What makes a good experiment?
The gold standard of causal inference is the Randomized Control Trial (RCT). In marketing, we refer to it as A/B testing.
An A/B test, also known as a split test, is a method to compare two versions of a web page or app against each other and determine which one performs better. Unlike an A/A test, where both groups are exposed to the same conditions, in an A/B test, the test and control groups are exposed to different variations. The key purposes and benefits of an A/B test are as follows:
- Evaluating the effectiveness of changes: By comparing two versions (A and B), where typically one is the original and the other contains one or more changes, you can understand the impact of these changes on user behavior
- Data-driven decision making: A/B testing provides empirical data on how a small change impacts user interaction, helping in making informed decisions
- Optimizing user experience and conversion rates: By testing how different versions...
