A/B testing (also known as split testing) is a method of advertising testing by which a baseline control sample is compared to a variety of single-variable test samples in order to improve response rates. A classic direct mail tactic, this method has been recently adopted within the interactive space to test tactics such as banner ads, emails and landing pages.

Significant improvements can be seen through testing elements like copy text, layouts, images and colors. However, not all elements produce the same improvements, and by looking at the results from different tests, it is possible to identify those elements that consistently tend to produce the greatest improvements.^[1]

Employers of this A/B testing method will distribute multiple samples of a test, including the control, to see which single variable is most effective in increasing a response rate or other desired outcome. The test, in order to be effective, must reach an audience of a sufficient size that there is a reasonable chance of detecting a meaningful difference between the control and other tactics: see Statistical power.

This method is different than multivariate testing which applies statistical modeling which allows a tester to try multiple variables within the samples distributed.

See also

• Choice Modelling
• Adaptive control
• Multivariate testing

References

External links

• An Introduction to A/B Testing