In this episode of WP Product Talk, hosts Katie Keith and Matt Cromwell interview Tom Carless, creator of the A/B Split Test plugin for WordPress. They discuss the importance of A/B testing for improving website conversions and share personal experiences and tips for maximizing testing effectiveness. Tom emphasizes the need for data-driven decisions when making changes to websites, offering insights on how to set up and run effective A/B tests, select key metrics, and interpret results.
Show Notes
Importance of A/B Testing
A/B testing is crucial for optimizing website conversions as it provides concrete data on which changes positively affect user behavior. Tom explains that making changes based on common sense alone can lead to misunderstandings about what truly drives sales and conversions.
Case Studies and Experiences
Katie shared her experience with A/B testing using Google Optimize and later switching to the A/B Split Test plugin. Tom also discussed a successful test on his plugin's checkout page that resulted in a 25% increase in conversions by reducing distractions.
Identifying What to Test
Tom recommends focusing on key pages within your conversion funnel for A/B testing, such as homepage and pricing pages. He suggests utilizing analytics to track engagement and identify where users drop off.
Testing During Events like Black Friday
There is a debate about whether to pause A/B testing during high-traffic events like Black Friday. Tom indicates that results can still be meaningful despite traffic fluctuations, but others might prefer to avoid unrelated tests during such peak times.
Suggestions for Effective A/B Tests
Tom advises maintaining simplicity in tests, focusing on small changes at a time, and using feedback mechanisms like live chat to gather ideas for improvements. The use of AI suggestions in planning tests was also highlighted.
Data Privacy and Compliance
The discussion covered the importance of GDPR compliance, as Tom’s A/B Split Test plugin does not store data on external servers, emphasizing privacy and control for users.
Continuous Learning Through Testing
Both Matt and Tom emphasized the iterative nature of A/B testing, where the goal is to continually refine and improve based on previous test results and user feedback.