a feature. The goal is to determine which version performs better based on metrics such as conversion, engagement, or revenue.

Experiment data is usually stored in tables that track which users were assigned to each variant and whether they completed a desired action. By analyzing this data, businesses can make informed decisions about product improvements.

A/B testing helps answer questions such as whether a new feature increases conversions, improves retention, or leads to higher revenue.

For beginners, the goal is to:

• Understand how experiments are structured
• Identify different variants in an experiment
• Measure outcomes such as conversions
• Compare performance between groups

• How users are distributed across variants
• Which variant leads to more conversions
• Revenue generated by each variant
• Overall experiment performance

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The Basic Pattern

SELECT column_name
FROM table_name;
  

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Examples

Example 1 — Count Users in Each Variant

Example 2 — Count Conversions by Variant

Example 3 — Calculate Revenue by Variant

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Practice Tasks (Your Turn!)

Task 1

Find the number of experiments by experiment status.

Task 2

Find the number of users assigned to each experiment.

Task 3

Find the total revenue generated across all experiments.