A/B testing helps us to make data-informed decisions in digital communications and marketing. It focuses on our audiences and their preferences on websites, social media, emails and ads. A/B testing can complement web, social, email and campaign analytics.
With A/B tests, we compare variations of digital content to determine which version performs best. The test results guide decisions in UI and UX design, content creation and marketing. We learn how small changes influence user behaviour and can improve user experience continuously.
Typical steps in A/B testing are:
- Form a hypothesis
- Create variants to test the hypothesis
- Run test
- Analyze the results
- Implement the changes based on the test results
If you want to learn more about A/B tests, please book a session with the Digital Strategy Team at University Relations.