Also known as section testing or group testing, it is a testing method in digital marketing to determine which users prefer using different versions of a product or feature. A/B testing is a method of comparing two versions of an application with each other to determine which solution performs better. A/B testing can also be used by product developers and designers to demonstrate the impact of new features or changes in user experience.
Why Choose A/B Testing?
A/B testing is reliable, easy, and clarifying. Its cost is low. These tests, which eliminate the guesswork in digital marketing, aim to increase performance based on data. You can use A/B testing for various marketing platforms such as social media, visual marketing, and much more.
A/B tests have gained value in parallel with the expansion of the digital marketing world. In traditional marketing methods, applying A/B testing to a magazine advertisement or billboard has brought serious cost problems. Advertising costs have been reduced with digital marketing and the number of interactions with A/B testing has been improved.
A/B testing plays an important role in campaign management, as it helps identify what works and what doesn’t. Thanks to A/B tests, we can collect understandable data on online products such as websites, mailings, or advertisements, and calculate under which conditions we can capture more interaction. For such reasons, companies prefer A/B testing.
There are multiple items that you can A/B test, we can exemplify with the list below.
- Logo
- Banner
- Ad placement
- Buttons
- Page layouts
- E-mails
- Newsletters
- Ads
- Text messages
- Website pages
- Mobile apps
Basic steps of planning and executing A/B testing
- Measuring and reviewing the performance baseline
- Determining the test target using the performance baseline
- Developing hypotheses about how your test will improve performance
- Identify test targets or locations
- Build versions A and B for testing
- Using a quality control tool to verify the installation
- Running the test
- Monitor and evaluate results using web and test analytics
- Apply your findings to improve the user experience
A/B Test Application Example
Google Plus’s A/B test mobile ad
Wanting to increase the number of users for Google+, Google also applied for A/B testing. It first posted the ad you see right below. These full-page ads, which we call Interstitial, generally perform better. However, that was not the case in Google’s advertising work. After the Interstitial ad was opened, 69% of mobile users left the website immediately, and only 9 percent installed the app on their device.
After realizing that this advertising effort would fail, Google came up with an ad that would disturb users less and managed to increase its mobile users by 17 percent a day later.
