What is A/B testing and what is the purpose?
A/B testing, also known as split testing, is a method of comparing two different versions of an ad to determine which one performs better. It allows you to make data-driven decisions and continuously improve your campaign’s performance. A/B testing can be applied to many elements of your ads, including ad creatives, headlines, descriptions, and more.
Creative Best Practices
- Image
- Test both vertical and horizontal images (9x16 and 16x9), making sure to separate them into different ad sets
- Run native-style images, eliciting curiosity and engagement
- Test different creative angles: science, medical, product showcase, customer testimonial, official announcements
- Video
- Test both 9x16 and 16x9 videos, making sure to separate them into different ad sets
- 9x16 UGC-style videos receive high engagement
- 15-30 second videos perform well, if you want to run longer videos consider driving users to a VSL
Best Practices: A/B Testing
- Creating New Ads
- Recommendation: Upload the new ad to a current ad set, making sure to keep the old and new ads running for a few days. If the new ads perform well, turn off the old ads. If not, keep the old ads live and turn off the new ads.
- Creative Budget
- To maximize the effectiveness of your A/B testing, please make sure that the daily budget for each ad set is greater than or equal to 10 times your goal CPA (Cost Per Acquisition). This helps you gather sufficient data for meaningful analysis.
- Creative Quantity
- We recommend uploading a maximum of 10 new creatives for each ad set. This ensures you have enough variations to test without overwhelming your budget.
- Performance Monitoring
- After launching new creatives, monitor their performance over a 7-day period. From there, you can assess whether the changes are having a positive impact on your campaign or not.
💡Pro Tip: Keep "Creative optimization" turned on when setting up your ad set. This allows our system to prioritize delivering your highest performing creatives.